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Sleep apnea (SA) is a sleep-breathing disorder accompanied by multiple complications. The SA detection method based on a single-lead electrocardiogram (ECG) has the characteristics of low power consumption and is desirable for the development of wearable equipment. This study proposed a dual-model deep learning method to perform representation learning and introduce long-term temporal dependence. First, the Christov algorithm was used to obtain the RR interval (RRI) of each 1-minute ECG segment, and the adaptive synthetic (ADASYN) sampling method was employed to synthesize the RRI series of the minority class to address the imbalanced learning problem. Then, a representation learning model based on the one-dimensional convolutional neural network (1DCNN-RLM) was built to extract the feature vector of the RRI series. Eventually, a temporal dependence model based on the bidirectional gated recurrent unit (BiGRU-TDM) was constructed to learn the state (SA/normal) transition pattern between the segments and complete the classification task. We employed the apnea-ECG database for experiments. For per-segment detection results, the accuracy, sensitivity, and specificity of this method were 91.1%, 88.9%, and 92.4%, respectively. The per-recording detection accuracy reached 100%. ADASYN alleviates the imbalance of sensitivity and specificity in classification results. The 1DCNN-RLM with powerful representation learning ability has extracted discriminative features. The BiGRU-TDM introduces the long-term time dependence of SA and improves classification performance. The results of this study substantiate that the proposed method is robust and has good transferability. This method provides a reference for the diagnosis of other diseases. [ABSTRACT FROM AUTHOR]

Cardiac arrhythmias that can lead to sudden cardiac death are common. Electrocardiograms (ECGs) offer valuable information about cardiac status and play a crucial role in evaluating patients with arrhythmia in clinical diagnosis. The development of machine learning technologies has made the computer-aided diagnosis of cardiac arrhythmias possible, which can improve the efficiency and quality of medical treatment. In this study, a new method for the automatic classification of heartbeats is developed. This paper presents a novel approach to detect arrhythmias in the inter-patient paradigm using a convolutional neural network (CNN) that integrates multiscale convolutional blocks, frequency convolutional block attention (FCBA) modules, and RR interval features. The proposed method also includes oversampling for the heartbeats of the minority class and the addition of random noise to increase training samples and alleviate the problem of data imbalance. We used the MIT-BIH-AR arrhythmia database , which is a globally recognized ECG database, to evaluate the classification performance of the proposed model in this paper. Based on the experiments, the sensitivities of the normal beats (N), supraventricular ectopic beats (SVEB), and ventricular ectopic beats (VEB) were 96.9%, 89.3%, and 93.3%, respectively. In the inter-patient heartbeat classification paradigm, the overall accuracy of the proposed method is 95.60% in classifying heartbeats. The study shows that the proposed scheme outperforms other published schemes in terms of classification results. • A heartbeat classification method combining deep neural networks and medical features is proposed. • The use of multiscale convolutional fusion allows the network to extract richer electrocardiogram (ECG) features. • A frequency convolutional block attention (FCBA) module is proposed to adjust the wight relationships. • Medical features, namely RR intervals, are embedded into our deep model to further improve the performance of heartbeat classification. • Accuracy of 95.6% was achieved under inter-patient scheme. [ABSTRACT FROM AUTHOR]

Accurate and early detection of cardiac arrhythmia present in an electrocardiogram (ECG) can prevent many premature deaths. Cardiac arrhythmia arises due to the improper conduction of electrical impulses throughout the heart. In this paper, we propose an improved RR interval-based cardiac arrhythmia classification approach. The Discrete Wavelet Transform (DWT) and median filters were used to remove high-frequency noise and baseline wander from the raw ECG. Next, the processed ECG was segmented after the determination of the QRS region. We extracted the primary feature RR interval and other statistical features from the beats to classify the Normal, Premature Ventricular Contraction (PVC), and Premature Atrial Contraction (PAC). The K-Nearest Neighbour (k-NN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), and Random Forest (RF) classifier were utilised for classification. Overall performance of SVM with Gaussian kernel achieved Se % = 99.28, Sp % = 99.63, +P % = 99.28, and Acc % = 99.51, which is better than the other classifiers used in this method. The obtained results of the proposed method are significantly better and more accurate. [ABSTRACT FROM AUTHOR]

• Method achieved a good result of 94.4% accuracy for apnea detection in segment. • Firstly employ deep residual network for apnea detectionbased on low-sample-rate data. • Weights of network can be fine-tuned on clinical. • Strong adaptivity. Model also works well using ECG-derived respiration (EDR) signal. Apnea is one of the most common conditions that causes sleep-disorder breathing. With growing number of patients worldwide, more and more patients suffer from complications of apnea. But most of them stay untreated due to the complex and time-consuming polysomnography (PSG) diagnosis method. Effective and precise diagnosis support system using electrocardiograph (ECG) is required. In this paper, we propose an approach using residual network to detect apnea based on RR intervals (intervals between R-peaks of ECG signal). In our model, we apply residual network to represent information carried by RR intervals. Moreover, we proposed a novel perspective, called dynamic autoregressive representation, to provide interpretation of representing the RR intervals by convolutional layers. This approach is tested for per-segment apnea detection using publicly available dataset on Physionet. 30 overnight recordings are used for training and 5 for testing. We achieve a good result of 94.4% accuracy, 93.0% sensitivity and 94.9% specificity. This result outperform other prevalent methods based on RR intervals. This model also shows its good adaptivity while using ECG-derived respiration signal (EDR) in experiments. Its extensiveness is evaluated and compared in experiments. The proposed model is also compared with deep neural networks using original ECG signals for apnea detection, and it achieves better result using fewer input samples. We develop a deep residual network to detect apnea on low-sample-rate RR intervals. The result suggests a possibility of representing RR intervals by neural network. The model showed strong adaptivity when using EDR input. [ABSTRACT FROM AUTHOR]

