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Tpeak-Tend interval (Tp-Te) is an indicator of transmural dispersion of repolarization, which is often related to ventricular arrhythmias. The relationship between Tp-Te and vasovagal syncope (VVS) has been reported in previous studies, but very little is known about Tp-Te in differentiation between psychogenic pseudosyncope (PPS) and VVS in children. We aim to discuss the predictive value of Tp-Te of supine and orthostatic electrocardiogram (ECG) in the diagnosis between PPS and VVS in children. The 31 children with PPS and 40 children with VVS from July 2014 to November 2023 were enrolled as the study group. Meanwhile, 30 healthy children who underwent a physical examination in the same hospital were selected as the control group. Tp-Te and QT interval in 12-lead supine and orthostatic ECG were measured. (1) Supine ECG: ① The Tp-Te/QT in PPS group was significantly lower than that in control group in II lead (P < 0.05). The Tp-Te/QTc in PPS group was significantly lower than that in control group in II and aVR lead (P < 0.05). There was no significant difference in Tp-Te between PPS group and control group (P > 0.05). ② The Tp-Te in VVS group was significantly higher than that in control group in aVR, aVF, and V 4 lead (P < 0.05). The Tp-Te/QT in VVS group was significantly higher than that in control group in aVF lead (P < 0.05). The Tp-Te/QTc in VVS group was significantly higher than that in control group in aVL, aVF and V 1 lead (P < 0.05). ③ The Tp-Te, Tp-Te/QT in PPS group were significantly lower than those in VVS group in some leads (P < 0.05). The Tp-Te/QTc in PPS group was significantly lower than that in VVS group in some leads. (2) Orthostatic ECG: ① The Tp-Te in PPS group was significantly higher than that in control group in V 1 , V 2 lead (P < 0.05). The Tp-Te/QT, Tp-Te/QTc in PPS group were significantly higher than those in control group in V 2 lead (P < 0.05). ② The Tp-Te and Tp-Te/QTc in V 4 lead, Tp-Te/QT in V 1 , V 4 lead in VVS group were significantly higher than those in control group (P < 0.05). ③ The Tp-Te in PPS group was significantly lower than that in VVS group in some leads (P < 0.05). The Tp-Te/QT in PPS group was significantly lower than that in VVS group in some leads (P < 0.05). The Tp-Te/QTc in PPS group was significantly lower than that in VVS group in some leads. (3) The receiver-operating characteristic (ROC) curve analysis: Tp-Te of supine ECG and orthostatic ECG in some leads had a predictive value on diagnosis between PPS and VVS (P < 0.05). Tp-Te, Tp-Te/QT, and Tp-Te/QTc of supine and orthostatic ECG in some leads have a certain predictive value in the diagnosis between PPS and VVS in children., Competing Interests: Declarations Competing interests The authors declare no competing interests. Ethics approval and consent to participate Written informed consent must had been obtained from a parent and/or legal guardian before HUTT. This research was approved by the Medical Ethics Committee of The Second Xiangya Hospital, Central South University (2014-012), and conforms to the principles stated in the Declaration of Helsinki., (© 2024. The Author(s).)

QRS detection within an electrocardiogram (ECG) is the basis of virtually any further processing and any error caused by this detection will propagate to further processing stages. However, standard benchmarking procedures of QRS detectors are seriously flawed because they report almost always close to 100% accuracy for any QRS detection algorithm. This is due to the use of large temporal error margins and noise-free ECG databases which grossly overestimate their performance. The use of a large fixed error margin masks temporal jitter between detection and ground truth measurements. Here, we present a new performance measure (JF) which combines temporal jitter with the F-score, and also an ECG database with decreasing levels of signal to noise ratios based on noise generated from different tasks. Our new performance measure JF fully encompasses all the types of errors that can occur, equally weights them and provides a percentage value which allows direct comparison between QRS detection algorithms. In combination with the new noisy ECG database, the JF performance measure now varies between 50% and 100% for different detectors and signal to noise conditions thereby making it possible to find the best detector for an application., Competing Interests: B.P. is CEO of Glasgow Neuro LTD which manufactures the Attys DAQ board. He has received a salary from Glasgow Neuro LTD which, however, did not provide any funding for this study. B.P. was involved in the design, data analysis, decision to publish, and preparation of the manuscript. However, this study is not about comparing different ECG measurement devices. The outcome of this study will be identical with other commercial data acquisition devices with identical specifications and the Attys does not alter the originally measured data in any way and thus does not introduce any bias. This does not alter our adherence to PLOS ONE policies on sharing data and materials. All data is available publicly and in its unprocessed format as measured by the Attys. There are no patents, products in development, or marketed products associated with this research to declare., (Copyright: © 2024 Porr, Macfarlane. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Characteristics of electrograms depend on the electrode design and distance to the electric source. Our aim was to assess the impact of electrode design and distance from the myocardial electric source on the unipolar and bipolar electrograms to deduce a far-field cut-off. We retrospectively analyzed left atrial electroanatomical maps of 25 patients acquired using an ablation catheter with a 4.5 mm tip-, mini- and 2 mm ring electrodes. The unipolar and bipolar electrograms were characterized based on peak-to-peak amplitude, signal duration, maximal slope, and relative power of the high frequency spectrum above 50 Hz (HF&#95;rel). The unipolar electrograms of ring electrodes showed an increased amplitude (140%), slope (150%) and HF&#95;rel (16% vs. 11%) compared to the tip- and mini-electrodes. The median amplitude, slope, and HF&#95;rel for the ring electrodes followed a power-law decay with distance with a steep decline up to 4 mm. This near-field cut-off can be identified based on a HF&#95;rel above 10% in unipolar electrograms. In conclusion, we observed a higher unipolar amplitude for small ring-electrodes compared to larger tip-electrodes. The rapid decay of the amplitude, slope, and HF&#95;rel up to a distance of 4 mm is suggestive for near-field cut-off identified based on HF&#95;rel above 50 Hz.Clinical Trial Registration: NCT04095559., (© 2024. The Author(s).)

Background. Using radar for non-contact measuring human vital signs has garnered significant attention due to its undeniable benefits. However, achieving reasonably good accuracy in contactless measurement senarios is still a technical challenge. Materials and methods. The proposed method includes two stages. The first stage involves the process of datasegmentation and signal channel selection. In the next phase, the raw radar signal from the chosen channel is subjected to modified Pan-Tompkins. Results. The experimental findings from twelve individuals demonstrated a strong agreement between the contactless radar and contact electrocardiography (ECG) devices for heart rate measurement, with correlation coefficient of 98.74 percentage; and the 95% limits of agreement obtained by radar and those obtained by ECG were 2.4 beats per minute. Conclusion. The results showed high agreement between heart rate calculated by radar signals and heart rate by electrocardiograph. This research paves the way for future applications using non-contact sensors to support and potentially replace contact sensors in healthcare., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Cardiovascular diseases are a cause of death making it crucial to accurately diagnose them. Electrocardiography plays a role in detecting heart issues such as heart attacks, bundle branch blocks and irregular heart rhythms. Manual analysis of ECGs is prone to mistakes and time consuming, underscoring the importance of automated methods. This study uses AI models like AlexNet and a dual branch model for categorizing ECG signals from the PTB Diagnostic ECG Database. AlexNet achieved a validation accuracy of 98.64% and a test set accuracy of 99% while the dual branch fusion network model achieved a test set accuracy of 99%. Data preprocessing involved standardizing, balancing and reshaping ECG signals. These models exhibited precision, sensitivity and specificity. In comparison to state of the arts' models such as Hybrid AlexNet SVM and DCNN LSTM our proposed models displayed performance. The high accuracy rates of 99% underscore their potential for ECG classification. These results validate the advantages of incorporating learning models into setups for automated ECG analysis providing adaptable solutions for various healthcare settings including rural areas., (© 2024. The Author(s).)

