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.
Highlights
As stated by the World Health Organization, cardiovascular diseases are the primary cause of death worldwide
In the learned metric space, set-based dissimilarity measures the distance between query and corpus sets, and the classification/ranking results can be determined by these distance scores
We can see that the proposed method significantly outperforms the compared widely-used approaches
Summary
As stated by the World Health Organization, cardiovascular diseases are the primary cause of death worldwide. Electrocardiogram (ECG) signal analysis is one of the most commonly used tools at the early stage. ECG signals record the cardiac electrical activity, and can provide important pathological information about human cardiac condition. It is impractical for doctors to analyze large amounts of ECG records in a short period of time, due to the limited ability of human eyes as well as the complicated variation of ECG signals themselves. As a non-invasive method for ECG signal analysis by CAD, heartbeat classification is important to recognize the heart arrhythmias. The ECG beats usually suffer from the changing amplitude and duration of waveforms caused by the real-scenario noises and the signal chaotic nature, which dramatically increase the challenge to decipher the hidden beat type information contained within the data
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