Contributions to Proceedings:
S. Pudukotai Dinakarrao, A. Jantsch:
"Arrhythmia Detection with Digital Hardware by Learning {ECG} Signal";
in: "ACM Great Lakes Symposium on VLSI 2018",
1;
issued by: ACM;
ACM Digital Library,
New York,
2018,
ISBN: 978-1-4503-5724-1,
495
- 498.
English 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.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1145/3194554.3194647
Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_275446.pdf
Created from the Publication Database of the Vienna University of Technology.