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This study aimed to evaluate the correlation between heart rate variability and driving fatigue through a physiological approach, examining the effects of oxygen concentrations ranging from normal to high. Driver fatigue, a factor in fatal accidents, has complex causes and varied symptoms. The ambiguity in reporting physiological changes due to mixed terminology such as fatigue and drowsiness prompted this study, which is based on the premise that physiological fatigue originates from the same mechanisms as stress. A driving scenario that induces fatigue was simulated, involving fifteen university student drivers. Experiments were conducted at oxygen concentrations of 21%, 30%, and 40%. Data were collected via an electrocardiogram system and analyzed statistically. Our findings reveal that at a 30% oxygen concentration, drivers showed a significant increase in SDNN and a decrease in LF/HF ratio, indicating enhanced autonomic nervous system stability and reduced sympathetic dominance. This intervention was found to effectively reduce and delay driving fatigue, demonstrating that oxygen supplementation at 30% concentration could notably improve traffic safety by mitigating driver fatigue. [ABSTRACT FROM AUTHOR]

Background/Objectives: This study aimed to evaluate an artificial intelligence (AI)-enhanced electrocardiogram (ECG) screening protocol for improved accuracy, efficiency, and risk stratification across six sports: handball, football, athletics, weightlifting, judo, and karate. Methods: For each of the six sports, resting 12-lead ECGs from healthy children and junior athletes were analyzed using AI algorithms trained on annotated datasets. Parameters included the QTc intervals, PR intervals, and QRS duration. Statistical methods were used to examine each sport's specific cardiovascular adaptations and classify cardiovascular risk predictions as low, moderate, or high risk. Results: The accuracy, sensitivity, specificity, and precision of the AI system were 97.87%, 75%, 98.3%, and 98%, respectively. Among the athletes, 94.54% were classified as low risk and 5.46% as moderate risk with AI because of borderline abnormalities like QTc prolongation or mild T-wave inversions. Sport-specific trends included increased QRS duration in weightlifters and low QTc intervals in endurance athletes. Conclusions: The statistical analyses and the AI-ECG screening protocol showed high precision and scalability for the proposed athlete cardiovascular health risk status stratification. Additional early detection research should be conducted further for diverse cohorts of individuals engaged in sports and explore other diagnostic methods that can help increase the effectiveness of screening. [ABSTRACT FROM AUTHOR]

This paper seeks to solve the classification problem of cardiac arrhythmias by using a small number of electrocardiogram (ECG) recordings. To offer a reasonable solution to this problem, a technique that combines a common matrix approach (CMA)-based classifier model with the Fourier decomposition method (FDM) is proposed. The FDM is responsible for generating time–frequency (T-F) representations of ECG recordings. The classification process is performed with feature images applied as input to the classifier model. The feature images are obtained after two-dimensional principal component analysis (2DPCA) of data matrices related to ECG recordings. Each data matrix is created by concatenating the ECG record itself, the Fourier transform, and the T-F representation on a single matrix. To verify the efficacy of the proposed method, various experiments are conducted with the MIT-BIH, Chapman, and PTB-XL databases. In the assessments using the MIT-BIH database under the inter-patient paradigm, we achieved a mean overall accuracy rate of 99.81%. The proposed method outperforms the majority of recent efforts, yielding rates exceeding 99% on nearly five performance metrics for the recognition of V- and S-class arrhythmias. It is found that, in the classification of four types of arrhythmias using ECG recordings from the Chapman database, our model surpasses recent works by reaching mean overall accuracy rates of 99.76% and 99.45% for the raw and de-noised ECG recordings, respectively. Similarly, five different forms of arrhythmias from the PTB-XL database were recognized with a mean overall accuracy of 98.71%. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are the primary cause of death worldwide. For accurate diagnosis of CVDs, robust and efficient ECG denoising is particularly critical in ambulatory cases where various artifacts can degrade the quality of the ECG signal. None of the present denoising methods preserve the morphology of ECG signals adequately for all noise types, especially at high noise levels. This study proposes a novel Fully-Gated Denoising Autoencoder (FGDAE) to significantly reduce the effects of different artifacts on ECG signals. The proposed FGDAE utilizes gating mechanisms in all its layers, including skip connections, and employs Self-organized Operational Neural Network (self-ONN) neurons in its encoder. Furthermore, a multi-component loss function is proposed to learn efficient latent representations of ECG signals and provide reliable denoising with maximal morphological preservation. The proposed model is trained and benchmarked on the QT Database (QTDB), degraded by adding randomly mixed artifacts collected from the MIT-BIH Noise Stress Test Database (NSTDB). The FGDAE showed the best performance on all seven error metrics used in our work in different noise intensities and artifact combinations compared with state-of-the-art algorithms. Moreover, FGDAE provides reliable denoising in extreme conditions and for varied noise compositions. The significantly reduced model size, 61% to 73% reduction, compared with the state-of-the-art algorithm, and the inference speed of the FGDAE model provide evident benefits in various practical applications. While our model performs best compared with other models tested in this study, more improvements are needed for optimal morphological preservation, especially in the presence of electrode motion artifacts. [ABSTRACT FROM AUTHOR]