Background: Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in adults using a 12-lead ECG; however, its efficacy in children is uncertain. We aimed to develop novel artificial intelligence-enabled ECG algorithms for LVSD and RVSD detection in pediatric patients., Methods and Results: We identified 10 142 unique pediatric patients (age≤18) with a 10-second, 12-lead surface ECG within 14 days of a transthoracic echocardiogram, performed between 2002 and 2022. LVSD was defined quantitatively by left ventricular ejection fraction (LVEF). RVSD was defined semiquantitatively. Novel pediatric models for LVEF ≤35% and LVEF <50% achieved excellent test areas under the curve of 0.93 (95% CI, 0.89-0.98) and 0.88 (95% CI, 0.83-0.94) respectively. The model to detect LVEF <50% had a sensitivity of 0.85, specificity of 0.80, positive predictive value of 0.095, and negative predictive value of 0.995. In comparison, the previously validated adult data-derived model for LVEF <35% achieved an area under the curve of 0.87 (95% CI, 0.84-0.90) for LVEF ≤35% in children. A novel pediatric model for any RVSD detection reached a test area under the curve of 0.90 (0.87-0.94)., Conclusions: An artificial intelligence-enabled ECG demonstrates accurate detection of both LVSD and RVSD in pediatric patients. While adult-trained models offer good performance, improvements are seen when training pediatric-specific models.

Background: The prevalence of abnormal electrocardiography (ECG) increases with aging, and these abnormalities may have an impact on anesthesia management. Although a normal ECG does not guarantee a healthy heart, an abnormal ECG can quickly identify a patient who is at high risk of cardiac complications., Objective: The aim of this study was to determine the prevalence and associated factors of preoperative abnormal electrocardiography among older surgical patients at selected teaching hospitals in southern Ethiopia, from February 15 to June 15, 2022., Methodology: A multicenter cross-sectional study was conducted at three randomly selected teaching hospitals in southern Ethiopia on 246 elderly surgical patients recruited consecutively. Data were entered into Epidata version 4.6, then exported and analyzed in STATA version 16. A binary logistic regression model was used to examine factors associated with abnormal ECG, and variables with a P-value < 0.2 were entered into the multivariate analysis to identify independent factors. Both crude and adjusted odds ratios were reported, and a P-value < 0.05 was considered statistically significant. The data were presented using frequencies, tables, charts, and figures., Result: In the current study, 120 (48.78%) of older surgical patients had abnormal preoperative ECGs. In terms of severity, 55.3% were classified as minor, while 44.16% were major ECG abnormalities. The most common ECG abnormalities were left axis deviation (LAD), left ventricular hypertrophy (LVH), and ST segment changes. The presence of comorbidity (AOR = 3.44, P = 0.001), age ≥ 70 years (AOR = 2.5, P = 0.011), history of angina (AOR = 5.9, P = 0.011), history of smoking (AOR = 5.07, P = 0.024) and urban residency (AOR = 1.89, P = 0.039) were the strongest risk factors for an abnormal ECG., Conclusion and Recommendation: Our study shows that older patients are more likely to have an abnormal ECG before surgery, regardless of symptoms or risk factors. Therefore, it is suggested that all older patients undergo preoperative ECG screening. Further prospective cohort studies are needed to investigate the impact and outcome of patients with preoperative abnormal ECG., (© 2024. The Author(s).)

Left ventricular (LV) global longitudinal strain (LVGLS) is versatile; however, it is difficult to obtain. We evaluated the potential of an artificial intelligence (AI)-generated electrocardiography score for LVGLS estimation (ECG-GLS score) to diagnose LV systolic dysfunction and predict prognosis of patients with heart failure (HF). A convolutional neural network-based deep-learning algorithm was trained to estimate the echocardiography-derived GLS (LVGLS). ECG-GLS score performance was evaluated using data from an acute HF registry at another tertiary hospital (n = 1186). In the validation cohort, the ECG-GLS score could identify patients with impaired LVGLS (≤ 12%) (area under the receiver-operating characteristic curve [AUROC], 0.82; sensitivity, 85%; specificity, 59%). The performance of ECG-GLS in identifying patients with an LV ejection fraction (LVEF) < 40% (AUROC, 0.85) was comparable to that of LVGLS (AUROC, 0.83) (p = 0.08). Five-year outcomes (all-cause death; composite of all-cause death and hospitalization for HF) occurred significantly more frequently in patients with low ECG-GLS scores. Low ECG-GLS score was a significant risk factor for these outcomes after adjustment for other clinical risk factors and LVEF. The ECG-GLS score demonstrated a meaningful correlation with the LVGLS and is effective in risk stratification for long-term prognosis after acute HF, possibly acting as a practical alternative to the LVGLS., (© 2024. The Author(s).)

Ventricular premature contractions (VPC) originating from right ventricular outflow tract is the most common type of ventricular arrhythmias in clinic settings, which can be effectively cured by catheter ablation. Interruption of the inferior vena cava (IVC) is a rare vascular anomaly resulting from aberrant development during embryogenesis. Herein, we reported a very rare case of VPC originating from right ventricular outflow tract (RVOT) with interruption of IVC, which was successfully ablated by transfemoral vein approach., (© 2024 The Author(s). Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.)

Background: This study aimed to define and explain a novel form of pacemaker-mediated arrhythmia which is initiated and sustained by atrial capture loss coinciding with ventriculoatrial (VA) conduction within the postventricular atrial refractory period (PVARP). Unlike repetitive nonreentrant VA synchrony (RNRVAS), in the pseudo-RNRVAS, the atrium is not stimulated due to pacing below the threshold level, rather than because of refractory atrial myocardium., Objective: The objective was to elucidate the mechanisms of this pseudo-RNRVAS, identify predisposing factors, and propose preventive strategies., Methods: Twenty-one patients with dual-chamber cardiac implantable electronic devices (CIEDs) exhibiting stable VA conduction within the PVARP were included. Pseudo-RNRVAS were induced by altering atrial amplitude and lower rate interval while keeping other CIED parameters constant., Results: Pseudo-RNRVAS developed after atrial capture loss in 20 out of 21 patients. Notably, 11 patients experienced pseudo-RNRVAS at heart rates ≤ 70 bpm, and 7 patients at AV delay ≤ 150 ms. The condition initiated immediately following the first ventricular pace beat after atrial capture loss in 18 patients. In six cases, pseudo-RNRVAS terminated intermittently and then restarted; in 14 cases, it did not resolve., Conclusion: Pseudo-RNRVAS can occur even at low heart rates and without specific predisposing factors seen in RNRVAS. Conditions that increase atrial pacing probability and threshold raise the likelihood of pseudo-RNRVAS. Early postimplantation may pose a heightened risk, correlating with pacemaker syndrome and susceptibility to heart failure., (© 2024 The Author(s). Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.)

Introduction: Although there is a plethora of literature on electrocardiographic changes following cardiac allotransplantation, there is little in the field of cardiac xenotransplantation. The only published literature to date is that of the first pig-to-human cardiac xenotransplantation. Here we take a close look at the electrocardiographic parameters in four non-human primate recipients of orthotopic cardiac xenotransplantation to develop baseline metrics for comparison., Methods: Orthotopic cardiac xenotransplantion was carried out in four non-human primate recipients. Electrocardiographic parameters were followed at various intervals using an internal hemodynamic monitoring system (DSI) as well as a standard 12-lead electrocardiogram (ECG). ECG intervals were then compared to published literature on porcine ECG intervals and pig-to-human cardiac xenotransplantation., Results: There were no significant differences observed between timepoints for HR, PR, QRS, QT, or QTc after cardiac xenotransplantation for each animal subject. ECG parameters were statistically similar to those of in situ mini-pig hearts in the literature. ECG parameters from the DSI on average were shorter than those from a traditional ECG, however, DSI parameters were consistent over time., Conclusion: These results demonstrate the possibility of conduction health for genetically engineered porcine donor hearts following cardiac xenotransplantation. Ongoing work to compare the results of an ECG in a porcine donor heart before and after implantation into a NHP is necessary to better characterize variables that may be at play in the function of the conduction system., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: The de Winter electrocardiogram (ECG) pattern is a rare presentation of ST-segment elevation myocardial infarction (STEMI) equivalent. The clinical profile of de Winter syndrome remains to be clarified., Methods: Medical records of 1865 consecutive patients with acute myocardial infarction admitted from November 2018 to July 2023 were screened. Twelve patients (0.6%) with de Winter syndrome were included. STEMI patients whose culprit vessel was the left anterior descending artery (LAD) but without de Winter ECG pattern were selected as controls after 1:2 matching for age and sex., Results: The de Winter syndrome patients were all male, aged 49.0 (46.3-52.5) years. The culprit lesion was the proximal LAD in seven patients (58.3%), the middle LAD in three (25%), the left main coronary artery in one (8.3%), and the ramus intermedius artery in the other. All of their culprit lesions had TIMI Thrombus Grade < 4, Cohen-Rentrop Score ≤ 2, and residual stenosis ≥ 80% after pretreatment with thrombus aspiration or balloon predilatation. Intracoronary imaging data were available in four patients, showing severe atherosclerotic stenosis. Compared with STEMI, de Winter syndrome had a higher prevalence of prior recurrent angina (75.0% vs. 37.5%, p = 0.034), better coronary collateralization (Cohen-Rentrop Score: 1 vs. 0, p = 0.001), lower thrombus burden (TIMI grade: 1 vs. 2, p = 0.005) but more severe atherosclerotic stenosis in the culprit lesions (90% vs. 60%, p < 0.001)., Conclusions: The culprit lesions of de Winter syndrome have a low thrombus burden, severe atherosclerotic stenosis, and poor collateral circulation protection., (© 2024 The Author(s). Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.)