• Pupil diameter fluctuates throughout the respiratory cycle in the resting state. • Respiratory fluctuation in pupil diameter was abolished by the cognitive state. • The cognitive state with the higher task engagement yielded more pupil dilation. • A different regulation may override respiratory-related control of pupil diameter. Pupil diameter fluctuation throughout the respiratory cycle is autonomically controlled in the resting state, as pupils dilate during inspiration and constrict during expiration. Furthermore, pupil size is differentially modulated by cognitive states between task engagement and disengagement. To determine whether respiratory-dependent fluctuations in pupil size are maintained during a cognitive task, we employed healthy human subjects performing a delayed matching-to-sample task with a short delay and measured their pupil sizes and R wave-to-R wave intervals (RRIs). We detected respiratory fluctuations in pupil size and the RRI during the delay period immediately before the discrimination stage of the task. During the discrimination stage, the cognitive state with the higher task engagement yielded more pupil dilation. However, respiratory fluctuations in pupil size were abolished, whereas those in the RRI were still discernible during the discrimination stage. Our results suggest that an alternative control mechanism involving the cognitive state associated with task engagement overrides the respiratory-related autonomic control of pupil diameter. [ABSTRACT FROM AUTHOR]

Background Prior work has found that linear heart rate variability (HRV) indices do not accurately reflect cardiac vagal control, and nonlinear indices of HRV have been proposed as alternative tools that may better capture cardiac vagal effects. We used progressive low dose atropine to induce changes in cardiac vagal tone to test the hypotheses that nonlinear HRV indices accurately reflect cardiac vagal control, and that their changes in response to low dose atropine correlate with those in RR interval. Methods Changes in RR interval and HRV indices during intravenous injections of saline (control) and 6 cumulative doses of atropine (from 1.4 to 7.2 μg/kg) during controlled breathing at 15 breaths per minute were assessed in 14 young healthy individuals. Results As expected, low dose atropine increased average RR interval (vagotonic effect). There was no strong association between vagotonic changes in RR interval and the majority of nonlinear HRV indices, either within or among subjects. Conclusions These data suggest an inconsistent relationship between responses of nonlinear HRV indices and RR interval to changes in cardiac vagal tone. Therefore, nonlinear HRV indices may not be reliable indices of cardiac vagal control in healthy humans. [ABSTRACT FROM AUTHOR]

This quantitative study delineates the influence of respiratory rate on cardiovascular signal in healthy subjects using granger causality approach. Electrocardiogram ( RR ), arterial blood pressure ( SBP ) and respiration ( RESP ) were simultaneously recorded for 5 min from 20 subjects during normal (13–20 cycles/min) and deep breathing (5 cycles/min) with equal inspiration and expiration time. During deep breathing mean RR remains same but the variance increases. Also deep breathing lowers blood pressure, increases baroreflex sensitivity and improves oxygen saturation. The traditional frequency domain methods based power spectrum analysis and coherence analysis lacks to measure coupling changes among physiological subsystems. Therefore, frequency domain Granger causality method based on directed coherence is proposed to detect the changes in coupling strength among cardiovascular signals under different respiration rates. Directed coherence spectrum can separate RESP , SBP and RR components from each recorded signals. The RESP component of both RR ( RR RESP ) and SBP ( SBP RESP ) increases (coherence > 0.5) significantly during deep breathing indicate that respiration affects both cardiac and vascular system but at normal breathing only cardiac system ( RR RESP ) get affected by respiration. Also a significant increase in coherence is observed on baroreflex direction ( RR SBP ) indicating that deep breathing controls blood pressure. Hence, the observed directed coherence spectrum helps to detect coupling changes among cardiac, vascular and respiratory signal during autonomic regulation. [ABSTRACT FROM AUTHOR]

Background: The electrocardiogram (ECG) is one of the most non-invasive techniques to give support to the atrial fibrillation (AF) diagnosis. Several authors use the temporal difference between two consecutive R waves, a method known as RR interval, to perform the AF diagnosis. However, RR interval-based analysis does not detect distortions on the other ECG waves.Purpose: Thus, the present work proposes a diagnostic decision support systems for AF based on higher order spectrum analysis of the voltage variation on the ECG..Methods: The proposed method was used aiming AF classifying. The classifier is composed by two screening stages: one based on the average and another on the average deviation of kurtosis of the ECG signals. Heartbeat obtained from the MIT-BIH atrial fibrillation and MIT-BIH normal were used.Results: ECG signal featured by kurtosis outperforms second order statistics based metrics in up to 476 times, and up to 110 times above the RR interval. The screening methods obtained sensitivity equal to 100% and specificity is up to 84.04%. The two screening methods combined provided an AF classifier with an accuracy rate at diagnosis of 100%. The results presented take into account windows of up to five heartbeats and a 99.73% confidence interval.Conclusion: The results obtained by the proposed method can be used to support decision-making in clinical practices with a diagnostic accuracy rate of 90.04% to 100%. [ABSTRACT FROM AUTHOR]

