ECG and EEG signals are very helpful in the early diagnosis of epileptic seizures. The research focuses on analysis of ECG and EEG signals applying a deep learning technique to study early prediction of epileptic seizure. Signal processing methods like Empirical Mode Decomposition, spectral analysis, and statistical methods were used. The algorithms were implemented in MATLAB, and the EEG and ECG data were collected from Physiobank and EPILEPSIAE databases.In the window-based analysis of low-frequency spectral area of EEG signals, 78.5% of the cases displayed a significant change as the windows progressed and the onset of seizure was approached. The spectral area of IMF components indicated a possible seizure prediction in 68.9% of the analyzed cases. Considering signals from individual EEG electrodes, the least percentage of seizure prediction was indicated by signals from T4 and F4 electrodes (52.3% and 40.7%, respectively, for spectral peaks and 23.8% and 29.6%, respectively, for spectral area). The results of regression analysis show that prediction of seizures can be possible around 20-30 minutes prior to the actual occurrence of seizures. KEYWORDS Electrocardiogram (ECG), Electroensephalogram (EEG), Empirical Mode Decomposition (EMD), epileptic seizure, machine learning 1 Concurrency Computat Pract Exper. 2020;32:e5111.wileyonlinelibrary.com/journal/cpe
This is a protocol for an progressive muscle strength training in a new domain of adaptive robot-assisted training interactions. The protocol was defined based on sports science literature, where the participants would be performing maximum voluntary contraction (MVC) trials at first, then taking a recovery break, then starting with a low-intensity task, and then gradually increasing the difficulty level. The protocol included a preparation stage, followed by initial measurements, a familiarisation session, and finally a performance session.
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