Combination Of Heart Rate Variability Research Articles

A Deep Belief Network Model for Automatic Atrial Fibrillation Detection

Atrial fibrillation is an abnormal heart rhythm categorized by the rapid and irregular pulse of the atrial chambers in the heart. The Electrocardiogram is normally used for the analysis of Atrial Fibrillation which is used to check a patient’s rhythm and electrical activity. The diagnosing method involved in this process is used to classify the electrocardiogram signal as normal or atrial fibrillation. In this analysis, Adaptive Dual Threshold Filter and Discrete Wavelet Transform methods are utilized to remove noises present in ECG signals representing Electromyogram, Power line interference, Baseline wandering, White Gaussian Noise, electrode motion artifact, and muscle contraction noises. In this process, a combination of heart rate variability and atrial activity is used to do the feature extraction. This work proposes a Chimp Optimization algorithm-based parameter-optimized Deep Belief classifier for effective Atrial Fibrillation detection. To validate the results comparison analysis is done with the three methods, Random Forest Classifier, Logistic Regression, and Decision Tree Classifier, to the check the outcomes of the proposed model. The simulation outcomes demonstrate that the accuracy of the proposed method achieves 10.81%, 10.81%, and 1.86% better than the existing methods.

Digitalization of perioperative traumatic stress in enhanced recovery after surgery program: current application and future prospect

Perioperative traumatic stress is a systemic nonspecific response caused by stimuli such as anesthesia, surgery, pain and anxiety, which lasts throughout the perioperative period.The continuous excessive stress response is not conducive to the postoperative rehabilitation of patients. Enhanced recovery after surgery (ERAS), a research hotspot of modern surgery, can significantly reduce perioperative pain and stress, thus promoting the rehabilitation of patients. With the progress of artificial intelligence and information technology, wearable, non-invasive, real-time heart rate variability (HRV) dynamic monitoring can effectively realize the digitalization of stress monitoring with low price, which is worthy of clinical application. Therefore, the use of HRV for digital monitoring of perioperative stress has a significant research value. Moreover, the combination of HRV and ERAS has shown its advantages and the prospect of clinical application is worthy of anticipating.

Detection of Episodes of Sleep Apnea and Hypopnea in ECG and EEG Signals by Machine Learning

The article is devoted to the application of machine learning methods for computerized detection of sleep apnea episodes based on the analysis of single-channel signals of the electrocardiogram (ECG) and electroencephalogram (EEG). To study the possibilities of machine learning to detect apnea based on ECG and EEG analysis, we used Apnea-ECG database and MIT-BIH polysomnographic database from PhysioNet, which contain annotations to each minute of records indicating the presence or absence of apnea/hypopnea at the current time. In order to apply machine learning methods to the problem of automated detection of sleep apnea/hypopnea episodes in ECG and EEG signals, long-term polysomnograms available in MIT-BIH polysomnographic database were segmented according to annotations into shorter sections lasting 30 seconds each. The study used 267 segments lasting 30 seconds for the class "norm", 258 segments for the class "apnea" and 273 segments for the class "hypopnea", a total of 798 simultaneous ECG and EEG recordings. The aim of this work is to identify and compare informative signs of sleep apnea episodes in terms of heart rate variability (HRV) and brain electrical activity, as well as the choice of classification methods that provide the highest accuracy for this task. Features of cardiorhythmograms in time and frequency domains, spectral-temporal and wavelet characteristics, as well as parameters of EEG signals based on energy ratio of EEG rhythms, Hearst index, Higuchi fractal dimension and sample entropy for EEG signals are considered. Using different sets of features, the accuracy of classifiers based on decision trees, discriminant analysis, support vector machines, k-nearest neighbor method, and ensemble training was determined. Based on this, combination of features and classifiers is proposed, which provides the highest accuracy of recognition of sleep apnea episodes according to single-channel ECG and EEG signals, taken separately and in the case of a combination of their features. The best results of classification of signals "norm", "apnea" and "hypopnea" were obtained for the model trained using weighted method k nearest neighbors with 25 features of HRV: the total percentage of correctly identified cases for three classes was 99.9% (797 correctly identified cases of 798). By reducing the number of HRV parameters to 9, the best machine learning result was achieved using the bagging ensemble algorithm with 30 decision trees: the total percentage of correctly identified cases for all three classes was 99.4% (793 correctly identified cases from 798: for "norm" - 265 cases from 267, for "apnea" - 257 cases from 258, for "hypopnea" - 271 cases from 273). The use of EEG parameters as features for apnea/hypopnea recognition showed worse results compared to HRV parameters. In this case, the best result of machine learning was achieved using support vector machines with quadratic kernel function: the total percentage of correctly identified cases for three classes was 91.9% and the signals corresponding to norm were most badly recognized (27 cases were classified as hypopnea, and in 9 cases - as sleep apnea). The combination of HRV and EEG parameters gave the best accuracy of 99.1%, but the results are comparable to using only HRV parameters. The obtained results indicate that HRV parameters allow recognizing sleep apnea and hypopnea with higher accuracy than EEG parameters, but EEG signal undoubtedly reflects signs of sleep apnea/hypopnea and also can be used for apnea recognition.

