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Beiträge in Tagungsbänden:

S. Pudukotai Dinakarrao, A. Jantsch:
"Arrhythmia Detection with Digital Hardware by Learning {ECG} Signal";
in: "ACM Great Lakes Symposium on VLSI 2018", 1; herausgegeben von: ACM; ACM Digital Library, New York, 2018, ISBN: 978-1-4503-5724-1, S. 495 - 498.



Kurzfassung englisch:
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.


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1145/3194554.3194647

Elektronische Version der Publikation:
https://publik.tuwien.ac.at/files/publik_275446.pdf


Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.