Real-time classification technique for early detection and prevention of myocardial infarction on wearable devices
Sopic, Dionisije ; Aminifar, Amin ; Aminifar, Amir LU
and Atienza, David
(2018)
2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017
p.1-4
- Abstract
Continuous monitoring of patients suffering from cardiovascular diseases and, in particular, myocardial infarction (MI) places a considerable burden on health-care systems and government budgets. The rise of wearable devices alleviates this burden, allowing for long-term patient monitoring in ambulatory settings. One of the major challenges in this area is to design ultra-low energy wearable devices for long-term monitoring of patients' vital signs. In this work, we present a real-time event-driven classification technique, based on support vector machines (SVM) and statistical outlier detection. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm.... (More)
Continuous monitoring of patients suffering from cardiovascular diseases and, in particular, myocardial infarction (MI) places a considerable burden on health-care systems and government budgets. The rise of wearable devices alleviates this burden, allowing for long-term patient monitoring in ambulatory settings. One of the major challenges in this area is to design ultra-low energy wearable devices for long-term monitoring of patients' vital signs. In this work, we present a real-time event-driven classification technique, based on support vector machines (SVM) and statistical outlier detection. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. This technique leads to a reduction in energy consumption and thus battery lifetime extension. We validate our approach on a well-established and complete myocardial infarction (MI) database (Physiobank, PTB Diagnostic ECG database [1]). Our experimental evaluation demonstrates that our real-time classification scheme outperforms the existing approaches in terms of energy consumption and battery lifetime by a factor of 3, while maintaining the classification accuracy at a medically-acceptable level of 90%.
(Less)
- author
- Sopic, Dionisije
; Aminifar, Amin
; Aminifar, Amir
LU
and Atienza, David
- publishing date
- 2018-03-23
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings
- pages
- 4 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017
- conference location
- Torino, Italy
- conference dates
- 2017-10-19 - 2017-10-21
- external identifiers
-
- scopus:85050108745
- ISBN
- 9781509058037
- DOI
- 10.1109/BIOCAS.2017.8325140
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2017 IEEE.
- id
- f7978016-0505-4208-9182-5fd5c8b362a4
- date added to LUP
- 2022-02-05 01:24:53
- date last changed
- 2022-04-22 07:31:27
@inproceedings{f7978016-0505-4208-9182-5fd5c8b362a4,
abstract = {{<p>Continuous monitoring of patients suffering from cardiovascular diseases and, in particular, myocardial infarction (MI) places a considerable burden on health-care systems and government budgets. The rise of wearable devices alleviates this burden, allowing for long-term patient monitoring in ambulatory settings. One of the major challenges in this area is to design ultra-low energy wearable devices for long-term monitoring of patients' vital signs. In this work, we present a real-time event-driven classification technique, based on support vector machines (SVM) and statistical outlier detection. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. This technique leads to a reduction in energy consumption and thus battery lifetime extension. We validate our approach on a well-established and complete myocardial infarction (MI) database (Physiobank, PTB Diagnostic ECG database [1]). Our experimental evaluation demonstrates that our real-time classification scheme outperforms the existing approaches in terms of energy consumption and battery lifetime by a factor of 3, while maintaining the classification accuracy at a medically-acceptable level of 90%.</p>}},
author = {{Sopic, Dionisije and Aminifar, Amin and Aminifar, Amir and Atienza, David}},
booktitle = {{2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings}},
isbn = {{9781509058037}},
language = {{eng}},
month = {{03}},
pages = {{1--4}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Real-time classification technique for early detection and prevention of myocardial infarction on wearable devices}},
url = {{http://dx.doi.org/10.1109/BIOCAS.2017.8325140}},
doi = {{10.1109/BIOCAS.2017.8325140}},
year = {{2018}},
}