Detecting Life-Threatening Arrhythmia with Machine Learning Algorithms
Date Issued
2016
Date
2016
Author(s)
Hong, Jen-Yee
Abstract
Sudden out-of-hospital cardiac arrest, one of the leading causes of death among adults, is frequently caused by ventricular fibrillation (VF). Prompt recognition of these life-threatening arrhythmias and early defibrillation treat- ment using an automated external defibrillator (AED) are crucial. Previous researchers proposed various VF detection algorithms, but most of them did not comply with the existing medical standards for AED development set by the American Heart Association (AHA). This thesis presents a machine- learning AED algorithm based on support vector machine. The development and evaluation processes of the algorithm carefully followed the AHA med- ical standards. With an overall sensitivity of 93.21 %, specificity 99.88 %, and precision of 89.28 %, the proposed algorithm satisfied all of the perfor- mance goals required by the AHA guideline. In addition, the dataset used in our study was more comprehensive then that used in previous studies and was reviewed by a physician to ensure its correctness. Therefore, it might be a better benchmark for future researches of AED algorithms.
Subjects
Arrhythmia
Ventricular fibrillation
Automatic external defibrillator
Electrocardiography
Signal processing
Machine learning
Type
thesis
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