Compressed Sensing Based Seizure Detection for an Ultra Low Power Multi-core Architecture

Roghayeh Aghazadeh, Fabio Montagna, Simone Benatti, Davide Rossi, Javad Frounchi
2018 2018 International Conference on High Performance Computing & Simulation (HPCS)  
Extracting information from brain signals in advanced Brain Machine Interfaces (BMI) often requires computationally demanding processing. The complexity of the algorithms traditionally employed to process multi-channel neural data, such as Principal Component Analysis (PCA), dramatically increases while scaling-up the number of channels and requires more power-hungry computational platforms. This could hinder the development of low-cost and low-power interfaces which can be used in wearable or
more » ... mplantable real-time systems. This work proposes a new algorithm for the detection of epileptic seizure based on compressively sensed EEG information, and its optimization on a low-power multi-core SoC for near-sensor data analytics: Mr. Wolf. With respect to traditional algorithms based on PCA, the proposed approach reduces the computational complexity by 4.4x in ARM Cortex M4-based MCU. Implementing this algorithm on Mr.Wolf platform allows to detect a seizure with 1 ms of latency after acquiring the EEG data for 1 s, within an energy budget of 18.4 µJ. A comparison with the same algorithm on a commercial MCU shows an improvement of 6.9x in performance and up to 18.4x in terms of energy efficiency.
doi:10.1109/hpcs.2018.00083 dblp:conf/ieeehpcs/AghazadehMBRF18 fatcat:sv6u6jm5bbexle2cm6rpxia2gu