An adaptive threshold algorithm based on wavelet in QRS detection

Electrocardiogram (ECG) signal has been widely used for cardiac diagnostic and pathological analysis. The QRS complex is the most string waveform within the ECG, and it carries large amounts of information. But it is always mixed with various noises, such as power line interference, baseline displacement and electromyography (EMG) noise. These noises bring obstacle to the diagnosis of cardiovascular diseases. Hence, eliminating these noises is the necessary and important way to analyze ECG signal. In this paper we elaborate a new algorithm based on wavelet transform and adaptive threshold after hundreds of experiments and simulations using MIT-BIH arrhythmia database signals. According to the results, this algorithm can remove noises efficiently with low distortion, so it can also meet the needs of the clinical treatment and pathological research.