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Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is polysomnography (PSG). A major challenge associated with this method is all the cables needed to connect the recording devices, making the examination more intrusive and usually requiring a clinical environment. This can have potential consequences on the test results and their accuracy. One simple way to assess the state of the central nervous system (CNS), a well-known indicator of sleep disorder, could be the use of a portable medical device. With this in mind, we implemented a simple model using both the RR interval (RRI) and its second derivative to accurately predict the awake and napping states of a subject using a feature classification model. For training and validation, we used a database providing measurements from nine healthy young adults (six men and three women), in which heart rate variability (HRV) associated with light-on, light-off, sleep onset and sleep offset events. Results show that using a 30 min RRI time series window suffices for this lightweight model to accurately predict whether the patient was awake or napping.

Abstract Angina pectoris is associated with adverse cardiovascular events. In this study, a Bi-directional Long Short-Term Memory (Bi-LSTM) prediction model with the Attention layer was established to explore the predictive value of the resting-state RR interval time series on the occurrence of angina pectoris. The data of this cohort study were from the Sleep Heart Health Study database, 2,977 people were included with the follow-up of 15 years. We used the RR interval time series of electrocardiogram signals in the resting state. The outcome variables were any angina events during the follow-up. We randomly divided 2,977 participants into training (n = 2680) and testing sets (n = 297) with a partition ratio of 9:1. The prediction model of Bi-LSTM with Attention layer was developed and the predictive performance was assessed. 1,236 had angina pectoris and 1,741 patients did not have angina pectoris during the follow-up period. The predictive performance of the Bi-LSTM model was great with the value of accuracy = 0.913, area under the curve (AUC) = 0.922, precision = 0.913 in the testing set. RR intervals may be the potential predictors of angina events. It is more and more convenient to obtain heart rate with the development of wearable devices; the Bi-LSTM prediction model established in this study is expected to provide support for the intelligent prediction of angina pectoris events.

Obstructive sleep apnea (OSA) atau sleep apnea adalah gangguan tidur langka yang memerlukan penggunaan sinyal aktivitas listrik, biasa disebut sebagai elektrogram (ECG), untuk dideteksi. Sinyal ECG terdiri dari bentuk gelombang, durasi, ritme bentuk gelombang, dan orientasi sinyal, yang dapat digunakan ahli jantung untuk mengevaluasi kondisi jantung pasien. Tujuan dari penelitian ini adalah untuk mendeteksi sleep apnea menggunakan dataset ECG yang ada. Sistem pendeteksi sleep apnea ini diharapkan mampu mendeteksi gangguan pada pasien secara dini dan membantu dokter mendiagnosis pasien secara lebih akurat dan cepat sehingga dapat memberikan penanganan lebih lanjut. Penelitian ini mengusulkan bagaimana mendeteksi sleep apnea dengan perangkat lunak dengan menggunakan parameter statistik dari sinyal interval RR dalam dataset sinyal ECG dan kemudian mengklasifikasikannya menggunakan metode DecisionTree. Proses deteksi sleep apnea yang peneliti usulkan menggunakan interval RR dan proses decision tree memiliki tingkat akurasi 99,5%.

A sharp deterioration of the patient’s condition against the backdrop of the development of life-threatening arrhythmias with symptoms of acute heart failure (AHF), multiple organ dysfunction syndrome (MODS) or cerebral edema (CE) can lead to the death of the patient. Since the known methods of automated diagnostics currently cannot accurately and promptly determine that the patient is in a life-threatening condition leading to the fatal outcome caused by AHF, MODS or CE, there is a need to develop appropriate methods. One of the ways to identify predictors of such a state is to apply machine learning methods to the collected datasets. In this article, we consider using data analysis methods to test the hypothesis that there is a predictor of death risk assessment, which can be derived from the previously obtained values of the ECG intervals, which gives a statistically significant difference for the ECG of the two groups of patients: those who suffered deterioration leading to the fatal outcome caused be MODS, AHF or CE, and those with favorable outcome. A method for unifying ECG data was proposed, which allow, based on the sequence of RR and QT intervals, to the construct of a number that is a characteristic of the patient's heart condition. Based on this characteristic, the patients are classified into groups: the main (patients with fatal outcome) and control (patients with favorable outcome). The resulting classification method lays the potential for the development of methods for identifying the patient's health condition, which will automate the detection of its deterioration. The novelty of the result lies in the confirmation of the hypothesis stated above, as well as the proposed classification criteria that allow solving the urgent problem of an automatic detection of the deterioration of the patient's condition.

