Research Article
Arrhythmia Detection with Antidictionary Coding and Its Application on Mobile Platforms
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@INPROCEEDINGS{10.1007/978-3-030-34833-5_5, author={Gilson Frias and Hiroyoshi Morita and Takahiro Ota}, title={Arrhythmia Detection with Antidictionary Coding and Its Application on Mobile Platforms}, proceedings={Body Area Networks: Smart IoT and Big Data for Intelligent Health Management. 14th EAI International Conference, BODYNETS 2019, Florence, Italy, October 2-3, 2019, Proceedings}, proceedings_a={BODYNETS}, year={2019}, month={11}, keywords={ECG Arrhythmia Antidictionary Werable Mobile}, doi={10.1007/978-3-030-34833-5_5} }- Gilson Frias
Hiroyoshi Morita
Takahiro Ota
Year: 2019
Arrhythmia Detection with Antidictionary Coding and Its Application on Mobile Platforms
BODYNETS
Springer
DOI: 10.1007/978-3-030-34833-5_5
Abstract
In response to the demand of memory efficient algorithms for electrocardiogram (ECG) signal processing and anomaly detection on wearable and mobile devices, an implementation of the antidictionary coding algorithm for memory constrained devices is presented. Pre-trained probabilistic models built from quantized ECG sequences were constructed in an offline fashion and their performance was evaluated on a set of test signals. The low complexity requirements of the models is confirmed with a port of a pre-trained model of the algorithm into a mobile device without incurring on excessive use of computational resources.
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