Key result
A real-time QRS complex detector based on discrete wavelet transform achieved 99.30% sensitivity and 99.61% positive predictivity for arrhythmia signals, and was implemented on an ARM microcontroller.
Why the study?
Does a DWT-based real-time QRS complex detector accurately detect QRS complexes in healthy and arrhythmic ECG signals?
Does a DWT-based real-time QRS complex detector accurately detect QRS complexes in healthy and arrhythmic ECG signals?
A novel DWT-based QRS detection algorithm implemented on an ARM microcontroller demonstrates high sensitivity and positive predictivity for both healthy and arrhythmic ECG signals in real-time.
Feasibility on embedded hardware encourages wearable ECG development; leaves open prospective clinical validation before adoption.
The electrocardiogram (ECG) is one of the most used tools to detect the health state of the heart. The QRS complex is an important waveform in an ECG signal and it serves as reference point for most ECG signal processing algorithms. In this paper a real-time QRS complex detector based on discrete wavelet transform (DWT) is proposed. In contrast with other algorithms, ours is able to automatically select the detail coefficients that will be used to detect the QRS complex after applying the DWT to the signal. The algorithm was compared with other published methods and evaluated with two datasets, one of them with arrhythmia and the other in healthy conditions. The results for the signals with arrhythmia were 99.30% for sensitivity, 99.61% for positive predictivity and 1.12% for error detection rate, while in the signals in healthy conditions the values were of 99.95%, 99.98% and 0.0006% respectively. Finally, the algorithm was implemented on an ARM microcontroller showing its real-time processing capability
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Rodríguez et al. (2018) studied Arrhythmia and healthy conditions (ECG signal processing). Real-time QRS complex detector based on discrete wavelet transform (DWT) vs. Other published methods was evaluated on Sensitivity, positive predictivity, and error detection rate. A real-time QRS complex detector based on discrete wavelet transform achieved 99.30% sensitivity and 99.61% positive predictivity for arrhythmia signals, and was implemented on an ARM microcontroller.
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