Aritmia merupakan salah satu gangguan jantung, yang mungkin tidak berbahaya dalam waktu dekat, tetapi dapat mengakibatkan abnormalitas jangka panjang. Meskipun tidak berbahaya, harus ada penanganan medis yang segera dilakukan dan perubahan lifestyle menjadi lebih baik. Deteksi aritmia umumnya dilakukan dengan elektrokardiograf (EKG) long recording menggunakan monitor Holter dan kemudian dianalisis ritmenya. Perekaman dengan Holter yang memerlukan waktu beberapa hari dapat mengganggu fisiologis pasien. Banyak penelitian telah dilakukan untuk membangun algoritme pendeteksian aritmia, dengan beragam sumber data, fitur, dan juga metode pendeteksian. Namun, permasalahan yang umum dihadapi oleh banyak peneliti adalah masalah waktu komputasi dan kompleksnya fitur yang dideteksi. Studi ini dilakukan untuk melakukan review terhadap penelitian yang sudah dilakukan terkait dengan penggunaan data, fitur, dan juga metode deep learning yang dapat menyelesaikan masalah waktu komputasi dan memungkinkan implementasi pada wearable devices. Studi diawali dengan pencarian literatur terkait, kemudian melihat basis data yang digunakan untuk membangun model deteksi. Selanjutnya, review dilanjutkan dengan menelaah fitur EKG yang digunakan dan juga metode deep learning yang diimplementasikan. Dari hasil review yang dilakukan, data yang umum digunakan adalah data yang bersumber dari dataset MIT-BIH, meskipun penggunaan data dinilai masih perlu pre-processing yang cukup rumit. Convolutional neural network (CNN) merupakan metode yang banyak digunakan, walaupun waktu komputasi menjadi salah satu pertimbangan. Analisis yang paling tepat untuk pendeteksian gangguan ritme dan memiliki waktu komputasi yang rendah adalah fitur interval sinyal EKG dengan analisis di domain waktu. Fitur tersebut nantinya akan digunakan sebagai masukan deep learning. Dengan begitu, waktu komputasi akan dapat banyak dikurangi, terutama ketika diterapkan pada wearable devices.

Recently, as interest in electrocardiogram monitoring has increased, research on real-time ECG signal analysis in daily life using lightweight embedded devices has increased. Abnormal beat detections in ECG signal analysis are an important research area to reduce processing time and cost for cardiac arrhythmia diagnosis. Abnormal beat detections can be divided into feature-based detection and shape-based detection. Feature-based detection finds it difficult to detect reliable fiducial points, and shape-based detection has difficulty detecting abnormal beats that are similar to normal beats. In this paper, we propose template cluster generation and abnormal beat detection using both detection methods. The proposed method shows robust detection of distorted normal beats by generating a template cluster rather than a single template. Moreover, abnormal beats that have normal shape can be detected using the RR interval, which is a highly reliable feature. Experiment results using the MIT-BIH arrhythmia database, provided by Physionet, showed the average processing times to generate a template cluster and detect abnormal beats for the 30-minute signal length were 1.21 seconds and 0.14 seconds, respectively. With manually adjusted thresholds, the specificity and accuracy achieved 93.00% and 97.94%, respectively. In the case of group 1 records obtained relatively stably, the specificity and accuracy achieved 99.27% and 99.44%.

