Early detection of human epileptic seizures based on intracortical local field potentials

Yun S. Park, Leigh R. Hochberg, Emad N. Eskandar, Sydney S. Cash, Wilson Truccolo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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 provide key signals to quickly and reliably detect human seizures.

Original languageEnglish (US)
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages323-326
Number of pages4
DOIs
StatePublished - Dec 1 2013
Externally publishedYes
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Other

Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
CountryUnited States
CitySan Diego, CA
Period11/6/1311/8/13

Fingerprint

Microelectrodes
Electroencephalography
Frequency bands
Bioelectric potentials
Kalman filters
Support vector machines
Inspection
Sampling
Costs

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

Cite this

Park, Y. S., Hochberg, L. R., Eskandar, E. N., Cash, S. S., & Truccolo, W. (2013). Early detection of human epileptic seizures based on intracortical local field potentials. In 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 (pp. 323-326). [6695937] https://doi.org/10.1109/NER.2013.6695937

Early detection of human epileptic seizures based on intracortical local field potentials. / Park, Yun S.; Hochberg, Leigh R.; Eskandar, Emad N.; Cash, Sydney S.; Truccolo, Wilson.

2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013. 2013. p. 323-326 6695937.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Park, YS, Hochberg, LR, Eskandar, EN, Cash, SS & Truccolo, W 2013, Early detection of human epileptic seizures based on intracortical local field potentials. in 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013., 6695937, pp. 323-326, 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013, San Diego, CA, United States, 11/6/13. https://doi.org/10.1109/NER.2013.6695937
Park YS, Hochberg LR, Eskandar EN, Cash SS, Truccolo W. Early detection of human epileptic seizures based on intracortical local field potentials. In 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013. 2013. p. 323-326. 6695937 https://doi.org/10.1109/NER.2013.6695937
Park, Yun S. ; Hochberg, Leigh R. ; Eskandar, Emad N. ; Cash, Sydney S. ; Truccolo, Wilson. / Early detection of human epileptic seizures based on intracortical local field potentials. 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013. 2013. pp. 323-326
@inproceedings{e36f4736b9164ae5badfd4920b4f5c27,
title = "Early detection of human epileptic seizures based on intracortical local field potentials",
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 provide key signals to quickly and reliably detect human seizures.",
author = "Park, {Yun S.} and Hochberg, {Leigh R.} and Eskandar, {Emad N.} and Cash, {Sydney S.} and Wilson Truccolo",
year = "2013",
month = "12",
day = "1",
doi = "10.1109/NER.2013.6695937",
language = "English (US)",
isbn = "9781467319690",
pages = "323--326",
booktitle = "2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013",

}

TY - GEN

T1 - Early detection of human epileptic seizures based on intracortical local field potentials

AU - Park, Yun S.

AU - Hochberg, Leigh R.

AU - Eskandar, Emad N.

AU - Cash, Sydney S.

AU - Truccolo, Wilson

PY - 2013/12/1

Y1 - 2013/12/1

N2 - 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 provide key signals to quickly and reliably detect human seizures.

AB - 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 provide key signals to quickly and reliably detect human seizures.

UR - http://www.scopus.com/inward/record.url?scp=84897712628&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84897712628&partnerID=8YFLogxK

U2 - 10.1109/NER.2013.6695937

DO - 10.1109/NER.2013.6695937

M3 - Conference contribution

AN - SCOPUS:84897712628

SN - 9781467319690

SP - 323

EP - 326

BT - 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013

ER -