This systematic review aims to summarize the consumer wearable devices used for collecting ECG signals, explore the models or algorithms employed in diagnosing and preventing heart-related diseases through ECG analysis, and discuss the challenges and future work related to adopting health monitoring using consumer wearable devices. Following the PRISMA method, we identified and reviewed 102 relevant papers from PubMed, IEEE, and Web of Science databases, covering the period from May 2013 to May 2023. This review comprehensively summarizes consumer wearable devices with ECG functions, available ECG datasets, and various algorithms for detecting cardiac diseases and monitoring long-term health. It also discusses the integration challenges and future directions in cardiac health monitoring. The results highlight a preference for deep learning algorithms, such as Convolutional Neural Networks (CNNs) and their variations, in analyzing ECG data due to the ability to automate feature extraction and reduce memory requirements. The review also discusses potential limitations of the current literature, including lack of reasoning and comparison of algorithms and limited data generalizability. By analyzing the current literature, this review provides an overview of state-of-the-art technologies, identifies key findings, and suggests potential avenues for future research and implementation.

Background: Patients with ST-segment elevation myocardial infarction (STEMI) may have higher hospitalization costs and poorer prognosis than non-ST-segment elevation myocardial infarction (NSTEMI)., Methods: A single-center retrospective study was conducted on 758 STEMI patients and 386 NSTEMI patients from January 1, 2020 to May 30, 2023 aimed to investigate the differences in cost and mortality., Results: STEMI patients had higher maximal troponin I (15,222.5 (27.18, 40,000.00) vs. 2731.5 (10.73, 27,857.25), p < 0.001) and lower left ventricular ejection fraction (LVEF) (56% (53%, 59%) vs. 57% (55%, 59%), p < 0.001) compared to NSTEMI patients. The clinical symptoms were mainly persistent or interrupted chest pain/distress in either STEMI or NSTEMI patients. STEMI patients had a significantly higher risk of combined hypotension than NSTEMI patients (8.97% vs. 3.89%, p = 0.002), and IABP was much more frequently used in the STEMI group with a statistical difference (2.90% vs. 0.52%, p = 0.015). STEMI patients have statistically higher hospitalization costs (RMB, ¥) (31,667 (25,337.79, 39,790) vs. 30,506.91 (21,405.96, 40,233.75), p = 0.006) and longer hospitalization days (10 (8, 11) vs. 9 (8, 11), p = 0.001) compared to NSTEMI patients. Although in-hospital mortality was higher in STEMI patients, the difference was not statistically significant (3.56% vs. 2.07%, p = 0.167). Multivariable logistic regression was performed and found that systolic blood pressure and NT-proBNP were risk factors for patient death (OR ≥ 1)., Conclusion: STEMI patients are more likely comorbid cardiogenic shock, heart failure complications with higher hospitalization costs and longer hospitalization days. And relatively more use of acute mechanical circulatory support devices such as IABP., Trial Registration: ChiCTR2300077885., (© 2024 The Author(s). Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.)

Objectives: This study aimed to evaluate the impact of hypothyroidism and levothyroxine (LT4) treatment on arrhythmic risk by concurrently analyzing multiple electrocardiogram (ECG) parameters such as the Index of Cardio-Electrophysiological Balance (iCEB), frontal QRS-T angle, Tpeak-Tend (Tp-e) interval/QT interval ratio, and QT dispersion (QTd)., Methods: This cross-sectional study included 132 adult patients with primary hypothyroidism who had been receiving LT4 treatment, and 132 demographically matched healthy controls. The hypothyroid group was also stratified by thyroid-stimulating hormone (TSH) levels (subclinical <4.5 and overt ≥ 4.5). Participants underwent a series of thyroid function and ECG measurements., Results: The hypothyroid and healthy control groups were matched for age and gender ( p  = 0.080; p  = 0.176). Participants with hypothyroidism had higher Tp-e/QT ratios, iCEB, median frontal QRS-T angle, and corrected QT dispersion (cQTd) than healthy controls ( p  = 0.004; p  = 0.025; p  = 0.004; p  = 0.004, respectively). In the overt group, the Tp-e/QT ratio, iCEB, and median frontal QRS-T angles were all higher ( p  = 0.012, p  = 0.037, and p  = 0.016, respectively). Logistic regression analysis indicated that a higher iCEB score (β = 0.60, p  = 0.003) was significant for the detection of arrhythmia risk. ROC analysis showed that iCEB had the highest sensitivity (0.80), moderate specificity (0.60), and AUC 0.70., Conclusion: Patients with hypothyroidism have a higher risk of arrhythmia. To assess this risk, it is important to analyze the Tp-e interval, iCEB, frontal QRS-T angle, and QTd. Differentiating between patients with subclinical and overt hypothyroidism can help minimize the risk of arrhythmia. iCEB is the most effective method for identifying arrhythmic risk. Using all these parameters can improve the accuracy of arrhythmic risk detection in patients with hypothyroidism.

Objectives: The calculation of left ventricular mass varies in different studies, and reference values of the voltage-to-mass ratio for diagnosing cardiac amyloidosis (CA) are lacking. This study aimed to determine the value of the voltage-to-mass ratio in diagnosing CA and provide an optimal cut-off value for different calculation methods., Methods: We reviewed the electrocardiograms and echocardiograms of 213 consecutive biopsy-proven CA patients, 236 hypertrophic cardiomyopathy (HCM) patients, 100 hypertensive heart disease patients, and 181 healthy controls. Left ventricular mass was calculated using linear and cross-sectional area (CSA) methods. The voltage-to-mass ratios were compared between the CA group and other groups. The voltage-to-mass ratio obtained was used to build multivariate logistic regression models that predicted the log odds of developing CA., Results: The CA group had a significantly lower voltage-to-mass ratio than the HCM, hypertensive heart disease, and healthy control groups. The voltage-to-mass ratio was an independent factor significantly associated with the CA diagnosis after adjusting for baseline characteristics. Linear and CSA methods yielded areas under the ROC curve of 0.86 and 0.90, respectively. Using the CSA method, the optimal cut-off was 16.42 mV/mm 2 /m 2 , with 89.0% sensitivity and 80.8% specificity., Conclusion: The voltage-to-mass ratio could differentiate patients with CA, HCM, and hypertensive heart disease from healthy controls, potentially providing an accurate and non-invasive alternative to current expensive and invasive diagnostic techniques., (© 2024 The Author(s). Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.)

Non-invasive radioablation using stereotactic body radiation therapy with X-ray has been proposed as a rescue treatment for refractory ventricular tachycardia (VT). However, there are concerns about the occurrence of late valvular or coronary disease. We treated VT originating from the aortic sinus cusp using the Bragg peak principle of a heavy ion beam, minimizing the dose to the aortic valve and coronary artery and providing an anti-arrhythmic effect and cardiac function recovery due to improved sympathetic nerve heterogeneity. We present a method for targeting sympathetic nerve distribution using 123 I-metaiodobenzylguanidine scintigraphy., (© 2024 The Author(s). Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.)

Coronary artery spasms (CAS) can manifest in various forms, from silent ischemia to severe cardiac events like myocardial infarction and sudden death. This case involves a 56-year-old male with recurrent ischemic chest pain and varying ECG signs. Cardiac catheterization revealed multiple coronary spasms that resolved spontaneously or with intracoronary nitroglycerin. The report emphasizes the severe presentations of multiple CAS and the importance of thorough diagnostic evaluation to avoid unnecessary interventions, highlighting the diagnostic challenges in managing such cases., (© 2024 The Author(s). Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.)

