Analysis Of RR Intervals Research Articles

From physiological complexity to data interactions-A case study of recordings from exercise monitoring.

The popularity of nonlinear analysis has been growing simultaneously with the technology of effort monitoring. Therefore, considering the simple methods of physiological data collection and the approaches from the information domain, we proposed integrating univariate and bivariate analysis for the rest and effort comparison. Two sessions separated by an intensive training program were studied. Nine subjects participated in the first session (S1) and seven in the second session (S2). The protocol included baseline (BAS), exercise, and recovery phase. During all phases, electrocardiogram (ECG) was recorded. For the analysis, we selected corresponding data lengths of BAS and exercise usually lasting less than 5 min. We found the utility of the differences between original data and their surrogates for sample entropy Sdiff and Kullback-Leibler divergence KLDdiff. Sdiff of heart rate variability was negative in BAS and exercise but its sensitivity for phases discrimination was not satisfactory. We studied the bivariate analysis of RR intervals and corresponding QT peaks by Interlayer Mutual Information (IMI) and average edge overlap (AVO) markers. While the IMI parameter decreases in exercise conditions, AVO increased in effort compared to BAS. These findings conclude that researchers should consider a bivariate analysis of extracted RR intervals and corresponding QT datasets, when only ECG is recorded during tests.

RETRACTED: Arrhythmia classification using ECG signal: A meta-heuristic improvement of optimal weighted feature integration and attention-based hybrid deep learning model

In healthcare facilities, the most common and least expensive diagnostic tool for monitoring electrical signals in the heart is the Electrocardiogram (ECG). Arrhythmia is nothing but abnormal rhythms in the heart. Arrhythmias can be dangerous and fatal. An ECG test can identify the type of arrhythmia that's causing the abnormal heartbeat. These necessitate lengthy, high-quality ECG segments, which may result in a few important episode omissions. Because of this, a low-cost, dependable, and automatic system for screening and treating arrhythmia becomes highly necessary. A novel method for the classification of arrhythmias is suggested on the basis of attention-based deep learning to satisfy the above-mentioned requirement. Signal collection, signal analysis, feature extraction, weighted fused features, and classification are the various phases of the proposed work. The input signal is initially obtained from benchmark data sources. The RR interval and QRS complex signal analysis are carried out next. A One-Dimensional Convolution Neural Network (1DCNN) is used to extract the deep features from the resulting RR interval and QRS complex, as well as the Higher-Order Statistical (HOS) features from the raw signal. The weighted fused feature selection is carried out after the above-mentioned three features have been obtained, where the weight is adjusted with the Recommended Adaptive Risk Rate-based Lemurs Optimization Algorithm (ARR-LO). Finally, the fused features are fed into the hybrid Attention model through the Long Short-Term Memory (LSTM) and Deep Temporal Convolution Network (DTCN), where ARR-LO is used to optimally determine some of the hyperparameters. The objectives of the study include developing an arrhythmia classification framework, constructing an adaptive attention-based hybrid deep learning model (HA-LSTM-DTCN), creating a new meta-heuristic algorithm (ARR-LO), and validating the suggested model against existing classifiers. The performance is compared with the conventional models and evaluated using a variety of metrics and the results from the simulation, the generated deep learning-based arrhythmia classification framework outperformed conventional prediction models such as DTCN, LSTM-DTCN, DNN, and LSTM classifiers in classifying the arrhythmia by 81.79%, 55.46%, and 80.73%, respectively and the overall accuracy of the proposed framework performs 94.425%. From the results, it was shown that generated AR-LO-HA-LSTM-DTCN model for arrhythmia classification improved the classification performance, thus making it useful in the quick diagnosis of arrhythmia disease. The findings show that the suggested arrhythmia classification model improves the classification performance, which can be used to quickly diagnose arrhythmia disease.