Background: Wearable technologies for monitoring cardiovascular parameters, including electrocardiography (ECG) and impedance cardiography (ICG), propose a challenging research subject. The expectancy for wearable devices to be unobtrusive and miniaturized sets a goal to develop smarter devices and better methods for signal acquisition, processing, and decision-making. Methods: In this work, non-standard electrode placement configurations (EPC) on the thoracic area and single arm were experimented for ECG signal acquisition. The locations were selected for joint acquisition of ECG and ICG, targeted to suitability for integrating into wearable devices. The methodology for comparing the detected signals of ECG was developed, presented, and applied to determine the R, S, and T waves and RR interval. An algorithm was proposed to distinguish the R waves in the case of large T waves. Results: Results show the feasibility of using non-standard EPCs, manifesting in recognizable signal waveforms with reasonable quality for post-processing. A considerably lower median sensitivity of R wave was verified (27.3%) compared with T wave (49%) and S wave (44.9%) throughout the used data. The proposed algorithm for distinguishing R wave from large T wave shows satisfactory results. Conclusions: The most suitable non-standard locations for ECG monitoring in conjunction with ICG were determined and proposed.

Arterial baroreflex assessment using vasoactive substances enables investigators to collect data pairs over a wide range of blood pressures and reflex reactions. These data pairs relate intervals between heartbeats or sympathetic neural activity to blood pressure values. In an X-Y plot the data points scatter around a sigmoidal curve. After fitting the parameters of a sigmoidal function to the data, the graph’s characteristics represent a rather comprehensive quantitative reflex description. Variants of the 4-parameter Boltzmann sigmoidal equation are widely used for curve fitting. Unfortunately, their ‘slope parameters’ do not correspond to the graph’s actual slope which complicates the analysis and bears the risk of misreporting. We propose a modified Boltzmann sigmoidal function with preserved goodness of fit whose parameters are one-to-one equivalent to the sigmoidal curve’s characteristics.

This paper is targeted in the area of biometric data enabled security by using machine learning for the digital health. The traditional authentication systems are vulnerable to the risks of forgetfulness, loss, and theft. Biometric authentication is has been improved and become the part of daily life. The Electrocardiogram (ECG) based authentication method has been introduced as a biometric security system suitable to check the identification for entering a building and this research provides for studying ECG-based biometric authentication techniques to reshape input data by slicing based on the RR-interval. The Overall Performance (OP) as a newly proposed performance measure is the combined performance metric of multiple authentication measures in this study. The performance of the proposed system using a confusion matrix has been evaluated and it has achieved up to 95% accuracy by compact data analysis. The Amang ECG (amgecg) toolbox in MATLAB is applied to the mean square error (MSE) based upper-range control limit (UCL) which directly affects three authentication performance metrics: the number of accepted samples, the accuracy and the OP. Based on this approach, it is found that the OP could be maximized by applying a UCL of 0.0028, which indicates 61 accepted samples within 70 samples and ensures that the proposed authentication system achieves 95% accuracy.

