scholarly journals Heart rate variability as a biomarker in chronic chagas cardiomyopathy patients with or without concomitant digestive involvement, for prediction of rassi score risk classes

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
L Silva ◽  
H T Moreira ◽  
M M Oliveira ◽  
L S S Cintra ◽  
A Schmidt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chagas Disease ◽  
Risk Group ◽  
Risk Groups ◽  
Support Vector ◽  
Mixed Form ◽  
Risk Class ◽  
Chagas Cardiomyopathy

Abstract Introduction The pathogenesis of Chronic Chagas Cardiomyopathy (CCC) is not yet fully elucidated. However, dysautonomia is one of the factors involved, in addition to being the essential mechanism in the pathogenesis of the Digestive Form of Chagas Disease (DFCD). The prognostic value of dysautonomia remains speculative, and there are no correlative studies of dysautonomia in CCC and DFCD. Purpose This study has three aims: a) to investigate in patients with CCC the relationship between cardiac dysautonomia, indirectly studied by heart rate variability (HRV), and the prognostic stratification assessed by the Rassi score; b) to compare the HRV in groups with isolated CCC and with the mixed form, i.e. CCC associated with DFCD; c) to evaluate the power of combining HRV indices to predict the risk class of each patient, using machine learning. Methods Thirty-one patients with CCC were classified into three risk groups (low, intermediate and high) according to their Rassi score and had two electrocardiograms (ECG) recorded, i.e. the conventional 12-lead and a single lead, the latter for a period of 10 to 20 minutes. From the single lead ECG, two equally sized RR series were generated and 31 HRV indices were calculated from each. The HRV was then compared between the three risk groups and also regarding the presence or not of concomitant digestive impairment. Taking HRV indices as inputs, four machine learning models were compared in its ability to predict the risk class of each patient. A previous step of attribute selection (sequential feature selection) was applied to identify the most relevant HRV indices for each algorithm. Results Comparing the HRV indices in the three risk groups obtained with the Rassi score, the phase entropy is decreased [0.91 (0.90, 0.91) vs 0.87 (0.86, 0.89); p=0.039] and the percentage of inflection points is increased [66.4 (63.5, 71.2) vs 58.2 (53.4, 63.3); p=0.032] in patients in the high-risk group, compared to the low-risk group. Of the 31 patients with CCC, 14 had the mixed form of the disease, i.e. with associated digestive impairment. In the latter, the triangular interpolation of the RR interval histogram decreased significantly [78.1 (62.5, 101.6) vs 121.1 (80.1, 146.5), p=0.046], while the absolute power in the low-frequency band decreased with strong trend to statistical significance [28.5 (17.1, 97.5) vs 86.9 (44.1, 171.7), p=0.06]. The best predictive model for each risk group was obtained with the Support Vector Machine, reaching an overall F1-score of 0.61. Conclusions The worst prognosis, indicated by the Rassi score, is associated with increased heart rate fragmentation. The combination of HRV indices enhanced the accuracy of the risk stratification. Compared to CCC the mixed form of Chagas' disease displays a decrease in the components of slow heart rate oscillation, suggesting a higher degree of sympathetic autonomic denervation associated with parasympathetic impairment. FUNDunding Acknowledgement Type of funding sources: Foundation. Main funding source(s): São Paulo Research Foundation (FAPESP)

scholarly journals Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity

2019 ◽  
Vol 9 (4) ◽  
pp. 45 ◽  
Author(s):  
Hugo F. Posada-Quintero ◽  
Jeffrey B. Bolkhovsky
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Memory Task ◽  
Autonomic Response ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Classification ◽  
Machine Learning Models

Indices of heart rate variability (HRV) and electrodermal activity (EDA), in conjunction with machine learning models, were used to identify one of three tasks a subject is performing based on autonomic response elicited by the specific task. Using non-invasive measures to identify the task performed by a subject can help to provide individual monitoring and guidance to avoid the consequences of reduced performance due to fatigue or other stressors. In the present study, sixteen subjects were enrolled to undergo three tasks: The psychomotor vigilance task (PVT), an auditory working memory task (the n-back paradigm), and a visual search (ship search, SS). Electrocardiogram (ECG) (for HRV analysis) and EDA data were collected during the tests. For task-classification, we tested four machine learning classification tools: k-nearest neighbor classifier (KNN), support vector machines (SVM), decision trees, and discriminant analysis (DA). Leave-one-subject-out cross-validation was used to evaluate the performance of the constructed models to prevent overfitting. The most accurate models were the KNN (66%), linear SVM (62%), and linear DA (62%). The results of this study showed that it is possible to identify the task a subject is performing based on the subject’s autonomic reactions (from HRV and EDA). This information can be used to monitor individuals within a larger group to assist in reducing errors caused by uncoordinated or poor performance by allowing for automated tracking of and communication between individuals.

