Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection
- Author(s): Yuanfa Wang 1 ; Zunchao Li 1 ; Lichen Feng 1 ; Hailong Bai 1 ; Chuang Wang 1
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View affiliations
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Affiliations:
1:
School of Microelectronics , Xi'an Jiaotong University , No. 28, Xianning West Road, Xi'an , People's Republic of China
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Affiliations:
1:
School of Microelectronics , Xi'an Jiaotong University , No. 28, Xianning West Road, Xi'an , People's Republic of China
- Source:
Volume 12, Issue 1,
January
2018,
p.
108 – 115
DOI: 10.1049/iet-cds.2017.0216 , Print ISSN 1751-858X, Online ISSN 1751-8598
An automatic detection system for distinguishing healthy, ictal, and inter-ictal EEG signals plays an important role in medical practice. This paper presents a very large scale integration (VLSI) architecture of three-class classification for epilepsy and seizure detection. In order to find out the most efficient three-class classification scheme for hardware implementation, several multiclass non-linear support vector machine (NLSVM) classifiers are compared and validated using software implementation. Finally, the one-against-one (OAO) multiclass NLSVM is selected due to its highest accuracy. The designed system consists of a discrete wavelet transform (DWT)-based feature extraction module, a modified sequential minimal optimization (MSMO) training module, and an OAO multiclass classification module. A lifting structure of Daubechies order 4 wavelet is introduced in three-level DWT to save circuit area and speed up the computational time. The MSMO is used for on-chip training. The circuit of the largest absolute value decision is designed to avoid the unclassifiable problem in the OAO multiclass classification. The designed system is implemented on a field-programmable gate array (FPGA) platform and evaluated using the publicly available epilepsy dataset. The experimental results demonstrate that the designed system achieves high accuracy with low-dimensional feature vectors.
Inspec keywords: electroencephalography; medical signal processing; support vector machines; feature extraction; discrete wavelet transforms
Other keywords: Daubechies order 4-wavelet; original sequential minimal optimisation; multiclass SVM classification; multiclass NLSVM classifiers; epileptic seizure detection; three-level DWT; hardware design; FPGA platform; three-class classification scheme; Lagrange multipliers; multiclass nonlinear support vector machine classifiers; automatic detection system; software implementation; discrete wavelet transform; DWT-based feature extraction module; absolute value decision; epilepsy; low-dimensional feature vectors; OAO multiclass classification module; inter-ictal EEG signals; healthy EEG signals; OAO multiclass NLSVM; modified sequential minimal optimisation; MSMO training module; on-chip training; hardware implementation; three-class classification VLSI system; one-against-one multiclass NLSVM
Subjects: Knowledge engineering techniques; Signal processing and detection; Digital signal processing; Electrodiagnostics and other electrical measurement techniques; Biology and medical computing; Bioelectric signals; Integral transforms; Integral transforms; Function theory, analysis
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