Journal
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Volume -, Issue -, Pages 298-301Publisher
IEEE
DOI: 10.1109/embc.2019.8856496
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Funding
- NIH [R01 HL136660]
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Atrial fibrillation (AF) is the most prevalent arrhythmia, resulting in varying and irregular heartbeats. AF increases risk for numerous cardiovascular diseases including stroke, heart failure and as a result, computer aided efficient monitoring of AF is crucial, especially for intensive care unit (ICU) patients. In this paper, we present an automated and robust algorithm to detect AF from ICU patients using electrocardiogram (ECG) signals. Several statistical parameters including root mean square of successive differences, Shannon entropy, Sample entropy and turning point ratio are calculated from the heart rate. A subset of the Medical Information Mart for Intensive Care (MIMIC) III database containing 36 subjects is used in this study. We compare the AF detection performance of several classifiers for both the training and blinded test data. Using the support vector machine classifier with radial basis kernel, the proposed method achieves 99.95% cross-validation accuracy on the training data and 99.88% sensitivity, 99.65% specificity and 99.75% accuracy on the blinded test data.
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