Low-Power Hardware Implementation of a Support Vector Machine Training and Classification for Neural Seizure Detection

Elhosary, Heba; Zakhari, Michael H.; Elgammal, Mohamed A.; Ghany, Mohamed A. Abd El; Salama, Khaled N.; Mostafa, Hassan

doi:10.1109/tbcas.2019.2947044

Cited by 33 publications

(31 citation statements)

References 39 publications

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“…A supervised machine learning algorithm SVM (support vector machine) that was first introduced by Vladimir N. Vapnik et al in 1963 [11] is used in the implemented design. SVM is widely used in statistical classification and regression analysis generally and as it has produced very promising results in detecting and predicting seizures onset [12][13][14]. Learning in SVM is a process in which a hyperplane that separates two labeled sets of training examples is determined.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a hardware implemented automatic seizure detection system using supervised machine learning that utilizes EEG signals is proposed. The proposed system is consolidated as follows: First, Features Extraction for training using Sequential Minimal Optimization (SMO) training accelerators which is used in [14]. Then, Feature extraction for classification through linear Support Vector Machine (SVM) classifier, after that a phase of validation is executed to verify the quality of the implemented system.…”

Section: Introductionmentioning

confidence: 99%

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Hardware Acceleration of High Sensitivity Power-Aware Epileptic Seizure Detection System Using Dynamic Partial Reconfiguration

et al. 2021

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In this paper, a high-sensitivity low-cost power-aware Support Vector Machine (SVM) training and classification based system, is hardware implemented for a neural seizure detection application. The training accelerator algorithm, adopted in this work, is the sequential minimal optimization (SMO). System blocks are implemented to achieve the best trade-off between sensitivity and the consumption of area and power. The proposed seizure detection system achieves 98.38% sensitivity when tested with the implemented linear kernel classifier. The system is implemented on different platforms: such as Field Programmable Gate Array (FPGA) Xilinx Virtex-7 board and Application Specific Integrated Circuit (ASIC) using hardwarecalibrated UMC 65nm CMOS technology. A power consumption evaluation is performed on both the ASIC and FPGA platforms showing that the ASIC power consumption is lower by at least 65% when compared with the FPGA counterpart. A power-aware system is implemented with FPGAs by the adoption of the Dynamic Partial Reconfiguration (DPR) technique that allows the dynamic operation of the system based on power level available to the system at the expense of degradation of the system accuracy. The proposed system exploits the advantages of DPR technology in FPGAs to switch between two proposed designs providing a decrease of 64% in power consumption.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hardware Acceleration of High Sensitivity Power-Aware Epileptic Seizure Detection System Using Dynamic Partial Reconfiguration

et al. 2021

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“…Previous studies primarily engaged in the feature extraction of seizure properties and classification of its activities. Several ML methods are widely employed in automatic epilepsy detection, such as multilayer perceptron (MLP) [5], dual-tree complex wavelet transforms (DTCWTs) [6,7], artificial neural network (ANN) [8], and support vector machine (SVM) 42 [9][10][11][12][13][14][15][16][17][18] ANN, MLP, and DTCWT require extensive training and the feature extraction method leads to a complicated design approach. It was reported in Wang et al [10] that SVM achieves better classification accuracy (ACC) than K-nearestneighbor (KNN), linear discriminant analysis, naïve Bayesian, and logistic regression for epileptic EEG classification.…”

Section: Introductionmentioning

confidence: 99%

“…Feature selection is also a key point of the classification of EEG signals, which reduces the classifier's computational complexity and improves the ACC of classification [35]. Statistical features, such as standard deviation, variance, and sample entropy, have been found to exhibit excellent performance in seizure detection [16,17]. Still, this type of feature calculation in hardware implementation is complex and needs more resources.…”

Section: Introductionmentioning

confidence: 99%

“…Many works have been developed for automatic seizure detection based on EEG and ML software systems [5][6][7][8][9][10]42]. Nevertheless, field-programmable gate array (FPGA) or system on chip (SoC)-based seizure detection systems are more comfortable and flexible for seizure patients to carry all the time [11][12][13][14][15][16][17][18]. In Yoo et al [11], a novel algorithm was implemented to detect seizures using scalp EEG with on-chip linear SVM.…”

Section: Introductionmentioning

confidence: 99%

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Very large scale integration implementation of seizure detection system with on‐chip support vector machine classifier

Shanmugam¹,

Selvathi²

2021

IET Circuits, Devices & Syst

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Epilepsy is one of the most common neurological disorders; it affects millions of people globally. Because of the risks to health that it causes, the study and analysis of epilepsy have been given considerable attention in the biomedical field. In a neurological diagnosis, an automated device for detecting seizures or epilepsy from an electroencephalogram (EEG) signal has a significant role. This research work proposes a very large scale integration implementation system for the automatic detection of seizures. Before classification, feature extraction was performed by discrete wavelet transform (DWT) and on-chip classification was performed by a linear support vector machine. The polyphase architecture of Daubechies fourth-order wavelet three-level DWT was used to minimize computational time. The systolic array architecture-based support vector machine classifier using parallel processing helps to minimize the computational complexity of the proposed method. This research work uses an open access EEG dataset. Hardware implementation was done on a field-programmable gate array (FPGA). Efficient results were produced compared with the existing system on chip (SoC) and FPGA seizure detection systems.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

2022

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Analysis & Sensing www.analysis-sensing.org Review doi.org/10.1002/anse.202200062Wearable devices have received significant attention recently for their ability to monitor critical physiological signals noninvasively, such as electrocardiography, electroencephalography, electromyography, and photoplethysmography. These biointegrated wearable systems can potentially fill gaps in conventional clinical practice by providing highly cost-effective health characterization and portable continuous health monitoring. Further, the physiological signals measured by wearables require post-processing to derive meaningful values, such as heart rate or blood oxygen saturation. This requirement, in conjunction with the smaller form factor and limited sensor count of the miniaturized systems, often necessitates robust signal processing and data analysis to approach the stringent performance specifications of conventional medical devices, and machine learning techniques have found success in filling this analytical role for their ability to learn complex functional relationships. Thus, this review outlines a systematic summary of the latest research on various wearable devices and their biosignal sensing and signal processing methods, emphasizing machine learning. We also discuss the developmental challenges and advantages of current machine-learning methods, while suggesting research directions for future studies.

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Low-Power Hardware Implementation of a Support Vector Machine Training and Classification for Neural Seizure Detection

Cited by 33 publications

References 39 publications

Hardware Acceleration of High Sensitivity Power-Aware Epileptic Seizure Detection System Using Dynamic Partial Reconfiguration

Hardware Acceleration of High Sensitivity Power-Aware Epileptic Seizure Detection System Using Dynamic Partial Reconfiguration

Very large scale integration implementation of seizure detection system with on‐chip support vector machine classifier

Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

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