Background: While COVID-19 is predominantly a respiratory disease, cardiovascular complications occur and are associated with worse outcomes. Electrocardiogram (ECG) abnormalities are frequently observed in hospitalized COVID-19 patients, some of which are associated with increased mortality. It is unclear whether ECG abnormalities occurring before hospitalization are associated with increased mortality. This quantitative systematic literature review aims to determine which ECG changes occurring before hospitalization are associated with mortality and discuss whether these findings can aid the assessment of patients and decision-making in the pre-hospital environment., Methods: A systematic search of the following digital databases was conducted: CINAL, PUBMED, MEDLINE, and Coronavirus Research Database. Eight cohort studies (primary papers) including COVID-19 patients with ECGs taken in the Emergency Department before hospitalization were selected for quantitative synthesis and results were obtained for the prevalence of ECG changes among survivors compared with non-survivors. Odds and hazard ratios for ECG abnormalities associated with mortality were also collected and compared., Results: Identification of ECG abnormalities on pre-hospitalization ECG is associated with increased mortality in COVID-19 patients. These ECG abnormalities include non-sinus rhythm, QTc prolongation, left bundle branch block, axis deviation, atrial fibrillation, atrial flutter, right ventricular strain patterns, ST segment changes, T wave abnormalities, and evidence of left ventricular hypertrophy., Conclusion: Electrocardiogram assessment in the pre-hospital environment may be beneficial when assessing COVID-19 patients and could help identify patients at increased risk of mortality., (© 2024 The Author(s). Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.)

Electrocardiogram (ECG) delineation is essential to the identification of abnormal cardiac status, especially when ECG signals are remotely monitored with wearable devices. The complexity and diversity of cardiac conditions generate numerous pathological ECG patterns, not only requiring the recognition of normal ECG but also addressing an extensive range of abnormal ECG patterns, posing a challenging task. Therefore, we propose an abnormal recognition-assisted network to integrate supplementary information on diverse ECG patterns. Simultaneously, we design an onset-offset aware loss to enhance precise waveform localization. Specifically, we establish a two-branch framework where ECG delineation serves as the target task, producing the final segmentation results. Additionally, the abnormal recognition-assisted network serves as an auxiliary task, extracting multi-label pathological information from ECGs. This joint learning approach establishes crucial correlations between ECG delineation and associated ECG abnormalities. The correlations enable the model to demonstrate sufficient generalization in the presence of diverse abnormal ECG patterns. Besides, onset-offset aware loss focuses intensively on wave onsets and offsets by applying biased weights to various waveform positions. This approach ensures a focus on precise localization, facilitating seamless integration into cross-entropy loss function. A large-scale wearable 12-lead dataset containing 4,913 signals is collected, offering an extensive range of ECG data for model training. Results demonstrate that our method achieves outstanding performance on two test datasets, attaining sensitivity of 94.97% and 94.27% and an error tolerance lower than 20 ms. Furthermore, our method is effective for various aberrant ECG signals, including ST-segment changes, atrial premature beats, and right and left bundle branch blocks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Silent myocardial infarction (SMI) frequently goes undetected, yet it is associated with increased cardiovascular morbidity and mortality. The impact of intensive systolic blood pressure (SBP) lowering on the risk of SMI in those with hypertension remains uncertain., Methods: In this post hoc analysis of the Systolic Blood Pressure Intervention Trial (SPRINT), participants with serial electrocardiograms (ECGs) during the trial were included. SPRINT investigated the benefit of intensive SBP lowering, aiming for < 120 mmHg compared to the standard SBP goal of < 140 mmHg. Incident SMI was defined as evidence of new MI on an ECG without adjudicated recognized myocardial infarction (RMI)., Results: During a median follow-up of 3.9 years, a total of 234 MI events (55 SMI and 179 RMI) occurred. Intensive, compared to standard, SBP lowering resulted in a lower rate of SMI (incidence rate 1.1 vs. 2.3 cases per 1000 person-years, respectively; HR [95% CI]: 0.48 [0.27-0.84]). Similarly, intensive, compared to standard, BP lowering reduced the risk of RMI (incidence rate 4.6 vs. 6.5 cases per 1000 person-years, respectively; HR [95% CI]: 0.71 [0.52-0.95]). No significant differences were noted between the strength of the association of intensive BP control on lowering the risk of SMI and RMI (p-value for HR differences = 0.23)., Conclusions: This study shows that in adults with hypertension, the benefits of intensive SBP lowering, compared with standard BP lowering, go beyond the prevention of RMI to include the prevention of SMI., Trial Registration: ClinicalTrials.gov Identifier: NCT01206062., (© 2024 The Author(s). Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.)

Feature attribution methods can visually highlight specific input regions containing influential aspects affecting a deep learning model's prediction. Recently, the use of feature attribution methods in electrocardiogram (ECG) classification has been sharply increasing, as they assist clinicians in understanding the model's decision-making process and assessing the model's reliability. However, a careful study to identify suitable methods for ECG datasets has been lacking, leading researchers to select methods without a thorough understanding of their appropriateness. In this work, we conduct a large-scale assessment by considering eleven popular feature attribution methods across five large ECG datasets using a model based on the ResNet-18 architecture. Our experiments include both automatic evaluations and human evaluations. Annotated datasets were utilized for automatic evaluations and three cardiac experts were involved for human evaluations. We found that Guided Grad-CAM, particularly when its absolute values are utilized, achieves the best performance. When Guided Grad-CAM was utilized as the feature attribution method, cardiac experts confirmed that it can identify diagnostically relevant electrophysiological characteristics, although its effectiveness varied across the 17 different diagnoses that we have investigated., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Cardiovascular risk assessment is a critical component of healthcare, guiding preventive and therapeutic strategies. In this study, we developed and evaluated an image-based electrocardiogram (ECG) analyzing an artificial intelligence (AI) model that estimates biological age and mortality risk., Methods: Using a dataset of 978 319 ECGs from 250 145 patients at Seoul National University Bundang Hospital, we developed a deep-learning model utilizing printed 12-lead ECG images to estimate patients' age (ECG-Age) and 1- and 5-year mortality risks. The model was validated externally using the CODE-15% dataset from Brazil., Results: The ECG-Age showed a high correlation with chronological age in both the internal and external validation datasets (Pearson's R = 0.888 and 0.852, respectively). In the internal validation, the direct mortality risk prediction models showed area under the curves (AUCs) of 0.843 and 0.867 for 5- and 1-year all-cause mortality, respectively. For 5- and 1-year cardiovascular mortality, the AUCs were 0.920 and 0.916, respectively. In the CODE-15%, the mortality risk predictions showed AUCs of 0.818 and 0.836 for the prediction of 5- and 1-year all-cause mortality, respectively. Compared to the neutral Delta-Age (ECG-Age - chronological age) group, hazard ratios for deaths were 1.88 [95% confidence interval (CI): 1.14-3.92], 2.12 (95% CI: 1.15-3.92), 4.46 (95% CI: 2.22-8.96) and 7.68 (95% CI: 3.32-17.76) for positive Delta-Age groups (5-10, 10-15, 15-20, >20), respectively., Conclusion: An image-based AI-ECG model is a feasible tool for estimating biological age and assessing all-cause and cardiovascular mortality risks, providing a practical approach for utilizing standardized ECG images in predicting long-term health outcomes., (Copyright © 2024 Italian Federation of Cardiology - I.F.C. All rights reserved.)

Background: Severe QT interval prolongation requires monitoring QTc intervals during anaesthesia with recommended therapeutic interventions at a threshold of 500 ms. The need for 12-lead ECG and lack of standardisation limit such monitoring. We determined whether automated continuous intraoperative QTc monitoring with 5-lead ECG measures QTc intervals comparable to 12-lead ECG and whether the interpretation of QTc intervals depends on the correction formulae and the patient's sex. We compared intraoperative QTc times to QTc times from resting ECGs of a population from the same region, to substantiate the hypothesis that patients under general anaesthesia may need specific treatment thresholds., Methods: In this prospective observational study, intraoperative QT/QTc intervals were automatically recorded using 12 and 5-lead ECG in 100 patients (44% males). QTc values were analysed for sex and formula-specific aspects after correction for heart rate according to Bazett, Fridericia, Hodges, Framingham, Charbit and QTcRAS, and compared to a regional community-based cohort. The level of significance was set to α = 0.05., Results: QT interval duration was not significantly different between 12-lead and 5-lead ECG (difference - 0.09 ms ± 8.5 ms, p = 0.793). The QTc interval duration significantly differed between the correction formulae (p < 0.001) and between sexes (p < 0.001). Mean intraoperative QTc duration was higher than in resting ECGs from a large community-based population with the same regional background (438 vs. 417 ms). The incidence of prolonged values >500 ms significantly depended on the correction formula (p < 0.001) and was up to tenfold higher in women versus men., Conclusion: Intraoperative QTc interval measurement using a 5-lead ECG is valid. Correction formulae and gender influence the intraoperative QTc interval duration and the incidence of pathologically prolonged values according to current limits. The consideration and definition of sex-specific normal limits for QTc times under general anaesthesia, therefore, warrant further investigation., (© 2024 The Author(s). Acta Anaesthesiologica Scandinavica published by John Wiley & Sons Ltd on behalf of Acta Anaesthesiologica Scandinavica Foundation.)