This study investigates the use of atrioventricular (AV) synchronization as an important diagnostic criterion for atrial fibrillation and flutter (AF) using one to twelve ECG leads. Heart rate, lead-specific AV conduction time, and P-/f-wave amplitude were evaluated by three representative ECG metrics (mean value, standard deviation), namely RR-interval (RRi-mean, RRi-std), PQ-interval (PQi-mean, PQI-std), and PQ-amplitude (PQa-mean, PQa-std), in 71,545 standard 12-lead ECG records from the six largest PhysioNet CinC Challenge 2021 databases. Two rhythm classes were considered (AF, non-AF), randomly assigning records into training (70%), validation (20%), and test (10%) datasets. In a grid search of 19, 55, and 83 dense neural network (DenseNet) architectures and five independent training runs, we optimized models for one-lead, six-lead (chest or limb), and twelve-lead input features. Lead-set performance and SHapley Additive exPlanations (SHAP) input feature importance were evaluated on the test set. Optimal DenseNet architectures with the number of neurons in sequential [1st, 2nd, 3rd] hidden layers were assessed for sensitivity and specificity: DenseNet [16,16,0] with primary leads (I or II) had 87.9–88.3 and 90.5–91.5%; DenseNet [32,32,32] with six limb leads had 90.7 and 94.2%; DenseNet [32,32,4] with six chest leads had 92.1 and 93.2%; and DenseNet [128,8,8] with all 12 leads had 91.8 and 95.8%, indicating sensitivity and specificity values, respectively. Mean SHAP values on the entire test set highlighted the importance of RRi-mean (100%), RR-std (84%), and atrial synchronization (40–60%) for the PQa-mean (aVR, I), PQi-std (V2, aVF, II), and PQi-mean (aVL, aVR). Our focus on finding the strongest AV synchronization predictors of AF in 12-lead ECGs would lead to a comprehensive understanding of the decision-making process in advanced neural network classifiers. DenseNet self-learned to rely on a few ECG behavioral characteristics: first, characteristics usually associated with AF conduction such as rapid heart rate, enhanced heart rate variability, and large PQ-interval deviation in V2 and inferior leads (aVF, II); second, characteristics related to a typical P-wave pattern in sinus rhythm, which is best distinguished from AF by the earliest negative P-peak deflection of the right atrium in the lead (aVR) and late positive left atrial deflection in lateral leads (I, aVL). Our results on lead-selection and feature-selection practices for AF detection should be considered for one- to twelve-lead ECG signal processing settings, particularly those measuring heart rate, AV conduction times, and P-/f-wave amplitudes. Performances are limited to the AF diagnostic potential of these three metrics. SHAP value importance can be used in combination with a human expert’s ECG interpretation to change the focus from a broad observation of 12-lead ECG morphology to focusing on the few AV synchronization findings strongly predictive of AF or non-AF arrhythmias. Our results are representative of AV synchronization findings across a broad taxonomy of cardiac arrhythmias in large 12-lead ECG databases.

Mental Workload (MW) has become an important problem in the design of man-machine systems, so its related detection and analysis technology has aroused extensive interest and concern. Researches show electrocardiogram (ECG) physiological signals can be an appropriate indicator to reflect the level of human MW. When the operator is in the state of high MW, the cardiac load will increase, and the period and shape of ECG signals accordingly change. Heart Rate Variability (HRV) is the most widely used ECG feature to assess the MW. However, its classification precision is not high enough. In order to improve this, multiple features of ECG signals are extracted to classify the MW in this paper. Besides the RR interval feature of HRV, the other three features, T and P wave power, QRS complex power and Sample Entropy (SampEn) of ECG waveform, are further extracted and applied to assess MW. The Support Vector Machine (SVM) is used to establish the classification model, and the grid search and cross-validation algorithm are used to optimize its parameters. The results show the MW classifier based on multiple features of ECG signals can evidently improve the classification accuracy. This will be helpful to realize a real time and online MW classification using ECG data.

Abnormal heart rhythms, also known as arrhythmias, can be life-threatening. AFIB and AFL are examples of arrhythmia that affect a growing number of patients. This paper describes a method that can support clinicians during arrhythmia diagnosis. We propose a deep learning algorithm to discriminate AFIB, AFL, and NSR RR interval signals. The algorithm was designed with data from 4051 subjects. With 10-fold cross-validation, the algorithm achieved the following results: ACC = 99.98%, SEN = 100.00%, and SPE = 99.94%. These results are significant because they show that it is possible to automate arrhythmia detection in RR interval signals. Such a detection method makes economic sense because RR interval signals are cost-effective to measure, communicate, and process. Having such a cost-effective solution might lead to widespread long-term monitoring, which can help detecting arrhythmia earlier. Detection can lead to treatment, which improves outcomes for patients.

