Team Members: Mahsa Shoaran, Benyamin A. Haghi, Milad Taghavi, Masoud Farivar
Epilepsy is a common neurological disorder affecting over 50 million people in the world. Approximately one third of epileptic patients exhibit seizures that are not controlled by medication. Despite substantial innovations in anti-seizure drug therapy, the proportion of patients with uncontrolled epilepsy has not changed, emphasizing the need for new treatment strategies. The development of new devices capable of performing a rapid and reliable seizure detection followed by brain stimulation holds great promises for improving the quality of life of millions of people with epileptic seizures worldwide.
The high density of neurons in neurobiological tissue requires a large number of electrodes to obtain the most accurate representation of neural activity and provide better control over the location of the stimulation sites or resected epileptic tissue. However, integrating hundreds of acquisition channels at relatively high sampling rates on-chip requires some type of data compression within the sensors to comply with the stringent bandwidth limitations for wireless transmission. In addition, a small size of the implantable system is critical to minimize potential clinical issues associated with implantation, while the total power consumption should be minimized to avoid heat generation inside the brain.
We investigated and evaluated common machine learning methods with focus on hardware complexity and overall performance. We recently developed a closed-loop seizure detection and prevention system. Combined with a novel and efficient feature extraction model, we showed that these classifiers quickly become competitive with more complex learning models, with only a small number of low-depth shallow decision trees. Fig.1 and Fig.3 show the proposed classifier which is fabricated in a 65nm TSMC process, consuming 41.2 nJ/class in a total area of 5401850. It supports 32 iEEG channels, and major frequency and time domain features (such as line length and different frequency bands). The proposed architecture is evaluated in automated seizure detection for epilepsy, using 3074 hours of intracranial EEG data from 26 patients with 393 seizures. Fig.2 show the performance evaluation of the algorithm. Fig.2a shows the average performance vs. number and depth of trees. Fig.2b show the average performance of different classifiers on different patients using iEEG data. Fig.2c shows the feature importance. Finally, latency of the algorithm vs. patient number has been shown on fig.2d. This patient-specific and energy-quality scalable classifier holds great promise for low-power sensor data classification in biomedical applications.
- Shoaran, B. A. Haghi, M. Taghavi, M. Farivar, and A. Emami, “Energy-Efficient Classification for Resource-Constrained Biomedical Applications”, IEEE Journal on Emerging and Selected Topics in Circuit and Systems, DOI 10.1109/JETCAS.2018.2844733
- Taghavi, B. A. Haghi, M. Shoaran, M. Farivar, and A. Emami, “A 41.2 nJ/class, 32-channel on-chip classifier for epileptic seizure detection,” Int. Conf. IEEE Eng. Medicine and Biology Society (EMBC), 2018.
- Shoaran, B. A. Haghi, M. Farivar, A. Emami, “Efficient Feature Extraction and Classification Methods in Neural Interfaces,” the Bridge, National Academy of Engineering, vol. 47, no. 4, pp. 31-35, Winter 2017.