The effect of a novel lifestyle intervention on autonomic and haemodynamic function in individuals with hypertrophic cardiomyopathy

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement no. 777204. Background Autonomic nervous system may play an important role in the pathophysiology of hypertrophic cardiomyopathy (HCM). Lifestyle interventions have been shown to improve signs and symptoms in individuals with heart failure. Purpose To evaluate the effect of a novel lifestyle intervention incorporating physical activity and dietary nitrate supplementation on cardiac autonomic and haemodynamic function in individuals with HCM. Methods Twenty-eight individuals with HCM were recruited into a single-centre pilot study (N=7 females, age 54±15 years) and randomised into the intervention (N=20) or control (N=8) group. Participants completed short-term (5-minute) non-invasive heart rate variability (HRV) measurements under resting (supine) conditions using the TaskForce (CNSystems, Graz, Austria). Frequency-domain HRV measures including spectral analysis of RR interval (i.e. absolute and normalised low frequency power (LF), high frequency power (HF) and LF/HF ratio) were recorded. Non-invasive haemodynamic variables including cardiac output (CO), and stroke volume (SV)were simultaneously recorded using bioimpedance technology integrated within the TaskForce. Intervention participants consumed 6 mmol of nitrate daily (concentrated beetroot juice) and increased and maintained their daily physical activity by ≥2000 steps/day above baseline for 16 weeks. Participants in the control group continued with their usual lifestyle and monitored daily step counts. Physical activity was evaluated objectively using pedometers (Omron Health, Japan). All participants were monitored weekly via telephone calls and average daily activity levels were recorded using diaries. However, participants in the control group were asked not to change their physical activity and dietary habits. Results Participants in the intervention group significantly increased their average physical activity by 3029 steps post intervention (i.e, 6234±2389 vs 9263± 3341 steps/day, p<0.01) compared to control (7559±3257 vs 7604± 2901 steps/day, p=0.96). There was a significant increase in post-intervention HF power (7.54±2.14 vs. 8.78±1.60 ms2, p<0.01) and LF power (6.89±2.33 vs. 8.17±1.55, p<0.01) respectively, but not in the control group (i.e., HF (7.05±3.39 vs. 7.64±2.56 ms2, p=0.33) and LF power (6.78±3.31 vs. 7.06±2.43 ms2 p=0.63)). There was a non-significant change in SV index in the intervention (33±9.83 vs. 29±8.56 ml/beat/m2, p=0.06) and control groups (34±6.27 vs. 33±8.1 ml/beat/m2, p=0.79). Similarly, there was no significant change in cardiac index in the intervention or control groups (intervention: 2.35±0.44 vs. 2.38±0.52 L/min/m2, p=0.77; control: 2.27±0.40 vs. 2.67±0.78, p=0.06). Conclusions Lifestyle intervention incorporating physical activity and dietary nitrate supplementation led to a significant increase in daily physical activity and improved parasympathetic activity in hypertrophic cardiomyopathy.

Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms.

Obstructive Sleep Apnea (OSA) is a respiratory disorder due to obstructive upper airway (mainly in the oropharynx) periodically during sleep. The common examination used to diagnose sleep disorders is Polysomnography (PSG). Diagnose with PSG feels uncomfortable for the patient because the patient's body is fitted with many sensors. This study aims to propose an OSA detection using the Fast Fourier Transform (FFT) statistics of electrocardiographic RR Interval (R interval from one peak to the peak of the pulse of the next pulse R) and machine learning algorithms. In this case-control study, data were taken from the Massachusetts Institute of Technology at Beth Israel Hospital (MIT-BIH) based on the Apnea ECG database (RR Interval). The machine learning algorithms were Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM). The OSA detection technique was designed and tested, and five features of the FFT were examined, namely mean (f1), Shannon entropy (f2), standard deviation (f3), median (f4), and geometric mean (f5). The OSA detection found the highest performance using ANN. Among the ANN types tested, the ANN with gradient descent backpropagation resulted in the best performance with accuracy, sensitivity, and specificity of 84.64%, 94.21%, and 64.03%, respectively. The lowest performance was found when LDA was applied. ANN with gradient-descent backpropagation performed higher than LDA, SVM, and KNN for OSA detection.

