Abstract
Automatic real-time ECG patterns detection and classification has great importance in early diagnosis and treatment of life-threatening cardiac arrhythmia [7]. In this paper, we developed an algorithm which could classify abnormal heartbeat at more than 85% accuracy. The ECG data of this research are provided by MIT-BIH Arrhythmia Database from Physionet. We extracted seven features from each ECG record to represent the ECG signal. Furthermore, Support Vector Machine and Multi-Layer Perceptron Neural Network are used for classification. We were able to achieve over 85% accuracy and with only 10% difference between sensitivity and specificity.
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Li, H., Boulanger, P. (2018). A Model-Based Approach for Arrhythmia Detection and Classification. In: Basu, A., Berretti, S. (eds) Smart Multimedia. ICSM 2018. Lecture Notes in Computer Science(), vol 11010. Springer, Cham. https://doi.org/10.1007/978-3-030-04375-9_37
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DOI: https://doi.org/10.1007/978-3-030-04375-9_37
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