Paper Info
Title
Compressed Sensing Based Seizure Detection for an Ultra Low Power Multi-core Architecture.
Abstract
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 implantable 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.
Year
Venue
Field
2018
HPCS
ARM architecture,Data analysis,Efficient energy use,Computer science,Support vector machine,Real-time computing,Feature extraction,Multi-core processor,Compressed sensing,Computational complexity theory
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Roghayeh Aghazadeh100.34
Fabio Montagna263.59
Simone Benatti38716.94
Davide Rossi441647.47
Javad Frounchi5244.78