Daily electrocardiogram (ECG) monitoring is helpful for the early detection of cardiopulmonary disorders. In particular, bathing poses a risk for abnormal cardiac beats, respiratory failure, and drowning owing to the thermal effect and water pressure on the body. Thus, there is a need for nonintrusive measurement techniques without the attachment of electrodes and any instrument operation, that is, a bathtub ECG monitoring system. In this paper, we describe the development of a new bathtub ECG monitoring system that has electrodes pasted outside the bathtub wall, and thus the capacitive coupling was made from the electrodes to the tap water through the bathtub wall. In a previous system, the decrease in the thickness of the bathtub wall and the location of a long-tape-type reference electrode to prevent oscillation by the incorporation of environmental noise were required to obtain a stable ECG signal. To resolve these drawbacks, two electrodes covered by an active shield with an amplifier were pasted to the outside surface of the bathtub wall without the decrease in thickness, near the bather's right scapula and left hip. To prevent the signal baseline fluctuation and oscillation caused by the environmental noise, the most suitable input impedance of the amplifier was also determined. In 13 healthy subjects (21.9 ± 1.94 years), the QRS components in ECG were successfully detected during bathing with a reasonable signal-to-noise ratio of more than 14.9 dB. Moreover, the intervals of the heartbeat and respiration obtained by the bathtub system and by the direct method agreed with each other. [ABSTRACT FROM AUTHOR]

Studies on the compounds of aromatic oils and their effects on psychophysiological changes in humans are often conducted separately. To obtain better validation, a suitable protocol is needed that can be extrapolated to large-scale olfactory stimulation experiments. Unfortunately, this type of study is still rarely performed. In this situation, we propose a randomized crossover pilot study on olfactory stimulation with aromatic oils in relation to changes in psychophysiological activity by focusing on white musk aromatic oil due to its popularity in the community. Chemical profiling by TDU-GC-MS (thermal desorption gas chromatography/mass spectrometry) was performed to understand the compounds of the aromatic oils presented. To understand the changes in the participants' impressions and mood states, POMS 2 (Profile of Mood States 2nd Edition) and VAS (Visual analogue scale) were performed in addition to physiological evaluation by using EEG (electroencephalogram), ECG (electrocardiogram) and salivary amylase measurements. The proposed pilot study showed "gorgeous", "sweet", and "like" impression toward white musk aromatic oil under VAS evaluation. Mood evaluation under POMS 2 variables such as Fatigue-Inertia (FI), Tension-anxiety (TA) and TMD (total mood disturbance) were significantly decreased under white musk aromatic oil inhalation. Under current protocol, we can also see the changes in autonomic activity and brain activity during olfactory stimulation. This pilot study could be the first step towards a larger sample size experiment on olfactory stimulation. This experiment has been registered to UMIN Clinical Trials Registry with register ID : UMIN000051972 on 24/08/2023. [ABSTRACT FROM AUTHOR]

Background: Traumatic brain injury (TBI) disrupts normal brain tissue and functions, leading to high mortality and disability. Severe TBI (sTBI) causes prolonged cognitive, functional, and multi-organ dysfunction. Dysfunction of the autonomic nervous system (ANS) after sTBI can induce abnormalities in multiple organ systems, contributing to cardiovascular dysregulation and increased mortality. Currently, detailed characterization of early autonomic dysfunction in the acute phase after sTBI is lacking. This study aims to use physiological waveform data collected from patients with sTBI to characterize early autonomic dysfunction and its association with clinical outcomes to prevent multi-organ dysfunction and improving patient outcomes. Objective: This data analysis protocol describes our pre-planned protocol using cardiac waveforms to evaluate early autonomic dysfunction and to inform multi-dimensional characterization of the autonomic nervous system (ANS) after sTBI. Methods: We will collect continuous cardiac waveform data from patients managed in an intensive care unit within a clinical trial. We will first assess the signal quality of the electrocardiogram (ECG) using a combination of the structural image similarity metric and signal quality index. Then, we will detect premature ventricular contractions (PVC) on good-quality ECG beats using a deep-learning model. For arterial blood pressure (ABP) data, we will employ a singular value decomposition (SVD)-based approach to assess the signal quality. Finally, we will compute multiple indices of ANS functions through heart rate turbulence (HRT) analysis, time/frequency-domain analysis of heart rate variability (HRV) and pulse rate variability, and quantification of baroreflex sensitivity (BRS) from high-quality continuous ECG and ABP signals. The early autonomic dysfunction will be characterized by comparing the values of calculated indices with their normal ranges. Conclusion: This study will provide a detailed characterization of acute changes in ANS function after sTBI through quantified indices from cardiac waveform data, thereby enhancing our understanding of the development and course of eAD post-sTBI. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is the most common persistent arrhythmia, and it is crucial to develop generalizable automatic AF detection methods. However, supervised AF detection is often limited in performance due to the difficulty in obtaining labeled data. To address the gap between limited labeled data and the requirements for model robustness and generalization in single-lead ECG AF detection, we proposed a semi-supervised contrastive learning method named MLMCL for AF detection. The MLMCL method utilizes the multi-level feature representations of the encoder to perform multiple contrastive learning to fully exploit temporal consistency, channel consistency, and label consistency. Meanwhile, it combines labeled and unlabeled data for pre-training to obtain robust features for downstream tasks. In addition, it uses the domain knowledge in the field of AF diagnosis for domain knowledge augmentation to generate hard samples and improve the distinguishability of ECG representations. In the cross-dataset testing mode, MLMCL had better performance and good stability on different test sets, demonstrating its effectiveness and robustness in the AF detection task. The comparison results with existing studies show that MLMCL outperformed existing methods in external tests. The MLMCL method can be extended and applied to multi-lead scenarios and has reference significance for the development of contrastive learning methods for other arrhythmia. [ABSTRACT FROM AUTHOR]

