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  • Title: Finding events of electrocardiogram and arterial blood pressure signals via discrete wavelet transform with modified scales.
    Author: Ghaffari A, Homaeinezhad MR, Akraminia M, Davaeeha M.
    Journal: Proc Inst Mech Eng H; 2010; 224(1):27-42. PubMed ID: 20225455.
    Abstract:
    A robust electrocardiogram (ECG) wave detection-delineation algorithm that can be applied to all ECG leads is developed in this study on the basis of discrete wavelet transform (DWT). By applying a new simple approach to a selected scale obtained from DWT, this method is capable of detecting the QRS complex, P-wave, and T-wave as well as determining parameters such as start time, end time, and wave sign (upward or downward). In the proposed method, the selected scale is processed by a sliding rectangular window of length n and the curve length in each window is multiplied by the area under the absolute value of the curve. In the next step, an adaptive thresholding criterion is conducted on the resulted signal. The presented algorithm is applied to various databases including the MIT-BIH arrhythmia database, European ST-T database, QT database, CinC Challenge 2008 database as well as high-resolution Holter data gathered in the DAY Hospital. As a result, the average values of sensitivity and positive prediction Se = 99.84 per cent and P+ = 99.80 per cent were obtained for the detection of QRS complexes with an average maximum delineation error of 13.7, 11.3, and 14.0 ms for the P-wave, QRS complex, and T-wave respectively. The presented algorithm has considerable capability in cases of a low signal-to-noise ratio, high baseline wander, and in cases where QRS complexes and T-waves appear with abnormal morphologies. Especially, the high capability of the algorithm in the detection of the critical points of the ECG signal, i.e. the beginning and end of the T-wave and the end of the QRS complex was validated by the cardiologist and the maximum values of 16.4 and 15.9 ms were recognized as absolute offset error of localization respectively. Finally, in order to illustrate an alternative capability of the algorithm, it is applied to all 18 subjects of the MIT-BIH polysomnographic database and the end-systolic and end-diastolic points of the blood pressure waveform were extracted and values of sensitivity and positive prediction Se = 99.80 per cent and P+ = 99.86 per cent were obtained for the detection of end-systolic, end-diastolic pulses.
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