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Among various Bio-medical signals, Electrocardiogram (ECG) is most important signal to diagnose Cardiac Arrhythmia (CA). However the noises added during ECG signal acquisition makes the system less accurate in CA detection. This paper presents a new ECG denoising approach based on noise reduction aspects in Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) domains. The main objective to use DWT is to remove the high frequency noise components like EMG noise which leads to significant cropping in QRS complexes and also lead to distortions at the beginning and ending of QRS complexes. This approach also designs an adaptive threshold which considers the average noise and signal powers to remove the noise form ECG signal. The segmentation of ECG signal involves to focus only on the noise dominant components such that the overall complexity also in the control. Further the EMD preserves the QRS complexes information by adapting the adaptive thresholding for the Intrinsic Mode Functions (IMFs) after finding the noise dominant IMFs from signal dominant IMFs. Simulation results are carried out through MIT-BIH Arrhythmia database and the performance is evaluated with respect to the performance metrics like Mean Square Error and Signal to Noise Ratio. The proposed approach provides better results compared to the conventional state-of the-art techniques.
Detection of abnormalities in the ECG signal to achieve an automatic diagnosis of several heart related diseases has become an increased research aspect. This paper focused to develop an automatic detection system to detect abnormalities in ECG. These abnormalities results in different cardiac arrhythmias. Towards the detection of different cardiac arrhythmias, this paper analyzed the ECG signal through Dual Tree Complex Wavelet Transform (DTCWT) as a feature extraction technique and further proposed a new selective band coding technique to extract only the informative features from the sub bands obtained from DTCWT. The novelty of this proposed system is to remove the redundant information, thereby achieving a fast and accurate detection results. Multi-Class Support Vector Machine (MC-SVM) is used for classification purpose. Extensive simulations are carried out for the MIT-BIH database and the performance is measured through the performance metrics such as Accuracy, Precision, Recall, False Positive Rate, F-Measure and overall computational time. The proposed method is also compared with conventional approaches to alleviate the performance enhancement in the detection of Cardiac Arrhythmias (CAs) with less time span.
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