A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm

Computer Methods and Programs in Biomedicine
Diptangshu PanditChee Peng Lim

Abstract

Detection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals. A lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals. The...Continue Reading

Citations

Nov 4, 2017·Computer Assisted Surgery·Xinghui LiXiaohao Wang
Aug 1, 2020·Physical and Engineering Sciences in Medicine·Jagdeep RahulLakhan Dev Sharma
Aug 18, 2020·Journal of Healthcare Engineering·Zhou ZhangZhangyong Li
Apr 1, 2018·Cardiovascular Engineering and Technology·Pooja SabherwalMonika Agrawal
Oct 19, 2020·Computer Methods and Programs in Biomedicine·Zhongyun YuanShengbo Sang
Mar 26, 2021·Computers in Biology and Medicine·Jagdeep RahulLakhan Dev Sharma
Aug 1, 2021·BMC Medical Informatics and Decision Making·Guixiang LiKai Wu

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