DocumentCode :
662950
Title :
Early detection of human epileptic seizures based on intracortical local field potentials
Author :
Park, Y.S. ; Hochberg, Leigh R. ; Eskandar, Emad N. ; Cash, Sydney S. ; Truccolo, Wilson
Author_Institution :
Brown Inst. for Brain Sci., Brown Univ., Providence, RI, USA
fYear :
2013
fDate :
6-8 Nov. 2013
Firstpage :
323
Lastpage :
326
Abstract :
The unpredictability of re-occurring seizures dramatically impacts the quality of life and autonomy of people with epilepsy. Reliable early seizure detection could open new therapeutic possibilities and thus substantially improve quality of life and autonomy. Though many seizure detection studies have shown the potential of scalp electroencephalogram (EEG) and intracranial EEG (iEEG) signals, reliable early detection of human seizures remains elusive in practice. Here, we examined the use of intracortical local field potentials (LFPs) recorded from 4×4-mm2 96-microelectrode arrays (MEA) for early detection of human epileptic seizures. We adopted a framework consisting of (1) sampling of intracortical LFPs; (2) denoising of LFPs with the Kalman filter; (3) spectral power estimation in specific frequency bands using 1-sec moving time windows; (4) extraction of statistical features, such as the mean, variance, and Fano factor (calculated across channels) of the power in each frequency band; and (5) cost-sensitive support vector machine (SVM) classification of ictal and interictal samples. We tested the framework in one-participant dataset, including 4 seizures and corresponding interictal recordings preceding each seizure. The participant was a 52-year-old woman suffering from complex partial seizures. LFPs were recorded from an MEA implanted in the participant´s left middle temporal gyrus. In this participant, spectral power in 0.3-10 Hz, 20-55 Hz, and 125-250 Hz changed significantly between ictal and interictal epochs. The examined seizure detection framework provided an event-wise sensitivity of 100% (4/4) and only one 20-sec-long false positive event in interictal recordings (likely an undetected subclinical event under further visual inspection), and a detection latency of 4.35 ± 2.21 sec (mean ± std) with respect to iEEG-identified seizure onsets. These preliminary results indicate that intracortical MEA recordings may provid- key signals to quickly and reliably detect human seizures.
Keywords :
Kalman filters; arrays; bioelectric potentials; biomedical electrodes; electroencephalography; feature extraction; medical disorders; medical signal processing; microelectrodes; neurophysiology; signal classification; signal denoising; signal sampling; statistical analysis; support vector machines; 20-sec-long false positive event; 96-microelectrode array; Fano factor calculation; Kalman filter; LFP denoising; SVM classification; complex partial seizures; cost-sensitive support vector machine classification; detection latency; early human epileptic seizure detection; epileptic patient autonomy; epileptic patient quality of life; event-wise sensitivity; frequency band; iEEG signal; iEEG-identified seizure onset; interictal epochs; interictal recording; interictal sample classification; intracortical LFP sampling; intracortical MEA recording; intracortical local field potentials; intracranial EEG; left middle temporal gyrus; moving time window; one-participant dataset; scalp electroencephalogram; size 4 mm; spectral power change; spectral power estimation; statistical feature extraction; therapy; visual inspection; Educational institutions; Electroencephalography; Epilepsy; Feature extraction; Noise reduction; Reliability; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on
Conference_Location :
San Diego, CA
ISSN :
1948-3546
Type :
conf
DOI :
10.1109/NER.2013.6695937
Filename :
6695937
Link To Document :
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