EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems

• URL: http://arxiv.org/abs/2309.07135v1
• Date: Mon, 28 Aug 2023 11:29:51 GMT
• Title: EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems
• Authors: Thorir Mar Ingolfsson, Upasana Chakraborty, Xiaying Wang, Sandor Beniczky, Pauline Ducouret, Simone Benatti, Philippe Ryvlin, Andrea Cossettini and Luca Benini
• Abstract summary: This paper introduces EpiDeNet, a new lightweight seizure detection network. Sensitivity-Specificity Weighted Cross-Entropy (SSWCE) is a new loss function that incorporates sensitivity and specificity. A three-window majority voting-based smoothing scheme combined with the SSWCE loss achieves 3x reduction of false positives to 1.18 FP/h.
• Score: 9.525786920713763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Epilepsy is a prevalent neurological disorder that affects millions of individuals globally, and continuous monitoring coupled with automated seizure detection appears as a necessity for effective patient treatment. To enable long-term care in daily-life conditions, comfortable and smart wearable devices with long battery life are required, which in turn set the demand for resource-constrained and energy-efficient computing solutions. In this context, the development of machine learning algorithms for seizure detection faces the challenge of heavily imbalanced datasets. This paper introduces EpiDeNet, a new lightweight seizure detection network, and Sensitivity-Specificity Weighted Cross-Entropy (SSWCE), a new loss function that incorporates sensitivity and specificity, to address the challenge of heavily unbalanced datasets. The proposed EpiDeNet-SSWCE approach demonstrates the successful detection of 91.16% and 92.00% seizure events on two different datasets (CHB-MIT and PEDESITE, respectively), with only four EEG channels. A three-window majority voting-based smoothing scheme combined with the SSWCE loss achieves 3x reduction of false positives to 1.18 FP/h. EpiDeNet is well suited for implementation on low-power embedded platforms, and we evaluate its performance on two ARM Cortex-based platforms (M4F/M7) and two parallel ultra-low power (PULP) systems (GAP8, GAP9). The most efficient implementation (GAP9) achieves an energy efficiency of 40 GMAC/s/W, with an energy consumption per inference of only 0.051 mJ at high performance (726.46 MMAC/s), outperforming the best ARM Cortex-based solutions by approximately 160x in energy efficiency. The EpiDeNet-SSWCE method demonstrates effective and accurate seizure detection performance on heavily imbalanced datasets, while being suited for implementation on energy-constrained platforms.

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