Cardiac autonomic nerve function in polycystic ovary syndrome is not related to oxidative stress: A Poincaré analysis of heart rate variability

Background: Polycystic ovary syndrome (PCOS) is a well recognized reproductive hormone disorder. It is often associated with dysautonomia and increased oxidative stress Objective: To assess the relationship between cardiac autonomic nerve function (CANF) and oxidative stress in patients with PCOS. Methods: This cross sectional study enrolled 30 newly diagnosed PCOS patients 20-35 years of age and 30 apparently healthy, regularly menstruating women of similar age and BMI. The patients were enrolled from outpatient Department of Endocrinology, BSMMU, Dhaka. CANF was assessed by poincare analysis of RR interval, a non linear method of Heart Rate Variability (HRV) analysis. HRV data of all subjects were recorded by a digital data acquisition device, Power lab 8/35, AD instruments, Australia, with lab chart software analysis. Oxidative stress was assessed by plasma catalase and plasma Malondialdehyde (MDA) levels. For statistical analysis unpaired “t” test and Pearson’s correlation test were used. Results: In this study, resting pulse rate, systolic blood pressure(SBP), diastolic blood pressure(DBP) were significantly higher (p<0.001, p<0.01, p<0.01 respectively) and standard deviation of the short term RR interval variability (SD1), ratio of short term and long term variability (SD1/SD2) ratio were significantly lower (p<0.001) in PCOS than those of healthy controls. In addition, plasma catalase levels was significantly lower (p<0.01) and plasma MDA levels was significantly higher (p<0.001) in PCOS patients compared to controls. On correlation analysis, SD1 showed negative correlation with plasma catalase and plasma MDA levels (p>0.05) in PCOS patients. In addition SD1/SD2 ratio showed positive correlation with plasma catalase and negative correlation with plasma MDA levels (p>0.05) in these group of patients. But all these correlations were statistically not significant. Conclusion: The present study reveals association of autonomic dysfunction and higher oxidative stress with PCOS but it failed to show evidence for any interrelationship between them. J Bngladesh Soc Physiol 2021;16(1): 88-94

Classification of De novo post-operative and persistent atrial fibrillation using multi-channel ECG recordings

Atrial fibrillation (AF) is the most sustained arrhythmia in the heart and also the most common complication developed after cardiac surgery. Due to its progressive nature, timely detection of AF is important. Currently, physicians use a surface electrocardiogram (ECG) for AF diagnosis. However, when the patient develops AF, its various development stages are not distinguishable for cardiologists based on visual inspection of the surface ECG signals. Therefore, severity detection of AF could start from differentiating between short-lasting AF and long-lasting AF. Here, de novo post-operative AF (POAF) is a good model for short-lasting AF while long-lasting AF can be represented by persistent AF. Therefore, we address in this paper a binary severity detection of AF for two specific types of AF. We focus on the differentiation of these two types as de novo POAF is the first time that a patient develops AF. Hence, comparing its development to a more severe stage of AF (e.g., persistent AF) could be beneficial in unveiling the electrical changes in the atrium. To the best of our knowledge, this is the first paper that aims to differentiate these different AF stages. We propose a method that consists of three sets of discriminative features based on fundamentally different aspects of the multi-channel ECG data, namely based on the analysis of RR intervals, a greyscale image representation of the vectorcardiogram, and the frequency domain representation of the ECG. Due to the nature of AF, these features are able to capture both morphological and rhythmic changes in the ECGs. Our classification system consists of a random forest classifier, after a feature selection stage using the ReliefF method. The detection efficiency is tested on 151 patients using 5-fold cross-validation. We achieved 89.07% accuracy in the classification of de novo POAF and persistent AF. The results show that the features are discriminative to reveal the severity of AF. Moreover, inspection of the most important features sheds light on the different characteristics of de novo post-operative and persistent AF.

RR interval analysis based on a newly developed PC program as a predictor of interventions in implantable cardioverter-defibrillator patients.

