Heart rate variability feature selection method for automated prediction of sudden cardiac death

Parsi, Ashkan; Byrne, Dallan; Glavin, Martin; Jones, Edward

doi:10.1016/j.bspc.2020.102310

Cited by 23 publications

(11 citation statements)

References 73 publications

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“…Initially, HRV analysis was introduced to investigate the vago-sympathetic balance and corresponding phenomena ( Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996 , later Task Force Society, 1996). However, the HRV features have been also shown as useful for detection of cardiac arrhythmias, as—technically speaking—they measure the irregularity of RR intervals (or heart rate) (e.g., Task Force Society, 1996 ; Bernston et al, 1997 ; Parsi et al, 2021a ). These features are calculated from the RR interval sequence (tachogram) and can be divided into three categories, i.e., time-domain (various statistical measures, such as mean or median value, standard deviation of normal interval (SDNN), root mean square of successive RR differences (RMSSD), and triangular interpolation index calculated from the histogram (TINN)), feature-domain (power and peak of very-low-, low-, and high-frequency bands, their ratios, etc.…”

Section: Ecg Analysismentioning

confidence: 99%

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Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

et al. 2022

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Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

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Section: Ecg Analysismentioning

confidence: 99%

“…A method based on only six time- and frequency-domain HRV features and a simple k-NN classifier can predict the sudden cardiac death from five-minute RR interval signals recorded by an implantable cardioverter defibrillator with an accuracy of 91.5% ( Parsi et al, 2021a ).…”

Section: Ecg Analysismentioning

confidence: 99%

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

et al. 2022

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“…However, we found the proposed methodology useful and adapted it to conduct a similar analysis for HRV parameters from an ECG [ 32 ]. Parsi et al [ 33 ] used HRV features from 1 min and 5 min ECG segments for the prediction of ventricular fibrillation (VF) and ventricular tachycardia (VT). First, a Student’s t-test was used to eliminate features with the lowest discriminatory properties.…”

Section: Introductionmentioning

confidence: 99%

“…Three classifiers were applied to predict the VT-VF event using an optimal number of features from each method (determined in the learning phase). In 1 min and 5 min segments, the best classification results in the test set were obtained using feature sets selected by MRMR (6 features in both cases) [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection

Buś

Jędrzejewski

Guzik

2022

JCM

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Heart rate is quite regular during sinus (normal) rhythm (SR) originating from the sinus node. In contrast, heart rate is usually irregular during atrial fibrillation (AF). Complete atrioventricular block with an escape rhythm, ventricular pacing, or ventricular tachycardia are the most common exceptions when heart rate may be regular in AF. Heart rate variability (HRV) is the variation in the duration of consecutive cardiac cycles (RR intervals). We investigated the utility of HRV parameters for automated detection of AF with machine learning (ML) classifiers. The minimum redundancy maximum relevance (MRMR) algorithm, one of the most effective algorithms for feature selection, helped select the HRV parameters (including five original), best suited for distinguishing AF from SR in a database of over 53,000 60 s separate electrocardiogram (ECG) segments cut from longer (up to 24 h) ECG recordings. HRV parameters entered the ML-based classifiers as features. Seven different, commonly used classifiers were trained with one to six HRV-based features with the highest scores resulting from the MRMR algorithm and tested using the 5-fold cross-validation and blindfold validation. The best ML classifier in the blindfold validation achieved an accuracy of 97.2% and diagnostic odds ratio of 1566. From all studied HRV features, the top three HRV parameters distinguishing AF from SR were: the percentage of successive RR intervals differing by at least 50 ms (pRR50), the ratio of standard deviations of points along and across the identity line of the Poincare plots, respectively (SD2/SD1), and coefficient of variation—standard deviation of RR intervals divided by their mean duration (CV). The proposed methodology and the presented results of the selection of HRV parameters have the potential to develop practical solutions and devices for automatic AF detection with minimal sets of simple HRV parameters. Using straightforward ML classifiers and the extremely small sets of simple HRV features, always with pRR50 included, the differentiation of AF from sinus rhythms in the 60 s ECGs is very effective.

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“…The entropy measures have been used to have been used to assist the prediction of cardiovascular disease outcomes [ 20 ]. They both have been applied to remote monitor of obese children [ 21 ], automated predict sudden cardiac death [ 22 ], and study the impact of smoking on heart rate variability among middle age men [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Effect of Percutaneous Coronary Intervention on Heart Rate Variability in Patients with Coronary Artery Disease

Yan

Liu

Yao

et al. 2021

Entropy

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Myocardial ischemia in patients with coronary artery disease (CAD) leads to imbalanced autonomic control that increases the risk of morbidity and mortality. To systematically examine how autonomic function responds to percutaneous coronary intervention (PCI) treatment, we analyzed data of 27 CAD patients who had admitted for PCI in this pilot study. For each patient, five-minute resting electrocardiogram (ECG) signals were collected before and after the PCI procedure. The time intervals between ECG collection and PCI were both within 24 h. To assess autonomic function, normal sinus RR intervals were extracted and were analyzed quantitatively using traditional linear time- and frequency-domain measures [i.e., standard deviation of the normal-normal intervals (SDNN), the root mean square of successive differences (RMSSD), powers of low frequency (LF) and high frequency (HF) components, LF/HF] and nonlinear entropy measures [i.e., sample entropy (SampEn), distribution entropy (DistEn), and conditional entropy (CE)], as well as graphical metrics derived from Poincaré plot [i.e., Porta’s index (PI), Guzik’s index (GI), slope index (SI) and area index (AI)]. Results showed that after PCI, AI and PI decreased significantly (p < 0.002 and 0.015, respectively) with effect sizes of 0.88 and 0.70 as measured by Cohen’s d static. These changes were independent of sex. The results suggest that graphical AI and PI metrics derived from Poincaré plot of short-term ECG may be potential for sensing the beneficial effect of PCI on cardiovascular autonomic control. Further studies with bigger sample sizes are warranted to verify these observations.

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Heart rate variability feature selection method for automated prediction of sudden cardiac death

Cited by 23 publications

References 73 publications

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection

Short-Term Effect of Percutaneous Coronary Intervention on Heart Rate Variability in Patients with Coronary Artery Disease

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