Objectives To assess the influence of 2nd-degree AV blocks (AVB) on RR interval-based heart rate variability (HRV) variables; to investigate the effect of using PP interval time series and of artifact filtering on HRV analyses; to investigate the influence of electrocardiogram (ECG) recording length and time of recording; and to calculate day-to-day variability and reference intervals of HRV variables. Animals Thirty healthy adult horses. Methods RR and PP interval time series were extracted from 10-h Holter ECGs and an automated filter was applied to the RR time series (RR f ). Time-domain HRV variables were calculated based on RR, PP, and RR f time series and their relation to the number of AVBs was assessed. Hourly 10- and 60-min segments were extracted to investigate the influence of segment length and recording time on HRV variables. Day-to-day variability and reference intervals of HRV variables were calculated. Results Variables of short-term HRV were significantly influenced by the number of AVBs when based on RR, but not when based on PP- and RR f time series. PP- and RR f -based HRV variables were in good agreement. The majority of HRV variables were influenced by recording time and ECG segment length. Day-to-day variability of HRV variables was low when based on 10-h ECG recordings but moderate to high when based on 60-min and 10-min recordings. Conclusions Second-degree AVBs significantly influence conventional RR-based, but not PP- and RR f -based time-domain HRV variables. However, PP and RR f analyses have limitations and recording length and time of recording must be considered. [ABSTRACT FROM AUTHOR]

• In this article, the CPSC 2018 dataset was used to train a ResRNN model. To classify the diseases into nine categories: normal, Arrhythmia (AF), I-atrioventricular block (AVB), left bundle branch block (LBBB), right bundle branch block (RBBB), premature atrial contraction (PAC), premature ventricular contractions (PVC), ST-segment depression (STD), ST-segment elevation (STSE) /(STE). • The ResRNN outperforms in identifying eight types of anomalies in 12-lead ECG data. • When level 4 rough coefficients with the Symlet-8 (Sym8) channel were used for organization, the best degree of exactness was obtained. • The ResRNN classifier has a normal accuracy of 91 percent when using ECG signals, which is much higher than the deep multiscale fusion neural network (DesNet) at 83 % and inception ResNet V2 at 80 %. Automated electrocardiogram (ECG) analysis cannot be employed in clinical practice due to the accuracy of the present models. Deep Neural Networks (DNNs) are models made up of stacking transformations that learn tasks by example. This technology has lately demonstrated remarkable performance in several activities, and its potential to improve clinical practice is highly anticipated. In this article, the China physiological signal challenge (CPSC)- 2018 dataset was used to train a ResRNN model. The ResRNN outperforms in identifying eight types of anomalies in 12-lead ECG data. When level 4 rough coefficients with the Symlet-8 (Sym8) channel were used for organization, the best degree of exactness was obtained. The ResRNN classifier has a normal accuracy of 91% when using ECG signals, which is much higher than the deep multiscale fusion neural network (DesNet) at 83 % and Inception ResNet V 2 at 80 %. As a result, the given methodology is far better than others. After preprocessing the signals, the ResRNN model classifies the diseases into nine categories: normal, Atrial fibrillation (AF), first degree-atrioventricular block (AVB), left bundle branch block (LBBB), right bundle branch block (RBBB), premature atrial contraction (PAC), premature ventricular contractions (PVC), ST-segment depression (STD), and ST-segment elevation (STSE) /(STE). [ABSTRACT FROM AUTHOR]

The QT interval of the electrocardiogram (ECG) is a measure of the duration of the ventricular depolarization and repolarization. In fish as in human, the QT interval is positively correlated with the RR interval of the ECG, a measure of the cardiac cycle length. Urotensin II (UII) is a neuropeptide that has been highly conserved from fish to human, and UII and its receptor (UT) are expressed in cardiovascular tissues including the heart. Although UII exerts potent cardiovascular actions, its possible effects on the QT interval have never been investigated. The goal of the present study was to provide insight into the potential effect of UII on the QT interval in an established in vivo trout model. To this end, the effects of UII on dorsal aortic blood pressure ( P DA ), RR, QT intervals and corrected QT (QTc) for RR interval, were investigated after intra-arterial (IA) injection of 5, 50 and 100 pmol UII. The effects of UII were compared to those of two structurally UII-related peptides (URPs), URP1 and URP2, and to those of arginine vasotocin (AVT), homolog of the mammalian arginine vasopressin. IA injection of vehicle or 5 pmol UII had no effect on the various parameters. At the 50-pmol dose, UII evoked its usual increase in P DA with a peak value observed 15 min after the injection (+ 22% from baseline, P < 0.001). This hypertensive effect of UII was accompanied by a significant increase in the RR interval (+ 18%, P < 0.001), i.e. a bradycardia, and these effects remained constant until the end of the recording. The highest dose of UII evoked similar hypertensive and bradycardic effects. Of interest, the QT interval did not change during the bradycardic action of UII (50 and 100 pmol) but the QTc interval significantly decreased. In trout pre-treated with urantide, a peptidic antagonist of UT, the hypertensive and bradycardic actions of 50 pmol UII were reduced 3-fold and no change occurred in the QT and QTc intervals. In trout pre-treated with blockers of the autonomic nervous system, the hypertensive effect of UII was maintained but no change appeared in RR, QT and QTc intervals. IA injections of 50 pmol URPs were without action on the preceding parameters. IA administration of 50 pmol AVT provoked quite similar increase in P DA , and elevation of the RR interval to those evoked by IA injection of UII but, in contrast to UII, AVT injection induced a highly significant and sustained prolongation of the QT interval compared to baseline (+ 7%, P < 0.001) without change in QTc. Our results are indicative of a lack of QT interval change during UII-evoked bradycardia but not after AVT-induced bradycardia and suggest for the first time that some compensatory mechanism specific for the UII peptide is working to stabilize the QT interval. Further research is needed to elucidate the mechanism involved in this action of UII. The potential for UII to prevent detrimental prolongation of cardiac ventricular repolarization might be questioned. [ABSTRACT FROM AUTHOR]