Prediction of sudden cardiac death remains a significant challenge. There is some evidence that ventricular ectopic activity could be regarded as a predictive marker. We carried out an analysis to explore whether premature ventricular complexes (PVCs) are a risk factor in implantable cardioverter-defibrillator (ICD) interventions. The study method was a RR interval series analysis (n = 184) of arrhythmic events and controls from the ICD. Study group consisted of patients with a mean age of 55 ±27 years; 74% of them were male, 85% were secondary prevention patients, 62% had coronary artery disease (CAD), 15% hypertropic cardiomyopathy (HCM), 15% dilated cardiomyopathy (DCM), and 8% diseases of other etiology. The mean follow-up time was 64 months (range: 3-126 months). The study population was divide into patients with at least 1 appropriate intervention ventricular tachycardia/ventricular fibrillation (VT/VF) (group A, n = 101) and controls without interventions (group B, n = 83). The number of PVC/4000 RR cycles, the shortest coupling intervals between a PVC and preceding R as well as the number of PVCs of very short (180-220 ms), short (220-280 ms) and different cycle lengths (CL) as well as the incidence of short-long-short (SLS) sequences were compared. The number of PVCs/4000 RR cycles was significantly higher in group A (263 ±32 compared to 43 ±17, p < 0.0001). The mean shortest PVC CL was significantly shorter in group A (320 ±13 compared to 400 ±38, p = 0.029). The number of PVCs with a very short CL was 1 ±0.4 compared to 0.1 ±0.1 (p = 0.028). The number of PVCs with a short CL was 5 ±1.2 compared to 0.6 ±0.4 (p = 0.0007) in groups A and B, respectively. The incidence of SLS sequences was significantly higher in group A than in group B (67 (94% of patients) and 4 (33% of patients) respectively (p < 0.0001)). Significant differences were found in the characteristics of PVCs and SLS sequences between patients with appropriate ICD interventions and controls. A newly developed basic computer program called PCRR was applied for RR interval analysis. This simple method could be a predictor of PVC burden and life-threatening arrhythmias in different populations.

Altered Cardiac Autonomic Regulation in Overweight and Obese Subjects: The Role of Age-and-Gender-Adjusted Statistical Indicators of Heart Rate Variability and Cardiac Baroreflex

In the context of functional determinants of cardiovascular risk, a simple excess in body weight, as indexed by a rise in body mass index (BMI), plays a significant, well-recognized causal role. Conversely, BMI reductions toward normal result in an improvement of risk. Obesity is associated with impaired cardiac autonomic regulation (CAR), through either vagal or sympathetic mechanisms, which could favor the tendency to foster hypertension. Here we study the changing properties of the relationship between increasing grades of BMI and CAR in a population of 756 healthy subjects (age 35.9 ± 12.41 years, 37.4% males, 21.6% overweight, and 16% obese). Evaluation of CAR is based on autoregressive spectral analysis of short-term RR interval and systolic arterial pressure variability, from which a multitude of indices, treated overall as autonomic nervous system (ANS) proxies, is derived. Inspection of the study hypothesis that elevated BMI conditions associate significantly with alterations of CAR, independently of age and gender, is carried out using a mix of statistical transformations, exploratory factor analysis, non-parametric testing procedures, and graphical tools particularly well suited to address alterations of CAR as a disturbed process. In particular, to remove the effects of the inter-individual variability, deriving from components like age, gender or ethnicity, and to reduce the number of ANS proxies, we set up six age-and-gender-adjusted CAR indicators, corresponding to four ANS latent domains (oscillatory, amplitude, pressure, and pulse), cardiac baroreflex regulation, and autonomic nervous system index (ANSI). An impairment of the CAR indicators is overall evident in the overweight group and more marked in the obesity group. Empirical evidence is strong (9/9 concordant non-parametric test results) for pressure domain, almost strong (8/9) for ANSI, medium-strong for baroreflex (6/9) and pulse (7/9), weak for oscillatory (2/9) and amplitude (1/9) domains. In addition, the distribution of the CAR indicators corresponding to pressure, pulse, baroreflex, and ANSI is skewed toward the unfavorable abscissa extremity, particularly in the obese group. The significant association of increased BMI with progressive impairments of CAR regarding specifically the pressure domain and the overall ANS performance might underscore the strong hypertensive tendency observed in obesity.

Renal Transplantation Significantly Improves Autonomic Function with Normalization of Baroreflex Sensitivity as Early as Three-Month Post-transplantation.