Background: In electrocardiographic imaging (ECGI), selecting an optimal regularization parameter (λ) is crucial for obtaining accurate inverse electrograms. The effects of signal and geometry uncertainties on the inverse problem regularization have not been thoroughly quantified, and there is no established methodology to identify when λ is sub-optimal due to these uncertainties. This study introduces a novel approach to λ selection using Tikhonov regularization and L-curve optimization, specifically addressing the impact of electrical noise in body surface potential map (BSPM) signals and geometrical inaccuracies in the cardiac mesh., Methods: Nineteen atrial simulations (5 of regular rhythms and 14 of atrial fibrillation) ensuring variability in substrate complexity and activation patterns were used for computing the ECGI with added white Gaussian noise from 40 dB to -3dB. Cardiac mesh displacements (1-3 cm) were applied to simulate the uncertainty of atrial positioning and study its impact on the L-curve shape. The regularization parameter, the maximum curvature, and the most horizontal angle of the L-curve (β) were quantified. In addition, BSPM signals from real patients were used to validate our findings., Results: The maximum curvature of the L-curve was found to be inversely related to signal-to-noise ratio and atrial positioning errors. In contrast, the β angle is directly related to electrical noise and remains unaffected by geometrical errors. Our proposed adjustment of λ, based on the β angle, provides a more reliable ECGI solution than traditional corner-based methods. Our findings have been validated with simulations and real patient data, demonstrating practical applicability., Conclusion: Adjusting λ based on the amount of noise in the data (or on the β angle) allows finding optimal ECGI solutions than a λ purely found at the corner of the L-curve. It was observed that the relevant information in ECGI activation maps is preserved even under the presence of uncertainties when the regularization parameter is correctly selected. The proposed criteria for regularization parameter selection have the potential to enhance the accuracy and reliability of ECGI solutions., Competing Interests: Declaration of competing interest M.S. Guillem. A.M. Climent. Are co-founders of Corify Care SL. R. Molero, J. Reventós-Presmanes, A.M. Climent. And M.S. Guillem get honorary from Corify Care SL. M.S. Guillem, L. Mont is shareholders of Corify Care SL. L. Mont reports honoraria as consultant, lecturer, and Advisory Board from Boston Scientific, Abbott Medical, Johnson & Johnson, and Medtronic. He is a shareholder of Galgo Medical SL. I. Roca-Luque has received honoraria as a lecturer and consultant from Abbott and Biosense Webster., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This review aims to provide hospitalists with a streamlined understanding of the evaluation of syncope, a commonly encountered complaint in hospital medicine. Authors conducted a literature review using the keyword 'syncope' to compile current evidence and recommendations regarding the categorization, risk stratification, and diagnostic evaluation of syncope. The review outlines the categorization of syncope into cardiac, neurally mediated, and orthostatic types, detailing the clinical features, etiologies, and differential diagnoses of each. It provides a structured framework for history-taking, physical examination, orthostatic vital sign assessment, and risk stratification to guide clinicians in efficiently identifying high-risk patients requiring urgent intervention. Additionally, diagnostic modalities including electrocardiography, echocardiography, and point-of-care ultrasound are discussed, along with their respective indications and limitations. By embracing standardized approaches and evidence-based practices outlined in this review, hospitalists can optimize resource utilization, streamline patient care, and improve outcomes in patients presenting with syncope.

Cardiovascular diseases represent the leading global cause of death, typically diagnosed and addressed through electrocardiograms (ECG), which record the heart's electrical activity. In recent years, there has been a notable surge in ECG recordings, driven by the widespread use of wearable devices. However, the limited availability of medical experts to analyze these recordings underscores the necessity for automated ECG analysis using computer-aided methods. In this study, we introduced 3DECG-Net, a deep learning model designed to detect and classify seven distinct heart states through the analysis of data fusion from 12-lead ECG in a multi-label framework. Our model leverages a residual architecture with a multi-head attention mechanism, undergoing training within a five-fold cross-validation scheme. By transforming 12-lead ECG signals into 3D data with the help of Recurrent Plot technique, 3DECG-Net achieves a noteworthy micro F1-score of 80.3 %, surpassing the performance of other state-of-the-art deep learning models developed for this specific task. Also, we present an ECG preprocessing framework to generate compact, high-quality ECG signals for potential application in future studies within this domain. We conduct an explainable AI experiment using Local Interpretable Model-agnostic Explanations (LIME) to elucidate the significance of each lead in accurately diagnosing specific arrhythmias, ensuring the logical processing of ECG data by 3DECG-Net. The findings of this study suggest that the proposed model is trustworthy and has the potential to be used as an effective diagnostic toolset for identifying heart arrhythmias. Its effectiveness can improve the diagnostic process, facilitate early treatment, and enhance overall efficiency in medical settings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Automatic pain assessment is an application in healthcare serving personalized pain care, and patients cannot self-report pain. Pain at the present is inferred from physiological dynamics at the present and in the near past. However, heterogeneous pain responses cross-subject and cross-type hinder accurate recognition of pain. This work solves the adaptive pain recognition problem across pain types. We concrete the adaptivity problem into recognizing both phasic/short and tonic/long pain from the physiological sequences of the same length. The adaptivity of the proposed solution (TCAtt-PainNet) was ensured by hybrid temporal-channel attention when fusing multivariate time-series of electrocardiogram (ECG) and galvanic skin response (GSR) features. The attention was obtained by learning the dependencies between the point at present and the sequence in the near past, where sequence point temporal attention was constructed via modified self-attention, and the following feature channel attention was constructed by squeeze-and-excitation temporal attention weighted deep feature sequence. The proposed solution successfully enhanced recognition adaptivity by addressing relevant information only from long input sequences when testing with tonic and phasic pain databases, making progress towards automatic pain assessment for real application scenarios with attributes unknown pain.

Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological signals can be used for security applications to achieve continuous authentication and user convenience due to passive data acquisition. This paper investigates an electrocardiogram (ECG) based biometric user authentication system using features derived from the Convolutional Neural Network (CNN) and self-supervised contrastive learning. Contrastive learning enables us to use large unlabeled datasets to train the model and establish its generalizability. We propose approaches enabling the CNN encoder to extract appropriate features that distinguish the user from other subjects. When evaluated using the PTB ECG database with 290 subjects, the proposed technique achieved an authentication accuracy of 99.15%. To test its generalizability, we applied the model to two new datasets, the MIT-BIH Arrhythmia Database and the ECG-ID Database, achieving over 98.5% accuracy without any modifications. Furthermore, we show that repeating the authentication step three times can increase accuracy to nearly 100% for both PTBDB and ECGIDDB. This paper also presents model optimizations for embedded device deployment, which makes the system more relevant to real-world scenarios. To deploy our model in IoT edge sensors, we optimized the model complexity by applying quantization and pruning. The optimized model achieves 98.67% accuracy on PTBDB, with 0.48% accuracy loss and 62.6% CPU cycles compared to the unoptimized model. An accuracy-vs-time-complexity tradeoff analysis is performed, and results are presented for different optimization levels.

