# Energy-Efficient Seizure Detection Suitable for low-power Applications

arXiv: https://arxiv.org/abs/2406.16948 · HTML: https://mlsanity.com/paper/2406.16948/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Temporal Convolution for Real-time Keyword Spotting on Mobile Devices](https://mlsanity.com/paper/1904.03814/) (2019)
- [AutoKWS: Keyword Spotting with Differentiable Architecture Search](https://mlsanity.com/paper/2009.03658/) (2020)
- [Hardware Accelerator and Neural Network Co-Optimization for Ultra-Low-Power Audio Processing Devices](https://mlsanity.com/paper/2209.03807/) (2022)
- [Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs](https://mlsanity.com/paper/2310.07895/) (2023)
- [A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models](https://mlsanity.com/paper/1808.03630/) (2018)
- [Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller](https://mlsanity.com/paper/1806.04549/) (2018)

## Abstract (arXiv)

Epilepsy is the most common, chronic, neurological disease worldwide and is typically accompanied by reoccurring seizures. Neuro implants can be used for effective treatment by suppressing an upcoming seizure upon detection. Due to the restricted size and limited battery lifetime of those medical devices, the employed approach also needs to be limited in size and have low energy requirements. We present an energy-efficient seizure detection approach involving a TC-ResNet and time-series analysis which is suitable for low-power edge devices. The presented approach allows for accurate seizure detection without preceding feature extraction while considering the stringent hardware requirements of neural implants. The approach is validated using the CHB-MIT Scalp EEG Database with a 32-bit floating point model and a hardware suitable 4-bit fixed point model. The presented method achieves an accuracy of 95.28%, a sensitivity of 92.34% and an AUC score of 0.9384 on this dataset with 4-bit fixed point representation. Furthermore, the power consumption of the model is measured with the low-power AI accelerator UltraTrail, which only requires 495 nW on average. Due to this low-power consumption this classification approach is suitable for real-time seizure detection on low-power wearable devices such as neural implants.