Robust detection of premature ventricular contractions using sparse signal decomposition and temporal features

Healthcare Technology Letters

article

M. Sabarimalai Manikandan¹  Barathram Ramkumar¹  Pranav S. Deshpande¹  Tilendra Choudhary¹ 

[1] School of Electrical Sciences, Indian Institute of Technology Bhubaneswar

关键词: electrocardiography;    medical signal detection;    medical signal processing;    knowledge based systems;    decision trees;    sparse signal decomposition;    temporal features;    automated PVC detection;    noise robust PVC detection;    premature ventricular contraction;    decision rules;    ECG signal sparse expansion;    electrocardiogram;    mixed dictionaries;    QRS complex;    P waves;    T waves;    baseline wanders;    muscle artefacts;    decision rule based detection algorithm;    MIT-BIH arrhythmia database;   

DOI  :  10.1049/htl.2015.0006

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

An automated noise-robust premature ventricular contraction (PVC) detection method is proposed based on the sparse signal decomposition, temporal features, and decision rules. In this Letter, the authors exploit sparse expansion of electrocardiogram (ECG) signals on mixed dictionaries for simultaneously enhancing the QRS complex and reducing the influence of tall P and T waves, baseline wanders, and muscle artefacts. They further investigate a set of ten generalised temporal features combined with decision-rule-based detection algorithm for discriminating PVC beats from non-PVC beats. The accuracy and robustness of the proposed method is evaluated using 47 ECG recordings from the MIT/BIH arrhythmia database. Evaluation results show that the proposed method achieves an average sensitivity of 89.69%, and specificity 99.63%. Results further show that the proposed decision-rule-based algorithm with ten generalised features can accurately detect different patterns of PVC beats (uniform and multiform, couplets, triplets, and ventricular tachycardia) in presence of other normal and abnormal heartbeats.

【 授权许可】

CC BY|CC BY-ND|CC BY-NC|CC BY-NC-ND