This paper presents a mixed-mode on-chip automatic frequency tuning technique to achieve a process-invariant lowpass cut-off frequency for biopotential amplifiers. It comprises of a sine-like test signal generation circuit, a digital scheme to minimize the deviations in the frequency of the test signal, a programmable switched capacitor array acting as the load of the biopotential amplifier, and a peak detector based digital capacitor selection logic. Results obtained show that the proposed technique curtails the deviation of the cut-off frequency across process (and temperature) variations. The complete scheme implemented in UMC 0.18 μ m CMOS technology consumes only 1.02 μ W average power approximately with 1.8 V supply. Moreover the complete tuning mechanism lasts for less than half a minute only. Its low power consumption and implementation on analog platform makes it integrable with the standard portable biomedical systems intended for remote monitoring. [ABSTRACT FROM AUTHOR]

Abstract Fatigue can cause human error, which is the main cause of accidents. In this study, the dynamic fatigue recognition of unmanned electric locomotive operators under high-altitude, cold and low oxygen conditions was studied by combining physiological signals and multi-index information. The characteristic data from the physiological signals (ECG, EMG and EM) of 15 driverless electric locomotive operators were tracked and tested continuously in the field for 2 h, and a dynamic fatigue state evaluation model based on a first-order hidden Markov (HMM) dynamic Bayesian network was established. The model combines contextual information (sleep quality, working environment and circadian rhythm) and physiological signals (ECG, EMG and EM) to estimate the fatigue state of plateau mine operators. The simulation results of the dynamic fatigue recognition model and subjective synchronous fatigue reports were compared with the field-measured signal data. The verification results show that the synchronous subjective fatigue and simulated fatigue estimation results are highly consistent (correlation coefficient r = 0.971**), which confirms that the model is reliable for long-term dynamic fatigue evaluation. The results show that the established fatigue evaluation model is effective and provides a new model and concept for dynamic fatigue state estimation for remote mine operators in plateau deep mining. Moreover, this study provides a reference for clinical medical research and human fatigue identification under high-altitude, cold and low-oxygen conditions.

INTRODUCTION: Acute coronary syndromes (ACS) encompass a spectrum of clinical conditions that result from a sudden occlusion or severe narrowing of the coronary arteries, leading to myocardial ischemia. The frontal QRS-T angle, a parameter measured on the electrocardiogram (ECG), has been proposed as a potential marker for cardiovascular events. This study aims to evaluate the effectiveness of the frontal QRS-T angle in differentiating between various subtypes of ACS (STEMI, non-STEMI, USAP) and stable angina pectoris (SAP). METHODS: A prospective observational study was conducted on patients admitted to the emergency department between January 9, 2023, and January 3, 2024. The study population included patients diagnosed with ACS or SAP and a control group without cardiac pathology. The frontal QRS-T angle was calculated from 12-lead ECGs. Statistical analyses, including Kruskal-Wallis and ROC curve analysis, were performed to assess the diagnostic utility of the QRS-T angle. RESULTS: The study included 169 patients, with a mean age of 61.96+-13.90 years. The frontal QRS-T angle was significantly higher in the STEMI group compared to other groups. ROC analysis demonstrated that the QRS-T angle could significantly differentiate between non-cardiac patients and those with STEMI, non-STEMI, and USAP. The frontal QRS-T angle was also significant in predicting mortality, with a cutoff value of 58.00, AUC of 0.825, sensitivity of 84.20%, and specificity of 72.00%. DISCUSSION AND CONCLUSION: The frontal QRS-T angle is a significant marker for distinguishing between different ACS subtypes and non-cardiac patients. Its role in identifying STEMI and predicting mortality highlights its potential utility in clinical practice.

(1) Background: Arrhythmias, or irregular heart rhythms, are a prevalent cardiovascular condition and are diagnosed using electrocardiogram (ECG) signals. Advances in deep learning have enabled automated analysis of these signals. However, the effectiveness of deep learning models depends greatly on the quality of signal preprocessing. This study evaluated the impact of different windowing techniques applied to Fourier transform-preprocessed ECG signals on the classification accuracy of deep learning models. (2) Methods: We applied three windowing techniques—Hamming, Hann, and Blackman—to transform ECG signals into the frequency domain. A one-dimensional convolutional neural network was employed to classify the ECG signals into five arrhythmia categories based on features extracted from each windowed signal. (3) Results: The Blackman window yielded the highest classification accuracy, with improved signal-to-noise ratio and reduced spectral leakage compared to the Hamming and Hann windows. (4) Conclusions: The choice of windowing technique significantly influences the effectiveness of deep learning models in ECG classification. Future studies should explore additional preprocessing methods and their clinical applications.

Abstract Burn patients often face elevated pain, anxiety, and depression levels. Music therapy adds to integrative care in burn patients, but research including electrophysiological measures is limited. This study reports electrophysiological signals analysis during Music-Assisted Relaxation (MAR) with burn patients in the Intensive Care Unit (ICU). This study is a sub-analysis of an ongoing trial of music therapy with burn patients in the ICU. Electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG) were recorded during MAR with nine burn patients. Additionally, background pain levels (VAS) and anxiety and depression levels (HADS) were assessed. EEG oscillation power showed statistically significant changes in the delta (p

