BrainFuseNet: Enhancing Wearable Seizure Detection Through EEG-PPG-Accelerometer Sensor Fusion and Efficient Edge Deployment

Thorir Mar Ingolfsson, Xiaying Wang, Upasana Chakraborty, Simone Benatti, Adriano Bernini, Pauline Ducouret, Philippe Ryvlin, Sándor Beniczky, Luca Benini, Andrea Cossettini

Abstract: This paper introduces BrainFuseNet , a novel lightweight seizure detection network based on the sensor fusion of electroencephalography (EEG) with photoplethysmography (PPG) and accelerometer (ACC) signals, tailored for low-channel count wearable systems. BrainFuseNet utilizes the Sensitivity-Specificity Weighted Cross-Entropy (SSWCE), an innovative loss function incorporating sensitivity and specificity, to address the challenge of heavily unbalanced datasets. The BrainFuseNet -SSWCE approach successfully detects $93.5\%$ seizure events on the CHB-MIT dataset ( $76.34\%$ sample-based sensitivity), for EEG-based classification with only four channels. On the PEDESITE dataset, we demonstrate a sample-based sensitivity and false positive rate of $60.66\%$ and $1.18$ FP/h, respectively, when considering EEG data alone. Additionally, we demonstrate that integrating PPG signals increases the sensitivity to $61.22\%$ (successfully detecting $92\%$ seizure events) while decreasing the number of false positives to $1.0$ FP/h. Finally, when ACC data are also considered, the sensitivity increases to $64.28\%$ (successfully detecting $95\%$ seizure events) and the number of false positives drops to only $0.21$ FP/h for sample-based estimations, with less than one false alarm per day when considering event-based estimations. BrainFuseNet is resource-friendly and well-suited for implementation on low-power embedded platforms, and we evaluate its performance on GAP9, a state-of-the-art parallel ultra-low power (PULP) microcontroller for tiny Machine Learning applications on wearables. The implementation on GAP9 achieves an energy efficiency of $21.43$ GMAC/s/W, with an energy consumption per inference of only $0.11$ mJ at high performance ( $412.54$ MMAC/s). The BrainFuseNet -SSWCE method demonstrates effective and accurate seizure detection on heavily imbalanced datasets while achieving state-of-the-art performance in the false positive rate and being well-suited for deployment on energy-constrained edge devices.