Classification of ECG using some novel features

Sarma, Pratiksha; Nirmala, S. R.; Sarma, Kandarpa Kumar

doi:10.1109/icetacs.2013.6691420

Cited by 11 publications

(1 citation statement)

References 3 publications

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“…Feature extraction plays an important role in the method pipeline because this stage establishes a platform for the subsequent procedures of pattern recognition and/or machine learning. Technologies to extract ECG signal features have been studied and developed from different angles, such as description of waveform morphologies, representation of waveband statistics, quantization of wavelet coefficients, and so forth [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Set-Based Discriminative Measure for Electrocardiogram Beat Classification

Qin

2017

Sensors

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Abstract:Computer aided diagnosis systems can help to reduce the high mortality rate among cardiac patients. Automatical classification of electrocardiogram (ECG) beats plays an important role in such systems, but this issue is challenging because of the complexities of ECG signals. In literature, feature designing has been broadly-studied. However, such methodology is inevitably limited by the heuristics of hand-crafting process and the challenge of signals themselves. To address it, we treat the problem of ECG beat classification from the metric and measurement perspective. We propose a novel approach, named "Set-Based Discriminative Measure", which first learns a discriminative metric space to ensure that intra-class distances are smaller than inter-class distances for ECG features in a global way, and then measures a new set-based dissimilarity in such learned space to cope with the local variation of samples. Experimental results have demonstrated the advantage of this approach in terms of effectiveness, robustness, and flexibility based on ECG beats from the MIT-BIH Arrhythmia Database.

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