SVM-based algorithm for recognition of QRS complexes in electrocardiogram

Mehta, S. S.; Lingayat, N. S.

doi:10.1016/j.rbmret.2008.03.006

Cited by 71 publications

(23 citation statements)

References 21 publications

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“…As the result the proposed method achieved detection rate at 99.10% that is nearly on par with other popular methods. However, the results done by Mehta et al were obtained from different database [4,5]. This paper shows that the STFT technique has potential to improve the performance in QRS complex detection.…”

Section: Discussionmentioning

confidence: 92%

“…The false positive (FP) presents number of detected beat from non beats. Mehta et al [4] n/a n/a 98.63% n/a Mehta et al [5] n/a n/a 99.30% n/a…”

Section: Resultsmentioning

confidence: 99%

“…Darrington proposed the fast method with minimal pre-process for real time QRS detection [3]. In addition to Mehta et al applied support vector machine (SVM) and K-means algorithms to detection QRS complexes [4,5]. Therefore, in this study we have proposed novel technique to detect the QRS complex of ECG surface.…”

Section: Introductionmentioning

confidence: 99%

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Development of QRS Detection using Short-time Fourier Transform based Technique

Uchaipichat¹,

Inban²

2010

IJCA

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This paper reports our study in QRS complex detection. The short-time Fourier transform (STFT) was employed in ECG filtering stage. The narrow rectangular window was used to transform ECG signals into time-frequency domain. The temporal information at 45 Hz from spectrogram was analyzed for detecting QRS locations. The automated thresholding combined with local maxima finding method was modified to find the QRS location. The data used in this study is MIT-BIH Arrhythmia database. As the results, our proposed technique achieved the detection rate better than 99% and fail ratio was 1.3%.

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Section: Discussionmentioning

confidence: 92%

“…The false positive (FP) presents number of detected beat from non beats. Mehta et al [4] n/a n/a 98.63% n/a Mehta et al [5] n/a n/a 99.30% n/a…”

Section: Resultsmentioning

confidence: 99%

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Development of QRS Detection using Short-time Fourier Transform based Technique

Uchaipichat¹,

Inban²

2010

IJCA

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“…So far, variant methods for ECG de-noising and R-wave detection have been proposed, including approaches of derivatives [10,11], digital filters [12][13][14][15], wavelet transform(WT) [1,[16][17][18][19][20][21][22][23][24][25][26], artificial neural network (ANN) [27,28], support vector machine (SVM) [29], k-means [30], empirical mode decomposition (EMD) [31], geometrical matching [32][33][34], combined threshold method [35,36], phase space method [37], Hilbert Transform method [38], and mixed approach [39,40]. Almost all of the methods listed above have some limitations.…”

Section: Introductionmentioning

confidence: 99%

“…The use of ANN and SVM require the training of specific model and adjustment of parameters, which needs massive complicated calculations. Hence, it is difficult to effectively address the balance between QRS enhancement and noise reduction in practice [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

Rui

Wang

et al. 2017

EURASIP J. Adv. Signal Process.

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Cardiovascular diseases are associated with high morbidity and mortality. However, it is still a challenge to diagnose them accurately and efficiently. Electrocardiogram (ECG), a bioelectrical signal of the heart, provides crucial information about the dynamical functions of the heart, playing an important role in cardiac diagnosis. As the QRS complex in ECG is associated with ventricular depolarization, therefore, accurate QRS detection is vital for interpreting ECG features. In this paper, we proposed a real-time, accurate, and effective algorithm for QRS detection. In the algorithm, a proposed preprocessor with a band-pass filter was first applied to remove baseline wander and power-line interference from the signal. After denoising, a method combining K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) was used for accurate QRS detection in ECGs with different morphologies. The proposed algorithm was tested and validated using 48 ECG records from MIT-BIH arrhythmia database (MITDB), achieved a high averaged detection accuracy, sensitivity and positive predictivity of 99.43, 99.69, and 99.72%, respectively, indicating a notable improvement to extant algorithms as reported in literatures.

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Application of Artificial Intelligence in Cardiovascular Medicine

Cheng

Manandhar

Aryal

et al. 2021

Comprehensive Physiology

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The advent of advances in machine learning (ML)-based techniques has popularized wide applications of artificial intelligence (AI) in various fields ranging from robotics to medicine. In recent years, there has been a surge in the application of AI to research in cardiovascular medicine, which is largely driven by the availability of large-scale clinical and multi-omics datasets. Such applications are providing a new perspective for a better understanding of cardiovascular disease (CVD), which could be used to develop novel diagnostic and therapeutic strategies. For example, studies have shown that ML has a substantial potential for early diagnosis of different types of CVD, prediction of adverse disease outcomes such as heart failure, and development of newer and personalized treatments. In this article, we provide an overview and discuss the current status of a wide range of AI applications, including machine learning, reinforcement learning, and deep learning, in cardiovascular medicine.

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SVM-based algorithm for recognition of QRS complexes in electrocardiogram

Cited by 71 publications

References 21 publications

Development of QRS Detection using Short-time Fourier Transform based Technique

Development of QRS Detection using Short-time Fourier Transform based Technique

A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

Application of Artificial Intelligence in Cardiovascular Medicine

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