Abstract Background Electrocardiograms (ECGs) in athletes commonly reveal findings related to physiologic adaptations to exercise, that may be difficult to discern from true underlying cardiovascular abnormalities. North American and European societies have published consensus statements for normal, borderline, and abnormal ECG findings for athletes, but these criteria are not based on established correlation with disease states. Additionally, data comparing ECG findings in athletes to non-athlete control subjects are lacking. Our objective was to compare the ECGs of collegiate athletes and non-athlete controls using Z-scores for digital ECG variables to better identify significant differences between the groups and to evaluate the ECG variables in athletes falling outside the normal range. Methods Values for 102 digital ECG variables on 7206 subjects aged 17–22 years, including 672 athletes, from Hawaii Pacific Health, University of Hawaii, and Rady Children’s Hospital San Diego were obtained through retrospective review. Age and sex-specific Z-scores for ECG variables were derived from normal subjects and used to assess the range of values for specific ECG variables in young athletes. Athletes with abnormal ECGs were referred to cardiology consultation and/or echocardiogram. Results Athletes had slower heart rate, longer PR interval, more rightward QRS axis, longer QRS duration but shorter QTc duration, larger amplitude and area of T waves, prevalent R’ waves in V1, and higher values of variables traditionally associated with left ventricular hypertrophy (LVH): amplitudes of S waves (leads V1-V2), Q waves (V6, III) and R waves (II, V5, V6). Z-scores of these ECG variables in 558 (83%) of the athletes fell within − 2.5 and 2.5 range derived from the normal population dataset, and 60 (8.9%) athletes had a Z-score outside the − 3 to 3 range. While 191 (28.4%) athletes met traditional voltage criteria for diagnosis of LVH on ECG, only 53 athletes (7.9%) had Z-scores outside the range of -2.5 to 2.5 for both S amplitude in leads V1-V2 and R amplitude in leads V5-6. Only one athlete was diagnosed with hypertrophic cardiomyopathy with a Z-score of R wave in V6 of 2.34 and T wave in V6 of -5.94. Conclusion The use of Z-scores derived from a normal population may provide more precise screening to define cardiac abnormalities in young athletes and reduce unnecessary secondary testing, restrictions and concern.

Complete heart block in women of childbearing age is rare, and incidental diagnosis during pregnancy is more uncommon. Hence, there remain no well-established guidelines on the management of patients with complete heart block presenting in labor. Here, we present a 26-year-old full-term primigravida, with no known previous cardiac history, in active labor with asymptomatic bradycardia in the 30–40s unresponsive to atropine augmentation. After multidisciplinary consultation, the decision was to proceed with delivery as planned without indication for a temporary pacemaker. The patient successfully delivered a full-term infant via operative vaginal delivery, with an ensuing cardiac workup completed postpartum.

Arrhythmias are among the diseases with high mortality rates worldwide, causing millions of deaths each year. This underscores the importance of real-time electrocardiogram (ECG) monitoring for timely heart disease diagnosis and intervention. Deep learning models, trained on ECG signals across twelve or more leads, are the predominant approach for automated arrhythmia detection in the AI-assisted medical field. While these multi-lead ECG-based models perform well in automatic arrhythmia detection, their complexity often restricts their use on resource-constrained devices. In this paper, we propose an efficient, lightweight arrhythmia classification model using a knowledge distillation technique to train a student model from a teacher model, tailored for embedded intelligence in wearable devices. The results show that the student model achieves 96.32% accuracy, which is comparable to the teacher model, with a remarkable compression ratio that is 1242.58 times smaller, outperforming other lightweight models. Enabled by the proposed model, we developed a wearable ECG monitoring system based on the STM32F429 Discovery kit and ADS1292R chip, achieving real-time arrhythmia detection on small wearable devices. [ABSTRACT FROM AUTHOR]

A cardiac arrhythmia is an abnormal heart rhythm caused by irregular heartbeats. Cardiac arrhythmias include atrial or ventricular fibrillation, right or left bundle branch block beats, and premature atrial or ventricular contractions. Different cardiac arrhythmias have distinct causes and clinical presentations. The type of cardiac arrhythmia must be identified to enable further intervention and treatment for addressing its underlying causes. In this study, we developed a convolutional neural network (CNN) model that extracts and classifies time-domain features to detect cardiac arrhythmias automatically in electrocardiogram (ECG) signals. This model employs endpoint detection to detect the activity of time-domain signals in accordance with a threshold for identifying the peak wave in ECG signals. These features are then transferred to two-dimensional (2D) color patterns that indicate abnormal heartbeats. Subsequently, a one-dimensional (1D) or 2D CNN classifier is employed to distinguish normal heartbeats from cardiac arrhythmias in raw ECG data. The proposed model was trained, tested, and validated on the Massachusetts Institute of Technology–Beth Israel Deaconess Medical Center Arrhythmia Database (commonly known as the MIT-BIH Arrhythmia Database), and it exhibited promising performance in cardiac arrhythmia recognition, as indicated by its precision, recall, F1 score, and accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method of classifying the severity of obstructive sleep apnea (OSA) using electrocardiogram (ECG) signals and deep learning. In our previous research, we presented an ECG-based signal segmentation-free model for OSA severity classification. Its key feature is using the unsegmented overnight ECG signal as input and directly predicting the four categories of OSA severity as output. The overall performance of our previous work has been demonstrated to significantly exceed those of most existing studies. On the basis of a preliminary study, a method of improving the accuracy of OSA severity classification is proposed in this paper. Modifications to the model architecture for OSA severity classification were made, and a squeeze-and-excitation network (SENet) was integrated into this work. Finally, our experimental results indicated that the accuracy of the four-category classification of OSA severity in this paper is 57.91%, which is slightly higher than 57.55% achieved in our previous research. [ABSTRACT FROM AUTHOR]

(1) Background: Arrhythmias, or irregular heart rhythms, are a prevalent cardiovascular condition and are diagnosed using electrocardiogram (ECG) signals. Advances in deep learning have enabled automated analysis of these signals. However, the effectiveness of deep learning models depends greatly on the quality of signal preprocessing. This study evaluated the impact of different windowing techniques applied to Fourier transform-preprocessed ECG signals on the classification accuracy of deep learning models. (2) Methods: We applied three windowing techniques—Hamming, Hann, and Blackman—to transform ECG signals into the frequency domain. A one-dimensional convolutional neural network was employed to classify the ECG signals into five arrhythmia categories based on features extracted from each windowed signal. (3) Results: The Blackman window yielded the highest classification accuracy, with improved signal-to-noise ratio and reduced spectral leakage compared to the Hamming and Hann windows. (4) Conclusions: The choice of windowing technique significantly influences the effectiveness of deep learning models in ECG classification. Future studies should explore additional preprocessing methods and their clinical applications. [ABSTRACT FROM AUTHOR]

