Automated Real-Time Epileptic Seizure Detection in Scalp EEG Recordings Using an Algorithm Based on Wavelet Packet Transform

• Published in: IEEE transactions on biomedical engineering Vol. 57; no. 7; pp. 1639 - 1651
• Main Authors: Zandi, Ali Shahidi, Javidan, Manouchehr, Dumont, Guy A., Tafreshi, Reza
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; Algorithms; Biological and medical sciences; Delay; EEG; Electrodiagnosis. Electric activity recording; Electroencephalography; Electroencephalography - methods; Epilepsy; Epilepsy - diagnosis; Epilepsy - physiopathology; Female; Frequency measurement; Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy; Humans; Investigative techniques, diagnostic techniques (general aspects); Male; Medical sciences; Middle Aged; Nervous system; Nervous system (semeiology, syndromes); Neurology; Pattern Recognition, Automated - methods; Predictive Value of Tests; Scalp; seizure detection; seizure focus lateralization; Signal Processing, Computer-Assisted; Testing; Wavelet analysis; Wavelet coefficients; wavelet packet (WP) transform; Wavelet packets; Wavelet transforms; seizure focus lateralization; Nervous system diseases; Epilepsy; EEG; Electrophysiology; wavelet packet (WP) transform; Electroencephalography; Algorithm; Cerebral disorder; Electrodiagnosis; seizure detection; Convulsion; Wavelet transformation; Central nervous system disease; Signal processing; Real time processing; Neurological disorder; Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2010.2046417

A novel wavelet-based algorithm for real-time detection of epileptic seizures using scalp EEG is proposed. In a moving-window analysis, the EEG from each channel is decomposed by wavelet packet transform. Using wavelet coefficients from seizure and nonseizure references, a patient-specific measure is developed to quantify the separation between seizure and nonseizure states for the frequency range of 1-30 Hz. Utilizing this measure, a frequency band representing the maximum separation between the two states is determined and employed to develop a normalized index, called combined seizure index (CSI). CSI is derived for each epoch of every EEG channel based on both rhythmicity and relative energy of that epoch as well as consistency among different channels. Increasing significantly during ictal states, CSI is inspected using one-sided cumulative sum test to generate proper channel alarms. Analyzing alarms from all channels, a seizure alarm is finally generated. The algorithm was tested on scalp EEG recordings from 14 patients, totaling 75.8 h with 63 seizures. Results revealed a high sensitivity of 90.5% , a false detection rate of 0.51 h-1 and a median detection delay of 7 s. The algorithm could also lateralize the focus side for patients with temporal lobe epilepsy.