FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN
Abstract
:1. Introduction
2. Material and Methods
2.1. Dataset Description
2.2. Feature Extraction
2.3. KNN Classifier
2.4. System Model
3. FPGA Hardware Architecture
3.1. Feature Extraction Datapath Architecture
3.2. KNN Processor Architecture
3.3. Simulation and Verification
4. FPGA Implementation
4.1. Experimental Setup
4.2. Resource Consumption
5. Results and Discussion
5.1. Dataset
5.2. Comparison of Hjorth Parameter Calculations between VHDL and Python
5.3. Classification Accuracy
5.4. Comparison with Previous Research
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Configuration | Correct Result | Accuracy | |||
---|---|---|---|---|---|
Ictal | Interictal | Normal | Total | ||
K = 1 | 30 | 33 | 29 | 92 | 85.18% |
K = 3 | 30 | 34 | 32 | 96 | 88.89% |
K = 5 | 31 | 34 | 31 | 96 | 88.89% |
K = 7 | 31 | 35 | 31 | 97 | 89.81% |
K = 9 | 31 | 35 | 32 | 98 | 90.74% |
Reference | Method | Classifier | Dataset | Result |
---|---|---|---|---|
Meddah et al., 2020 [33] | DWT, PCA | SVM | Bonn University, 2 classes (O+ Z, S) | 98.67% |
Jose et al., 2020 [18] | Energy, PSD, spectral entropy of EEG sub-band | ELM | Bonn University, 2 classes (S, Z) | 98.5% |
Sarić et al., 2020 [17] | Time–frequency features of CWT | MLP-ANN | TUH EEG Corpus, 3 classes (FNS, GNSZ, NS) | 95.14% |
Sahani et al., 2021 [19] | Optimized VMD | Semi-supervised reduced deep CNN (RDCNN) | Bonn University, 2 classes (S, Z) | 99.37% |
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Rizal, A.; Hadiyoso, S.; Ramdani, A.Z. FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN. Electronics 2022, 11, 3026. https://doi.org/10.3390/electronics11193026
Rizal A, Hadiyoso S, Ramdani AZ. FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN. Electronics. 2022; 11(19):3026. https://doi.org/10.3390/electronics11193026
Chicago/Turabian StyleRizal, Achmad, Sugondo Hadiyoso, and Ahmad Zaky Ramdani. 2022. "FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN" Electronics 11, no. 19: 3026. https://doi.org/10.3390/electronics11193026
APA StyleRizal, A., Hadiyoso, S., & Ramdani, A. Z. (2022). FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN. Electronics, 11(19), 3026. https://doi.org/10.3390/electronics11193026