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The electrocardiographic (ECG) signal is the electrical manifestation of the contractile activity of the heart, and is the most commonly used biomedical signal for the detection of arrhythmia and diagnosis of cardiovascular diseases (Rangayyan, 2002; Tompkins, 1993). In clinical practice, however, surface recording of the ECG signal (with the frequency range of 0.05-250 Hz), obtained by placing electrodes on the subject’s skin, is susceptible to several different types of artifacts. The dominant artifacts in an ambulatory ECG recording include:
Baseline Wander. Drift of the baseline is a type of low-frequency (< 0.5 Hz) artifact and usually caused by respiration or movement of the patient.
Physiological Artifacts. This type of artifact is mainly induced by muscular contractions. Electrode-motion artifact has a wide frequency range (1-5,000 Hz) and is generally considered to be the most troublesome, because it can mimic the appearance of ectopic beats and cannot be removed easily by simple filters.
Random Noise. Random noise could be due to the thermal effect in the instrumentation amplifiers, the recording system, and pickup of ambient electromagnetic signals by cables (Rangayyan, 2002). Random noise usually appears with high frequency; its frequency range depends on the specific source. In real-time clinical monitoring systems used during surgery, electrosurgical noise is a significant obstacle to be overcome.
External Interference. Examples of environmental interference are those caused by 50 or 60 Hz power-supply lines, electrode motion, radiation from lights, and radio-frequency emissions from nearby medical devices.
The stage of artifact removal is crucial in ECG monitoring systems, and fundamental for many other ECG processing applications, e.g., beat classification (Afonso, Tompkins, Nguyen, & Luo, 1999; Hu, Palreddy, & Tompkins, 1997), QRS detection (Meyer, Gavela, & Harris, 2006; Hu, Tompkins, Urrusti, & Afonso, 1993), analysis of arrhythmia (Thakor & Zhu, 1991), extraction of the fetal ECG signal from the maternal abdominal ECG (Kanjilal, Palit, & Saha, 1997; Khamene & Negahdaripour, 2000), classification of myocardial ischemia (Silipo & Marchesi, 1998), diagnosis of atrial fibrillation (Yang, Devine, & Macfarlane, 1994), ECG-based sleep apnea detection (Mita, 2007), and ECG signal data compression (Zigel, Cohen, & Katz, 2000; Hamilton, Thomson, & Sandham, 1995). Denoising the inherently nonstationary ECG signals calls for adaptive filters whose impulse response can be automatically adjusted according to the time-varying characteristics of the signal and artifacts.