Arrhythmia detection in electrocardiogram (ECG) signals is essential for monitoring cardiovascular health. Current automated arrhythmia classification methods frequently encounter difficulties in detecting multiple cardiac abnormalities, particularly when dealing with imbalanced datasets. This paper proposes a novel deep learning approach for the detection and classification of arrhythmias in ECG signals using a Hybrid Residual Network (Hybrid ResNet). Our method employs a Hybrid Residual Network architecture that integrates standard convolution, depthwise separable convolution, and residual connections to enhance the feature extraction efficiency and classification accuracy. To guarantee superior input signals, we preprocess the ECG signals by removing baseline drift with a high-pass Butterworth filter, denoising via discrete wavelet transform, and segmenting heartbeat cycles through R-peak detection. Additionally, we rectify the class imbalance in the MIT-BIH Arrhythmia Database by applying the Synthetic Minority Oversampling Technique (SMOTE), therefore enhancing the model's ability to detect infrequent arrhythmia types. The suggested system achieves a classification accuracy of 99.09% on the MIT-BIH dataset, surpassing conventional convolutional neural networks and other state-of-the-art methodologies. Compared to existing approaches, our strategy exhibits superior effectiveness and robustness in managing diverse irregular heartbeats and arrhythmias. [ABSTRACT FROM AUTHOR]

Background: Electrocardiograms (ECGs) in athletes commonly reveal findings related to physiologic adaptations to exercise, that may be difficult to discern from true underlying cardiovascular abnormalities. North American and European societies have published consensus statements for normal, borderline, and abnormal ECG findings for athletes, but these criteria are not based on established correlation with disease states. Additionally, data comparing ECG findings in athletes to non-athlete control subjects are lacking. Our objective was to compare the ECGs of collegiate athletes and non-athlete controls using Z-scores for digital ECG variables to better identify significant differences between the groups and to evaluate the ECG variables in athletes falling outside the normal range. Methods: Values for 102 digital ECG variables on 7206 subjects aged 17–22 years, including 672 athletes, from Hawaii Pacific Health, University of Hawaii, and Rady Children's Hospital San Diego were obtained through retrospective review. Age and sex-specific Z-scores for ECG variables were derived from normal subjects and used to assess the range of values for specific ECG variables in young athletes. Athletes with abnormal ECGs were referred to cardiology consultation and/or echocardiogram. Results: Athletes had slower heart rate, longer PR interval, more rightward QRS axis, longer QRS duration but shorter QTc duration, larger amplitude and area of T waves, prevalent R' waves in V1, and higher values of variables traditionally associated with left ventricular hypertrophy (LVH): amplitudes of S waves (leads V1-V2), Q waves (V6, III) and R waves (II, V5, V6). Z-scores of these ECG variables in 558 (83%) of the athletes fell within − 2.5 and 2.5 range derived from the normal population dataset, and 60 (8.9%) athletes had a Z-score outside the − 3 to 3 range. While 191 (28.4%) athletes met traditional voltage criteria for diagnosis of LVH on ECG, only 53 athletes (7.9%) had Z-scores outside the range of -2.5 to 2.5 for both S amplitude in leads V1-V2 and R amplitude in leads V5-6. Only one athlete was diagnosed with hypertrophic cardiomyopathy with a Z-score of R wave in V6 of 2.34 and T wave in V6 of -5.94. Conclusion: The use of Z-scores derived from a normal population may provide more precise screening to define cardiac abnormalities in young athletes and reduce unnecessary secondary testing, restrictions and concern. Key Points: • Athletes had slower heart rate, longer PR interval, greater QRS axis, longer QRS duration, shorter QTc interval, higher peak amplitude of S waves in leads V1 and V2, Q waves in leads III and V6, R waves in leads II, V5, and V6 compared to control subjects. • However, most of the athletes had ECG variable Z-scores within range of -2.5 and 2.5 (83%) and − 3 and 3 (91.1%), all of which had no identified cardiac pathologies. • ECG assessment in athletes utilizing Z-scores derived from normal subjects may guide clinical decision making regarding secondary screening. [ABSTRACT FROM AUTHOR]

Echocardiography (ECG) is a noninvasive technology that is widely used for recording heartbeats and diagnosing cardiac arrhythmias. However, interpreting ECG signals is challenging and may require substantial time from medical specialists. The evolution of technology and artificial intelligence has led to advances in the study and development of automatic arrhythmia classification systems to aid in medical diagnoses. Within this context, this paper introduces a framework for classifying cardiac arrhythmias on the basis of a multimodal convolutional neural network (CNN) with an adaptive attention mechanism. ECG signal segments are transformed into images via the Hilbert space-filling curve (HSFC) and recurrence plot (RP) techniques. The framework is developed and evaluated using the MIT-BIH public database in alignment with AAMI guidelines (ANSI/AAMI EC57). The evaluations accounted for interpatient and intrapatient paradigms, considering variations in the input structure related to the number of ECG leads (lead MLII and V1 + MLII). The results indicate that the framework is competitive with those in state-of-the-art studies, particularly for two ECG leads. The accuracy, precision, sensitivity, specificity and F1 score are 98.48%, 94.15%, 80.23%, 96.34% and 81.91%, respectively, for the interpatient paradigm and 99.70%, 98.01%, 97.26%, 99.28% and 97.64%, respectively, for the intrapatient paradigm. [ABSTRACT FROM AUTHOR]

