Detection and classification of ECG noises using decomposition on mixed codebook for quality analysis

Healthcare Technology Letters

article

Pramendra Kumar¹  Vijay Kumar Sharma¹ 

[1] Department of Computer and Communication Engineering, Manipal University Jaipur

关键词: medical signal processing;    electrocardiography;    medical signal detection;    AWGN;    signal denoising;    Gaussian noise;    signal classification;    PLI;    additive white Gaussian noise;    AWGN;    signal decomposition;    mixed codebook;    ECG local waves;    ECG noises;    decomposed signals;    noisy ECG signals;    muscle artefact;    Technology-Boston Beth Israel Hospital arrhythmia database;    spectral-bound waveforms;    MIT-BIH polysmnographic database;    Fantasia database;   

DOI  :  10.1049/htl.2019.0096

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

In this Letter, a robust technique is presented to detect and classify different electrocardiogram (ECG) noises including baseline wander (BW), muscle artefact (MA), power line interference (PLI) and additive white Gaussian noise (AWGN) based on signal decomposition on mixed codebooks. These codebooks employ temporal and spectral-bound waveforms which provide sparse representation of ECG signals and can extract ECG local waves as well as ECG noises including BW, PLI, MA and AWGN simultaneously. Further, different statistical approaches and temporal features are applied on decomposed signals for detecting the presence of the above mentioned noises. The accuracy and robustness of the proposed technique are evaluated using a large set of noise-free and noisy ECG signals taken from the Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH polysmnographic database and Fantasia database. It is shown from the results that the proposed technique achieves an average detection accuracy of above 99% in detecting all kinds of ECG noises. Furthermore, average results show that the technique can achieve an average sensitivity of 98.55%, positive productivity of 98.6% and classification accuracy of 97.19% for ECG signals taken from all three databases.

【 授权许可】

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