The most common cause of mortality in chronic kidney disease patients is cardiovascular events. Cardiovascular autonomic dysfunction is likely to contribute high incidence of cardiovascular mortality, and in addition to adrenergic overdrive in these patients, there is the presence of impaired reflex control of both sympathetic and parasympathetic outflow to the heart and vasculature. Very few studies are available which show that renal transplantation (RT) improves the baroreflex function along with improvement in cardiovascular variability parameters. This prospective study was designed for the assessment of the autonomic function, i.e., heart rate variability (HRV), blood pressure variability (BPV), and baroreflex sensitivity (BRS) in end-stage renal disease (ESRD) patients before RT and three and six months after RT and to study the effects of RT on cardiac and vascular autonomic tone and on BRS. We studied 81 ESRD patients prospectively slated for RT but only 64 patients (mean age: 33 years) completed both three and six months visits after RT for autonomic function study. Patients were evaluated in detail clinically as well as routine biochemical parameters were done on every three visits. Baroreflex function was quantified by the sequence method. Assessment of short-term HRV and BPV were done using power spectrum analysis of RR intervals and systolic BP by frequency domain analysis. The parameters of HRV after RT showed significant changes in high-frequency domain measures six months post-RT but not in low frequency. HRV in total power was also statistically significant as early as three months postrenal transplant and remained at six months. The favorable effect of RT on decreasing BPV and improving BRS is seen by as early as three months.

Detection of Atrial Fibrillation on Stroke Units: Comparison of Manual versus Automatic Analysis of Continuous Telemetry

Background: Detection of atrial fibrillation (AF) is one of the primary diagnostic goals for patients on a stroke unit. Physician-based manual analysis of continuous ECG monitoring is regarded as the gold standard for AF detection but requires considerable resources. Recently, automated computer-based analysis of RR intervals was established to simplify AF detection. The present prospective study analyzes both methods head to head regarding AF detection specificity, sensitivity, and overall effectiveness. Methods: Consecutive stroke patients without history of AF or proof of AF in the admission ECG were enrolled over the period of 7 months. All patients received continuous ECG telemetry during the complete stay on the stroke unit. All ECGs underwent automated analysis by a commercially available program. Blinded to these results, all ECG tracings were also assessed manually. Sensitivity, specificity, time consumption, costs per day, and cost-effectiveness were compared. Results: 216 consecutive patients were enrolled (70.7 ± 14.1 years, 56% male) and 555 analysis days compared. AF was detected by manual ECG analysis on 37 days (6.7%) and automatically on 57 days (10.3%). Specificity of the automated algorithm was 94.6% and sensitivity 78.4% (28 [5.0%] false positive and 8 [1.4%] false negative). Patients with AF were older and had more often arterial hypertension, higher NIHSS at admission, more often left atrial dilatation, and a higher CHA2DS2-VASc score. Automation significantly reduced human resources but was more expensive compared to manual analysis alone. Conclusion: Automatic AF detection is highly specific, but sensitivity is relatively low. Results of this study suggest that automated computer-based AF detection should be rather complementary to manual ECG analysis than replacing it.

Clinical Usefulness of New R-R Interval Analysis Using the Wearable Heart Rate Sensor WHS-1 to Identify Obstructive Sleep Apnea: OSA and RRI Analysis Using a Wearable Heartbeat Sensor.

Obstructive sleep apnea (OSA) is highly associated with cardiovascular diseases, but most patients remain undiagnosed. Cyclic variation of heart rate (CVHR) occurs during the night, and R-R interval (RRI) analysis using a Holter electrocardiogram has been reported to be useful in screening for OSA. We investigated the usefulness of RRI analysis to identify OSA using the wearable heart rate sensor WHS-1 and newly developed algorithm. WHS-1 and polysomnography simultaneously applied to 30 cases of OSA. By using the RRI averages calculated for each time series, tachycardia with CVHR was identified. The ratio of integrated RRIs determined by integrated RRIs during CVHR and over all sleep time were calculated by our newly developed method. The patient was diagnosed as OSA according to the predetermined criteria. It correlated with the apnea hypopnea index and 3% oxygen desaturation index. In the multivariate analysis, it was extracted as a factor defining the apnea hypopnea index (r = 0.663, p = 0.003) and 3% oxygen saturation index (r = 0.637, p = 0.008). Twenty-five patients could be identified as OSA. We developed the RRI analysis using the wearable heart rate sensor WHS-1 and a new algorithm, which may become an expeditious and cost-effective screening tool for identifying OSA.