We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (19–32 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska sleepiness scale (KSS) were polled every five minutes. ECG recordings were divided into 5-min segments, each associated with the mean of one self- and two observer-KSS ratings. Those mean KSS values were binarized to obtain two classes not drowsy and drowsy. The Q-, R- and T-waves in the recordings were detected; R-peak positions were manually reviewed; the Q- and T-detection method was tested against the manual annotations of Physio-net’s QT database. Power spectral densities of RR intervals (RR-PSD) and quantiles of the empirical distribution of heart-rate corrected QTc intervals were estimated. Support-vector machines and random-holdout cross-validation were used for the estimation of the classification error. Using either RR-PSD or QTc features yielded mean test errors of 79.3 ± 0.3 % and 82.7 ± 0.5 %, respectively. Merging RR and QTc features improved the accuracy to 88.3 ± 0.2 %. QTc intervals of the class drowsy were generally prolonged com-pared to not drowsy. Our findings indicate that the inclusion of QT intervals contribute to the discrimination of driver sleepiness.

An unconstrained visual display terminal (VDT) visual fatigue monitoring method based on BCG is studied. A VDT visual fatigue test is designed and implemented in this study, also acceleration signals are obtained by using an acceleration sensor, which is installed on the seat back. Then, BCG is obtained by adaptive threshold wavelet denoising, FFT, frequency domain filtering in the range of heart rate, and IFFT. Low frequency (LF) is extracted after the RR interval and JJ interval are calculated by the wavelet algorithm. Curves of two kinds of LF with the usage of VDT were obtained and were identical. Thus, the authors can draw conclusions that BCG can replace ECG to achieve unconstrained monitoring of VDT visual fatigue and LF gradually increases with VDT visual fatigue becoming more and more serious. Finally, we compare LF curves of BCG and ECG during use VDT to draw a conclusion that BCG can replace ECG to monitor VDT visual fatigue in an unconstrained way. Also, the confidence interval is used to verify the reliability of SD subjective evaluation, and the conclusion is reliable.

Introduction: Carbon nanotubes (CNTs) are novel candidates in nanotechnology with a variety of increasing applications in medicine and biology. Therefore the investigation of nanomaterials’ biocompatibility can be an important topic. The aim of present study was to investigate the CNTs impact on cardiac heart rate among rats. Methods: Electrocardiogram (ECG) signals were recorded before and after injection of CNTs on a group with six rats. The heart rate variability (HRV) analysis was used for signals analysis. The rhythm-to-rhythm (RR) intervals in HRV method were computed and features of signals in time and frequency domains were extracted before and after injection. Results: Results of the HRV analysis showed that CNTs increased the heart rate but generally these nanomaterials did not cause serious problem in autonomic nervous system (ANS) normal activities. Conclusion: Injection of CNTs in rats resulted in increase of heart rate. The reason of phenomenon is that multiwall CNTs may block potassium channels. The suppressed and inhibited IK and potassium channels lead to increase of heart rate.

Background: Knowledge of the onset of atrial fibrillation (AF) episodes in patients with paroxysmal atrial fibrillation (PAF) will enable them to better manage this condition. Current advances in mobile technology allow RR interval data to be obtained in real time. An analysis technique using RR interval data is presented with a view to alert a subject before a PAF episode. Method: The method is based on a time series of standard deviation and 0.99 quantile values of the spectral entropy, constructed from RR data. The RR data are taken from three time periods. The first time period has no occurrences of AF for 45 min to either side of the time period. The second time period just precedes an AF attack. Both of these are of thirty minutes duration. The third time period of approximately 5 min follows the second, and is when AF occurs. Results: Twenty-two PAF subjects were studied and in all cases there was a steady increase in the values of these indices as the onset of the AF attack approached. Conclusion: This method of analysis of RR interval data shows potential use to alert a PAF subject before the onset of an AF episode.

