Key result
A new methodology for extracting features for heartbeat morphology classification outperformed other well-known strategies when evaluated on the MIT-BIH Arrhythmia database.
Why the study?
Existing ECG heartbeat morphology classification methods suffer from high signal variability, and combining multiple approaches makes it difficult to infer individual contributions.
Does the new feature extraction methodology improve QRS morphology classification in ECG signals compared to existing strategies?
Does the new feature extraction methodology improve QRS morphology classification in ECG signals compared to existing strategies?
A new feature extraction methodology for QRS morphology classification outperforms existing strategies on the MIT-BIH Arrhythmia database.
The electrocardiogram (ECG) is a non-invasive method to detect cardiovascular diseases (CVD), the most common cause of death in the world. The recognition of heartbeat morphologies present in the ECG signal is an effective way to detect CVDs prematurely. Many approaches were developed for this purpose, such as the use of Wavelets, High Order Statistics (HOS), Local Binary Patterns (LBP), Random Projection, Fiducial points, and Hermite Polynomials. Unfortunately, most parts of these approaches suffer from the high variability of ECG signal features and conditions. Also, it is common to use more than one of them simultaneously, which makes it hard to infer the contributions of each one. This work presents a new robust methodology to extract features for heartbeat morphology classification. Moreover, we introduce new labels for a small set of morphologies present in MIT-BIH Arrhythmia database, taking into account only the QRS complex (the 3 more representative waves of a heartbeat) instead of the whole heartbeat. We evaluate each approach in isolation and the results show that our method outperforms other well-known strategies.
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Caldas et al. (2020) studied Cardiovascular diseases. New methodology to extract features for heartbeat morphology classification vs. Other well-known strategies (Wavelets, HOS, LBP, Random Projection, Fiducial points, Hermite Polynomials) was evaluated on Heartbeat morphology classification performance. A new methodology for extracting features for heartbeat morphology classification outperformed other well-known strategies when evaluated on the MIT-BIH Arrhythmia database.
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