A classification scheme for ventricular arrhythmias using wavelets analysis

Balasundaram, Krishnanand; Massé, Stéphane; Nair, Krishnakumar; Umapathy, Karthikeyan

doi:10.1007/s11517-012-0980-y

Cited by 32 publications

(22 citation statements)

References 45 publications

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“…They have tested their algorithm using cleaned and noisy ECG signals and reported a sensitivity and specificity values of 92 and 75% for the classification of VT and VF using features of cleaned ECG signal. In another study, Balasundaram et al ( 2013 ) have applied Eigen decomposition using SVD on the wavelet coefficients matrix of ECG to evaluate features. They have considered linear discriminant analysis (LDA) classifier for the classification of VT and non-VT episodes and reported an overall accuracy of 97.10%.…”

Section: Discussionmentioning

confidence: 99%

“…Our proposed method has higher performance regarding sensitivity and specificity values as 81.08 and 95.28% for the classification of VT and VF episodes as compared to the approach in Namarvar and Shahidi ( 2004 ). The approach reported by Balasundaram et al ( 2013 ) has used less ECG instances as compared to the proposed method. Moreover, OH et al ( 2017 ) have considered only shock vs. non-shock classification scheme and their approach have better performance as compared to our method.…”

Section: Discussionmentioning

confidence: 99%

“…One of the important step in the state of the art methods is the extraction of features from ECG signal for detection of VT/VF episodes. The most common features used for detection of VT and VF detection are based on complexity measure (CPLX) (Zhang et al, 1999 ), threshold crossing intervals (TCI) (Thakor et al, 1990 ), VF filter leakage measure (VFF) (), spectral algorithm (SPEC) (Barro et al, 1989 ), phase space representation (PSR) (Amann et al, 2007 ), autocorrelation function (ACF) (Chen et al, 1987 ), band-pass filter and count (Jekova and Krasteva, 2004 ), covariance, area and frequency bins of binary signal (Jekova, 2007 ), time-frequency analysis (Millet-Roig et al, 1999 ), and wavelet transforms (Addison et al, 2000 ; Balasundaram et al, 2013 ). The performance of five different VF detection algorithm has been compared in (Jekova, 2000 ).…”

Section: Introductionmentioning

confidence: 99%

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Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform

et al. 2018

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Accurate detection and classification of life-threatening ventricular arrhythmia episodes such as ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) from electrocardiogram (ECG) is a challenging problem for patient monitoring and defibrillation therapy. This paper introduces a novel method for detection and classification of life-threatening ventricular arrhythmia episodes. The ECG signal is decomposed into various oscillatory modes using digital Taylor-Fourier transform (DTFT). The magnitude feature and a novel phase feature namely the phase difference (PD) are evaluated from the mode Taylor-Fourier coefficients of ECG signal. The least square support vector machine (LS-SVM) classifier with linear and radial basis function (RBF) kernels is employed for detection and classification of VT vs. VF, non-shock vs. shock and VF vs. non-VF arrhythmia episodes. The accuracy, sensitivity, and specificity values obtained using the proposed method are 89.81, 86.38, and 93.97%, respectively for the classification of Non-VF and VF episodes. Comparison with the performance of the state-of-the-art features demonstrate the advantages of the proposition.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform

et al. 2018

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“…MP is an iterative signal decomposition technique that expresses a signal x ( t ) as a linear combination of functions selected from an overcomplete dictionary of TF basis functions [ 17 ]. The algorithm has been successful in creating high-resolution TF representations of biomedical signals [ 25 – 27 ]. In this study, we apply the MP algorithm to the AA signal obtained from the preprocessing step.…”

Section: Methodsmentioning

confidence: 99%

Developing a New Computer-Aided Clinical Decision Support System for Prediction of Successful Postcardioversion Patients with Persistent Atrial Fibrillation

Sterling

Huang

Ghoraani

2015

Computational and Mathematical Methods in Medicine

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We propose a new algorithm to predict the outcome of direct-current electric (DCE) cardioversion for atrial fibrillation (AF) patients. AF is the most common cardiac arrhythmia and DCE cardioversion is a noninvasive treatment to end AF and return the patient to sinus rhythm (SR). Unfortunately, there is a high risk of AF recurrence in persistent AF patients; hence clinically it is important to predict the DCE outcome in order to avoid the procedure's side effects. This study develops a feature extraction and classification framework to predict AF recurrence patients from the underlying structure of atrial activity (AA). A multiresolution signal decomposition technique, based on matching pursuit (MP), was used to project the AA over a dictionary of wavelets. Seven novel features were derived from the decompositions and were employed in a quadratic discrimination analysis classification to predict the success of post-DCE cardioversion in 40 patients with persistent AF. The proposed algorithm achieved 100% sensitivity and 95% specificity, indicating that the proposed computational approach captures detailed structural information about the underlying AA and could provide reliable information for effective management of AF.

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“…In many cases, cardiologists need to manually analyze ECG recordings acquired from patients over several days, making the task very troublesome and time-consuming [8]. The automatic annotation of ECG waves is important for understanding the regulation of heartbeats during sleep and hypertension [9][10][11][12][13][14], for diagnosing several cardiac arrhythmias [15][16][17][18][19][20][21][22], and for detecting other atrial disorders. Many research methods have been devoted to annotating the QRS complex (R-peaks) [23].…”

Section: Introductionmentioning

confidence: 99%

P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

et al. 2020

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Electrocardiogram (ECG) signal analysis is a critical task in diagnosing the presence of any cardiac disorder. There are limited studies on detecting P-waves in various atrial arrhythmias, such as atrial fibrillation (AFIB), atrial flutter, junctional rhythm, and other arrhythmias due to P-wave variability and absence in various cases. Thus, there is a growing need to develop an efficient automated algorithm that annotates a 2D printed version of P-waves in the well-known ECG signal databases for validation purposes. To our knowledge, no one has annotated P-waves in the MIT-BIH atrial fibrillation database. Therefore, it is a challenge to manually annotate P-waves in the MIT-BIH AF database and to develop an automated algorithm to detect the absence and presence of different shapes of P-waves. In this paper, we present the manual annotation of P-waves in the well-known MIT-BIH AF database with the aid of a cardiologist. In addition, we provide an automatic P-wave segmentation for the same database using a fully convolutional neural network model (U-Net). This algorithm works on 2D imagery of printed ECG signals, as this type of imagery is the most commonly used in developing countries. The proposed automatic P-wave detection method obtained an accuracy and sensitivity of 98.56% and 98.78%, respectively, over the first 5 min of the second lead of the MIT-BIH AF database (a total of 8280 beats). Moreover, the proposed method is validated using the well-known automatically and manually annotated QT database (a total of 11,201 and 3194 automatically and manually annotated beats, respectively). This results in accuracies of 98.98 and 98.9%, and sensitivities of 98.97 and 97.24% for the automatically and manually annotated QT databases, respectively. Thus, these results indicate that the proposed automatic method can be used for analyzing long-printed ECG signals on mobile battery-driven devices using only images of the ECG signals, without the need for a cardiologist.

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A classification scheme for ventricular arrhythmias using wavelets analysis

Cited by 32 publications

References 45 publications

Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform

Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform

Developing a New Computer-Aided Clinical Decision Support System for Prediction of Successful Postcardioversion Patients with Persistent Atrial Fibrillation

P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

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