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.

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