FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN
The EEG is one of the main medical instruments used by clinicians in the analysis and diagnosis of epilepsy through visual observations or computers. Visual inspection is difficult, time-consuming, and cannot be conducted in real time. Therefore, we propose a digital system for the classification of...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/19/3026 |
| _version_ | 1797479871045173248 |
|---|---|
| author | Achmad Rizal Sugondo Hadiyoso Ahmad Zaky Ramdani |
| author_facet | Achmad Rizal Sugondo Hadiyoso Ahmad Zaky Ramdani |
| author_sort | Achmad Rizal |
| collection | DOAJ |
| description | The EEG is one of the main medical instruments used by clinicians in the analysis and diagnosis of epilepsy through visual observations or computers. Visual inspection is difficult, time-consuming, and cannot be conducted in real time. Therefore, we propose a digital system for the classification of epileptic EEG in real time on a Field Programmable Gate Array (FPGA). The implemented digital system comprised a communication interface, feature extraction, and classifier model functions. The Hjorth descriptor method was used for feature extraction of activity, mobility, and complexity, with KNN was utilized as a predictor in the classification stage. The proposed system, run on a The Zynq-7000 FPGA device, can generate up to 90.74% accuracy in normal, inter-ictal, and ictal EEG classifications. FPGA devices provided classification results within 0.015 s. The total memory LUT resource used was less than 10%. This system is expected to tackle problems in visual inspection and computer processing to help detect epileptic EEG using low-cost resources while retaining high performance and real-time implementation. |
| first_indexed | 2024-03-09T21:52:01Z |
| format | Article |
| id | doaj.art-94a5c5cb75cb4e539772ebb54380e44f |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T21:52:01Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-94a5c5cb75cb4e539772ebb54380e44f2023-11-23T20:05:03ZengMDPI AGElectronics2079-92922022-09-011119302610.3390/electronics11193026FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNNAchmad Rizal0Sugondo Hadiyoso1Ahmad Zaky Ramdani2School of Electrical Engineering, Telkom University, Bandung 40257, IndonesiaSchool of Applied Science, Telkom University, Bandung 40257, IndonesiaSchool of Applied Science, Telkom University, Bandung 40257, IndonesiaThe EEG is one of the main medical instruments used by clinicians in the analysis and diagnosis of epilepsy through visual observations or computers. Visual inspection is difficult, time-consuming, and cannot be conducted in real time. Therefore, we propose a digital system for the classification of epileptic EEG in real time on a Field Programmable Gate Array (FPGA). The implemented digital system comprised a communication interface, feature extraction, and classifier model functions. The Hjorth descriptor method was used for feature extraction of activity, mobility, and complexity, with KNN was utilized as a predictor in the classification stage. The proposed system, run on a The Zynq-7000 FPGA device, can generate up to 90.74% accuracy in normal, inter-ictal, and ictal EEG classifications. FPGA devices provided classification results within 0.015 s. The total memory LUT resource used was less than 10%. This system is expected to tackle problems in visual inspection and computer processing to help detect epileptic EEG using low-cost resources while retaining high performance and real-time implementation.https://www.mdpi.com/2079-9292/11/19/3026EEGepilepticdigital systemFPGAreal-time |
| spellingShingle | Achmad Rizal Sugondo Hadiyoso Ahmad Zaky Ramdani FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN Electronics EEG epileptic digital system FPGA real-time |
| title | FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN |
| title_full | FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN |
| title_fullStr | FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN |
| title_full_unstemmed | FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN |
| title_short | FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN |
| title_sort | fpga based implementation for real time epileptic eeg classification using hjorth descriptor and knn |
| topic | EEG epileptic digital system FPGA real-time |
| url | https://www.mdpi.com/2079-9292/11/19/3026 |
| work_keys_str_mv | AT achmadrizal fpgabasedimplementationforrealtimeepilepticeegclassificationusinghjorthdescriptorandknn AT sugondohadiyoso fpgabasedimplementationforrealtimeepilepticeegclassificationusinghjorthdescriptorandknn AT ahmadzakyramdani fpgabasedimplementationforrealtimeepilepticeegclassificationusinghjorthdescriptorandknn |