Comparative assessment of drivers' stress induced by autonomous and manual driving with heart rate variability parameters and machine learning analysis of electrodermal activity

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
P Zontone ◽  
A Affanni ◽  
R Bernardini ◽  
D Brisinda ◽  
L Del Linz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Autonomous Driving ◽  
Motion Artifacts ◽  
Simultaneous Increase ◽  
Support Vector ◽  
Physiological Status ◽  
Learning Analysis

Abstract Background Nowadays research on Autonomous Driving Systems (ADS) and attention towards novel technology allowing real-time assessment of car drivers' psycho-physiological status is growing, to quantify driver's stress during semi-autonomous or autonomous driving assistance and to investigate human reaction to different types of ADS. We present a system for automatic stress detection with combined machine learning analysis of Skin Potential Response (SPR) and electrocardiographic (ECG) recordings to compare the driver's stress reaction during both manual and autonomous driving sessions carried out in a dynamic professional drive simulator (PDS). Methods All data were acquired after informed consent from 14 healthy volunteers (HVs) in the Vi-grade (Udine) PDS. Two SPR signals (one from each hand) and three chest ECG leads were recorded. A Motion Artifact (MA) removal algorithm was used to remove motion artifacts from SPR signals. A cleaned, single SPR signal, obtained as the RMS value by combining the two original signals, was then sent together with the time-variation of heart rate (HR) to a Machine Learning (ML) classification algorithm, i.e., a Support Vector Machine (SVM), based on some specific features of this signal. The output of the SVM provides a series of labels, that indicate the presence or lack of stress episodes during the driving experiment. Stress occurrence was also independently quantified with heart rate variability (HRV) analysis in the time (TD) and frequency (FD) domains and with non-linear (NL) methods. Results All participants completed the driving protocol consisting of two subsequent sessions, one with conventional manual (MD) and the other with autonomous (AD) driving settings, along a highway where some unexpected events occurred, inducing different level stress response. Figure 1 shows an example of time variant changes of the RMS SPR signal and of the HR of one tested individual during both experimental settings. A simultaneous increase of both SPR and HR signal is apparent during the stress episodes correctly identified by the SVM (gray shadows). Discriminant analysis of FD (VLF, LF and HF) and NL (SD1, SD2, Entropy and Recurrence Plot) HRV parameters, independently assessed by two researchers blind to SVM results, differentiated between stress induced by MD and AD (accuracy: 88,4% cross-correlated) in good agreement with automatic SVM assessment. In general stress level was lower during the AD, being all HRV parameters not significantly modified from baseline rest. SPR amplitude eventually increased also during AD, but SVM efficiently differentiated between AD and MD stress anyhow. Conclusions The proposed method for automatic assessment of stress reactions of car drivers with SVM of SPR and HR signals is reliable, in both MD and AD scenarios. The results seem to evidence that MD is in general more demanding than AD inducing higher activation of sympathetic nervous system, especially in critical situations. Figure 1. Time variance of SPR and HR. Funding Acknowledgement Type of funding source: None

Prediction of echocardiographic parameters in Chagas disease using heart rate variability and machine learning

2021 ◽  
Vol 67 ◽  
pp. 102513
Author(s):  
Luiz Eduardo Virgilio Silva ◽  
Henrique Turin Moreira ◽  
Mariani Mendes Madisson Bernardo ◽  
André Schmidt ◽  
Minna Moreira Dias Romano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chagas Disease ◽  
Echocardiographic Parameters

scholarly journals Heart rate variability: Analysis of time-domain indices in patients with chronic Chagas disease before and after an exercise program