A Detector for Premature Atrial and Ventricular Complexes.

The relationship between premature atrial complexes (PACs) and atrial fibrillation (AF), stroke and myocardium degradation is unclear. Current PAC detectors are beat classifiers that attain low sensitivity on PAC detection. The lack of a proper PAC detector hinders the study of the implications of this event and its monitoring. In this work a PAC and ventricular detector is presented. Two PhysioNet open-source databases were used: the long-term ST database (LTSTDB) and the supraventricular arrhythmia database (SVDB). A combination of heart rate variability (HRV) and morphological features were used to classify beats. Morphological features were extracted from the ECG as well as on the 4th scale of the discrete wavelet transform (DWT). After feature selection, a random forest algorithm was trained for a binary classification of PAC (S) vs. others and for a multi-labels classification to discriminate between normal (N), S and ventricular (V) beats. The algorithm was tested in a 10-fold cross-validation following a patient-wise train-test division (i.e., no beats belonging to the same patient were included both in the test and train set). The resultant median sensitivity, specificity and positive predictive value (PPV) were 99.29, 99.54, and 100% for (N), 95.83, 99.39, and 35.68% for (S), 100, 99.90, and 79.63% for (V). The proposed method attains a greater PAC and ventricular beat sensitivity and PPV than the state-of-the-art classifiers.

Examining the influence of emotional arousal and scam preventive messaging on susceptibility to scams

With the increase in scams globally and the elusive methods of perpetrators, law enforcement agencies have turned to public education and awareness programs to decrease the number of scam victims. This has also raised a need to look into the psychology of scams and how they can be prevented. Emotional arousal has been shown to hinder cognitive decision-making processes in scam victims, subsequently influencing them to fall prey to scams. Despite this, messages used in scam prevention campaigns have been framed in ways that appeal to rational cognitive processes. This exploratory study examined two research questions: do (a) the type of messaging used in scam prevention posters (cognitive-focused poster vs. emotion-normalising poster) and (b) emotional arousal (positive arousal vs. negative arousal vs. no arousal) influence one’s susceptibility to scams? Susceptibility to scams was measured through participants’ intention to purchase items during a fake e-commerce scam scenario. Emotional arousal was measured with a combination of heart rate variability (HRV) data obtained through wearable heart rate trackers, and self-report scales. Results of the study showed that participants who viewed emotion-normalising posters demonstrated a lower susceptibility to scams compared to those who viewed the cognitive-focused poster. However, emotional arousal was not found to have any influence on one’s susceptibility to scams. These findings serve to inform crime prevention campaigns by law enforcement. Findings from this exploratory study can also encourage further research into the scam prevention research.

Automatic sleep staging using heart rate variability, body movements, and recurrent neural networks in a sleep disordered population.

To validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected sleep disordered patients. We applied a previously designed algorithm for automatic sleep staging using long short-term memory recurrent neural networks to model sleep architecture. The classifier uses 132 HRV features computed from electrocardiography and activity counts from accelerometry. We retrained our algorithm using two public datasets containing both healthy sleepers and sleep disordered patients. We then tested the performance of the algorithm on an independent hold-out validation set of sleep recordings from a wide range of sleep disorders collected in a tertiary sleep medicine center. The classifier achieved substantial agreement on four-class sleep staging (wake/N1-N2/N3/rapid eye movement [REM]), with an average κ of 0.60 and accuracy of 75.9%. The performance of the sleep staging algorithm was significantly higher in insomnia patients (κ = 0.62, accuracy = 77.3%). Only in REM parasomnias, the performance was significantly lower (κ = 0.47, accuracy = 70.5%). For two-class wake/sleep classification, the classifier achieved a κ of 0.65, with a sensitivity (to wake) of 72.9% and specificity of 94.0%. This study shows that the combination of HRV, body movements, and a state-of-the-art deep neural network can reach substantial agreement in automatic sleep staging compared with polysomnography, even in patients suffering from a multitude of sleep disorders. The physiological signals required can be obtained in various ways, including non-obtrusive wrist-worn sensors, opening up new avenues for clinical diagnostics.

