Review and Comparison of QRS Detection Algorithms for Arrhythmia Diagnosis

Kirti,; Sohal, Harsh; Jain, Shruti

doi:10.1109/ispcc48220.2019.8988503

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…To guarantee high performance, pre-processing and post-processing steps were usually required, at an increased computing cost. Other reviews and benchmarks focused on pre-processing and decision-making steps [ 62 , 63 , 64 ]. Van and Podmasteryev reviewed in detail four ML-based algorithms [ 65 ], including artificial neural network (ANN) [ 66 ], k-nearest neighbor (k-NN) algorithm [ 67 ], k-means [ 68 ], and SVM [ 69 ].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision

Mehri

Calmon²,

Odille

et al. 2023

Sensors

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Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases. Using the proposed approach, high F1-scores of 99.80% and 99.64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99.88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99.39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision

Mehri

Calmon²,

Odille

et al. 2023

Sensors

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A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis

Saleem

Khandoker

Alkhodari

et al. 2022

Sci Rep

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Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily attend to ectopic beats but do not consider technical issues that affect the ECG. A secondary aim of this study was to investigate the effect of increasing increments of artifacts on 24 of the most used HRV measures. A two-step preprocessing approach for denoising HRV is introduced which targets each type of noise separately. First, the technical artifacts in the ECG are eliminated by applying complete ensemble empirical mode decomposition with adaptive noise. The second step removes physiological artifacts from the HRV signal using a combination filter of single dependent rank order mean and an adaptive filtering algorithm. The performance of the two-step pre-processing tool showed a high correlation coefficient of 0.846 and RMSE value of 7.69 × 10–5 for 6% of added ectopic beats and 6 dB Gaussian noise. All HRV measures studied except HF peak and LF peak are significantly affected by both types of noise. Frequency measures of Total power, HF power, and LF power and fragmentation measures; PAS, PIP, and PSS are the most sensitive to both types of noise.

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