BrainFuseNet: Enhancing Wearable Seizure Detection through EEG-PPG-accelerometer Sensor Fusion and Efficient Edge Deployment

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.