Seizure Detection Algorithm Based on Hybrid Morphological Filtering Optimization Multiscale Amplitude Integrated EEG

Automatic seizure detection algorithms have significant implications for improving patients’ quality of life. However, the absence of multiscale analysis in these algorithms regarding electroencephalography (EEG) signals may lead to overlooking crucial seizure features. To address this limitation, our study aims to develop a novel seizure detection algorithm based on hybrid morphological filtering optimization multiscale amplitude integrated EEG (aEEG). First, multichannel EEG signals, collected by EEG sensors, are preprocessed with asymmetric filtering, rectification, envelope detection, multiscale time compression and signal smoothing to obtain multiscale aEEG. Second, morphological filter (MF), multiscale MF (MSMF), and average combination difference MF (ACDMF) are hybridized to filter, analyze, and reconstruct the signals, thereby emphasizing the seizures. Thereafter, optimal aEEG signal is selected using the channel and time compression scale (TCS) joint optimization algorithm based on the kappa (Ka), and this signal is utilized for seizure detection. Finally, it is evaluated on the CHB-MIT database using multiple validation methods, achieving satisfactory experimental results. The results indicate that the model attains accuracy (ACC), specificity (SPE), sensitivity (SEN), <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}{1}$ </tex-math></inline-formula> score (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}{1}$ </tex-math></inline-formula>), and Ka of 97.60%, 96.56%, 98.63%, 97.60%, and 95.19%, respectively.