Synchronization provides an insight into underlying the interaction mechanisms among the bivariate time series and has recently become an increasing focus of interest. In this study, we proposed a new cross entropy measure, named cross fuzzy measure entropy (C-FuzzyMEn), to detect the synchronization of the bivariate time series. The performances of C-FuzzyMEn, as well as two existing cross entropy measures, i.e., cross sample entropy (C-SampEn) and cross fuzzy entropy (C-FuzzyEn), were first tested and compared using three coupled simulation models (i.e., coupled Gaussian noise, coupled MIX( p ) and coupled Henon model) by changing the time series length, the threshold value for entropy and the coupling degree. The results from the simulation models showed that compared with C-SampEn, C-FuzzyEn and C-FuzzyMEn had better statistical stability and compared with C-FuzzyEn, C-FuzzyMEn had better discrimination ability. These three measures were then applied to a cardiovascular coupling problem, synchronization analysis for RR and pulse transit time (PTT) series in both the normal subjects and heart failure patients. The results showed that the heart failure group had lower cross entropy values than the normal group for all three cross entropy measures, indicating that the synchronization between RR and PTT time series increases in the heart failure group. Further analysis showed that there was no significant difference between the normal and heart failure groups for C-SampEn (normal 2.13 ± 0.37 vs. heart failure 2.07 ± 0.16, P = 0.36). However, C-FuzzyEn had significant difference between two groups (normal 1.42 ± 0.25 vs. heart failure 1.31 ± 0.12, P < 0.05). The statistical difference was larger for two groups when performing C-FuzzyMEn analysis (normal 2.40 ± 0.26 vs. heart failure 2.15 ± 0.13, P < 0.01). [ABSTRACT FROM AUTHOR]

Abstract: The study investigated whether the beat-to-beat QT interval variability relationship to the mean heart rate and the RR interval variability depended on the cardiovascular autonomic status changed by postural positioning. Repeated long-term 12-lead Holter recordings were obtained from 352 healthy subjects (mean age 32.7 ± 9.1 years, 176 females) while they underwent postural provocative tests involving supine, unsupported sitting and unsupported standing positions. Each recording was processed as a sequence of overlapping 10-second segments. In each segment, the mean RR interval, the coefficients of variance of the RR intervals (RRCV) and the QT intervals (QTCV) were obtained. In each subject, these characteristics, corresponding to different postural positions, were firstly averaged and secondly used to obtain within-subject correlation coefficients between the different characteristics at different postural positions. While the within-subject means of RRCV generally decreased when changing the position from supine to sitting and to standing (4.53 ± 1.95%, 4.12 ± 1.51% and 3.26 ± 1.56% in females and 3.99 ± 1.44%, 4.00 ± 1.24% and 3.53 ± 1.32% in males respectively), the means of QTCV systematically increased during these position changes (0.96 ± 0.40%, 1.30 ± 0.56% and 1.88 ± 1.46% in females and 0.85 ± 0.30%, 1.13 ± 0.41% and 1.41 ± 0.59% in males, respectively). The intra-subject relationship between QTCV, RRCV and mean RR intervals was highly dependent on postural positions. The study concludes that no universally applicable normalization of the QT interval variability for the heart rate and/or the RR interval variability should be assumed. In future studies of the QT variability, it seems preferable to report on the absolute values of QT variability, RR variability and mean heart rate separately. [Copyright &y& Elsevier]

Background: Although J-wave elevation in the inferolateral leads could be related to idiopathic ventricular fibrillation (IVF), little is known about the pathophysiologic characteristics of J-wave elevation in patients with IVF. Objective: This study aimed to determine the relationship between augmentation of J-wave elevation and changes in RR interval or autonomic nervous activities in patients with IVF. Methods: Eight patients with IVF and 22 controls with J-wave elevation (≥0.1 mV) in lead V5 were studied. The J-wave amplitude was automatically measured in lead CM5 of a digital Holter electrocardiogram, and the J–RR relationship was determined. Based on the analysis of heart rate variability, the relationship between the J-wave amplitude and the natural logarithm of high-frequency (HF) components (J–ln HF relationship) or the ratio of low frequency (LF) components to HF components (J–LF/HF relationship) was also determined. Results: The J–RR slope (mm/s) was greater in patients with IVF than in controls (3.5 ± 0.7 vs 2.4 ± 0.8; P <.01), as was J-wave amplitude (mm) at an RR interval of 1.2 seconds (2.8 ± 0.9 vs 2.0 ± 0.6; P <.05). The J-wave amplitude was correlated positively with ln HF and negatively with LF/HF, and the slopes of both J–ln HF and J–LF/HF regression lines were greater in patients with IVF than in controls. During an entire 24-hour period, there was no difference between the 2 groups in either HF or LF/HF. Nine of the total 11 episodes (82%) of spontaneous ventricular fibrillation occurred between 18:00 and 6:00. Conclusions: In patients with IVF as compared with control subjects, J-wave elevation was more strongly augmented during bradycardia and was associated with an increase in vagal activity. This could be related to the occurrence of ventricular fibrillation predominantly at night in patients with IVF. [ABSTRACT FROM AUTHOR]