This paper investigates the use of continuous morse wavelet transform (CWT) coefficients as inputs to convolutional neural networks (CNNs) for electrocardiogram (ECG) biometric authentication. We evaluate the performance and generalization of pre-trained SqueezeNet architecture using the ECG-ID Database. Our approach involves extracting 10 scalograms from each subject's ECG signals and employing gradient descent optimization during training. The models demonstrate high accuracy, achieving over 90% on both training and validation datasets, indicating robust performance and minimal overfitting. Further analysis using the F1 confidence curve and ROC curve reveals a balanced trade-off between precision and recall, with an optimal F1 score of 0.84 and an AUC of 0.84, respectively. Additionally, we explore the impact of different CWT parameter settings, including Voice per Octave (VPO), symmetry parameter (gamma), and time-bandwidth product (P2). The optimal VPO of 41 yields an AUC of 0.87 and an F1 score of 0.84. The best performance is achieved with gamma values greater than 2 and time-bandwidth products between 45 and 80, enhancing time localization and frequency resolution. In this study, the significance of fine-tuning wavelet parameters to improve the effectiveness of ECG biometric systems is demonstrated, demonstrating the potential of combining CWT and CNNs for reliable biometric authentication. [ABSTRACT FROM AUTHOR]

Introduction: Acute coronary syndromes (ACS) encompass a spectrum of clinical conditions that result from a sudden occlusion or severe narrowing of the coronary arteries, leading to myocardial ischemia. Early and accurate diagnosis of ACS is critical to improv e patient outcomes. The frontal QRS-T angle, a parameter measured on the electrocardiogram (ECG), has been proposed as a potential marker for cardiovascular events. This study aims to evaluate the effectiveness of the frontal QRS -T angle in differentiating between various subtypes of ACS (STEMI, non-STEMI, USAP) and stable angina pectoris (SAP). Materials and Methods: A prospective observational study was conducted on patients admitted to the emergency department between January 9, 2023, and January 3, 2024. The study population included patients diagnosed with ACS or SAP and a control group without cardiac pathology. The frontal QRS-T angle was calculated from 12-lead ECGs. Statistical analyses, including Kruskal -Wallis and ROC curve analysis, were performed to assess the diagnostic utility of the QRS-T angle. Results: The study included 169 patients, with a mean age of 61.96±13.90 yea rs. The frontal QRS-T angle was significantly higher in the STEMI group compared to other groups. ROC analysis demonstrated that the QRS-T angle could significantly differentiate between noncardiac patients and those with STEMI, non-STEMI, and USAP. The cutoff value for differentiating non-cardiac patients from those with STEMI was >30, with an AUC of 0.784, sensit ivity of 81.25%, and specificity of 66.67%. The frontal QRS-T angle was also significant in predicting mortality, with a cutoff value of 58.00, AUC of 0.825, sensitivity of 84.20%, and specificity of 72.00%. Conclusion: The frontal QRS-T angle is a significant marker for distinguishing between different ACS subtypes and non-cardiac patients. Its role in identifying STEMI and predicting mortality highlights its potential utility in clinical practice. Further studies are needed to validate these findings and explore the broader implications of the QRS-T angle in cardiovascular diagnosis and management. [ABSTRACT FROM AUTHOR]

Complete heart block in women of childbearing age is rare, and incidental diagnosis during pregnancy is more uncommon. Hence, there remain no well‑established guidelines on the management of patients with complete heart block presenting in labor. Here, we present a 26‑year‑old full‑term primigravida, with no known previous cardiac history, in active labor with asymptomatic bradycardia in the 30–40s unresponsive to atropine augmentation. After multidisciplinary consultation, the decision was to proceed with delivery as planned without indication for a temporary pacemaker. The patient successfully delivered a full‑term infant via operative vaginal delivery, with an ensuing cardiac workup completed postpartum. [ABSTRACT FROM AUTHOR]

Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and interference. In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. It adaptively modifies time points, which improves the alignment between both signals. In contrast, the Temporal Convolutional Network model trains on the original signal, and gradient descent optimization is utilized to improve the selection of time points. Evaluation of both strategies' efficacy is performed by calculating signal distances at lower and higher sampling rates. First, a collection of synthetic data points that resembled the P-QRS-T wave was used to train the model. Then, the ECG-ID database for real data analysis was used. Filtering to remove baseline wander followed by evaluation and testing were carried out in the real patient data. The results, in particular MSE = 0.0005, RMSE = 0.0216, and Pearson's CC = 0.9904 for 120 sps in the case of the transformer patient data model, provide viable paths for maintaining the precision and dependability of ECG-based diagnostic systems at much lower sampling rate. Outcomes indicate that both techniques are effective at improving the fidelity between the original and modified ECG signals. [ABSTRACT FROM AUTHOR]

With heart health issues attracting much attention, wearable electrocardiogram (ECG) monitoring devices show a broad market prospect. This paper develops a generic ECG pre-processing algorithm and proposes a method for the single-lead ECG classification problem based on model stacking. Features such as RR-intervals, power spectrum, and higher-order statistics are computed and grouped into three classes. The support vector machine (SVM) classifier is trained separately based on each class of features, and subsequently, a fourth SVM classifier is trained on the prediction results of the three SVM classifiers at the first layer. To obtain more realistic results and better comparisons with previous studies, the algorithm follows the ANSI/AAMI EC57:2012 standard and is tested on a real ECG database. The experimental results show that the algorithm in this paper better overcomes the impact of the data imbalance problem and obtains good results.

