Pudukotai Dinakarrao, S. M., & Jantsch, A. (2018). ADDHard: Arrhythmia Detection with Digital Hardware by Learning ECG Signal. In Proceedings of the 2018 on Great Lakes Symposium on VLSI. ACM, Austria. ACM Digital Library. https://doi.org/10.1145/3194554.3194647
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Book Title:
Proceedings of the 2018 on Great Lakes Symposium on VLSI
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Abstract:
Anomaly detection in Electrocardiogram (ECG) signals facilitates the diagnosis of cardiovascular diseases i.e., arrhythmias. Existing methods, although fairly accurate, demand a large number of computational resources. Based on the pre-processing of ECG signal, we present a low-complex digital hardware implementation (ADDHard) for arrhythmia detection. ADDHard has the advantages of low-power consumption and a small foot print. ADDHard is suitable especially for resource constrained systems such as body wearable devices. Its implementation was tested with the MIT-BIH arrhythmia database and achieved an accuracy of 97.28% with a specificity of 98.25% on average.