Cardiovascular diseases are the leading cause of death around the world. As a result, low-cost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a low-cost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical low-cost sensor, achieving good results without requiring any calibration of the raw data acquired. In addition, it has been validated with a well-accepted public electrocardiograph (ECG) data base, obtaining 87.65%, 91.84%, and 91.31% in terms of sensitivity, specificity and accuracy, respectively.

Cardiac autonomic neuropathy (CAN), one of the major complications in diabetes, if detected at the subclinical stage allows for effective treatment and avoiding further complication including cardiovascular pathology. Surface ECG (Electrocardiogram)-based diagnosis of CAN is useful to overcome the limitation of existing cardiovascular autonomic reflex tests traditionally used for CAN identification in clinical settings. The aim of this paper is to analyze the changes in the mechanical function of the ventricles in terms of systolic-diastolic interval interaction (SDI) from a surface ECG to assess the severity of CAN progression [no CAN, early CAN (ECAN) or subclinical CAN, and definite CAN (DCAN) or clinical CAN]. ECG signals recorded in supine resting condition from 72 diabetic subjects without CAN (CAN-) and 70 diabetic subjects with CAN were analyzed in this paper. The severity of CAN was determined by Ewing's Cardiovascular autonomic reflex tests. Fifty-five subjects of the CAN group had ECAN and 15 subjects had DCAN. In this paper, we propose an improved version of the SDI parameter (i.e., TQ/RR interval ratio) measured from the electrical diastolic interval (i.e., TQ interval) and the heart rate interval (i.e., RR interval). The performance of the proposed SDI measure was compared with the performance of the existing SDI measure (i.e., QT/TQ interval ratio). The proposed SDI parameter showed significant differences among three groups (no CAN, ECAN, and DCAN). In addition, the proposed SDI parameter was found to be more sensitive in detecting CAN progression than other ECG interval-based features traditionally used for CAN diagnosis. The modified SDI parameter might be used as an alternative measure for the Ewing autonomic reflex tests to identify CAN progression for those subjects who are unable to perform the traditional tests. These findings could also complement the echocardiographic findings of the left ventricular diastolic dysfunction by providing additional information about alteration in systolic and diastolic intervals in heart failure.

Development of new multimedia technologies allows us to receive better quality of audio and video content. Quality of Experience (QoE) evaluates given content from the consumer’s perspective. This measurement allows to evaluate not only visual and audible quality, but also general acceptability of provided service. QoE evaluation is getting popular between engineers, designers, retailers who wants to provide high quality content for consumers. QoE is generally evaluated subjectively by surveys. It is possible to find relationship between physiological signals measured while user is consuming audiovisual content and make the subjective evaluation of this experience. This paper investigates relationship between heart rate and QoE while user is watching 1 min duration video recordings on three different devices. Heart rate was calculated as mean RR interval for each recording. Mean RR intervals of 0.848 s, 0.869 s and 0.884 s were calculated for low, medium and high QoE device configurations, respectively. ANOVA analysis results indicates a relation between heart rate and QoE level. The results can help to develop further the investigations of QoE level and heart rate relationship for various subjective assessment, device configurations and content provided. Santrauka Tobulėjančios technologijos suteikia galimybę matyti vis geresnį vaizdo ir garso turinį. Matuojant patirtą kokybę (angl. Quality of Experience (QoE)) galima įvertinti vartotojui rodomą turinį, taip pat įvertinama ne tik vaizdo ir garso kokybė, bet ir bendras teikiamos paslaugos priimtinumas. QoE įvertinimas vis dažniau taikomas įvairių sričių inžinierių, dizainerių, pardavėjų, kurie nori pateikti kokybišką multimedijos turinį vartotojams. QoE dažniausiai įvertinama subjektyviai, vykdant vartotojų apklausas. Matuojant fiziologinius signalus ir vartotojui parodant audiovizualinį turinį galima ieškoti ryšio tarp gautų signalų ir vartotojo subjektyvių įvertinimų. Straipsnyje tiriamas ryšys tarp širdies ritmo ir QoE vartotojui žiūrint 1 minutės trukmės vaizdo įrašus, esant trims skirtingiems QoE lygiams. Širdies ritmas gautas skaičiuojant atstumų tarp gretimų elektrokardiogramos (ECG) signalo R tipo dantelių (RR intervalų) vidurkį kiekvienam 1 minutės trukmės įrašui. Gautos vidutinio RR intervalo vertės buvo 0,848 s, 0,869 s ir 0,884 s. Tai atitiko QoE žemo, vidutinio ir aukšto lygio prietaisų konfigūracijas. Atliktus ANOVA rezultatų analizę galima daryti prielaidą, kad egzistuoja ryšys tarp širdies ritmo ir QoE lygio pokyčio. QoE lygio įtaka širdies ritmui gali būti išsamiau tiriama atsižvelgiant į subjektyvų vartotojo vertinimą, skirtingas prietaisų konfigūracijas ir rodomą turinį. Reikšminiai žodžiai: patirta kokybė QoE; ECG; širdies ritmas; RR intervalas; fiziologiniai matavimai.

