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This study proposes electrocardiogram (ECG) identification based on non-fiducial feature extraction using window removal method, nearest neighbor (NN), support vector machine (SVM), and linear discriminant analysis (LDA). In the pre-processing stage, Daubechies 4 is used to remove the baseline wander and noise of the original signal. In the feature extraction and selection stage, windows are set at a time interval of 5 s in the preprocessed signal, while autocorrelation, scaling, and discrete cosine transform (DCT) are applied to extract and select features. Thereafter, the window removal method is applied to all of the generated windows to remove those that are unrecognizable. Lastly, in the classification stage, the NN, SVM, and LDA classifiers are used to perform individual identification. As a result, when the NN is used in the Normal Sinus Rhythm (NSR), PTB diagnostic, and QT database, the results indicate that the subject identification rates are 100%, 99.40% and 100%, while the window identification rates are 99.02%, 97.13% and 98.91%. When the SVM is used, all of the subject identification rates are 100%, while the window identification rates are 96.92%, 95.82% and 98.32%. When the LDA is used, all of the subject identification rates are 100%, while the window identification rates are 98.67%, 98.65% and 99.23%. The proposed method demonstrates good results with regard to data that not only includes normal signals, but also abnormal signals. In addition, the window removal method improves the individual identification accuracy by removing windows that cannot be recognized.
Woo-Hyuk Jung; Sang-Goog Lee. ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method. Applied Sciences 2017, 7, 1205 .
AMA StyleWoo-Hyuk Jung, Sang-Goog Lee. ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method. Applied Sciences. 2017; 7 (11):1205.
Chicago/Turabian StyleWoo-Hyuk Jung; Sang-Goog Lee. 2017. "ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method." Applied Sciences 7, no. 11: 1205.
In this paper, we present a method for detecting the R-peak of an ECG signal by using an singular value decomposition (SVD) filter and a search back system. The ECG signal was detected in two phases: the pre-processing phase and the decision phase. The pre-processing phase consisted of the stages for the SVD filter, Butterworth High Pass Filter (HPF), moving average (MA), and squaring, whereas the decision phase consisted of a single stage that detected the R-peak. In the pre-processing phase, the SVD filter removed noise while the Butterworth HPF eliminated baseline wander. The MA removed the remaining noise of the signal that had gone through the SVD filter to make the signal smooth, and squaring played a role in strengthening the signal. In the decision phase, the threshold was used to set the interval before detecting the R-peak. When the latest R-R interval (RRI), suggested by Hamilton et al., was greater than 150% of the previous RRI, the method of detecting the R-peak in such an interval was modified to be 150% or greater than the smallest interval of the two most latest RRIs. When the modified search back system was used, the error rate of the peak detection decreased to 0.29%, compared to 1.34% when the modified search back system was not used. Consequently, the sensitivity was 99.47%, the positive predictivity was 99.47%, and the detection error was 1.05%. Furthermore, the quality of the signal in data with a substantial amount of noise was improved, and thus, the R-peak was detected effectively.
Woo-Hyuk Jung; Sang-Goog Lee. An R-peak detection method that uses an SVD filter and a search back system. Computer Methods and Programs in Biomedicine 2012, 108, 1121 -1132.
AMA StyleWoo-Hyuk Jung, Sang-Goog Lee. An R-peak detection method that uses an SVD filter and a search back system. Computer Methods and Programs in Biomedicine. 2012; 108 (3):1121-1132.
Chicago/Turabian StyleWoo-Hyuk Jung; Sang-Goog Lee. 2012. "An R-peak detection method that uses an SVD filter and a search back system." Computer Methods and Programs in Biomedicine 108, no. 3: 1121-1132.