VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability
Abstract : Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consists of a feature extraction (FE) module and an SVM module. The FE module performs the threelevel Daubechies discrete wavelet transform to fit the physiological bands of the electroencephalogram (EEG) signal and extracts the time–frequency domain features reflecting the nonstationary signal properties. The SVM module integrates the modified sequential minimal optimization algorithm with the table-driven-based Gaussian kernel to enable efficient on-chip learning. The presented design is verified on an Altera Cyclone II field-programmable gate array and tested using the two publicly available EEG datasets. Experiment results show that the designed VLSI system improves the detection accuracy and training efficiency.
? The conventional implementation of most classification algorithms is resource intensive such that devices in existence today sacrifice the classification accuracy and latency to meet the power and size constraints.
? However, the existing closed-loop neuromodulation devices are too simplistic and lack sufficient on-chip processing and intelligence.
? The existing closed-loop devices mainly rely on simple comparison of a pre-selected biomarker (typically from 1 out of 4 channels) against a fixed threshold.
? Next-generation closed-loop neuromodulation systems will require significant improvements in the existing devices.
? The impact of multiplication can be substantially less than in typical voltage-based circuits.
? However, training classical SVM is solving a quadratic programming (QP) problem which is computationally complex and energy-consuming, so integrating an efficient SVM training algorithm is very important.
? Multiplication substantially increases the dynamic range required, which has a severe impact on voltage-based analog circuits.
? Moreover, recent studies report the impact of using temporal patterns delivered via multiple contacts in enhancing plasticity and symptom relief , highlighting the benefits of high-channel-count stimulation.
• To facilitate a fair comparison and guide design choices among various on-chip classifiers, we propose a new energy-area (E-A) efficiency figure of merit that evaluates hardware efficiency and multi-channel scalability.
• The design proposed is an 8-channel closed loop neuromodulation system for DBS, that was verified using stereo-EEG (sEEG) electrodes.
• In this work, we propose the Depth-Variant Tree Ensemble (DVTE), a novel low-latency variation of conventional ensemble methods.
• We implemented the DVTE classifier in hardware to demonstrate the efficacy of the proposed cost-aware learning approach.
? This paper proposes a VLSI design of nonlinear SVM-based seizure detection system, which integrates the MSMO algorithm and the db4 DWT for achieving efficient on-chip training capability and high detection accuracy.
? The proposed detection system has better performance on Specific Dataset than Randomized Dataset in both training efficiency and detection accuracy.
? The boundaries are also used to check the optimality of the samples, which avoids the optimality-satisfaction assumption in the SMO algorithm and consequently requires fewer iterations and performs more efficiently on all the benchmark datasets.
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