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Research Paper | Electronics & Communication Engineering | India | Volume 3 Issue 11, November 2014
Design & Analysis of Multimode Monitoring of Electrocardiogram (ECG) and Electromyography (EMG) Signals with USB interface or Human Computer Interaction System
Pradeep Kumar Jaisal | Dr. Sushil Kumar [7] | Dr. S. P. Shukla [2]
Abstract: The electrocardiogram (ECG) and electromyography (EMG) is an important physiological signal that helps determine the state of the cardiovascular & muscular system; however, this signal is often corrupted by interfering noise. Baseline wander is a commonly seen noise in ECG & EMG recordings and can be caused by respiration, changes in electrode impedance, and motion. Baseline wander can mask important information from the ECG & EMG, and if it is not properly removed, crucial diagnostic information contained in the ECG & EMG will be lost or corrupted. Therefore, it is vital to effectively eliminate baseline wander before any further processing of ECG such as feature extraction. The simplest method of baseline wander (drift) removal is the use of a high-pass filter that blocks the drift and passes all main components of ECG though the filter. The main components of ECG include the P-wave, QRS-complex, and Twave. Specifically, the PR-Segment, ST-Segment, PR-Interval, and QT-Interval are considered as the main segments of the ECG. Each of these intervals/segments has its corresponding frequency components, and according to the American Health Association (AHA), the lowest frequency component in the ECG signal is at about 0.05Hz. However, a complete baseline removal requires that the cut-off frequency of the high-pass filter be set higher than the lowest frequency in the ECG; otherwise some of the baseline drift will pass through the filter. The frequency of the baseline wander high-pass filter is usually set slightly below 0.5Hz. Therefore, knowing that the actual ECG & EMG signal has components between 0.05Hz and 0.5Hz, the fore mentioned simple approach for baseline removal distorts and deforms the ECG & EMG signals.
Keywords: Electrocardiogram ECG, Surface Electromyography SEMG, Motor System, Matlab
Edition: Volume 3 Issue 11, November 2014,
Pages: 392 - 397