Real-Time Cardiac Arrhythmia Classification Using Memristor Neuromorphic Computing System

Cardiac arrhythmia is known to be one of the most common causes of death worldwide. Therefore, development of efficient arrhythmia detection techniques is essential to save patients' lives. In this paper, we introduce a new real-time cardiac arrhythmia classification using memristor neuromorphi...

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Bibliographic Details
Published in2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2018; pp. 2567 - 2570
Main Authors Hassan, Amr M., Khalaf, Aya F., Sayed, Khaled S., Li, Hai Helen, Chen, Yiran
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2018
Subjects
Biological neural networks
Electrocardiography
Hardware
Memristors
Neuromorphics
Neurons
Training
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Summary:Cardiac arrhythmia is known to be one of the most common causes of death worldwide. Therefore, development of efficient arrhythmia detection techniques is essential to save patients' lives. In this paper, we introduce a new real-time cardiac arrhythmia classification using memristor neuromorphic computing system for classification of 5 different beat types. Neuromorphic computing systems utilize new emerging devices, such as memristors, as a basic building block. Hence, these systems provide excellent trade-off between real-time processing, power consumption, and overall accuracy. Experimental results showed that the proposed system outperforms most of the methods in comparison in terms of accuracy and testing time, since it achieved 96.17% average accuracy and 34 ms average testing time per beat.
ISSN:1557-170X
1558-4615
DOI:10.1109/EMBC.2018.8512868