Topological Data Analysis Approach to Extract the Persistent Homology Features of Ballistocardiogram Signal in Unobstructive Atrial Fibrillation Detection

Jiang, Fangfang; Xu, Bowen; Zhu, Ziyu; Zhang, Biyong

doi:10.1109/jsen.2022.3153647

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…We used one of the most effective filter-based algorithms for feature selection, i.e., MRMR [ 48 ], which has recently been relatively widely used in ML [ 22 , 29 , 30 , 31 , 32 , 33 , 49 ]. The MRMR algorithm maximizes the relevance (ability) of the set of features for correct classification and minimizes the redundancy between the features.…”

Section: Methodsmentioning

confidence: 99%

“…However, the database was relatively small (from 324 60 s segments to 5504 10 s segments). Jiang et al [ 32 ] studied AF detection in a 24 s ballistocardiogram using several ML classifiers. They used MRMR to select the most relevant among several novel nonlinear persistent homology features and studied the impact of the number of features on classification performance.…”

Section: Introductionmentioning

confidence: 99%

“…The readings and measurements are not as reliable as an ECG. 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).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Jiang and collaborators studied non-invasive atrial fibrillation using TDA on ballistocardiographic (BCG) data [27]. BCG refers to the measurement and recording of the ballistic forces generated by the ejection of blood from the heart during each cardiac cycle.…”

Section: Ecg Data and Heart Rate Signalsmentioning

confidence: 99%

Topological Data Analysis in Cardiovascular Signals: An Overview

Hernández-Lemus,

Miramontes,

Martínez-García

2024

Entropy

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Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structures in a broad range of data analytics problems including the study of cardiovascular signals. Here, we aim to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment in the form of a literature or narrative review. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA shows great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.

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Multi-scale attention convolutional neural network for noncontact atrial fibrillation detection using BCG

Su,

Zhao,

Huang

et al. 2024

Biomedical Signal Processing and Control

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Topological Data Analysis Approach to Extract the Persistent Homology Features of Ballistocardiogram Signal in Unobstructive Atrial Fibrillation Detection

Cited by 6 publications

References 28 publications

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

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

Topological Data Analysis in Cardiovascular Signals: An Overview

Multi-scale attention convolutional neural network for noncontact atrial fibrillation detection using BCG

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