Paper Info
Title
Scalable And Energy Efficient Seizure Detection Based On Direct Use Of Compressively-Sensed Eeg Data On An Ultra Low Power Multi-Core Architecture
Abstract
Extracting information from dense multi-channel neural sensors for accurate diagnosis of brain disorders necessitates computationally expensive and advanced signal processing approaches to analyze the massive volume of recorded data. Compressive Sensing (CS) is an efficient method for reducing the computational complexity and power consumption in the resource-constrained multi-site neural systems. However, reconstructing the signal from compressed measurements is computationally intensive, making it unsuitable for real-time applications such as seizure detection. In this paper, a seizure detection algorithm is proposed to overcome these limitations by circumventing the reconstruction phase and directly processing the compressively sampled EEG signals. The Lomb-Scargle Periodogram (LSP) is used to extract the spectral energy features of the compressed data. Performance of the seizure detector using non-linear support vector machine (SVM) classifier, tested on 24 patients of the CHB-MIT data-set for compression ratios (CR) of 1-64x, is 96-93%, 92-87%, 0.95-0.91, and <1 s for sensitivity, accuracy, the area under the curve, and latency, respectively. A power-efficient classification method based on the utilization of dual linear SVM classifiers is proposed. The proposed classification method based on the dual linear SVM classification achieved better classification performance compared to commonly used classifiers, such as K-nearest neighbor, random forest, artificial neural network, and linear SVM, while consuming low power in comparison to non-linear SVM kernels. The hardware-optimized implementation of this algorithm is proposed on a low-power multi-core SoC for near-sensor data analytics: Mr. Wolf. Optimized implementation of this algorithm on Mr. Wolf platform leads to detecting a seizure with an energy budget of 18.4 mu J and 3.9 mu J for a compression ratio of 24x using non-linear SVM classifier and the dual linear SVM based classification method, respectively.
Year
DOI
Venue
2020
10.1016/j.compbiomed.2020.104004
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
EEG, Compressed sensing, Lomb-scargle periodogram, Support vector machine, Embedded systems, Ultra-low power
Journal
125
ISSN
Citations 
PageRank 
0010-4825
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Roghayeh Aghazadeh100.34
Javad Frounchi2244.78
Fabio Montagna363.59
Simone Benatti48716.94