Electrocardiogram (ECG) signal is the electrical recording of heart activity. The Electrocardiogram (ECG) reflects the activities and the attributes of the human heart and reveals very important hidden information. The information is extracted by means of ECG signal analysis to gain insights that are very crucial in explaining and identifying various pathological conditions, but the ECG signal can be distorted with noise. Noise can be any interference due to motion artifacts or due to power equipment that are present where ECG had been taken. A typical computer based ECG analysis system includes a signal pre-processing, beats detection and feature extraction stages, followed by classification. Automatic identification of arrhythmias from the ECG is one important biomedical application of pattern recognition. Moreover ECG signal processing has become a prevalent and effective tool for research and clinical practices. The motion artifacts are effectively removed from the ECG signal which is shown by beat detection on noisy and cleaned ECG signals after LMS and NLMS processing. This paper focuses on ECG signal processing using Least Mean Square (LMS) and Normalized Least Mean Square (NLMS), which has received increasing attention as a signal conditioning and feature extraction technique for biomedical application.
Signal Preprocessing, Pattern recognition, Noise
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