[논문]Optimal Length of Heart Rate Variability Data and Forecasting Time for Ventricular Fibrillation Prediction Using Machine Learning

Jeong, Da Un; Taye, Getu Tadele; Hwang, Han-Jeong; Lim, Ki Moo

doi:10.1155/2021/6663996

[해외논문] Optimal Length of Heart Rate Variability Data and Forecasting Time for Ventricular Fibrillation Prediction Using Machine Learning 원문보기

Computational and mathematical methods in medicine : CMMM, v.2021, 2021년, pp.6663996 -

Jeong, Da Un (Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea) , Taye, Getu Tadele (Health Informatics Units, School of Public Health, Mekelle University, Mekelle, Ethiopia) , Hwang, Han-Jeong (Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea) , Lim, Ki Moo (Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea)

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Abstract ▼ AI-Helper

Ventricular fibrillation (VF) is a cardiovascular disease that is one of the major causes of mortality worldwide, according to the World Health Organization. Heart rate variability (HRV) is a biomarker that is used for detecting and predicting life-threatening arrhythmias. Predicting the occurrence of VF in advance is important for saving patients from sudden death. We extracted features from seven HRV data lengths to predict the onset of VF before nine different forecast times and observed the prediction accuracies. By using only five features, an artificial neural network classifier was trained and validated based on 10-fold cross-validation. Maximum prediction accuracies of 88.18% and 88.64% were observed at HRV data lengths of 10 and 20 s, respectively, at a forecast time of 0 s. The worst prediction accuracy was recorded at an HRV data length of 70 s and a forecast time of 80 s. Our results showed that features extracted from HRV signals near the VF onset could yield relatively high VF prediction accuracies.

참고문헌 (19)

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[해외논문] Optimal Length of Heart Rate Variability Data and Forecasting Time for Ventricular Fibrillation Prediction Using Machine Learning 원문보기

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[해외논문] Optimal Length of Heart Rate Variability Data and Forecasting Time for Ventricular Fibrillation Prediction Using Machine Learning 원문보기

Abstract ▼ AI-Helper

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