Key result
Abnormal beat detection using a Random Forest classifier on compressed ECGs achieved a true positive rate of 69.5% and a false positive rate of 32.8%.
Why the study?
Can abnormal heartbeats be accurately detected in compressed ECGs using machine learning?
Can abnormal heartbeats be accurately detected in compressed ECGs using machine learning?
Effect estimate: TPR 69.5%, FPR 32.8%
Machine learning can identify abnormal heartbeats directly from compressed ECG signals, potentially reducing computational load for wireless transmission.
May support ML-based abnormal beat detection in compressed ECGs; hypothesis-generating and requires prospective validation before clinical use.
The electrocardiogram (ECG) is an important measurement for diagnosing heart disease. Transmission of continuous ECG over a wireless network can be taxing; therefore, compressing the ECG can reduce the load on wireless networks. On the other hand, reconstructing the ECG for analysis can be computationally intensive. As such, diagnosing heart diseases from compressed ECG is desired. Abnormal beat detection using machine learning in the compressed domain is proposed. The ECG was compressed using a wavelet-based morphological feature preserving compression algorithm The compression algorithm was applied on 84 ECG records available in Long Term Atrial Fibrillation Database (LTAFDB) achieving an average compression rate of 4.17:1. Abnormal beats in the compressed signals were classified using a Random Forest trained using a randomly under-sampled training set. The achieved true positive rate was 69.5% and the false positive rate was 32.8%. The results indicate that identification of abnormal beats in compressed ECGs is possible. Future work will explore detection of abnormal beats in compressively sensed ECG.
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Abdelazez et al. (2018) studied Abnormal heartbeats (n=84). Random Forest machine learning on compressed ECGs was evaluated on True positive rate and false positive rate for abnormal beat detection (TPR 69.5%, FPR 32.8%). Abnormal beat detection using a Random Forest classifier on compressed ECGs achieved a true positive rate of 69.5% and a false positive rate of 32.8%.