Detection of ECG characteristic points using wavelet transforms

Abstract

An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points. With the multiscale feature of WT's, the QRS complex can be distinguished from high P or T waves, noise, baseline drift, and artifacts. The relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated. By using this method, the detection rate of QRS complexes is above 99.8% for the MIT/BIH database and the P and T waves can also be detected, even with serious baseline drift and noise.

A assessment of multiscale-based peak detection algorithm using MIT/BIH Arrhythmia Database

“…일반적으로 사용되고 있는 피크 검출 방법에는 대표적으로 역치값 설정 기술(Adaptive threshold) [3], 미분(Differentiation) [8], 웨이블 렛 변환(Wavelet transform) [2], 힐버트 변환(Hilbert transform) [ 11Hz, Cuiwei [9] 8～58.8Hz, Sahambi [10] 3～40Hz, Benitez [11] 8～20Hz, Moraes [12] 9～30Hz, Mahmoodabadi [13] …”

Motor Unit Action Potential Duration: Measurement and Significance

“…This technique has provided promising results in the analysis of various electrophysiological signals such as blink reflex [49], EMG and electrocardiographic recordings [50][51][52], electroencephalographic signals for analysis of epileptic activity [53], and event-related potentials [54]. By regarding transformed EMG signals at a suitable scale in the DWT domain, it is possible to evade high frequency noise and low frequency BL fluctuations.…”

A dual-mode ECG processor with difference-insensitive QRS detection and lossless compression

“…We put forward an adaptive difference sensitivity scaling (ADSS) mechanism that tunes the TH DS dynamically to make it applicable for different situations. The scaling is based on the running estimate of TR, as depicted in equation (4).…”

“…Ieong [3] simplified the algorithm in [4] to a single scale wavelet transform which achieves high detection performance and low power consumption. But these QRS detection methods have little concern about noise interference which should be taken good care of, as wearable device are sensitive to various noises.…”