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

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  • Farnaz Forooghifar, Embedded Systems Laboratory
  • ,
  • Amin Aminifar, Western Norway University of Applied Sciences
  • ,
  • Tomas Teijeiro, Lund University
  • ,
  • Amir Aminifar, Aarhus Universitet
  • ,
  • Jesper Jeppesen
  • Sandor Beniczky
  • David Atienza, Embedded Systems Laboratory

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%.

OriginalsprogEngelsk
Titel2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021
Antal sider4
ForlagIEEE
Udgivelsesårjun. 2021
Artikelnummer9458555
ISBN (trykt)978-1-6654-3025-8
ISBN (Elektronisk)9781665419130
DOI
StatusUdgivet - jun. 2021
Begivenhed3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021 - Washington, USA
Varighed: 6 jun. 20219 jun. 2021

Konference

Konference3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021
LandUSA
ByWashington
Periode06/06/202109/06/2021

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