This paper proposes a system for predicting the effect of electrical defibrillation using spectral feature parameters. The proposed method consists of two-stage prediction. The first stage involves predicting whether electrical defibrillation is "Successful" or "Ineffective." As the next stage, if the proposed prediction system determines "Ineffective," the proposed system discriminates between "VF recurrence" or "Failure" for electrical defibrillation. To develop the prediction system, feature parameters for the target electrocardiograms (ECGs) were first extracted by using the wavelet transform and spectral analysis. Next, effective feature parameters for prediction are selected through an analysis of variance. Moreover, in the preprocessing phase, the Synthetic Minority Oversampling Technique method and standardization are introduced. Finally, support vector machines with some kernel functions and the regularization method are utilized to predict the three states, i.e., "Successful," "Failure," and "VF recurrence," for electrical defibrillation in two phases. In this paper, we present our analysis method for ECGs and evaluate the effectiveness of the proposed prediction system., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hidetoshi Oya reports a relationship with Japan Society for the Promotion of Science that includes: funding grants., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Patients with congenital long QT syndrome (LQTS) are prone to ventricular dysrhythmia but may be initially asymptomatic with a normal QTc interval on resting electrocardiogram (ECG). Albuterol is listed as a medication that poses a "special risk" to patients with congenital LQTS, but its effects have been rarely described. We present a case of previously unknown, asymptomatic congenital LQTS unmasked by albuterol in an adolescent with asthma., Case Report: A 12-year-old girl with a history of asthma presented to the emergency department (ED) with shortness of breath, wheezing, and tachycardia for 24 h, consistent with acute asthma exacerbation. She received two doses of her home albuterol inhaler 2 h prior to presentation. Initial ECG demonstrated a QTc of 619 ms. Her remaining history, clinical examination, and laboratory workup, including electrolytes, were unremarkable. She was observed with cardiac monitoring before being discharged from the ED in stable condition for next-day outpatient pediatric cardiology follow-up. Resting office ECGs revealed QTcs from 440-470 ms. Exercise stress test revealed QTc prolongation of 520 ms and 500 ms at minute-2 and minute-4 of recovery, respectively. Genetic testing revealed heterozygous pathogenic variants in KCNQ1, consistent with type 1 LQTS. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: Albuterol may be a cause of marked QTc prolongation in ED patients with underlying congenital LQTS, which can be a diagnostic clue in previously unidentified patients. Extreme QTc prolongation also serves as an indication in the ED for Cardiology consultation, laboratory evaluation for electrolyte imbalances, and observation with cardiac monitoring., Competing Interests: Declaration of competing interest No conflicts of interest to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

In response to increasing data privacy regulations, this work examines the use of federated learning for deep residual networks to diagnose cardiac abnormalities from electrocardiogram (ECG) data. This approach allows medical institutions to collaborate without exchanging raw patient data. We utilize the publicly available data from the PhysioNet/Computing in Cardiology Challenge 2021, featuring diverse ECG databases, to compare the classification performance of three federated learning methods against both central training with data sharing and isolated training scenarios. We show that federated learning outperforms ECG classifiers trained in isolation. In particular, our findings demonstrate that a globally trained model fine-tuned to specific local datasets surpasses non-collaborative approaches. This shows that models trained in federation learn general features that can be tailored to specific tasks. Furthermore, federated learning almost matches the performance of central training with data sharing on out-of-distribution data from non-participating institutions. These results highlight the ability of federated learning in developing models that generalize well across diverse patient data, without the need to share data among institutions, thus addressing data privacy concerns.

The QT interval, an electrocardiographic temporal representation of the ventricular depolarization and repolarization, is an integral parameter that must be carefully evaluated to gather critical information regarding electrical instability that may cause malignant ventricular dysrhythmias or sudden cardiac death. The QT interval is affected by several inheritable and acquired factors, such as genetic mutations, electrolyte disturbances, and medication interactions. We strongly believe that prompt and accurate recognition of any QT interval abnormalities is critical in many clinical settings. This concise review article highlights the importance of accurate measurement of the QT interval, enhances understanding of the most prevalent factors yielding abnormalities within the QT interval and the prognostic value of the QT interval, as well as provides several key practical reminders for healthcare professionals to strengthen our clinical practice., Competing Interests: Declaration of competing interest The author has no financial or other conflicts of interest to disclose., (Copyright © 2024 Southern Society for Clinical Investigation. Published by Elsevier Inc. All rights reserved.)

High-frequency ultrasound (HFUS; >30 MHz) Doppler imaging has been widely used in the imaging of small animals and humans because of its high resolution. Vector Doppler imaging (VDI) has certain advantages for visualizing complex flow patterns independent of the Doppler angle. However, no commercial HFUS VDI system is currently available; therefore, several studies have connected an ultrasound research platform (Verasonics Vantage 256) with an HFUS array transducer for HFUS VDI. Unfortunately, the maximum frame rate of this system is only 10 kHz at an operational frequency of 40 MHz because of limitations related to data transmission hardware, thereby restricting the maximum detectable velocity of Doppler measurements. To address this drawback, in the present study, an electrocardiography (ECG)-gating-based HFUS VDI system was developed to avoid Doppler flow aliasing in data acquisition by ultrasound research platform at its maximum frame rate of 10 kHz. The developed method aligns all tilted plane waves with the ECG R-wave, which avoids the trade-off between frame rate and tilted angles number in conventional VDI. The performance of the proposed data acquisition method in HFUS VDI was verified using a steady-flow phantom, for which estimation errors were less than 10% under different flow settings. In animal studies, peak flow velocities in the carotid artery, left ventricle, and aortic arch of wild-type mice were measured (approximately 55, 655, and 765 mm/s, respectively). Also, the HFUS VDI from the mitral regurgitation mice model was obtained to present the complex flow patterns through the proposed method. In contrast to the conventional method, no Doppler aliasing occurs in the proposed method because the frame rate is sufficient. The experimental results indicate the developed HFUS VDI has the potential to become a useful tool for vector flow visualization in small animals, even under a high flow velocity.

Automatic electrocardiogram (ECG) classification provides valuable auxiliary information for assisting disease diagnosis and has received much attention in research. The success of existing classification models relies on fitting the labeled samples for every ECG type. However, in practice, well-annotated ECG datasets usually cover only limited ECG types. It thus raises an issue: conventional classification models trained with limited ECG types can only identify those ECG types that have already been observed in the training set, but fail to recognize unseen (or unknown) ECG types that exist in the wild and are not included in training data. In this work, we investigate an important problem called open-world ECG classification that can predict fine-grained observed ECG classes and identify unseen classes. Accordingly, we propose a customized method that first incorporates clinical knowledge into contrastive learning by generating "hard negative" samples to guide learning diagnostic ECG features (i.e., distinguishable representations), and then performs multi-hypersphere learning to learn compact ECG representations for classification. The experiment results on 12-lead ECG datasets (CPSC2018, PTB-XL, and Georgia) demonstrate that the proposed method outperforms the state-of-the-art methods. Specifically, our method achieves superior accuracy than the comparative methods on the unseen ECG class and certain seen classes. Overall, the investigated problem (i.e., open-world ECG classification) helps to draw attention to the reliability of automatic ECG diagnosis, and the proposed method is proven effective in tackling the challenges. The code and datasets are released at https://github.com/betterzhou/Open&#95;World&#95;ECG&#95;Classification., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Shuang Zhou reports a relationship with National University of Singapore that includes: non-financial support. Shuang Zhou reports a relationship with Peking University that includes: non-financial support. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: Cardiac cephalalgia, once seen as a rare symptom of coronary artery disease, is now more recognized. It often comes with chest discomfort and autonomic dysfunction, worsened by physical activity. However, not all cases have chest symptoms or are activity induced. This report presents a case of cardiac cephalalgia and reviews 46 previous cases., Method: We discuss a unique case where a patient had headache attacks without chest symptoms, autonomic dysfunction, or triggers. We reviewed English case reports of cardiac cephalalgia from 1982 to 2022 using PubMed ( http://www.ncbi.nlm.nih.gov/pubmed )., Results: A 69-year-old man presented with a sudden headache without triggers or typical symptoms. Coronary computed tomography angiography (CTA) showed diffuse stenosis in the left anterior descending and the first diagonal branch arteries. His headache improved after percutaneous coronary intervention. Cardiac cephalalgia is usually marked by severe headaches, autonomic signs, and often affects the occipital region. Electrocardiogram (ECG) might not always show abnormalities, and chest pain is not always present. In such cases, elevated cardiac enzymes can be crucial for diagnosis., Conclusion: When a headache is the sole symptom of an acute coronary event, consider moderate to severe intensity, older age at onset, occipital localization, and autonomic signs. ECG, cardiac enzymes, and coronary CTA are valuable for accurate diagnosis and treatment., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Establishing the precise mechanism of cardiac arrhythmias in the electrophysiology laboratory is one of the main requisites for a successful and safe ablation. This article provides an organized approach to the differential diagnosis of narrow and wide complex tachycardias based on the analysis of electrical activation patterns, followed by specific pacing maneuvers in each case., Competing Interests: Disclosures The authors have no conflicts of interest to disclose., (Copyright © 2024 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.)