Electrocardiogram (ECG) signal has been commonly used to analyze the complexity of heart rate variability (HRV). For this, various entropy methods have been considerably of interest. The multiscale entropy (MSE) method, which makes use of the sample entropy (SampEn) calculation of coarse-grained time series, has attracted attention for analysis of HRV. However, the SampEn computation may fail to be defined when the length of a time series is not enough long. Recently, distribution entropy (DistEn) with improved stability for a short-term time series has been proposed. Here, we propose a novel multiscale DistEn (MDE) for analysis of the complexity of short-term HRV by utilizing a moving-averaging multiscale process and the DistEn computation of each moving-averaged time series. Thus, it provides an improved stability of entropy evaluation for short-term HRV extracted from ECG. To verify the performance of MDE, we employ the analysis of synthetic signals and confirm the superiority of MDE over MSE. Then, we evaluate the complexity of short-term HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The experimental results exhibit that MDE is capable of quantifying the decreased complexity of HRV with aging and CHF disease with short-term HRV time series.

Background: Use of tobacco in cigarettes indeed represents a health concern of growing magnitude among cigarette smokers. As a consequence of its addictive qualities, the consumption of cigarettes often becomes a lifelong habit, with cumulative and deleterious effects on health. Smokeless tobacco has been advocated as a substitute for cigarette smoking. On the contrary, the use of smokeless tobacco is fraught with health risks and needs to be discouraged. Modernisation of life style has affected the population even in rural areas, leading to addiction. Smokeless tobacco is presumed to be less of an evil by the rural folk. Khaini addicts are likely to suffer from mental and physical exhaustion, leading to stress. Khaini is tobacco with slaked lime. This is important in view of the prevailing socio-economic as well as the healthcare system available in any developing country. Therefore, the present study is designed to measure the heart rate variability (HRV) among this special group of the population. Materials and Methods: Fifteen male subjects were enrolled from the rural farm labors′ population who regularly chew khaini. They were free from any type of physical and mental health issues and were non-smokers. A similar group of 15 male subjects anthropometrically matched, who did not chew khaini and did not use tobacco in any form, participated as controls. Each individual volunteer was subjected to an HRV analysis on three occasions: the first record was performed on the first day of their arrival in the hospital as subjects during the lean season. The second observation was recorded after 3 months. The final HRV analysis was performed after 6 months of the first recording. Two types of parameters were analyzed: time domain and frequency domain. Statistical analysis was performed using a paired t-test within the study group and the unpaired t-test between non-khaini chewing controls and subjects on Day 1 of the recording. Results: There was no statistically significant difference between the controls and the study group on Day 1 of recording. Both the HRV parameters - time and frequency domain, showed decreased values during the third month and sixth month recording as compared with the first week recording. The decrease is much more during the sixth month recording as compared with the third month recording. A statistically significant decrease is observed in the mean RR interval, heart rate, very large frequency and large frequency only when the first week recording is compared with the third month recording, but when the first week recording is compared with the sixth month recording, a significant decrease in the mean RR interval and heart rate is found. Conclusion: Subjects involved in taking khaini are likely to undergo a lot of physical and mental stress thus affecting their autonomic status. This adds up to the stress induced by their occupation. HRV analysis using short-term electrocardiogram recordings was used to detect changes consequent with this stress.

