Efficient and robust ventricular tachycardia and fibrillation detection method for wearable cardiac health monitoring devices

Healthcare Technology Letters

article

Eedara Prabhakararao¹  M. Sabarimalai Manikandan¹ 

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

关键词: diseases;    electrocardiography;    medical signal processing;    feature extraction;    patient monitoring;    discrete cosine transforms;    signal denoising;    muscle;    ventricular tachycardia;    rapid ventricular tachycardia;    fibrillation detection method;    wearable cardiac health monitoring devices;    automatic external defibrillator;    electrocardiogram;    ECG;    discrete cosine transform;    noise suppression;    zero-crossing rate estimation;    VTVF detection;    peak-to-peak interval;    ECG arrhythmias;    PhysioNet databases;    feature extraction;    baseline wanders;    powerline interference;    muscle artefacts;   

DOI  :  10.1049/htl.2016.0010

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

In this Letter, the authors propose an efficient and robust method for automatically determining the VT and VF events in the electrocardiogram (ECG) signal. The proposed method consists of: (i) discrete cosine transform (DCT)-based noise suppression; (ii) addition of bipolar sequence of amplitudes with alternating polarity; (iii) zero-crossing rate (ZCR) estimation-based VTVF detection; and (iv) peak-to-peak interval (PPI) feature based VT/VF discrimination. The proposed method is evaluated using 18,000 episodes of different ECG arrhythmias taken from 6 PhysioNet databases. The method achieves an average sensitivity (Se) of 99.61%, specificity (Sp) of 99.96%, and overall accuracy (OA) of 99.92% in detecting VTVF and non-VTVF episodes by using a ZCR feature. Results show that the method achieves a Se of 100%, Sp of 99.70% and OA of 99.85% for discriminating VT from VF episodes using PPI features extracted from the processed signal. The robustness of the method is tested using different kinds of ECG beats and various types of noises including the baseline wanders, powerline interference and muscle artefacts. Results demonstrate that the proposed method with the ZCR, PPI features can achieve significantly better detection rates as compared with the existing methods.

【 授权许可】

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