Myocardial infarction (MI), referred to as a heart attack, is a life-threatening condition that happens due to blood clots, typically, blood flow to a portion of the heart muscle is blocked. The cardiac muscle may become permanently damaged if there is insufficient oxygen and blood flow to the affected area. It's crucial to treat MI as soon as possible because even a small delay might have serious effects. The primary diagnostic tool to track and identify the signs of MI is the electrocardiogram (ECG). The complexity of MI signals combined with noise makes it difficult for clinicians to make a precise and prompt diagnosis. It might be laborious and time-consuming to manually analyse an enormous quantity of ECG data. Therefore, techniques for autonomously diagnosing from the ECG data are required. There have been numerous research on the topic of MI espial, but the majority of the algorithms are cognitively intensive when working with empirical data. The current study suggests a unique method for the efficient and reliable identification of MI. We employed circulant singular spectrum analysis (CSSA) for baseline wander removal, a 4-stage Savitzky-Golay (SG) filter to expunge powerline interference from the ECG signal and segmented in the preprocessing stage. Thus segmented ECG has been decomposed using CSSA, entropy based features are extracted. The best features are selected by using binary Harris hawk optimization (BHHO) and to machine learning (ML) classifiers like Naive Bayes, Decision tree, K-nearest neighbor (KNN), Support vector machine (SVM), and Ensemble subspace KNN. Our suggested method has been examined from both class as well as subject oriented perspectives. While the subject-oriented technique uses data from one patient for testing while using data from the other subjects for training, the class-wise strategy divides data as test data as well as training data regardless of subjects. We succeeded in achieving accuracy ( A c % ) of 99.8, sensitivity ( S e % ) of 99, and 100 specificity ( S p %) under the class-oriented approach. Similarly, for the subject wise strategy we achieved a mean A c % , S e %, and S p % of 85.2, 83.1, and 84.5, respectively.

Background: Dynamic changes in the fQRS complex between the initial and follow-up ECG in patients with acute pulmonary embolism (APE) have rarely been studied., Objective: The purpose of this study was to investigate the significance of dynamic changes in the fragmented QRS complex in APE patients., Methods: APE patients (n = 222) were divided into three groups based on their ECG data to determine whether there were dynamic changes in the fQRS complex from admission to follow-up at one month: the fQRS shallower group (n = 49), fQRS deeper group (n = 25) and fQRS unchanged group (n = 148). Each patient was observed and followed for 12 months., Results: Cox multivariate logistic regression analysis indicated that the dynamic deeper fQRS complex was an independent predictor of long-term mortality (HR: 5.563, 95 % CI: 1.079-28.678, P = 0.040) in patients with APE. Kaplan-Meier curve analysis revealed that the event-free survival of the fQRS shallower group significantly increased relative to that of the fQRS deeper group and that of the fQRS deeper group significantly decreased relative to that of the fQRS unchanged group and shallower group (P = 0.022, P = 0.041)., Conclusion: Compared with the deeper fQRS complex, the dynamic shallower fQRS complex was an indicator of a good prognosis in APE patients, while the dynamic deeper fQRS complex indicated a poor prognosis. Dynamical changes in fQRS may assist clinicians in risk stratification and individualized treatment for APE, as well as in predicting APE regression or progression., Competing Interests: Declaration of competing interest The authors declared no conflicts of interest with respect to the authorship and/or publication of this article., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Slower adaptation of the QT interval to sudden changes in heart rate has been identified as a risk marker of ventricular arrhythmia. The gradual changes observed in exercise stress testing facilitates the estimation of the QT-RR adaptation time lag., Methods: The time lag estimation is based on the delay between the observed QT intervals and the QT intervals derived from the observed RR intervals using a memoryless transformation. Assuming that the two types of QT interval are corrupted with either Gaussian or Laplacian noise, the respective maximum likelihood time lag estimators are derived. Estimation performance is evaluated using an ECG simulator which models change in RR and QT intervals with a known time lag, muscle noise level, respiratory rate, and more. The accuracy of T-wave end delineation and the influence of the learning window positioning for model parameter estimation are also investigated., Results: Using simulated datasets, the results show that the proposed approach to estimation can be applied to any changes in heart rate trend as long as the frequency content of the trend is below a certain frequency. Moreover, using a proper position of the learning window for exercise so that data compensation reduces the effect of nonstationarity, a lower mean estimation error results for a wide range of time lags. Using a clinical dataset, the Laplacian-based estimator shows a better discrimination between patients grouped according to the risk of suffering from coronary artery disease., Conclusions: Using simulated ECGs, the performance evaluation of the proposed method shows that the estimated time lag agrees well with the true time lag.

Electrocardiogram (ECG) is a critical biomedical signal and plays an imperative role in diagnosing cardiovascular disorders. During ECG data acquisition in clinical environment, noise is frequently present. Various noises such as powerline interference (PLI) and baseline wandering (BLW) distort the ECG signal which may lead to incorrect interpretation. Consequently, substantial emphasis has been dedicated to ECG denoising for reliable diagnosis and analysis. In this study, a novel hybrid ECG denoising method based on variational mode decomposition (VMD) and the empirical wavelet transform (EWT) is presented. For effective denoising using the VMD and EWT approach, the noisy ECG signal is decomposed within narrow-band variational mode functions (VMFs). The aim is to remove noise from these narrow-band VMFs. In current approach, the centre frequency of each VMF was computed and utilized to design an adaptive wavelet filter bank using EWT. This leads to effective removal of noise components from the signal. The proposed approach was applied to ECG signals obtained from the MIT-BIH Arrhythmia database. To evaluate the denoising performance, noise sources from the MIT-BIH Noise Stress Test Database (NSTDB) are used for simulation. The assessment of denoising performance in based on two key metrics: the percentage-root-mean-square difference (PRD) and the signal-to-noise ratio (SNR). The findings of the simulation experiment demonstrate that the suggested method has lower percentage root mean square difference and higher signal-to-noise ratio as compared to existing state of the art denoising methods. An average output SNR of 24.03 was achieved, along with a 5% reduction in PRD.

Objective: The severity of atrial fibrillation (AF) can be assessed from intra-operative epicardial measurements (high-resolution electrograms), using metrics such as conduction block (CB) and continuous conduction delay and block (cCDCB). These features capture differences in conduction velocity and wavefront propagation, but ignore complementary properties such as the morphology of the action potentials. In this work, we focus on such complementary properties, and derive features to detect variations in the atrial potential waveforms., Methods: We show that the spatial variation of atrial potential morphology during a single beat may be described by changes in the singular values of the epicardial measurement matrix. The method is non-parametric and requires little preprocessing. A corresponding singular value map points at areas subject to fractionation and block. Further, we developed an experiment where we simultaneously measure electrograms (EGMs) and a multi-lead ECG., Results: The captured data showed that the normalized singular values of the heartbeats during AF are higher than during SR, and that this difference is more pronounced for the (non-invasive) ECG data than for the EGM data, if the electrodes are positioned at favorable locations., Conclusion: Overall, the singular value-based features are a useful indicator to detect and evaluate AF., Significance: The proposed method might be beneficial for identifying electropathological regions in the tissue without estimating the local activation time.

Purpose: Sleep studies are important to evaluate sleep and sleep-related disorders. The standard test for evaluating sleep is polysomnography, during which several physiological signals are recorded separately and simultaneously with specialized equipment that requires a technologist. Simpler recordings that can model the results of a polysomnography would provide the benefit of expanding the possibilities of sleep recordings., Methods: Using the publicly available sleep data set from the multiethnic study of atherosclerosis and 1769 nights of sleep, we extracted a distinct data subset with engineered features of the biomarkers collected by actigraphic, oxygenation, and electrocardiographic sensors. We then applied scalable models with recurrent neural network and Extreme Gradient Boosting (XGBoost) with a layered approach to produce an algorithm that we then validated with a separate data set of 177 nights., Results: The algorithm achieved an overall performance of 0.833 accuracy and 0.736 kappa in classifying into four states: wake, light sleep, deep sleep, and rapid eye movement (REM). Using feature analysis, we demonstrated that heart rate variability is the most salient feature, which is similar to prior reports., Conclusions: Our results demonstrate the potential benefit of a multilayered algorithm and achieved higher accuracy and kappa than previously described approaches for staging sleep. The results further the possibility of simple, wearable devices for sleep staging. Code is available at https://github.com/NovelaNeuro/nEureka-SleepStaging ., (Copyright © 2023 by the American Clinical Neurophysiology Society.)