Application of intrinsic band function technique for automated detection of sleep apnea using HRV and EDR signals

Sleep apnea is the most common sleep disorder that causes respiratory, cardiac and brain diseases. The heart rate variability (HRV) and the electrocardiogram-derived respiration (EDR) signals to capture the cardio-respiratory information and the features extracted from these two signals have been used for the detection of sleep apnea. Detection of sleep apnea using the combination of HRV and EDR signals may provide more information. This paper proposes a novel method for the automated detection of sleep apnea based on the features extracted from HRV and EDR signals. The method involves the extraction of features from the intrinsic band functions (IBFs) of both EDR and HRV signals, and the classification using kernel extreme learning machine (KELM). The IBFs of HRV and EDR signals are evaluated using the Fourier decomposition method (FDM). The energy and the fuzzy entropy (FE) features are extracted from these IBFs. The kernel extreme learning machine (KELM) classifier with four kernel functions such as ‘linear’, ‘polynomial’, ‘radial basis function (RBF)’ and ‘cosine wavelet kernel’ is used for the automated detection of sleep apnea. The proposed technique yielded a sensitivity and a specificity of 78.02% and 74.64%, respectively using the public database. The method outperformed some of the reported works using HRV and EDR signals.

Comparative Analysis of the Discriminative Capacity of EEG, Two ECG-Derived and Respiratory Signals in Automatic Sleep Staging

Highly accurate classification of sleep stages is possible based on EEG signals alone. However, reliable and high quality acquisition of these signals in the home environment is difficult. Instead, electrocardiogram (ECG) and Respiratory (Res) signals are easier to record and may offer a practical alternative for home monitoring of sleep. Therefore, automatic sleep staging was performed using ECG, Res (thoracic excursion) and EEG signals from 31 nocturnal recordings of the Sleep Heart Health Study (SHHS) polysomnography Database. Feature vectors were extracted from 0.5 min (standard) epochs of sleep data by time-domain, frequency domain, time-frequency and nonlinear methods and optimized by using the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) method. These features were then classified by using a SVM. Classification based upon EEG features produced a Correct Classification Ratio CCR=0.92. In comparison, features derived from ECG signals alone, that is the combination of Heart Rate Variability (HRV), and ECG-Derived Respiration (EDR) signals produced a CCR=0.54, while those features based on the combination of HRV and (thoracic) Res signals resulted in a CCR=0.57. Overall comparison of the results based on standard epochs of EEG signals with those obtained from 5-minute (long) epochs of cardiorespiratory signals, revealed that acceptable CCR=0.81 and discriminative capacity (Accuracy=89.32%, Specificity=92.88% and Sensitivity=78.64%) were also achievable when using optimal feature sets derived from long epochs of the latter signals in sleep staging. In addition, it was observed that the presence of some artifacts (like bigeminy) in the cardiorespiratory signals reduced the accuracy of automatic sleep staging more than the artifacts that contaminated the EEG signals.

Diagnosis of major depressive disorder by combining multimodal information from heart rate dynamics and serum proteomics using machine-learning algorithm

ObjectiveMajor depressive disorder (MDD) is a systemic and multifactorial disorder that involves abnormalities in multiple biochemical pathways and the autonomic nervous system. This study applied a machine-learning method to classify MDD and control groups by incorporating data from serum proteomic analysis and heart rate variability (HRV) analysis for the identification of novel peripheral biomarkers. MethodsThe study subjects consisted of 25 drug-free female MDD patients and 25 age- and sex-matched healthy controls. First, quantitative serum proteome profiles were analyzed by liquid chromatography-tandem mass spectrometry using pooled serum samples from 10 patients and 10 controls. Next, candidate proteins were quantified with multiple reaction monitoring (MRM) in 50 subjects. We also analyzed 22 linear and nonlinear HRV parameters in 50 subjects. Finally, we identified a combined biomarker panel consisting of proteins and HRV indexes using a support vector machine with recursive feature elimination. ResultsA separation between MDD and control groups was achieved using five parameters (apolipoprotein B, group-specific component, ceruloplasmin, RMSSD, and SampEn) at 80.1% classification accuracy. A combination of HRV and proteomic data achieved better classification accuracy. ConclusionsA high classification accuracy can be achieved by combining multimodal information from heart rate dynamics and serum proteomics in MDD. Our approach can be helpful for accurate clinical diagnosis of MDD. Further studies using larger, independent cohorts are needed to verify the role of these candidate biomarkers for MDD diagnosis.