Electrocardiogram Beat Classification Using Data Filtration Technique and Support Vector Machine

This work addresses the automatic classification of arrhythmia beats into four generalized classes as described by the Association for the Advancement of Medical Instrumentation (AAMI) standard. We propose a method that includes time-series, statistical and frequency features of RR-interval, DWT, and EMD analysis of QRS morphology. Also, a data filtration technique using support vector selection and under-sampling is applied to find those features as well as data points having significant prediction capabilities. While testing the above combination on MIT-BIH arrhythmia database, adopting the inter-patient paradigm, we achieved 70%, 99.79%, 64.5%, and 80.55% Se and 61.76%, 94.64%, 83.22%, and 77.48% PPV for F, N, SVEB, and VEB classes respectively. Further, the proposed method reduced the classifier’s complexity through feature selection and computation time by data reduction while maintaining the generalization capability of the model. Another finding includes the significant contribution that RR-interval, 180–360 Hz and 0–45 Hz band power, and non-linear statistical characteristics have in distinguishing the arrhythmia classes. The feature and data selection criterion used is F -score and one-class classification by RBF-SVM respectively. The classifier used for building the final model is SVM with the cubic kernel.

RR interval analysis for the distinction between stress, physical activity and no activity using a portable ECG.

Methods based exclusively on heart rate hardly allow to differentiate between physical activity, stress, relaxation, and rest, that is why an additional sensor like activity/movement sensor added for detection and classification. The response of the heart to physical activity, stress, relaxation, and no activity can be very similar. In this study, we can observe the influence of induced stress and analyze which metrics could be considered for its detection. The changes in the Root Mean Square of the Successive Differences provide us with information about physiological changes. A set of measurements collecting the RR intervals was taken. The intervals are used as a parameter to distinguish four different stages. Parameters like skin conductivity or skin temperature were not used because the main aim is to maintain a minimum number of sensors and devices and thereby to increase the wearability in the future.

Lying Awake at Night: Cardiac Autonomic Activity in Relation to Sleep Onset and Maintenance

Insomnia, i.e., difficulties initiating and/or maintaining sleep, is one of the most common sleep disorders. To study underlying mechanisms for insomnia, we studied autonomic activity changes around sleep onset in participants without clinical insomnia but with varying problems with initiating or maintaining sleep quantified as increased sleep onset latency (SOL) and wake after sleep onset (WASO), respectively. Polysomnography and electrocardiography were simultaneously recorded in 176 participants during a single night. Cardiac autonomic activity was assessed using frequency domain analysis of RR intervals and results show that the normalized spectral power in the low frequency band (LFnu) after sleep onset was significantly higher in participants with long SOL compared to participants with short SOL. Furthermore, the normalized spectral power in the high frequency band (HFnu) was significantly lower in participants with long SOL as compared to participants with short SOL over 3 time periods (first 10 min in bed intending to sleep, 10 min before, and 10 min after sleep onset). These results suggest that participants with long SOL are more aroused in all three examined time periods when compared to participants with short SOL, especially for young adults (20–40 years). As there is no clear consensus on the cutoff for an increased WASO, we used a data-driven approach to explore different cutoffs to define short WASO and long WASO groups. LFnu, HFnu, and LF/HF differed between the long and the short WASO groups. A higher LFnu and LF/HF and a lower HFnu was observed in participants with long WASO for most cutoffs. The highest effect size was found using the cutoff of 66 min. Our findings suggest that autonomic cardiac activity has predictive value with respect to sleep characteristics pertaining to sleep onset and maintenance.

Symbolic Recurrence Analysis of RR Interval to Detect Atrial Fibrillation.