Abstract: An in-home sleep monitoring system was developed previously in our laboratory for monitoring electrocardiography (ECG) and respiratory signals. However, the ECG signal acquired with this system is prone to high-grade noise caused by motion artifact. Since the detection of the QRS complexes with high accuracy is very important in a computer-based analysis of the ECG, a high accuracy QRS detection algorithm is developed and based on the combination of heart rate indicators and morphological ECG features. The proposed algorithm is tested both on 16h data acquired using the two sensors of our cardiorespiratory belt system, i.e., the polyvinylidene fluoride (PVDF) film and the conductive fabric sheets, and on all 48 records of the MIT/BIH Arrhythmia Database. Satisfying results are obtained for both databases, the sensitivity S e and positive predictivity P + were calculated for each case and results show S e =[96.98%, 93.76%] and P + =[97.81%, 99.48%] for conductive fabric and PVDF film sensors, respectively, and S e =99.77% and P + =99.64% in the case of the MIT/BIH Arrhythmia Database. Further, heart rate variability (HRV) measures were calculated using our system and a commercial system. A comparison between systems’ results is done to show the usefulness of our developed algorithm used with our cardiorespiratory belt sensor. [Copyright &y& Elsevier]

Abstract: Although heart rate dependency of QT interval is well known, the relationship of other electrocardiogram (ECG) parameters to heart rate has been researched less intensively. This study investigated the heart rate dependencies of QT interval, PQ interval, and QRS width in 40 healthy subjects (18 women; mean age, 30.4 ± 8.1 years). In each subject, 3 long-term (approximately 13 hours) 12-lead ECGs were obtained for 3 day-time periods with gaps of 2 to 3 weeks between repeated recordings. In each recording, approximately 230 ECG measurements of QT interval, PQ interval, and QRS width were made, each preceded by stable heart rate. For each recording, linear regression slopes of QT/RR, PQ/RR, and QRS/RR relationships were obtained. Intrasubject SDs of individual values were compared with intersubject SDs of intrasubject means to test the individuality of the relationships. The intrasubject means of the slope values were also compared between sexes. The individual SDs of the QT/RR regression slopes were 0.0116 ± 0.0065, whereas the population SD of intrasubject means was 0.0245 (P = 3.6 × 10−15). For the PQ/RR slopes and QRS/RR slopes, these values were 0.0085 ± 0.0050 vs 0.0314 (P = 7.9 × 10−28) and 0.001 89 ± 0.001 57 vs 0.005 50 (P = 2.6 × 10−17), respectively. The QT/RR slopes were steeper in women than in men (0.194 ± 0.019 vs 0.168 ± 0.022, 0.0005), whereas the QRS/RR slopes were, on average, negative in women while positive in men (−0.001 38 ± 0.0045 vs 0.003 35 ± 0.0054, P = .005). There were no sex differences in the PQ/RR slopes (0.054 ± 0.032 in women vs 0.055 ± 0.031 in men, P = .95). Thus, not only the heart rate dependency of QT interval but also the rate dependencies of PQ interval and of QRS width show high intrasubject stability with substantial intersubject differences. [Copyright &y& Elsevier]

Background: Circadian QTc changes have been reported, with conflicting results. Spontaneous QTc variability is important for pharmaceutical cardiac safety studies. Objective: The purpose of this study was to investigate QTc variability in accurately measured and heart-rate corrected daytime data of healthy subjects. Methods: Continuous 12-lead ECGs were recorded in 53 healthy volunteers. For each recording, ∼320 ECG samples of 10 seconds were obtained throughout the daytime period, all preceded by stable heart rates. In each ECG sample, QT interval was measured on superimposed 12 leads by two independent cardiologists and reconciled. Four RR-interval expressions were used: (a) average of the first three RR of the ECG sample, (b) RR average of the 10-second sample, (c) average of RR intervals in a 2-minute history, and (d) RR intervals of an independently established individual QT/RR hysteresis profile. For all RR-interval expressions, QT intervals were corrected using the Fridericia formula and individually optimized curvature correction. QTc variability was measured by intraindividual QTc standard deviations. Results: With Fridericia correction and the RR expressions (a) to (d), QTc variability obtained was (a) 9.45 ± 1.70 ms, (b) 7.80 ± 1.48 ms, (c) 6.37 ± 1.64 ms, and (d) 5.81 ± 1.75 ms. With individualized correction, QTc variability was (a) 8.16 ± 1.71 ms, (b) 6.71 ± 1.41 ms, (c) 5.22 ± 1.13 ms, and (d) 4.56 ± 1.18 ms. All differences (b) vs (a), (c) vs (b), and (d) vs (c) were highly statistically significant (P <10−12 in all cases). Conclusion: Previously reported large QTc variability largely results from methodologic imprecision. Little QTc variability is present in daytime recordings of healthy subjects. Consequently, QT-related pharmaceutical cardiac safety studies can be made smaller without decreasing their power. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute a feature vector for the following classifier. Two neural networks, including a probabilistic neural network (PNN) and a back-propagation neural network (BPNN), are employed as classifiers. ECG samples attributing to eight different beat types were sampled from the MIT-BIH arrhythmia database for experiments. The results show high classification accuracy of over 98% with either of the two classifiers. Between them, the PNN shows a slightly better performance than BPNN in terms of accuracy and robustness to the number of ICA-bases. The impressive results prove that the integration of independent component analysis and neural networks, especially PNN, is a promising scheme for the computer-aided diagnosis of heart diseases based on ECG. [Copyright &y& Elsevier]