2013 ◽  
Vol 32 (3) ◽  
pp. 219-227 ◽  
Author(s):  
Marcus Vinicius Amaral da Silva Souza ◽  
Carla Cristiane Santos Soares ◽  
Juliana Rega de Oliveira ◽  
Cláudia Rosa de Oliveira ◽  
Paloma Hargreaves Fialho ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Chagas Disease ◽  
Time Domain ◽  
Exercise Program ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis ◽  
Before And After ◽  
Chronic Chagas Disease

Detecting Pedestrian Situation Awareness in Real-Time: Algorithm Development Using Heart Rate Variability and Phone Position

2018 ◽  
Vol 62 (1) ◽  
pp. 1579-1583
Author(s):  
Yourui Tong ◽  
Bochen Jia ◽  
Yi Wang ◽  
Si Yang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Real Time ◽  
Situation Awareness ◽  
Mental State ◽  
Time Algorithm ◽  
Support Vector ◽  
Vehicle Design ◽  
Automated Vehicle ◽  
Hrv Analysis

To help automated vehicles learn surrounding environments via V2X communications, it is important to detect and transfer pedestrian situation awareness to the related vehicles. Based on the characteristics of pedestrians, a real-time algorithm was developed to detect pedestrian situation awareness. In the study, the heart rate variability (HRV) and phone position were used to understand the mental state and distractions of pedestrians. The HRV analysis was used to detect the fatigue and alert state of the pedestrian, and the phone position was used to define the phone distractions of the pedestrian. A Support Vector Machine algorithm was used to classify the pedestrian’s mental state. The results indicated a good performance with 86% prediction accuracy. The developed algorithm shows high applicability to detect the pedestrian’s situation awareness in real-time, which would further extend our understanding on V2X employment and automated vehicle design.

scholarly journals A machine learning approach to identifying objective biomarkers of anxiety and stress

2019 ◽  
Author(s):  
Arjun Ramakrishnan ◽  
Adam Pardes ◽  
William Lynch ◽  
Christopher Molaro ◽  
Michael Louis Platt
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Trait Anxiety ◽  
Video Game ◽  
Social Stress ◽  
Internal State ◽  
Self Report ◽  
Support Vector ◽  
High Trait ◽  
High Trait Anxiety

AbstractAnxiety and stress-related disorders are highly prevalent and debilitating conditions that impose an enormous burden on society. Sensitive measurements that can enable early diagnosis could mitigate suffering and potentially prevent onset of these conditions. Self-reports, however, are intrusive and vulnerable to biases that can conceal the true internal state. Physiological responses, on the other hand, manifest spontaneously and can be monitored continuously, providing potential objective biomarkers for anxiety and stress. Recent studies have shown that algorithms trained on physiological measurements can predict stress states with high accuracy. Whether these predictive algorithms generalize to untested situations and participants, however, remains unclear. Further, whether biomarkers of momentary stress indicate trait anxiety – a vulnerability foreshadowing development of anxiety and mood disorders – remains unknown. To address these gaps, we monitored skin conductance, heart rate, heart rate variability and EEG in 39 participants experiencing physical and social stress and compared these measures to non-stressful periods of talking, rest, and playing a simple video game. Self-report measures were obtained periodically throughout the experiment. A support vector machine trained on physiological measurements identified stress conditions with ~96% accuracy. A decision tree that optimally combined physiological and self-report measures identified individuals with high trait anxiety with ~84% accuracy. Individuals with high trait anxiety also displayed high baseline state anxiety but a muted physiological response to acute stressors. Overall, these results demonstrate the potential for using machine learning tools to identify objective biomarkers useful for diagnosing and monitoring mental health conditions like anxiety and depression.

scholarly journals Trends in Heart-Rate Variability Signal Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Syem Ishaque ◽  
Naimul Khan ◽  
Sri Krishnan
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Well Being ◽  
Good Health ◽  
Machine Learning Techniques ◽  
Video Gaming ◽  
Clinical Events ◽  
Learning Techniques

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

scholarly journals Preoperative Heart Rate Variability During Sleep Predicts Vagus Nerve Stimulation Outcome Better in Patients With Drug-Resistant Epilepsy

2021 ◽  
Vol 12 ◽  
Author(s):  
Xi Fang ◽  
Hong-Yun Liu ◽  
Zhi-Yan Wang ◽  
Zhao Yang ◽  
Tung-Yang Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Outcome Prediction ◽  
Healthy Controls ◽  
Drug Resistant ◽  
Sleep State ◽  
Drug Resistant Epilepsy

Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices.Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model.Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1).Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

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