Anxiety Misdiagnosed as Dementia? A Complex Case Successfully Treated Using a Multimodal Biofeedback Approach

A sixty-three year old woman with a recent diagnosis of dementia sought biofeedback treatment for depression, anxiety, and cognitive impairment. Quantitative electroencephalography (QEEG) revealed brainwave patterns that could be associated with anxiety as well as possible dementia. The context of her forgetfulness and disorientation was explored and discovered to coincide with anxiety-related hyperarousal. Following a combination of heart rate variability (HRV) and neurofeedback (EEG biofeedback), she reported significant reductions in depression, anxiety, and cognitive impairment.

Design and implementation of a control system reflecting the level of analgesia during general anesthesia

Measuring and ensuring an adequate level of analgesia in patients are of increasing interest in the area of automated drug delivery during general anesthesia. Therefore, the aim of this investigation was to develop a control system that may reflect the intraoperative analgesia value. Our hypothesis was that a feedback controller could be applied in clinical practice safely and at an adequate quality of analgesia. The purpose of this study was to evaluate the practical feasibility of such a system in a clinical setting. The control system for the level of analgesia described in this paper relies on a parameter combination of heart rate variability (HRV), heart rate (HR), and blood pressure (mean arterial pressure, MAP), which serve as input variables for an expert system. For this fuzzy system, the experience of the participating anesthesiologists was translated into a set of fuzzy rules. In a pilot trial, the control system for automated titration of remifentanil, a short-acting opioid, was tested combined with a closed-loop propofol infusion system for hypnosis. Ten adult patients (4 women, 6 men), aged 22-52 years (median, 45 years; range, 29-49 years), with an American Society of Anesthesiologists physical status class I or II and who were scheduled for elective trauma surgery in a supine position were enrolled in this prospective trial. The precision of the system was calculated using internationally defined performance parameters. There was no human intervention necessary during the computer-controlled administration of propofol and remifentanil, and operating conditions were satisfactory in all patients. All patients assessed the quality of anesthesia as "good" to "very good". Median performance error, median absolute performance error, and wobble for HR and MAP during maintenance of anesthesia were -8.98 (5.32), 10.08 (4.17), and 2.68 (1.29) and -4.51 (12.73), 13.63 (2.27), and 3.90 (2.08) [mean (SD)], respectively. The control system, reflecting the level of analgesia during general anesthesia designed and evaluated in this study, allows for a clinically practical, nearly fully automated infusion of an opioid during medium-length surgical procedures with acceptable technical requirements and an adequate precision.

An Intelligent Scoring System and Its Application to Cardiac Arrest Prediction

Traditional risk score prediction is based on vital signs and clinical assessment. In this paper, we present an intelligent scoring system for the prediction of cardiac arrest within 72 h. The patient population is represented by a set of feature vectors, from which risk scores are derived based on geometric distance calculation and support vector machine. Each feature vector is a combination of heart rate variability (HRV) parameters and vital signs. Performance evaluation is conducted on the leave-one-out cross-validation framework, and receiver operating characteristic, sensitivity, specificity, positive predictive value, and negative predictive value are reported. Experimental results reveal that the proposed scoring system not only achieves satisfactory performance on determining the risk of cardiac arrest within 72 h but also has the ability to generate continuous risk scores rather than a simple binary decision by a traditional classifier. Furthermore, the proposed scoring system works well for both balanced and imbalanced datasets, and the combination of HRV parameters and vital signs shows superiority in prediction to using HRV parameters only or vital signs only.