Abstract: Introduction: Although guinea pigs are suitable for in vivo QT assessment of newly discovered drugs at the pre-clinical stage because of the similarity of the ion channels between the guinea pig heart and the human, there is limited data available regarding the characteristics of QT interval in conscious guinea pigs. Aging is one of several factors which have been shown to affect the QT interval in humans and animals. In the present study, we examined the influence of age on QT and RR intervals in conscious guinea pigs. Methods: Electrocardiograms were recorded from female Hartley guinea pigs at the age of 6 weeks (young; n =6) and 23 months (old; n =4) via a telemetry system. The QT and RR intervals were measured during daytime and nighttime, and following intravenous bolus injection of E-4031 (0.1 mg/kg) or terfenadine (4 mg/kg). Comparisons were made to determine group differences in: (1) the normal values of the QT and RR intervals, (2) the best-fit QT-correction formula, (3) the circadian rhythm of QT and RR intervals, and (4) drug effects on repolarization. Results: The normal values of QT and RR intervals in the old group were significantly longer than those in the young group. The best-fit formula for correcting QT interval was a modified Bazett''s formula for both young and old groups. The old group did not show the nocturnal variation of either QT or RR interval. Terfenadine caused significantly greater QTc prolongation in the old group compared to the young. Discussion: Aging affects resting QT and RR intervals in conscious female guinea pigs, a factor which should be considered when examining the effects of compounds on cardiac repolarization. Also, the present study suggests a possibility that age can affect QTc prolongation induced by some IKr blockers. [Copyright &y& Elsevier]

Abstract: In this paper, an electrocardiogram (ECG) beat classification system based on wavelet transformation and probabilistic neural network (PNN) is proposed to discriminate six ECG beat types. The ECG beat signals are first decomposed into components in different subbands using discrete wavelet transformation. Three sets of statistical features of the decomposed signals as well as the AC power and the instantaneous RR interval of the original signal are exploited to characterize the ECG signals. A PNN follows to classify the feature vectors. The result shows a promising accuracy of 99.65%, with equally well recognition rates of over 99% throughout all type of ECG beats. Only 11 features are required to attain this high accuracy, which is substantially smaller in quantity than that in other methods. These observations prove the effectiveness and efficiency of the proposed method for computer-aided diagnosis of heart diseases based on ECG signals. [Copyright &y& Elsevier]

Abstract: Background and purpose: Sleep affects the RR interval in electrocardiogram (ECG) recordings and ventricular repolarization differentially in men and women. Compared to men, pre-menopausal women have a more pronounced shortening of RR interval and prolongation of QT and QT corrected (QTc, by Bazett''s formula) ECG waves during rapid eye movement (REM) sleep. The aim of the present study was to evaluate sleep-related RR and QT changes: (1) with the physiological decline in female hormones occurring with menopause, and (2) after hormone replacement therapy with estrogen and progesterone (HRT). Patients and methods: We analyzed ECG recordings from 14 post-menopausal women (48–61 years old) who underwent polysomnography before HRT (T1) and after 6 months of HRT (T2) with estrogen and progesterone. Eight of the post-menopausal women (48–54 years) were also compared to eight age-matched pre-menopausal women. In all subjects, mean RR interval, mean QT interval and QTc, were obtained from 1-min recordings selected from wakefulness, stage 2 and REM sleep. Results: Pre-menopausal and post-menopausal women showed similar changes in RR, QT and QTc intervals from wakefulness through sleep. Specifically, in both pre-menopausal and post-menopausal women the RR interval was shorter during REM sleep compared to wakefulness (P=0.009) and stage 2 sleep (P=0.001); the QT interval was more prolonged during stage 2 (P=0.002) and REM (P=0.006); and the QTc interval was significantly prolonged during stage 2 (P=0.01) and REM (P=0.0003) sleep compared to wakefulness. Among post-menopausal women, both before and after HRT (T1 and T2), RR interval shortened significantly during REM compared to wakefulness (P=0.03) and to stage 2 (P=0.002); the absolute QT interval was longer during stage 2, compared to both wakefulness (P<0.001) and REM (P<0.001); the QTc interval was increased during REM sleep compared to wakefulness (P=0.003). Conclusions: Sleep-related RR and QT changes in women are not altered by menopausal status nor by post-menopausal hormonal replacement with estrogen and progesterone. [Copyright &y& Elsevier]

