A wavelet-based ECG delineation algorithm for 32-bit integer online processing

Abstract

BackgroundSince the first well-known electrocardiogram (ECG) delineator based on Wavelet Transform (WT) presented by Li et al. in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave) would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementations of this method adopt non-linear operators such as root mean square (RMS) or floating point algebra, which are computationally demanding.MethodsThis paper presents a 32-bit integer, linear algebra advanced approach to online QRS detection and P-QRS-T waves delineation of a single lead ECG signal, based on WT.ResultsThe QRS detector performance was validated on the MIT-BIH Arrhythmia Database (sensitivity Se = 99.77%, positive predictive value P+ = 99.86%, on 109010 annotated beats) and on the European ST-T Database (Se = 99.81%, P+ = 99.56%, on 788050 annotated beats). The ECG delineator was validated on the QT Database, showing a mean error between manual and automatic annotation below 1.5 samples for all fiducial points: P-onset, P-peak, P-offset, QRS-onset, QRS-offset, T-peak, T-offset, and a mean standard deviation comparable to other established methods.ConclusionsThe proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, in spite of a simple structure built on integer linear algebra.

A Modular Low-Complexity ECG Delineation Algorithm for Real-Time Embedded Systems

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Real-time premature ventricular contractions detection based on Redundant Discrete Wavelet Transform

“…The Wavelet Transform (WT) has been widely used in recent years in various areas and several applications, such as power system quality (Andrade and Leao, 2014;Arrais et al, 2014;Costa, 2014), signal processing (Pedireddi and Srinivasan, 2010;Saleh et al, 2012), biomedical engineering (Arrais et al, 2016;Dalvi et al, 2016;Di Marco and Chiari, 2011;Kim et al, 2011;Madeiro et al, 2009), among others. WT provides a compact and flexible analysis in time and frequency domain, allowing different resolution levels, besides presenting various base functions (Wavelet families), which allow a more specific analysis in order to adapt the analysis of signal behavior and so achieve the best possible results (Burrus et al, 1997;Mallat, 1989Mallat, , 2008Percival and Walden, 2000).…”

System Architecture for Low-Power Ubiquitously Connected Remote Health Monitoring Applications With Smart Transmission Mechanism

“…3 from the Lead I ECG signal thus plays a prominent role in calculating the required features and helps in classifying the patient. A wavelet transform based advanced approach for feature extraction is discussed in [12]. An algorithm of ECG feature extraction for mobile health care applications with low complexity is presented in [13].…”

“…The multi scale features of wavelet transforms are made use in [14] to detect the features of ECG signal. For a detailed description of several feature extraction algorithms available, kindly refer to [12]- [14]. In below sub sections the functionality of adaptive rule engine and seamless hand-off controller are discussed in detail.…”