Key result
The proposed automated cardiac event change detection method achieved an average sensitivity of 99.76%, positive predictivity of 94.58%, and overall accuracy of 94.32% in determining ECG waveform changes.
Population
Normal and abnormal ECG signals including different types of arrhythmia beats, heart rates and signal quality
Authors
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May enable efficient continuous ECG monitoring; leaves open prospective clinical validation.
The proposed automated low-complexity cardiac event change detection method demonstrates high accuracy and sensitivity for continuous ECG monitoring, potentially reducing battery and bandwidth requirements.
Satija et al. (2016) conducted a letter in Arrhythmia / Cardiac event monitoring. Cardiac event change detection (CECD) method was evaluated on Determining the changes in heartbeat waveforms of the ECG signals. The proposed automated cardiac event change detection method achieved an average sensitivity of 99.76%, positive predictivity of 94.58%, and overall accuracy of 94.32% in determining ECG waveform changes.
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