Abstract: Background and purpose: We questioned the role of respiratory events in obstructive sleep apnea syndrome (OSAS) and of upper airway resistance syndrome (UARS) on heart rate (HR) during sleep, paying specific attention to the termination of the abnormal breathing events and examining the presence of arousals or termination with only central nervous system (CNS) activation. Patients and methods: Twenty patients, 10 with UARS and 10 with mild OSAS, were studied. A nocturnal polysomnogram was performed including measurement of respiratory variables and pulse transit time (PTT). According to the presence or absence of a PTT event indicative of autonomic nervous system (ANS) activation, 148 events were extracted after having been randomly chosen in each represented sleep stage, with or without an electroencephalogram (EEG) arousal >1.5s. RR interval (RRI) in electrocardiogram (ECG) recordings, as well as heart rate variability, was calculated during 60 and 120s, respectively. Period amplitude analysis (PAA) was applied for RR-interval analysis, and fast Fourier transformation (FFT) was applied to perform HR variability analysis. Results: Visually scored EEG arousal was significantly associated with an increase in sympathetic index of heart rate, while PTT was associated with a drop in parasympathetic index, after the respiratory events. Patients with mild OSAS presented persistently shorter RRI when compared to patients with UARS. The latter also exhibited a significant decrease in parasympathetic index (High Frequency (HF)) at the termination of a respiratory event. Conclusion: The HF component was only significantly decreased in patients with UARS, which indicates a predominant involvement of the parasympathetic tone in patients with UARS in comparison to those with OSAS. [Copyright &y& Elsevier]

Abstract: Objective: We tested the hypothesis that the reductions of the changes in the respective influence of the cardiac sympathetic and vagal activity control and delta EEG activity with aging alter the interactions between the heart rate variability (HRV) and the delta sleep EEG power band. Methods: A polysomnography was performed on 16 healthy young men and 19 healthy middle-aged men across the first 3 NREM–REM cycles. Spectral analysis was applied to electrocardiogram and electroencephalogram recordings. High Frequency (HFnu) of HRV as well as the maximum of cross-spectrum, coherency, gain and phase shifts between HFnu and delta sleep EEG power band were compared between both groups. Results: Young men experienced more deep sleep than middle-aged men (P&#60;0.001). In middle-aged subjects, HFnu was lower than the HFnu of their younger counterparts (P&#60;0.001), but they showed similar increases during NREM sleep and similar decreases during REM sleep as the young subjects. Cross-spectrum values, coherency, gain and phase shifts between HFnu and delta were identical between the two groups. Modifications in HFnu show parallel changes and precede changes in delta EEG band by a similar leads of 11±6min in young men and 9±7min in middle-aged men (P=0.23). Conclusions: Reduced changes in the respective influence of the cardiac sympathetic and vagal activity and delta EEG activity with progressive aging do not alter the relationship and phase difference between changes in the relative predominant cardiac vagal activity and delta power in middle-aged men. Significance: Interaction between the cardiac sympathetic and vagal activity with delta EEG activity is maintained in middle-aged men. [Copyright &y& Elsevier]

Background: Heart rate variability (HRV) is a marker of cardiac autonomic tone. Depressed HRV has been reported in patients with AIDS. We conducted this study to find out if HRV is depressed in human immunodeficiency virus (HIV)-positive individuals without AIDS. Methods: We studied prospectively HRV by spectral analysis of short-term electrocardiography (ECG) monitoring in 21 HIV-positives (33±11 years) and in 18 healthy volunteers (31±9 years). None of the HIV-positives had any clinical evidence of autonomic or cardiac dysfunction. Echocardiography was also performed in HIV-seropositives to rule out left ventricular (LV) systolic dysfunction. All these individuals did not have any evidence of AIDS. Results: Mean CD4+ lymphocyte count was 426±166/mm3. The ejection fraction (EF%) of HIV patients was 62.4±6.4.The total power of HRV was reduced significantly in HIV-positive individuals (p<0.00001). All the components of HRV were reduced. Conclusions: HRV is reduced in HIV-seropositive individuals in early stages of infection as well without any clinical evidence of autonomic dysfunction. This may serve as an early marker of future global sympatho-vagal imbalance. [Copyright &y& Elsevier]

Objective: We investigated the interactions between heart rate variability and sleep electroencephalogram power spectra.Methods: Heart rate and sleep electroencephalogram signals were recorded in 8 healthy young men. Spectral analysis was applied to electrocardiogram and electroencephalogram recordings. Spectral components of RR intervals were studied across sleep stages. The cross-spectrum maximum was determined as well as coherencies, gains and phase shifts between normalized high frequency of RR intervals and all electroencephalographic frequency bands, calculated over the first 3 NREM-REM cycles.Results: RR intervals increased from awake to NREM and decreased during REM. Normalized low frequency decreased from awake to NREM and increased during REM while normalized high frequency evolved conversely. Low to high frequency ratio developed in opposition to RR intervals. Coherencies between normalized high frequency and power spectra were high for all bands. The gain was highest for delta band. Phase shift between normalized high frequency and delta differed from zero and modifications in normalized high frequency preceded changes in delta by 41±14°.Conclusions: Our study demonstrates that: (1) all electroencephalographic power bands are linked to normalized high frequency; (2) modifications in cardiac vagal activity show predominantly parallel changes and precede changes in delta band by a phase shift corresponding to a lead of 12±5 min. [Copyright &y& Elsevier]

