scholarly journals Atrial Fibrillation Classification with Smart Wearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7233
Author(s):  
Jayroop Ramesh ◽  
Zahra Solatidehkordi ◽  
Raafat Aburukba ◽  
Assim Sagahyroon
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Gold Standard ◽  
Normal Sinus Rhythm ◽  
Binary Classification ◽  
Wearable Devices ◽  
Deep Convolutional Neural Networks ◽  
Pulse Rate Variability ◽  
Medical Grade

Atrial fibrillation (AF) is a type of cardiac arrhythmia affecting millions of people every year. This disease increases the likelihood of strokes, heart failure, and even death. While dedicated medical-grade electrocardiogram (ECG) devices can enable gold-standard analysis, these devices are expensive and require clinical settings. Recent advances in the capabilities of general-purpose smartphones and wearable technology equipped with photoplethysmography (PPG) sensors increase diagnostic accessibility for most populations. This work aims to develop a single model that can generalize AF classification across the modalities of ECG and PPG with a unified knowledge representation. This is enabled by approximating the transformation of signals obtained from low-cost wearable PPG sensors in terms of Pulse Rate Variability (PRV) to temporal Heart Rate Variability (HRV) features extracted from medical-grade ECG. This paper proposes a one-dimensional deep convolutional neural network that uses HRV-derived features for classifying 30-s heart rhythms as normal sinus rhythm or atrial fibrillation from both ECG and PPG-based sensors. The model is trained with three MIT-BIH ECG databases and is assessed on a dataset of unseen PPG signals acquired from wrist-worn wearable devices through transfer learning. The model achieved the aggregate binary classification performance measures of accuracy: 95.50%, sensitivity: 94.50%, and specificity: 96.00% across a five-fold cross-validation strategy on the ECG datasets. It also achieved 95.10% accuracy, 94.60% sensitivity, 95.20% specificity on an unseen PPG dataset. The results show considerable promise towards seamless adaptation of gold-standard ECG trained models for non-ambulatory AF detection with consumer wearable devices through HRV-based knowledge transfer.

Nonlinear Characterization of Heart Rate Variability in Normal Sinus Rhythm, Atrial Fibrillation and Congestive Heart Failure

2016 ◽  
Author(s):  
Syed Hassan Zaidi ◽  
Imran Akhtar ◽  
Syed Imran Majeed ◽  
Tahir Zaidi ◽  
Muhammad Saif Ullah Khalid
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Sinus Rhythm ◽  
Time Series Data ◽  
Normal Sinus Rhythm ◽  
Series Data ◽  
Normal Sinus

This paper highlights the application of methods and techniques from nonlinear analysis to illustrate their far superior capability in revealing complex cardiac dynamics under various physiological and pathological states. The purpose is to augment conventional (time and frequency based) heart rate variability analysis, and to extract significant prognostic and clinically relevant information for risk stratification and improved diagnosis. In this work, several nonlinear indices are estimated for RR intervals based time series data acquired for Healthy Sinus Rhythm (HSR) and Congestive Heart Failure (CHF), as the two groups represent different cases of Normal Sinus Rhythm (NSR). In addition to this, nonlinear algorithms are also applied to investigate the internal dynamics of Atrial Fibrillation (AFib). Application of nonlinear tools in normal and diseased cardiovascular states manifest their strong ability to support clinical decision support systems and highlights the internal complex properties of physiological time series data such as complexity, irregularity, determinism and recurrence trends in cardiovascular regulation mechanisms.

scholarly journals Comparative Analysis of Heart Rate Variability Parameters for Arrhythmia and Atrial Fibrillation using ANOVA

2018 ◽  
Vol 11 (4) ◽  
pp. 1841-1849 ◽  
Author(s):  
Kirti Kirti ◽  
Harsh Sohal ◽  
Shruti Jain
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Normal Sinus Rhythm ◽  
Health Conditions ◽  
Statistical Comparison ◽  
Cardiac Health ◽  
Normal Sinus ◽  
Gender And Health ◽  
Anova Technique

Heart Rate Variability (HRV) is an important criterion to check the cardiac health. Sudden HRV signifies the unhealthy condition of the heart, particularly when the person is suffering from a cardiac disease. HRV parameters on different patients of different ages, gender and health conditions are observed using time domain, geometrical domain and frequency domain. Statistical comparison is done on three different databases MIT/BIH Normal Sinus Rhythm (NSR), MIT/BIH Arrhythmia (AR) and MIT/BIH Atrial Fibrillation (AF) using Analysis of Variance (ANOVA) technique. We have extracted twenty HRV features from all the three domains, which show weak, moderate or strong significant changes as per the relation during comparison with respective databases. Out of twenty only nine features are selected which shows noticeable difference between three databases. Later, the selected features will be used for classification in future.

scholarly journals Comparison of two oscillometric blood pressure monitors in subjects with atrial fibrillation

