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

Abstract

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A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

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Optimal SSA‐based wideband digital differentiator design for cardiac QRS complex detection application

“…Several QRS complex detection techniques have been reported in the recent literature. These include quadratic filter, level crossing sampling‐based analog to digital conversion logic, integrate and fire sampling, least mean square algorithm‐based adaptive linear predictor, empirical mode decomposition (EMD), multiscale mathematical morphology (MM), sigmoidal radial basis function artificial neural network (ANN), max‐min difference (MMD) algorithm, filter banks (FBs), quadratic spline wavelet transform (WT), daubechies (db10) WT, Harr WT, wavelet filter bank, digital filtering with dynamic threshold, combination of WT, derivative, and Hilbert transform (HT), adaptive MM, phase space reconstruction and box‐scoring calculation, ECG structural analysis (SA), relative energy (RE), parallel delta modulator (PDM), modified S‐transform (ST), and deterministic finite automata (DFA) …”

“…The Shannon energy and Hilbert transform‐based methods detect several false peaks for long pause ECG signals . The QRS detection accuracy of the methods reported in the literature is very poor, which reduces the diagnostic correctness and operational reliability, whereas, the high accuracy methods employ costly signal processing operations.…”

<em></em>Heartbeat Abnormality Detection using Machine Learning Models and Rate Variability (HRV) Data

“…ECG analysis is very widely explored area and various methods have been proposed for ECG analysis [2]. In previous works we have explored light weight ECG analysis and abnormality detection [3][4][5][6].…”