作者: Varun Bajaj , Ram Bilas Pachori
DOI: 10.1007/S13534-013-0084-0
关键词: Electroencephalography 、 Temporal lobe 、 Pattern recognition 、 Computer science 、 EEG-fMRI 、 Epileptic seizure 、 Hilbert–Huang transform 、 Analytic signal 、 Epilepsy 、 Artificial intelligence 、 Anesthesia 、 Word error rate 、 Biomedical engineering
摘要: Epileptic seizure is generated by abnormal synchronization of neurons the cerebral cortex patients, which commonly detected electroencephalograph (EEG) signals. In this paper, intracranial EEG signals have been used to detect focal temporal lobe epilepsy. This paper presents a new method based on empirical mode decomposition (EMD) for detection epileptic seizures. The proposed uses Hilbert transformation intrinsic functions (IMFs), obtained EMD process that provides analytic signal representation IMFs. instantaneous area measured from trace windowed IMFs rules-based experiment results are included show effectiveness performance evaluation has performed computing sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative (NPV) and error rate (ERD). compared existing methods detecting epilepsy provided with increased accuracy.