Deep learning has been widely applied in the field of electrocardiogram (ECG) signal analysis due to its powerful data representation capability. However, supervised methods require a large amount of labeled data, and ECG data annotation is typically time-consuming and costly. Additionally, supervised methods are limited by the finite data types in the training set, resulting in limited generalization performance. Therefore, how to leverage massive unlabeled ECG signals for data mining and universal feature representation has become an urgent problem to be addressed. Self-supervised learning (SSL) is an effective approach to address the issue of missing annotated ECG data and improve the transfer ability of the model by learning generalized features from unlabeled data using pre-defined proxy tasks. However, existing surveys on self-supervised learning mostly focus on the domains of images or temporal signals, and there is a relative lack of comprehensive reviews on self-supervised learning in the ECG domain. To fill this gap, this paper provides a comprehensive review of advanced self-supervised learning methods used in the field of ECG. Firstly, a systematic summary and classification of self-supervised learning methods for ECG are presented, starting from two learning paradigms—contrastive and predictive. The basic principles of different categories of methods are elaborated, and the characteristics of each method are analyzed in detail, highlighting the advantages and limitations of each approach. Subsequently, a summary is provided for the commonly used datasets and application scenarios in ECG self-supervised learning, along with a review of data augmentation methods frequently applied in the ECG domain, offering a systematic reference for subsequent research. Finally, an in-depth discussion is presented on the current challenges of self-supervised learning within the ECG field, and future directions for the development of ECG self-supervised learning are explored, providing guidance for subsequent research in the field.

Background The integration of Artificial Intelligence (AI) in electrocardiographic (ECG) devices has become a pivotal area of research, particularly during the COVID-19 pandemic. These technologies are essential for enhancing cardiac diagnosis and monitoring. Methods This study assesses current trends, key contributors, and collaborative networks in the field of AI-enhanced ECG devices. We utilized a comprehensive analysis, using the Biblioshiny library from Bibliometrix for data exploration of data extracted from the Scopus database and VOSViewer for creating and visualizing maps. These tools were played an important role in conducting an in-depth analysis of the relationships and developments within the field. Results The analysis shows a significant increase in publications related to AI-enhanced ECG devices, with a marked surge during the COVID-19 pandemic. Despite the growing interest and technological advancements, the study exposes a notable disparity in the geographical distribution of research contributions, highlighting substantial under-representation of African researchers. This gap is attributed to infrastructural, financial constraints, and limited collaborative networks within the continent. Conclusion The rapid evolution and increasing importance of AI in ECG devices underscore the need for more inclusive research practices. There is a critical need to integrate and promote contributions from under-represented regions, particularly Africa, to ensure a globally diverse perspective in tackling health challenges. This study calls for enhanced participation and support for African researchers to bridge the existing research gap and foster global health equity.

Obstructive sleep apnea (OSA) detection using single-lead electrocardiograms (ECGs) has advanced significantly with the integration of artificial intelligence (AI). This review explores how AI enhances feature extraction and machine learning algorithms to improve OSA detection. The RR interval in electrocardiographic data is particularly valued for its ease of identification and low error rate. We review a range of machine learning and deep learning techniques employed in OSA detection. This review offers insights into developing single-lead ECG-based OSA detection systems by analyzing database availability, feature extraction methods, and machine learning approaches. [ABSTRACT FROM AUTHOR]

Introduction: The QRS complex is the most prominent waveform within the electrocardiograph (ECG) signal. The accurate detection of the QRS complex is an essential step in the ECG analysis algorithm, which can provide fundamental information for the monitoring and diagnosis of the cardiovascular diseases. Methods: Seven public ECG datasets were used in the experiments. A simple and effective QRS complex detection algorithm based on the deep neural network (DNN) was proposed. The DNN model was composed of two parts: a feature pyramid network (FPN) based backbone with dual input channels to generate the feature maps, and a location head to predict the probability of point belonging to the QRS complex. The depthwise convolution was applied to reduce the parameters of the DNN model. Furthermore, a novel training strategy was developed. The target of the DNN model was generated by using the points within 75 milliseconds and beyond 150 milliseconds from the closest annotated QRS complexes, and artificial simulated ECG segments with high heart rates were generated in the data augmentation. The number of parameters and floating point operations (FLOPs) of our model was 26976 and 9.90M, respectively. Results: The proposed method was evaluated through a cross-dataset test and compared with the sophisticated state-of-the-art methods. On the MITBIH NST, the proposed method demonstrated slightly better sensitivity (95.59% vs. 95.55%) and lower presicion (91.03% vs. 92.93%). On the CPSC 2019, the proposed method have similar sensitivity (95.15% vs.95.13%) and better precision (91.75% vs. 82.03%). Discussion: Experimental results show the proposed algorithm achieved a comparable performance with only a few parameters and FLOPs, which would be useful for the application of ECG analysis on the wearable device. [ABSTRACT FROM AUTHOR]

