Comput. Biol. Med. - A novel real-time patient-specific seizure diagnosis algorithm based on analysis of EEG and ECG signals using spectral and spatial features and improved particle swarm optimization classifier.

Tópicos

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Resumo

This paper proposes a novel real-time patient-specific seizure diagnosis algorithm based on analysis of electroencephalogram (EEG) and electrocardiogram (ECG) signals to detect seizure onset. In this algorithm, spectral and spatial features are selected from seizure and non-seizure EEG signals by Gabor functions and principal component analysis (PCA). Furthermore, four features based on heart rate acceleration are extracted from ECG signals to form feature vector. Then a neural network classifier based on improved particle swarm optimization (IPSO) learning algorithm is developed to determine an optimal nonlinear decision boundary. This classifier allows to adjust the parameters of the neural network classifier, efficiently. This algorithm can automatically detect the presence of seizures with minimum delay which is an important factor from a clinical viewpoint. The performance of the proposed algorithm is evaluated on a dataset consisting of 154 h records and 633 seizures from 12 patients. The results indicate that the algorithm can recognize the seizures with the smallest latency and higher good detection rate (GDR) than other presented algorithms in the literature.

Resumo Limpo

paper propos novel realtim patientspecif seizur diagnosi algorithm base analysi electroencephalogram eeg electrocardiogram ecg signal detect seizur onset algorithm spectral spatial featur select seizur nonseizur eeg signal gabor function princip compon analysi pca furthermor four featur base heart rate acceler extract ecg signal form featur vector neural network classifi base improv particl swarm optim ipso learn algorithm develop determin optim nonlinear decis boundari classifi allow adjust paramet neural network classifi effici algorithm can automat detect presenc seizur minimum delay import factor clinic viewpoint perform propos algorithm evalu dataset consist h record seizur patient result indic algorithm can recogn seizur smallest latenc higher good detect rate gdr present algorithm literatur

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