The hypothesis that hypercapnia-induced differences in heart rate variability (HRV) would emerge during early maturation was tested using a developing porcine model. Piglets were randomly assigned to either exposed (10% CO2 for 1 h) or control (100% O2) conditions, and then to one of three study groups: (a) 5–8 days old, (b) 13–15 days old, (c) 26–34 days old. Experiments were performed on pairs of age- and litter-matched animals that were anesthetized, paralyzed, and artificially ventilated. HRV was evaluated using power spectral analysis, SD of differences between successive RR intervals, and cardiac interval analysis. Statistical comparisons of simultaneously studied animals were made at baseline, 15 and 55 min after onset of hypercapnia, and 2 h after offset of hypercapnia. Our analyses revealed that only HRV of 26–34-day-old animals differed significantly from values of control animals. Cardiac intervals of those animals were distributed in such a manner that hypercapnia likely elicited coactivation of sympathetic and parasympathetic systems. Comparison of the distribution of cardiac intervals for other animals showed that 5–8-day-old animals had high frequency of balanced intervals at baseline that remained so during hypercapnia. Given that such coactivation may be neuroprotective, the paucity of balanced intervals in 13–15-day-old animals could mean that the end of the second postnatal week is associated with increased vulnerability. [Copyright &y& Elsevier]

• A low computational load and dynamic thresholding based QRS complex detection method. • The dynamic threshold value is computed for both upward and downward QRS complex simultaneously. • The proposed method was applied on five different full-length databases, MITBIH AD, FTD, ESTD, NSDT, and FTDB, has given AETR of 2.35 s, 3.8 s, 3.69 s, 1.99 s, 0.53 s, respectively. • The proposed method is tested on total 195 records and 672,472 beats of five different ECG databases, which yields the 99.70 and 99.69, average Se%, and +P% respectively. QRS-complex detection is a primitive step in the detection of cardiac disorder using electrocardiogram (ECG). Abnormal and varying peaks, baseline wander and other noise are the main challenges in accurate QRS-complex detection. In this paper, A window-based FIR filter is used to eliminate the high-frequency noise. Next, the ECG is enhanced to the power third after multiplication followed by normalization and moving average process to retain dynamic QRS-complex. Then baseline and root mean square (RMS) value of first three seconds of the signal are used for initial thresholding, later dynamic thresholding process was utilized to update the threshold value after the detection of four R-peaks. The threshold value was automatically updated using the previous threshold value, R-peak amplitude, RR-interval, and RR-intervals means. Kurtosis coefficient computation is used for discarding prominent T-wave and further this technique located the QRS-complex accurately in the raw ECG signal. The proposed method was applied to Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia Database (MIT-BIH AD), Fantasia Database (FTD), European ST-T Database (ESTD), MIT-BIH Noise Stress Test Database (NSTD), and Direct Fetal ECG Database (FTD) for its evaluation and validation. The overall sensitivity rate of 99.70% and positive predictivity rate of 99.69% have been achieved. The proposed method is based on dynamic thresholding and simple decision rules, which makes this method computationally efficient. Wide validation over five different databases proves the robustness of this method. [ABSTRACT FROM AUTHOR]

• A simple but efficient heuristic method for atrial fibrillation detection. • A novel AF feature extraction strategy based on Poincaré plot of first-order differential RR interval. • The advantage of distinguishing AF from non-AF with complicated arrhythmias. With the popularity of wearable mobile devices, increasing numbers of applications use single-lead electrocardiograms (ECGs), which provides information about heart rate and rhythm to detect cardiac diseases. Atrial fibrillation (AF) is the most common arrhythmia in clinical practice, it is very important to detect AF early. In this study, we propose a Poincaré plot-based method for AF detection that has the advantages of requiring few calculations and short detection segments. Our method first extracts the first-order differential RR interval (ΔRRI) sequences from a segmented ECG data, and then the polar coordinate transformation is performed on the Poincaré plot of the ΔRRI to obtain the phase-based distribution. Two features, the distribution width D w and the average distribution height D h are extracted from the phase-based distribution to classify the AF and non-AF episodes. Five PhysioNet databases were used for the assessment, which contain 3,843.3 hours of data from 229 subjects. Long-Term AF Database was selected as the training set, and the remaining four databases were used as the testing sets. In the testing sets, the results yield good sensitivity/specificity (97.91%/99.14%) with 60 second ECG signal segments, which are better than those of the existing the Poincaré plot-based methods, and the method also performs well with 20 second ECG signal segments (sensitivity/specificity of 97.28%/98.35%). The detection performance of this method is good under different condition, and has the advantage of easy implementation, which makes it promising for application in the detection and monitoring of AF. [ABSTRACT FROM AUTHOR]

We introduce average entropy (AE) as a disorder measure of heart rate time series. The AE measurement reflects both the probability distribution of all discrete values in a time series and the randomness of the series. The AE analysis is able to differentiate the congestive heart failure (CHF), the healthy, and the atrial fibrillation (AF) subjects for both short and long heart rate time series. The differentiation accuracy of AE is higher than 90%. In addition, since the meaning of "disorder" is easily confused with "complexity", we explore the relation between them by analyzing heart rate time series. Specifically, we demonstrate that the complexity measured by entropy of entropy (EoE) versus the disorder measured by AE plot of heart rate time series exhibits a distinct inverted U relation. • AE reflects the disorder of both probability distribution & randomness of a series. • AE analysis is able to differentiate the CHF, the healthy, and the AF subjects. • EoE (complexity) vs. AE (disorder) exhibits a distinct inverted U curve. [ABSTRACT FROM AUTHOR]