Atrial fibrillation (AF) is a sustained cardiac arrhythmia associated with stroke, heart failure, and related health conditions. Though easily diagnosed upon presentation in a clinical setting, the transient and/or intermittent emergence of AF episodes present diagnostic and clinical monitoring challenges that would ideally be met with automated ambulatory monitoring and detection. Current approaches to address these needs, commonly available both in smartphone applications and dedicated technologies, combine electrocardiogram (ECG) sensors with predictive algorithms to detect AF. These methods typically require extensive preprocessing, preliminary signal analysis, and the integration of a wide and complex array of features for the detection of AF events, and are consequently vulnerable to over-fitting. In this paper, we introduce the application of symbolic recurrence quantification analysis (SRQA) for the study of ECG signals and detection of AF events, which requires minimal pre-processing and allows the construction of highly accurate predictive algorithms from relatively few features. In addition, this approach is robust against commonly-encountered signal processing challenges that are expected in ambulatory monitoring contexts, including noisy and non-stationary data. We demonstrate the application of this method to yield a highly accurate predictive algorithm, which at optimal threshold values is 97.9% sensitive, 97.6% specific, and 97.7% accurate in classifying AF signals. To confirm the robust generalizability of this approach, we further evaluated its performance in the implementation of a 10-fold cross-validation paradigm, yielding 97.4% accuracy. In sum, these findings emphasize the robust utility of SRQA for the analysis of ECG signals and detection of AF. To the best of our knowledge, the proposed model is the first to incorporate symbolic analysis for AF beat detection.

Extended Central Tendency Measure and difference plot for heart rate variability analysis

Heart rate variability (HRV) is a non-invasive alternative to analyze the role of the autonomic nervous system (ANS) on heart functioning. Many tools have been developed to analyze collected cardiac data. Among them, the Central Tendency Measure (CTM) is a quantitative method for variability analysis of RR intervals. The values of the CTM must be between 0 and 1 (inclusive) for different radius, which follows the intrinsic characteristics of each time series. Using the conventional CTM, the successive differences of the time series may be calculated, and it can classify and differentiate the disturbances in the ANS involving HRV. This method was extended (e-CTM) to analyze the differences between RR interval time series. In this extension, a new parameter is added, which allows analysis of long time intervals, instead of successive and adjacent RR intervals. The ability of the e-CTM to differentiate the groups of the RR interval time series was verified with 145 RR interval time series divided into three groups: subjects with congestive heart failure, healthy subjects, and nurses during one hour of their workday. Results evidence that the new parameter added differentiates the group with pathology (and subsequent impairment of ANS) and group under stress at work (temporary impairment of ANS). These results suggest that the e-CTM is capable of detection long-term variations in the HRV according to the ANS impairment.

How the insula speaks to the heart: Cardiac responses to insular stimulation in humans.

Despite numerous studies suggesting the role of insular cortex in the control of autonomic activity, the exact location of cardiac motor regions remains controversial. We provide here a functional mapping of autonomic cardiac responses to intracortical stimulations of the human insula. The cardiac effects of 100 insular electrical stimulations into 47 epileptic patients were divided into tachycardia, bradycardia, and no cardiac response according to the magnitude of RR interval (RRI) reactivity. Sympathetic (low frequency, LF, and low to high frequency powers ratio, LF/HF ratio) and parasympathetic (high frequency power, HF) reactivity were studied using RRI analysis. Bradycardia was induced by 26 stimulations (26%) and tachycardia by 21 stimulations (21%). Right and left insular stimulations induced as often a bradycardia as a tachycardia. Tachycardia was accompanied by an increase in LF/HF ratio, suggesting an increase in sympathetic tone; while bradycardia seemed accompanied by an increase of parasympathetic tone reflected by an increase in HF. There was some left/right asymmetry in insular subregions where increased or decreased heart rates were produced after stimulation. However, spatial distribution of tachycardia responses predominated in the posterior insula, whereas bradycardia sites were more anterior in the median part of the insula. These findings seemed to indicate a posterior predominance of sympathetic control in the insula, whichever the side; whereas the parasympathetic control seemed more anterior. Dysfunction of these regions should be considered when modifications of cardiac activity occur during epileptic seizures and in cardiovascular diseases.

Cardiovascular autonomic profile in women with constitutional hypotension.