It is hypothesized that a key characteristic of electrocardiogram (ECG) signal is its nonlinear dynamic behaviour and that the nonlinear component changes more significantly between normal and arrhythmia conditions than the linear component. The usual statistical descriptors used in RR (R to R) interval analysis do not capture the nonlinear disposition of RR interval variability. In this paper we explore a novel approach to extract the features from nonlinear component of the RR interval signal using Teager energy operator (TEO). The key feature of Teager energy is that it models the energy of the source that generated the signal rather than the energy of the signal itself. Hence any deviations in regular rhythmic activity of the heart get reflected in the Teager energy function. The classification evaluated on MIT-BIH database, with RR interval and mean of Teager energy computed over RR interval as features, exhibits an average accuracy that exceeds 99.79%.

In this paper, a new method for the detection of apnea/hypopnea periods in physiological data is presented. The method is based on the intelligent combination of an integrated sensor system for long-time cardiorespiratory signal monitoring and dedicated signal-processing packages. Integrated sensors are a PVDF film and conductive fabric sheets. The signal processing package includes dedicated respiratory cycle (RC) and QRS complex detection algorithms and a new method using the respiratory cycle variability (RCV) for detecting apnea/hypopnea periods in physiological data. Results show that our method is suitable for online analysis of long time series data.

The circadian rhythm of the mean heart rate and RR and QT intervals in electrocardiograms were determined for Spilotes pullatus in normal cycle (day/light and night/dark) conditions, during springtime. The statistical analysis of the results revealed significant differences among time intervals, using Tukey's test. Heart rate and body temperatures demonstrated considerable variations in their respective values over the 24h monitoring period and long circadian rhythmicity in the electrocardiogram intervals (p

Background: Caregivers of chronically bedridden patients are likely to suffer from mental and physical exhaustion leading to stress. This is important in view of the prevailing socioeconomic as well as the healthcare system available in a developing country like India. Therefore, the present study is designed to measure heart rate variability (HRV) among this special group of population who give care to long-term bedridden patients. Materials and Methods: Fifteen female subjects were enrolled from among attendants of patients admitted with an immediate history of a cerebrovascular accident with locomotor deficits, who required constant care. They were free from any type of physical and mental health issues, nonsmokers and nonaddicts. Each care giver was subjected to HRV analysis on three occasions: The first record was performed in the 1 st week of their arrival in the hospital as caregivers. The second observation was recorded after 3 months of caregiving. The final HRV analysis was done after 6 months of care giving. Two types of parameters were analyzed: Time domain and frequency domain. Statistical analysis was done using paired t-test. Results: Both the HRV parameters: Time and frequency domain, showed decreased values during the 3 rd and 6 th month recording as compared to the 1 st week recording. The decrease is much more during the 6 th month recording as compared to the 3 rd month recording. Statistically significant decrease is observed in mean RR interval, heart rate, very low frequency (VLF), and LF only when the 1 st week recording is compared with the 3 rd month recording, but when the 1 st week recording was compared with the 6 th month recording significant decrease was found in mean RR interval and heart rate. Conclusion: Subjects involved in taking care of chronically ill bedridden patients are likely to undergo a lot of physical and mental stress, thus affecting their autonomic status. HRV analysis using short term electrocardiogram (ECG) recording was used to detect changes consequent to this stress. It was found that almost all HRV parameters measuring heart rate complexity were decreased in the period of caregiving.