Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Self-Aware Anomaly-Detection for Epilepsy Monitoring on Low-Power Wearable Electrocardiographic Devices

Forooghifar, Farnaz ; Aminifar, Amin ; Teijeiro, Tomas ; Aminifar, Amir LU orcid ; Jeppesen, Jesper ; Beniczky, Sandor and Atienza, David (2021) 3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021 In 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021
Abstract

Low-power wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints concerning time and location, on one hand, and fulfilling long-term tracking, on the other hand. In the case of epileptic seizures, as the attacks infrequently occur, using an anomaly detection approach reduces the need to record long hours of data for each patient before detecting the successive coming seizures. In this work, by combining the concepts of self-aware system and anomaly detection, we propose an energy-efficient system to detect epileptic seizures on single-lead electrocardiographic signals, which is personalized after analyzing the first seizure of the patient. This system, then, uses a simple... (More)

Low-power wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints concerning time and location, on one hand, and fulfilling long-term tracking, on the other hand. In the case of epileptic seizures, as the attacks infrequently occur, using an anomaly detection approach reduces the need to record long hours of data for each patient before detecting the successive coming seizures. In this work, by combining the concepts of self-aware system and anomaly detection, we propose an energy-efficient system to detect epileptic seizures on single-lead electrocardiographic signals, which is personalized after analyzing the first seizure of the patient. This system, then, uses a simple anomaly-detection model, whenever the model is deemed reliable, and uses a more complex model otherwise. We show that after the personalization, the number of patients, for which the method provides high sensitivity, can reach 26 out of 43 patients with the false alarm rate (FAR) of 4 alarms/day. Thus, the number of responders to the system is increased by 24%, while the FAR is only increased by one alarm/day, compared to the system that just uses the simple model. This benefit occurs while the system complexity decreases by 27.7% compared to the complex model. After adding the two-level (simple and complex) anomaly-detection, the complexity is tuned between 72.3% and 37.6% of the complex model. Similarly, the sensitivity is tuned between 66.5% and 60.3%.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
anomaly detection, epileptic seizures, low-power, self-awareness, wearable devices
host publication
2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021
series title
2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021
article number
9458555
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021
conference location
Washington, United States
conference dates
2021-06-06 - 2021-06-09
external identifiers
  • scopus:85113304022
ISBN
9781665419130
DOI
10.1109/AICAS51828.2021.9458555
language
English
LU publication?
yes
additional info
Funding Information: This work has been partially supported by the ML-Edge Swiss National Science Foundation (NSF) Research project (GA No. 200020182009/1), the PEDESITE Swiss NSF Sinergia project (GA No. SCRSII5 193813/1), the RESoRT project financed by Fondation Botnar (Application no. REG-19-019), and the WASP Program funded by the Knut and Alice Wallenberg Foundation. Publisher Copyright: © 2021 IEEE.
id
0a4fca91-f4af-4c5e-ad57-d75c64ed7701
date added to LUP
2022-01-31 02:09:34
date last changed
2022-04-27 07:28:24
@inproceedings{0a4fca91-f4af-4c5e-ad57-d75c64ed7701,
  abstract     = {{<p>Low-power wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints concerning time and location, on one hand, and fulfilling long-term tracking, on the other hand. In the case of epileptic seizures, as the attacks infrequently occur, using an anomaly detection approach reduces the need to record long hours of data for each patient before detecting the successive coming seizures. In this work, by combining the concepts of self-aware system and anomaly detection, we propose an energy-efficient system to detect epileptic seizures on single-lead electrocardiographic signals, which is personalized after analyzing the first seizure of the patient. This system, then, uses a simple anomaly-detection model, whenever the model is deemed reliable, and uses a more complex model otherwise. We show that after the personalization, the number of patients, for which the method provides high sensitivity, can reach 26 out of 43 patients with the false alarm rate (FAR) of 4 alarms/day. Thus, the number of responders to the system is increased by 24%, while the FAR is only increased by one alarm/day, compared to the system that just uses the simple model. This benefit occurs while the system complexity decreases by 27.7% compared to the complex model. After adding the two-level (simple and complex) anomaly-detection, the complexity is tuned between 72.3% and 37.6% of the complex model. Similarly, the sensitivity is tuned between 66.5% and 60.3%.</p>}},
  author       = {{Forooghifar, Farnaz and Aminifar, Amin and Teijeiro, Tomas and Aminifar, Amir and Jeppesen, Jesper and Beniczky, Sandor and Atienza, David}},
  booktitle    = {{2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021}},
  isbn         = {{9781665419130}},
  keywords     = {{anomaly detection; epileptic seizures; low-power; self-awareness; wearable devices}},
  language     = {{eng}},
  month        = {{06}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021}},
  title        = {{Self-Aware Anomaly-Detection for Epilepsy Monitoring on Low-Power Wearable Electrocardiographic Devices}},
  url          = {{http://dx.doi.org/10.1109/AICAS51828.2021.9458555}},
  doi          = {{10.1109/AICAS51828.2021.9458555}},
  year         = {{2021}},
}