Constitutional hypotension (CHT) is defined as a SBP below 105 mmHg. As autonomic-related symptoms are frequently reported in CHT, these symptoms suggest that the cardiovascular autonomic control is perturbed in individuals with CHT. We investigated the autonomic cardiovascular control of 15 women with CHT and 12 women with NBP (SBP >110 mmHg). We monitored BP and ECG for autonomic function test. Supine and head up tilt (HUT) spectral analysis of RR interval and BP variability, baroreflex sensitivity and plasma levels of plasma renin activity and aldosterone were determined. M-mode echocardiogram was used to determine the left ventricle mass. SBP and DBP were lower in CHT (97 ± 1.5 and 54 ± 1.5 mmHg) than in NBP (126 ± 3 and 70 ± 4 mmHg, P < 0.001 for both), whereas heart rate was comparable (65 ± 1.5 and 63 ± 3 bpm). CHT compared with NBP had lower Valsalva's ratio and BP phase IV overshooting, 1.7 ± 0.07 vs. 2 ± 0.07 (P < 0.05) and 19 ± 2.4, and 28 ± 3 mmHg (P < 0.05), respectively. BRSseq, alpha LF and LFRR/HFRR were greater in CHT (29.2 ± 0.7 and 39.1 ± 4.7 ms/mmHg and 1.4 ± 0.2) compared with NBP (25 ± 1.6 and 20.1 ± 2.5 ms/mmHg and 0.7 ± 01, [P < 05, for all]). LFSAP was lower in CHT (0.8 ± 0.2) than in NBP (1.5 ± 0.3 mmHg, P < 0.02). HUT data were similar. Supine and HUT aldosterone and PRA were higher in CHT. Left ventricle mass was lower in CHT. We conclude that the cardiovascular autonomic control in women with CHT is characterized by a low sympathetic vascular tone and increased baroreceptor sensitivity. Also, it seems that these women have a compensated primary hypovolemia, which warrants further investigation.

Interictal electrocardiographic alternations in patients with drug-resistant epilepsy

PurposePrevious studies suggested the possible role of autonomic dysfunction in sudden unexpected death in epilepsy (SUDEP). The aim of this study is to assess the interictal ECG alternations especially heart rate variability (HRV), as a marker of autonomic dysfunction, in patients with drug-resistant epilepsy and determine the effect of epilepsy type and duration, seizure frequency and anti-epileptic drugs (AEDs) on ECG findings. MethodsIn this comparative cross-sectional study, the interictal ECG parameters of 64 consecutive patients with drug-resistant epilepsy and the same number of age and sex-matched controls were analyzed. Epilepsy type and duration, seizure frequency, MRI findings and patients’ anti-convulsive medications were determined. ResultsOur study showed significant longer mean PR interval, shorter mean QRS duration, shorter mean QTc interval and longer corrected QT interval dispersion (QTcd) in patients with epilepsy compared to healthy subjects. The analysis of RR intervals revealed reduced RR standard deviation (SDNN), which is a marker of reduced HRV. A positive linear correlation was found between QRS duration and epilepsy duration. No significant correlation was found between taking a certain kind of AED, and ECG alternations, except for mild QTcd prolongation in patients taking valproate. ConclusionOur study showed clinically important alternations in interictal ECG parameters in patients with drug-resistant epilepsy which could result in sudden cardiac death.

Autonomic cardiac activity in adults with short and long sleep onset latency.

Autonomic cardiac activity during sleep has been widely studied. Research has mostly focused on cardiac activity between different sleep stages and wakefulness as well as between normal and pathological sleep. This work investigates autonomic activity changes during sleep onset in healthy subjects with long and short sleep onset latency (SOL). Polysomnography (PSG) and electrocardiography (ECG) were simultaneously recorded in 186 healthy subjects during a single night. Autonomic activity was assessed based on frequency domain analysis of RR intervals and results show that the analysis of RR intervals differs significantly between the short SOL and the long SOL groups. We found that the spectral power in the low frequency band (LF) was significantly higher in the long SOL group compared to the short SOL group in the first 10 minutes in bed intended to sleep. There was no significant difference for LF and the spectral power in the high frequency band (HF) 10 minutes before and after sleep onset between the two groups. Only in the short SOL group there was a significant increase in HF from the first 10 minutes in bed intended to sleep to 10 minutes before SO, while LF decreased significantly in both groups. The effect of time (5.5-min bin) on the heart rate variability (HRV) features around sleep onset showed that both LF and HF differed significantly during the period surrounding sleep onset only in the short SOL group.