ArticleFrontiers in physiology2018
Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform.
Article in Frontiers in physiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 53 citations in OpenAlex.
- High accuracy distinction of shockable and non-shockable arrhythmias in abnormal classes through wavelet transform with pseudo differential like operators.Scientific reports · 2023Article
- Diagnosis of sudden cardiac arrest using principal component analysis in automated external defibrillators.Scientific reports · 2023Article
- A review of progress and an advanced method for shock advice algorithms in automated external defibrillators.Biomedical engineering online · 2022Review
- Editorial: Machine Learning and Deep Learning for Physiological Signal Analysis.Frontiers in physiology · 2022Article
- LDIAED: A lightweight deep learning algorithm implementable on automated external defibrillators.PloS one · 2022Article
- Prediction of Multiple sclerosis disease using machine learning classifiers: a comparative study.Journal of preventive medicine and hygiene · 2021Article
- Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network.Journal of medical systems · 2020Article
Corrections and comments
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Authors and funding
6 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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