2010 ◽  
Vol 33 (1) ◽  
pp. 54 ◽  
Author(s):  
Tyler S Lamb ◽  
Amar Thakrar ◽  
Mahua Ghosh ◽  
Merne P Wilson ◽  
Thomas W Wilson
Keyword(s):  
Atrial Fibrillation ◽  
Blood Pressure ◽  
Health Care ◽  
Heart Rate ◽  
Health Care Professionals ◽  
Gold Standard ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
Therapeutic Decisions ◽  
Blood Pressure Monitors

Objective: To compare blood pressure readings obtained with two commonly used oscillometric monitors: Omron HEM 711 AC (OM) and Welch-Allyn 52000 series NIBP/oximeter (WA) with mercury sphygmomanometers (Merc) in subjects with atrial fibrillation. Methods: We recruited 51 hemodynamically stable subjects with atrial fibrillation. Fifty four subjects in normal sinus rhythm served as controls. Supine blood pressure readings in each arm were recorded simultaneously using one monitor and Merc. The second monitor then replaced the first and readings were repeated. Merc was then switched to the opposite arm, and both monitors retested. Apical heart rates were ascertained with a stethoscope. We used the averaged, same arm Merc readings as “gold standard”. Results: Automated blood pressure readings were obtained in all control subjects and in all but three of those with atrial fibrillation. Both monitors, and operators, noted a difference between apical and radial/brachial pulse rates: apical-recorded: Merc 6.1±15.0; OM 5.5±13.7; WA 10.0±21.2 beats per minute. Both monitors were accurate in controls: over 90% of readings were within 10 mmHg of averaged Merc, and both achieved European Hypertension Society standards. In subjects with atrial fibrillation, about one quarter of all oscillometric readings differed from Merc by more than 10 mmHg. Both falsely high and falsely low readings occurred, some up to 30 mmHg. There was no relation between accuracy and heart rate. Conclusions: Single blood pressure readings, taken with oscillometric monitors in subjects with atrial fibrillation differ, often markedly, from those taken manually. Health care professionals should record multiple readings manually, using validated instruments when making therapeutic decisions.

scholarly journals Correlation of heart rate recovery and heart rate variability with atrial fibrillation progression

2021 ◽  
Vol 49 (11) ◽  
pp. 030006052110578
Author(s):  
Gwang-Seok Yoon ◽  
Seong-Huan Choi ◽  
Sung Woo Kwon ◽  
Sang-Don Park ◽  
Sung-Hee Shin ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Autonomic Function ◽  
Normal Sinus Rhythm ◽  
Heart Rate Recovery ◽  
Normal Sinus ◽  
Metabolic Equivalents ◽  
Younger Age ◽  
Treadmill Exercise Testing

Objective To examine the combination of heart rate recovery (HRR) and heart rate variability (HRV) in predicting atrial fibrillation (AF) progression. Methods Data from patients with a first detected episode of AF who underwent treadmill exercise testing and 24-h Holter electrocardiography were retrospectively analysed. Autonomic dysfunction was verified using HRR values. Sympathetic and parasympathetic modulation was analysed by HRV. AF progression was defined as transition from the first detected paroxysmal episode to persistent/permanent AF. Results Of 306 patients, mean LF/HF ratio and HRR did not differ significantly by AF progression regardless of age (< or ≥65 years). However, when the LF/HF ratio was divided into tertiles, in patients aged <65 years, the mid LF/HF (1.60–2.40) ratio was significantly associated with lower AF progression rates and longer maintenance of normal sinus rhythm. For patients aged <65 years, less metabolic equivalents were related to higher AF progression rates. For patients aged ≥65 years, a low HRR was associated with high AF progression rates. Conclusion In relatively younger age, high physical capacity and balanced autonomic nervous system regulation are important predictors of AF progression. Evaluation of autonomic function assessed by age could predict AF progression.

Effect of amiodarone on heart rate variability in patients with newly diagnosed atrial fibrillation (clinical observation)

2020 ◽  
pp. 81-85
Author(s):  
E. P. Popova ◽  
O. T. Bogova ◽  
S. N. Puzin ◽  
D. A. Sychyov ◽  
V. P. Fisenko
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Drug Therapy ◽  
Heart Function ◽  
Newly Diagnosed ◽  
Class Iii ◽  
Spectral Parameters ◽  
Patient Will ◽  
The Individual

Spectral analysis of heart rate variability gives an idea of the role of the autonomic nervous system in the regulation of chronotropic heart function. This method can be used to evaluate the effectiveness of drug therapy. Drug therapy should be carried out taking into account the individual clinical form of atrial fibrillation. Information about the vegetative status of the patient will undoubtedly increase the effectiveness of treatment. In this study, spectral parameters were studied in patients with newly diagnosed atrial fibrillation. The effect of antiarrhythmic drug class III amiodarone on the spectral parameters of heart rate variability was studied.

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

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