Key result
An FPGA-based digital system using the Hjorth descriptor and KNN achieved up to 90.74% accuracy in classifying normal, inter-ictal, and ictal EEG within 0.015 seconds.
Why the study?
Visual inspection of EEG for epilepsy diagnosis is difficult, time-consuming, and cannot be conducted in real time.
An FPGA-based system using Hjorth descriptors and KNN can classify epileptic EEG signals in real-time with high accuracy and low resource utilization.
May enable real-time FPGA EEG classification; leaves open prospective clinical validation in epilepsy monitoring.
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.
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Rizal et al. (2022) studied Epilepsy. FPGA-based digital system using Hjorth descriptor and KNN was evaluated on Accuracy in normal, inter-ictal, and ictal EEG classifications. An FPGA-based digital system using the Hjorth descriptor and KNN achieved up to 90.74% accuracy in classifying normal, inter-ictal, and ictal EEG within 0.015 seconds.
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