The classification of ECG signals is a critical process because it guides the diagnosis of the proper treatment process for the patient. However, any form of disturbance with ECG signals can be highly conspicuous because of the mechanics involved in data acquisition from living beings, which has a significant impact on the classification procedure. The purpose of this research work is to advance ECG signal classification results by employing numerous denoising methods and, in turn, boost the accuracy of cardiovascular diagnoses. To simulate realistic conditions, we added various types of noise to ECG data, including Gaussian, salt and pepper, speckle, uniform, and exponential noise. To overcome the interference of noise from environments in the obtained ECG signals, we employed wavelet transform, median filter, Gaussian filter, and the hybrid of the wavelet and median filters. The proposed hybrid denoising method has better results than the other methods because of the use of wavelet multi-scale analysis and the ability of the median filter to avoid the loss of vital ECG characteristics. Thus, despite a certain proximity in the values, the hybrid method is significantly more accurate and reliable, as evidenced by the mean squared error (MSE), mean absolute error (MAE), R-squared, and Pearson correlation coefficient. More specifically, the hybrid approach provided an MSE of 0.0012 and an MAE of 0.025, the R-squared value for this study was 0.98, and the Pearson correlation coefficient was 0.99, which provides a very good resemblance to the original ECG confirmation. The proposed classification model is based on the modified lightweight CNN or MLCNN that was trained using the noisy and the denoised data. The findings demonstrated that by applying the denoised data, the testing accuracy, precision, recall, and F1 scores achieved 0.92, 0.91, 0.90, and 0.91 for the datasets, while the noisy data achieved 0.80, 0.78, 0.82, and 0.80, respectively. In this study, the signal quality and denoising methods were found to enhance ECG signal classification and diagnostic accuracy while encouraging proper preprocessing in future studies and applications for real-time ECG for cardiac care.

In the recent years, there has been a burgeoning interest in Takotsubo syndrome (TTS), which is renowned as a specific form of reversible myocardial dysfunction. Despite the extensive literature available on TTS, clinicians still face several practical challenges associated with the diagnosis and management of this phenomenon. This potentially results in the underdiagnosis and improper management of TTS in clinical practice. The present paper, the first part (part-1) of the consensus report, aims to cover diagnostic and therapeutic challenges associated with TTS along with certain recommendations to combat these challenges., Competing Interests: Conflict of Interest: The authors declare that they have no conflict of interest.

Wearable devices have a significant impact on society, and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using Internet of Things solutions can be a crucial tool for emergency management, reducing risks in rescue operations and ensuring the safety of workers. The massive amounts of data, high network traffic, and computational demands of a typical monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities. This study presents a Cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable device. The system monitors vital signs such as electrocardiograms and breathing patterns during work sessions, and it is able to manage real-time alarm events to a personnel management center. In this study, 900 paramedics and emergency workers were monitored using wearable devices over a period of 12 months. Data from these devices were collected, processed via Cloud infrastructure, and analyzed to assess the system's reliability and scalability. The results showed a significant improvement in worker safety and operational efficiency. This study demonstrates the potential of Cloud-based systems and Internet of Things devices in enhancing emergency response efforts., Competing Interests: Gian Angelo Geminiani was employed by G&G Technologies Srl, Marcello Modica was employed by LogConsulting, Antonio Augello was employed by Accyourate SpA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Smart clothes equipped with flexible sensing systems provide a comfortable means to track health status in real time. Although these sensors are flexible and small, the core signal-processing units still rely on a conventional printed circuit board (PCB), making current health-monitoring devices bulky and inconvenient to wear. In this study, a printed fabric-based hybrid circuit was designed and prepared-with a series of characteristics, such as surface/sectional morphology, electrical properties, and stability-to study its reliability. Furthermore, to verify the function of the fabric-based circuit, simulations and measurements of the circuit, as well as the collection and processing of a normal adult's electrophysiological signals, were conducted. Under 10,000 stretching and bending cycles with a certain elongation and bending angle, the resistance remained 0.27 Ω/cm and 0.64 Ω/cm, respectively, demonstrating excellent conductivity and reliability. Additionally, the results of the simulation and experiment showed that the circuit can successfully amplify weak electrocardiogram (ECG) signals with a magnification of 1600 times with environmental filtering and 50 Hz of industrial frequency interference. This technology can monitor human electrophysiological signals, such as ECGs, electromyograms (EMGs), and joint motion, providing valuable practical guidance for the unobtrusive monitoring of smart clothes.

Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and precise diagnosis to slow CAD progression. Electrocardiogram (ECG) and phonocardiogram (PCG), conveying abundant disease-related information, are prevalent clinical techniques for early CAD diagnosis. Nevertheless, most previous methods have relied on single-modal data, restricting their diagnosis precision due to suffering from information shortages. To address this issue and capture adequate information, the development of a multi-modal method becomes imperative. In this study, a novel multi-modal learning method is proposed to integrate both ECG and PCG for CAD detection. Along with deconvolution operation, a novel ECG-PCG coupling signal is evaluated initially to enrich the diagnosis information. After constructing a modified recurrence plot, we build a parallel CNN network to encode multi-modal information, involving ECG, PCG and ECG-PCG coupling deep-coding features. To remove irrelevant information while preserving discriminative features, we add an autoencoder network to compress feature dimension. Final CAD classification is conducted by combining support vector machine and optimal multi-modal features. The experiment is validated on 199 simultaneously recorded ECG and PCG signals from non-CAD and CAD subjects, and achieves high performance with accuracy, sensitivity, specificity and f1-score of 98.49%, 98.57%,98.57% and 98.89%, respectively. The result demonstrates the superiority of the proposed multi-modal method in overcoming information shortages of single-modal signals and outperforming existing models in CAD detection. This study highlights the potential of multi-modal deep-coding information, and offers a wider insight to enhance CAD diagnosis.

Rationale: Intravenous catheter placement in the healthy upper extremity is preferred for chemotherapy in patients with breast cancer. Common venous accesses are peripherally inserted central catheters (PICCs) and totally implantable intravenous port catheters (TIVPs). In this case, a patient with breast cancer had a history of infusion port placement through the left internal jugular vein, with ipsilateral innominate vein stenosis after placement. The patient was re-treated with a PICC placed ectopically through the left upper limb into the intrathoracic vein. After multidisciplinary consultation, a transfemoral PICC combined with intracavitary electrocardiography (IC-ECG) was performed to establish venous access. This case can assist PICC catheterization nurses in developing optimal venous access strategies tailored to the specific situations of patients in similar situations. Through adequate evaluations and optimal selection of venous access, the success rate of disposable catheterization can be improved, and the risk of complications reduced., Patient Concerns: A 53-year-old female with breast cancer had a history of infusion port, with ipsilateral innominate vein stenosis after placement. The patient was re-treated with a PICC placed ectopically through the left upper limb into the intrathoracic vein., Diagnoses: An axial computed tomography (CT) image before totally implantable venous access port (TIVP) placement and An axial CT 103 days after TIVP placement shows diffuse stenosis of the left innominate vein, associated with infusion port placement through the left internal jugular vein., Interventions: After multidisciplinary consultation, a transfemoral PICC combined with intracavitary electrocardiography (IC-ECG) was performed to establish venous access., Outcomes: The patient's lower limb PICC was left in place for 201 days with no complications, completing the full treatment cycle., Lessons: This case presents a rare and insightful clinical scenario. For patients with a history of infusion port placement, particularly via the left internal jugular vein, careful analysis of the innominate vena cava and examination of chest wall vein exposure are essential to determining the optimal vascular access strategy., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Impedance cardiography (ICG) is essential in evaluating cardiac function in patients with cardiovascular diseases. Aiming at the problem that the measurement of ICG signal is easily disturbed by motion artifacts, this paper introduces a de-noising method based on two-step spectral ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA). Firstly, the first spectral EEMD-CCA was performed between ICG and motion signals, and electrocardiogram (ECG) and motion signals, respectively. The component with the strongest correlation coefficient was set to zero to suppress the main motion artifacts. Secondly, the obtained ECG and ICG signals were subjected to a second spectral EEMD-CCA for further denoising. Lastly, the ICG signal is reconstructed using these share components. The experiment was tested on 30 subjects, and the results showed that the quality of the ICG signal is greatly improved after using the proposed denoising method, which could support the subsequent diagnosis and analysis of cardiovascular diseases.