<div class="csl-bib-body">
<div class="csl-entry">Pudukotai Dinakarrao, S. M., & Jantsch, A. (2018). ADDHard: Arrhythmia Detection with Digital Hardware by Learning ECG Signal. In <i>Proceedings of the 2018 on Great Lakes Symposium on VLSI</i>. ACM, Austria. ACM Digital Library. https://doi.org/10.1145/3194554.3194647</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/76454
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dc.description.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.
en
dc.publisher
ACM Digital Library
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dc.title
ADDHard: Arrhythmia Detection with Digital Hardware by Learning ECG Signal
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dc.type
Konferenzbeitrag
de
dc.type
Inproceedings
en
dc.relation.publication
Proceedings of the 2018 on Great Lakes Symposium on VLSI
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dc.relation.isbn
9781450357241
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dc.relation.doi
10.1145/3194554
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dc.type.category
Full-Paper Contribution
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dc.publisher.place
New York
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tuw.booktitle
Proceedings of the 2018 on Great Lakes Symposium on VLSI
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tuw.peerreviewed
true
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tuw.relation.publisher
ACM
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tuw.relation.publisherplace
New York
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tuw.researchTopic.id
I2
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tuw.researchTopic.name
Computer Engineering and Software-Intensive Systems