Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals

Afkhami, Rashid Ghorbani; Azarnia, Ghanbar; Tinati, Mohammad Ali

doi:10.1016/j.patrec.2015.11.018

Cited by 169 publications

(64 citation statements)

References 19 publications

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“…Also, the mixture of experts used in [11] with temporal-frequency domain features from the wavelet transform does not provide further insights of the classification boundaries which could be extrapolated into a medical interpretation. Although decision trees (applied in [10]) are known for being algorithms used in Business Applications for their great intuitive design and modeling, these along with the features extracted from HOS and GMM may represent a difficult task to find some interoperability; and Ensemble learning makes it even harder.…”

Section: Resultsmentioning

confidence: 99%

“…Martis et al [9] applied the bispectrum computation in each beat and PCA to create the features that ultimately fed NN and support vector machines (SVMs) algorithms to classify between five types of heartbeats including the normal heartbeats (NBs) and premature ventricular contraction (PVC) beats. Afkhami et al [10] derived morphological, statistical, and temporal features from the heartbeats amid probability density function extracted from the Gaussian mixture modeling (GMM) parameters to train an ensemble of decision trees. Javadi et al [11] extracted features using the wavelet transform from key morphological shapes of the ECG and combined negative correlation learning with mixture of experts to train a negatively correlated NNs (neural networks).…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Classification Models for Premature Ventricular Contraction Detection on ECG Traces

Casas

Avitia

González-Navarro

et al. 2018

Journal of Healthcare Engineering

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According to the American Heart Association, in its latest commission about Ventricular Arrhythmias and Sudden Death 2006, the epidemiology of the ventricular arrhythmias ranges from a series of risk descriptors and clinical markers that go from ventricular premature complexes and nonsustained ventricular tachycardia to sudden cardiac death due to ventricular tachycardia in patients with or without clinical history. The premature ventricular complexes (PVCs) are known to be associated with malignant ventricular arrhythmias and sudden cardiac death (SCD) cases. Detecting this kind of arrhythmia has been crucial in clinical applications. The electrocardiogram (ECG) is a clinical test used to measure the heart electrical activity for inferences and diagnosis. Analyzing large ECG traces from several thousands of beats has brought the necessity to develop mathematical models that can automatically make assumptions about the heart condition. In this work, 80 different features from 108,653 ECG classified beats of the gold-standard MIT-BIH database were extracted in order to classify the Normal, PVC, and other kind of ECG beats. Three well-known Bayesian classification algorithms were trained and tested using these extracted features. Experimental results show that the F1 scores for each class were above 0.95, giving almost the perfect value for the PVC class. This gave us a promising path in the development of automated mechanisms for the detection of PVC complexes.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian Classification Models for Premature Ventricular Contraction Detection on ECG Traces

Casas

Avitia

González-Navarro

et al. 2018

Journal of Healthcare Engineering

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show abstract

“…The features used to represent a heartbeat are usually extracted from cardiac rhythm or time/frequency domains, in which the RR -Interval is reported as one of the most widely used feature [2, 3, 7–10]. RR -Interval holds indispensable information about heart rhythms and has capacity to discriminate the disease heartbeats from the normal ones.…”

Section: Related Workmentioning

confidence: 99%

“…RR -Interval holds indispensable information about heart rhythms and has capacity to discriminate the disease heartbeats from the normal ones. Other features, such as the higher order statistics (HOS) [7, 11], wavelet coefficients [12–17], morphological amplitudes [2, 18], signal energy [17], and random projection features [19, 20], can also be commonly found in the literature. As irrelevant features could cause negative impacts to the classification performance and decrease the generalization power, different feature selection techniques have been applied to clear up the noise and reduce the feature dimension, such as the floating sequential search [4] and the weighted linear discriminant model with a forward-backward search strategy [21].…”

Section: Related Workmentioning

confidence: 99%

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A pyramid-like model for heartbeat classification from ECG recordings

Sun

Rong

et al. 2018

PLoS ONE

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Heartbeat classification is an important step in the early-stage detection of cardiac arrhythmia, which has been identified as a type of cardiovascular diseases (CVDs) affecting millions of people around the world. The current progress on heartbeat classification from ECG recordings is facing a challenge to achieve high classification sensitivity on disease heartbeats with a satisfied overall accuracy. Most of the work take individual heartbeats as independent data samples in processing. Furthermore, the use of a static feature set for classification of all types of heartbeats often causes distractions when identifying supraventricular (S) ectopic beats. In this work, a pyramid-like model is proposed to improve the performance of heartbeat classification. The model distinguishes the classification of normal and S beats and takes advantage of the neighbor-related information to assist identification of S bests. The proposed model was evaluated on the benchmark MIT-BIH-AR database and the St. Petersburg Institute of Cardiological Technics(INCART) database for generalization performance measurement. The results reported prove that the proposed pyramid-like model exhibits higher performance than the state-of-the-art rivals in the identification of disease heartbeats as well as maintains a reasonable overall classification accuracy.

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Using normalized echo state network to detect abnormal ECG patterns

Wang

Ding

Wang

et al. 2023

Int J Imaging Syst Tech

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Cardiovascular disease (CVD) stands as a prominent contributor to human mortality. Electrocardiogram (ECG) represents a widely adopted noninvasive method employed by clinicians to detect and diagnose CVDs. Nonetheless, conventional ECG‐based detection approaches for cardiac disorders tend to be time‐consuming and inefficient, necessitating the need for more effective solutions. Recent studies have highlighted the effectiveness of the echo state network (ESN) in detecting abnormal ECG patterns. However, traditional ESN models often face challenges such as unstable training and convergence difficulties due to variations in the range of reservoir state values. To address this issue, this study introduces a novel approach called the normalized echo state network (NESN). The NESN method normalizes the states of all neurons within the reservoir before applying the nonlinear activation function. In our study, we conducted performance evaluations of the proposed model using the MIT‐BIH arrhythmia database. We performed a synergistical analysis to investigate the impact of reservoir parameters on the network performance. The experimental results demonstrated promising outcomes, with an accuracy of 99.1% and an F1‐score of 96.4%. Specifically, for detecting abnormal ECG patterns, our model achieved a sensitivity of 90.2%, a positive predictive value of 96.6%, and a specificity of 99.8%. These results highlight the superior performance of our classifier compared to most traditional mainstream heartbeat detection methods and ring topology ESN model.

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Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals

Cited by 169 publications

References 19 publications

Bayesian Classification Models for Premature Ventricular Contraction Detection on ECG Traces

Bayesian Classification Models for Premature Ventricular Contraction Detection on ECG Traces

A pyramid-like model for heartbeat classification from ECG recordings

Using normalized echo state network to detect abnormal ECG patterns

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