Ubiquitous emotion-aware computing

Emotions are a crucial element for personal and ubiquitous computing. What to sense and how to sense it, however, remain a challenge. This study explores the rare combination of speech, electrocardiogram, and a revised Self-Assessment Mannequin to assess people's emotions. 40 people watched 30 International Affective Picture System pictures in either an office or a living-room environment. Additionally, their personality traits neuroticism and extroversion and demographic information (i.e., gender, nationality, and level of education) were recorded. The resulting data were analyzed using both basic emotion categories and the valence--arousal model, which enabled a comparison between both representations. The combination of heart rate variability and three speech measures (i.e., variability of the fundamental frequency of pitch (F0), intensity, and energy) explained 90% (p < .001) of the participants' experienced valence--arousal, with 88% for valence and 99% for arousal (ps < .001). The six basic emotions could also be discriminated (p < .001), although the explained variance was much lower: 18---20%. Environment (or context), the personality trait neuroticism, and gender proved to be useful when a nuanced assessment of people's emotions was needed. Taken together, this study provides a significant leap toward robust, generic, and ubiquitous emotion-aware computing.

Assessment of autonomic function in patients with acute myocardial infarction or diabetes mellitus by heart rate variability, ventricular late potential and QT dispersion.

To compare the efficacy and sensitivity of heart rate variability (HRV), QT dispersion (QTd) and ventricular late potential (VLP) examination in judging autonomic function. Thirty three patients with acute myocardial infarction (AMI) and 33 patients with diabetes mellitus (DM), all of whom were diagnosed with autonomic neuropathy determined by a standard test of cardiovascular autonomic function, were examined by HRV (timing domain methods), QTd and VLP. Thirty three normal individuals served as controls. The mean SD of the normal R-R interval (SDNN) in both the AMI and DM groups was significantly less than that in the control group (p< 0.01); and of course, the QTd of these groups was significantly greater than that of the controls (p< 0.01). The VLP positive rate of the AMI and DM groups were much higher than that of the control group (p< 0.001). SDNN was shown to be significantly negatively correlated to QTd (r= -0.45); and significantly negatively correlated to VLP (r= -0.47); QTd was shown to be positively, though not significantly, correlated to VLP (r=0.48). QTd could be looked as sieving index; HRV could be looked as routine examination of cardiovascular autonomic function, especially SDNN; the combination of HRV and VLP could improve the accuracy of diagnosis.

Comparison of [ 123I]metaiodobenzylguanidine kinetics with heart rate variability and plasma norepinephrine level

Background. [ 123I]Metaiodobenzylguanidine (MIBG) imaging has been used to assess cardiac sympathetic nerve abnormalities. We evaluated the clinical significance of myocardial MIBG imaging as a measure of cardiac sympathetic nervous system function by comparing it to heart rate variability and plasma norepinephrine level. Methods and Results. In 211 subjects, we analyzed heart rate variability with 24-hour electrocardiography, performed scintigraphy with MIBG, and measured plasma norepinephrine levels. Time and frequency domain measures of heart rate variability were calculated with the Marquette heart rate variability program (Marquette Electronics, Milwaukee, Wis.). Early and late myocardial MIBG uptakes were measured at 15 and 150 minutes after injection, respectively. MIBG clearance rate from the heart and heart-to-lung and heart-to-mediastinum ratios of MIBG activities were calculated. On the whole, heart rate variability, including low-frequency power, correlated positively, but modestly so, with late MIBG uptake and negatively with MIBG clearance rate. The plasma norepinephrine level correlated negatively with late MIBG uptake and with heart rate variability, including low-frequency power, and positively with MIBG clearance rate. Similar correlations were also observed in patient subgroups with coronary artery disease, diabetes mellitus, and renal failure, but these correlations were weak ( R 2 < 0.5). Conclusions. Increased cardiac sympathetic nervous system activity may be associated with increased myocardial MIBG clearance and decreased heart rate variability, including low-frequency power. Because these associations were not strong, however, the combination of heart rate variability with MIBG may allow an interactive assessment of the cardiac autonomic nervous system.

Multifactorial Prediction of Arrhythmic Events after Myocardial Infarction. Combination of Heart Rate Variability and Left Ventricular Ejection Fraction with Other Variables

Autonomic dysfunction has recently been shown to identify postinfarction patients at a high risk of arrhythmic events. Therefore, the predictive characteristics of heart rate variability and the left ventricular ejection fraction in combination with other prognostic variables--mean heart rate, late potentials, and ventricular ectopic beat frequency greater than 10/hour (VE10)--were examined in 417 postinfarction patients. The heart rate variability index was the most important factor for the stratification of patients at high risk of arrhythmic events after myocardial infarction and optimum stratification was based on the combination of the heart rate variability index with late potentials or with frequent ventricular ectopic beats.