Patient-specific ECG beat classification technique

Healthcare Technology Letters

article

Manab K. Das¹  Samit Ari¹ 

[1] Department of Electronics and Communication Engineering, National Institute of Technology

关键词: electrocardiography;    medical signal processing;    signal classification;    feature extraction;    support vector machines;    least squares approximations;    transforms;    patient-specific electrocardiogram beat classification technique;    ECG;    critical heart condition;    automated diagnostic system;    ventricular ectopic beat;    supra ventricular ectopic beat;    Stockwell transform;    S-transform;    bacteria foraging optimisation;    BFO algorithm;    least mean square-based multiclass support vector machine;    morphological feature extraction;    timing features;    combined feature vector;    optimised feature vector;    LMS-based multiclass SVM classifier;    Lagrange multiplier;    automated diagnosis;    weight vector;    classification error minimization;    MIT-BIH arrhythmia database;    St;    Petersburg Institute of Cardiological Technics database;    INCART database;   

DOI  :  10.1049/htl.2014.0072

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

Electrocardiogram (ECG) beat classification plays an important role in the timely diagnosis of the critical heart condition. An automated diagnostic system is proposed to classify five types of ECG classes, namely normal (N), ventricular ectopic beat (V), supra ventricular ectopic beat (S), fusion (F) and unknown (Q) as recommended by the Association for the Advancement of Medical Instrumentation (AAMI). The proposed method integrates the Stockwell transform (ST), a bacteria foraging optimisation (BFO) algorithm and a least mean square (LMS)-based multiclass support vector machine (SVM) classifier. The ST is utilised to extract the important morphological features which are concatenated with four timing features. The resultant combined feature vector is optimised by removing the redundant and irrelevant features using the BFO algorithm. The optimised feature vector is applied to the LMS-based multiclass SVM classifier for automated diagnosis. In the proposed technique, the LMS algorithm is used to modify the Lagrange multiplier, which in turn modifies the weight vector to minimise the classification error. The updated weights are used during the testing phase to classify ECG beats. The classification performances are evaluated using the MIT-BIH arrhythmia database. Average accuracy and sensitivity performances of the proposed system for V detection are 98.6% and 91.7%, respectively, and for S detections, 98.2% and 74.7%, respectively over the entire database. To generalise the capability, the classification performance is also evaluated using the St. Petersburg Institute of Cardiological Technics (INCART) database. The proposed technique performs better than other reported heartbeat techniques, with results suggesting better generalisation capability.

【 授权许可】

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