Smart healthcare systems integrated with advanced deep neural networks enable real‐time health monitoring, early disease detection, and personalized treatment. In this work, a novel 3D AND‐type flash memory array with a rounded double channel for computing‐in‐memory (CIM) architecture to overcome the limitations of conventional smart healthcare systems: the necessity of high area and energy efficiency while maintaining high classification accuracy is proposed. The fabricated array, characterized by low‐power operations and high scalability with double independent channels per floor, exhibits enhanced cell density and energy efficiency while effectively emulating the features of biological synapses. The CIM architecture leveraging the fabricated array achieves high classification accuracy (93.5%) for electrocardiogram signals, ensuring timely detection of potentially life‐threatening arrhythmias. Incorporated with a simplified spike‐timing‐dependent plasticity learning rule, the CIM architecture is suitable for robust, area‐ and energy‐efficient in‐memory arrhythmia detection systems. This work effectively addresses the challenges of conventional smart healthcare systems, paving the way for a more refined healthcare paradigm. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) represent a significant health concern in the present era, with Electrocardiogram (ECG) serving as a crucial bio-signal for their detection. Efficient health monitoring necessitates rapid and precise diagnosis, thereby mandating the utilization of Compressive Sensing (CS) alongside Machine Learning (ML) algorithms. CS functions as a sensing methodology that reduces sample numbers by capturing sparse or compressible representations, simplifying and expediting the acquisition process. In this proposed study, ECG signals are compressively sensed and preprocessed using CS reconstruction algorithms, followed by the application of various ML algorithms for diagnostic purposes. The assessment of the reconstructed ECG signal entails the assessment of Peak Signal-to-Noise Ratio (PSNR) values and Percentage Root-mean-square Difference (PRD). Concurrently, ML algorithms are evaluated based on metrics including accuracy, specificity, and sensitivity. This work demonstrates exceptional performance in terms of acquisition time and computational complexity through the application of CS technology. Comparative analysis with the existing methodologies for CVD diagnosis reveals the proposed approach's remarkable efficacy. Notably, the reduction in data volume and hardware complexity serves as a significant advantage over conventional methods. The integration of CS and ML algorithms in the proposed methodology proves highly effective in diagnosing CVDs, achieving a classification accuracy of 94.7%. These results underscore the methodology's ability to deliver both speed and accuracy in diagnosis, positioning it as a promising approach for health monitoring. [ABSTRACT FROM AUTHOR]

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BackgroundTraumatic brain injury (TBI) disrupts normal brain tissue and functions, leading to high mortality and disability. Severe TBI (sTBI) causes prolonged cognitive, functional, and multi-organ dysfunction. Dysfunction of the autonomic nervous system (ANS) after sTBI can induce abnormalities in multiple organ systems, contributing to cardiovascular dysregulation and increased mortality. Currently, detailed characterization of early autonomic dysfunction in the acute phase after sTBI is lacking. This study aims to use physiological waveform data collected from patients with sTBI to characterize early autonomic dysfunction and its association with clinical outcomes to prevent multi-organ dysfunction and improving patient outcomes.ObjectiveThis data analysis protocol describes our pre-planned protocol using cardiac waveforms to evaluate early autonomic dysfunction and to inform multi-dimensional characterization of the autonomic nervous system (ANS) after sTBI.MethodsWe will collect continuous cardiac waveform data from patients managed in an intensive care unit within a clinical trial. We will first assess the signal quality of the electrocardiogram (ECG) using a combination of the structural image similarity metric and signal quality index. Then, we will detect premature ventricular contractions (PVC) on good-quality ECG beats using a deep-learning model. For arterial blood pressure (ABP) data, we will employ a singular value decomposition (SVD)-based approach to assess the signal quality. Finally, we will compute multiple indices of ANS functions through heart rate turbulence (HRT) analysis, time/frequency-domain analysis of heart rate variability (HRV) and pulse rate variability, and quantification of baroreflex sensitivity (BRS) from high-quality continuous ECG and ABP signals. The early autonomic dysfunction will be characterized by comparing the values of calculated indices with their normal ranges.ConclusionThis study will provide a detailed characterization of acute changes in ANS function after sTBI through quantified indices from cardiac waveform data, thereby enhancing our understanding of the development and course of eAD post-sTBI.

Diabetes Mellitus (DM) is a persistent metabolic disorder which is characterized by increased blood glucose level in the blood stream. Initially, DM occurs while the insulin secretion in the pancreas has a disability to secrete or to use hormone for the metabolic process. Moreover, there are different types of DM depending on the physiological process, and the types include Type1 DM, Type2 DM and Gestational DM. Electrocardiography (ECG) waves are used to detect the abnormal heartbeats and cannot directly detect DM, but the wave abnormality can indicate the possibility and presence of DM. Whereas the Photoplethysmography (PPG) signals are a non-invasive method used to detect changes in blood volume that can monitor BG changes. Furthermore, the detection and classification of DM using PPG and ECG can involve analyzing the functional performance of these modalities. By extracting the features like R wave (W1) and QRS complex (W2) in the ECG signals and Pulse Width (S1) and Pulse Amplitude Variation (S2) can detect DM and can be classified into DM and Non-DM. The authors propose a Novel architecture in the basis of Encoder Decoder structure named as Obstructive Encoder Decoder module. This module extracts the specific features and the proposed novel Obstructive Erasing Module remove the remaining artifacts and then the extracted features are fed into the Multi-Uni-Net for the fusion of the two modalities and the fused image is classified using EXplainable Machine Learning (EX-ML). From this classification the performance metrics like Accuracy, Precision, Recall, F1-Score and AUC can be determined.

Cardiovascular diseases claim over 17 million lives annually. Prevention involves adopting healthy habits and regular check-ups, ideally outside hospitals to reduce healthcare costs, leveraging telemedicine tools. However, diagnosing CVDs outside hospitals can be challenging due to noise interference in electrocardiograms (ECGs), necessitating the use of Signal Quality Assessment (SQA) systems. This paper presents a MATLAB toolbox for automated ECG Signal Quality Assessment, featuring a novel method. Furthermore, the toolbox can extract up to 37 Signal Quality Indices (SQIs), commonly used as features in machine learning-based SQA. Therefore, our software has the potential to facilitate the healthcare process, resulting in efficient and cost-effective cardiovascular care.