Background: ECG is an important tool in the diagnosis of ischemic heart disease and arrhythmia. Computerized automatic diagnostic tools may help clinicians in diagnosing these diseases and give early warning when the ECG is continuously monitored. Their success depends on the availability of reliable ECG wave identification systems. The conventional algorithms include the use of derivative-based methods and non-linear filtering. In the past decades, the wavelet transform has been advocated. Although investigators have compared the performance of wavelet transform with the conventional algorithms on QRS detection, research is still needed on the performance of these algorithms on P and T wave detection. In this study, we compared the accuracy of a derivative-based method and the wavelet transform in P, R and T wave detection. Methods: ECG signals were downloaded from 48 files of the European ST-T database. We extracted 11 one-minute recordings to cover a variety of ECG morphologies. The signals were filtered by a bandpass filter. The derivative-based method identified the ECG waves by applying rules on the smoothed differentiated signal. For the wavelet transform, the first derivative of a Gaussian was used as the basis function. The number of P, R and T waves correctly identified by the derivative-based method and the wavelet transform were compared. Results: 806 ECG beats were analyzed. 89.4% of the P waves were identified correctly using wavelet (compared to 80.2% for the derivative-based method). The lack of statistical significance (p=0.07) may be due to a lack of power. 99.0% of R waves were identified correctly using wavelet compared to 98.8% for the derivative-based method. 91.8 % of the T waves were correctly identified using wavelet compared to 77.3% for the derivative- based method (p<0.05). Conclusion: The wavelet-based method was shown to be superior to the conventional derivative-based method especially in T wave identification.
Kwok, H., Giorgi, A., Fenici, R., Raffone, A., Identification of Electrocardiogram Characteristic Points: Wavelet Transform versus Derivative-based Method, Abstract de <<American College of Cardiology Annual Scientific Session 2004>>, (New Orleans, 07-10 March 2004 ), <<JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY>>, 2004; (Marzo): 400A-400A [http://hdl.handle.net/10807/18647]
Identification of Electrocardiogram Characteristic Points: Wavelet Transform versus Derivative-based Method
Fenici, Riccardo;
2004
Abstract
Background: ECG is an important tool in the diagnosis of ischemic heart disease and arrhythmia. Computerized automatic diagnostic tools may help clinicians in diagnosing these diseases and give early warning when the ECG is continuously monitored. Their success depends on the availability of reliable ECG wave identification systems. The conventional algorithms include the use of derivative-based methods and non-linear filtering. In the past decades, the wavelet transform has been advocated. Although investigators have compared the performance of wavelet transform with the conventional algorithms on QRS detection, research is still needed on the performance of these algorithms on P and T wave detection. In this study, we compared the accuracy of a derivative-based method and the wavelet transform in P, R and T wave detection. Methods: ECG signals were downloaded from 48 files of the European ST-T database. We extracted 11 one-minute recordings to cover a variety of ECG morphologies. The signals were filtered by a bandpass filter. The derivative-based method identified the ECG waves by applying rules on the smoothed differentiated signal. For the wavelet transform, the first derivative of a Gaussian was used as the basis function. The number of P, R and T waves correctly identified by the derivative-based method and the wavelet transform were compared. Results: 806 ECG beats were analyzed. 89.4% of the P waves were identified correctly using wavelet (compared to 80.2% for the derivative-based method). The lack of statistical significance (p=0.07) may be due to a lack of power. 99.0% of R waves were identified correctly using wavelet compared to 98.8% for the derivative-based method. 91.8 % of the T waves were correctly identified using wavelet compared to 77.3% for the derivative- based method (p<0.05). Conclusion: The wavelet-based method was shown to be superior to the conventional derivative-based method especially in T wave identification.
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