Key result
A discrete wavelet transform-based algorithm for QRS detection achieved an accuracy of 99.366% on the EDB database and 98.89% on the LTSTDB database.
Why the study?
The QRS complex represents the most important part of the ECG signal, motivating studies for QRS recognition.
A novel discrete wavelet transform-based algorithm demonstrates high accuracy (>98%) for QRS complex detection in standard ECG databases.
May advance automated ECG analysis tools; leaves open prospective clinical validation before practice adoption.
QRS represented the most important part of ECG signal, so different researches and studies are performed for QRS recognition. In this paper, a new technique by using wavelet transform is used for de-noising ECG signal by using adaptive threshold, then DWT used to separate the high frequency from the low component, then compute the statistical information from low frequencies to be used in threshold computation, Based on these statics features, lower and upper threshold are calculated, which are updated according to number of peaks that are detected until two thresholds give same number of peaks, also the detected peaks are updated according to average R–R time. Results of (EDB) database was (Acc = 99.366%), while (LTSTDB) database was (Acc = 98.89%). The results are compared with other work and it is show that the proposed method gave better performance and can be used for QRS detection.
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Aqeel M. Hamad alhussainy (2020) studied ECG signal analysis. Discrete wavelet transform (DWT) based QRS detection algorithm vs. Other algorithms was evaluated on Accuracy of QRS detection. A discrete wavelet transform-based algorithm for QRS detection achieved an accuracy of 99.366% on the EDB database and 98.89% on the LTSTDB database.
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