Key result
An extreme gradient boosting-based beat-level ECG analysis method achieved 99.14% accuracy on the MITDB and 98.68% accuracy on a wearable single-lead ECG dataset, outperforming other models.
Why the study?
Wearable single-lead ECG signal acquisition suffers from varying conditions and severe noise, leaving room for improvement in accuracy and efficiency for beat-level automated diagnosis without spare reference channels.
An extreme gradient boosting-based method using new morphological features provides highly accurate and robust heartbeat classification for wearable single-lead ECGs.
May improve wearable ECG classification; leaves open prospective clinical validation before adoption.
Wearable electrocardiogram (ECG) monitoring devices have enabled everyday ECG collection in our daily lives. However, the condition of ECG signal acquisition using wearable devices varies and wearable ECG signals could be interfered with by severe noises, resulting in great challenges of computer-aided automated ECG analysis, especially for single-lead ECG signals without spare channels as references. There remains room for improvement of the beat-level single-lead ECG diagnosis regarding accuracy and efficiency. In this paper, we propose new morphological features of heartbeats for an extreme gradient boosting-based beat-level ECG analysis method to carry out the five-class heartbeat classification according to the Association for the Advancement of Medical Instrumentation standard. The MIT-BIH Arrhythmia Database (MITDB) and a self-collected wearable single-lead ECG dataset are used for performance evaluation in the static and wearable ECG monitoring conditions, respectively. The results show that our method outperforms other state-of-the-art models with an accuracy of 99.14% on the MITDB and maintains robustness with an accuracy of 98.68% in the wearable single-lead ECG analysis.
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Zhu et al. (2021) studied Arrhythmia. Extreme gradient boosting-based beat-level ECG analysis method vs. Other state-of-the-art models was evaluated on Five-class heartbeat classification accuracy. An extreme gradient boosting-based beat-level ECG analysis method achieved 99.14% accuracy on the MITDB and 98.68% accuracy on a wearable single-lead ECG dataset, outperforming other models.
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