Pre-ictal epileptic seizure prediction based on ECG signal analysis

Arijit Ghosh; Anasua Sarkar; Tarak Das; Piyali Basak

doi:10.1109/I2CT.2017.8226263

Pre-ictal epileptic seizure prediction based on ECG signal analysis

Arijit Ghosh, Anasua Sarkar, Tarak Das, Piyali Basak

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

Abstract

Epileptic seizures demonstrate a clear effect of the dominating behavior of the autonomic nervous system on the cardiovascular system, especially on the Heart Rate Variation. Recording of Electroencephalogram (EEG) for detection of the onset of epileptic seizure had been used for constructing automatic seizure detection algorithm. Electrocardiogram (ECG) also can be used to evaluate Heart Rate Variability in patients with epileptic seizures. This paper studies 5 minutes pre-ictal ECG signal acquired from subjects suffering from Frontal Lobe Epilepsy with demonstrated seizures, whose age ranges from 18-28. ECG is acquired for both normal and epileptic subjects with demonstrated seizures. After detection of QRS complex using modified Pan-Tompkins algorithm, we perform three analysis methods to evaluate the features that uniquely help to predict an epileptic seizure. Time domain, Frequency domain and Non linear features provide accurate results in this prediction. The process can be used for implementing an automated prediction of pre-ictal (before seizure) period for seizure using only ECG signal.

Original language	English
Title of host publication	2017 2nd International Conference for Convergence in Technology, I2CT 2017
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	920-925
Number of pages	6
Volume	2017-January
ISBN (Electronic)	9781509043071
DOIs	https://doi.org/10.1109/I2CT.2017.8226263
Publication status	Published - 2017 Dec 18
Externally published	Yes
Event	2nd International Conference for Convergence in Technology, I2CT 2017 - Pune, India Duration: 2017 Apr 7 → 2017 Apr 9

Conference

Conference	2nd International Conference for Convergence in Technology, I2CT 2017
Country/Territory	India
City	Pune
Period	2017/04/07 → 2017/04/09

Free keywords

Biomedical signal processing
Electrocardiogram
Frontal Lobe Epilepsy
Heart Rate Variation
Linear analysis
Non linear analysis
Pre-ictal epoch
Seizure prediction

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10.1109/I2CT.2017.8226263

Cite this

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title = "Pre-ictal epileptic seizure prediction based on ECG signal analysis",

abstract = "Epileptic seizures demonstrate a clear effect of the dominating behavior of the autonomic nervous system on the cardiovascular system, especially on the Heart Rate Variation. Recording of Electroencephalogram (EEG) for detection of the onset of epileptic seizure had been used for constructing automatic seizure detection algorithm. Electrocardiogram (ECG) also can be used to evaluate Heart Rate Variability in patients with epileptic seizures. This paper studies 5 minutes pre-ictal ECG signal acquired from subjects suffering from Frontal Lobe Epilepsy with demonstrated seizures, whose age ranges from 18-28. ECG is acquired for both normal and epileptic subjects with demonstrated seizures. After detection of QRS complex using modified Pan-Tompkins algorithm, we perform three analysis methods to evaluate the features that uniquely help to predict an epileptic seizure. Time domain, Frequency domain and Non linear features provide accurate results in this prediction. The process can be used for implementing an automated prediction of pre-ictal (before seizure) period for seizure using only ECG signal.",

keywords = "Biomedical signal processing, Electrocardiogram, Frontal Lobe Epilepsy, Heart Rate Variation, Linear analysis, Non linear analysis, Pre-ictal epoch, Seizure prediction",

author = "Arijit Ghosh and Anasua Sarkar and Tarak Das and Piyali Basak",

year = "2017",

month = dec,

day = "18",

doi = "10.1109/I2CT.2017.8226263",

language = "English",

volume = "2017-January",

pages = "920--925",

booktitle = "2017 2nd International Conference for Convergence in Technology, I2CT 2017",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2nd International Conference for Convergence in Technology, I2CT 2017 ; Conference date: 07-04-2017 Through 09-04-2017",

}

Ghosh, A, Sarkar, A, Das, T & Basak, P 2017, Pre-ictal epileptic seizure prediction based on ECG signal analysis. in 2017 2nd International Conference for Convergence in Technology, I2CT 2017. vol. 2017-January, IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 920-925, 2nd International Conference for Convergence in Technology, I2CT 2017, Pune, India, 2017/04/07. https://doi.org/10.1109/I2CT.2017.8226263

Pre-ictal epileptic seizure prediction based on ECG signal analysis. / Ghosh, Arijit; Sarkar, Anasua; Das, Tarak et al.
2017 2nd International Conference for Convergence in Technology, I2CT 2017. Vol. 2017-January IEEE - Institute of Electrical and Electronics Engineers Inc., 2017. p. 920-925.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

T1 - Pre-ictal epileptic seizure prediction based on ECG signal analysis

AU - Ghosh, Arijit

AU - Sarkar, Anasua

AU - Das, Tarak

AU - Basak, Piyali

PY - 2017/12/18

Y1 - 2017/12/18

N2 - Epileptic seizures demonstrate a clear effect of the dominating behavior of the autonomic nervous system on the cardiovascular system, especially on the Heart Rate Variation. Recording of Electroencephalogram (EEG) for detection of the onset of epileptic seizure had been used for constructing automatic seizure detection algorithm. Electrocardiogram (ECG) also can be used to evaluate Heart Rate Variability in patients with epileptic seizures. This paper studies 5 minutes pre-ictal ECG signal acquired from subjects suffering from Frontal Lobe Epilepsy with demonstrated seizures, whose age ranges from 18-28. ECG is acquired for both normal and epileptic subjects with demonstrated seizures. After detection of QRS complex using modified Pan-Tompkins algorithm, we perform three analysis methods to evaluate the features that uniquely help to predict an epileptic seizure. Time domain, Frequency domain and Non linear features provide accurate results in this prediction. The process can be used for implementing an automated prediction of pre-ictal (before seizure) period for seizure using only ECG signal.

AB - Epileptic seizures demonstrate a clear effect of the dominating behavior of the autonomic nervous system on the cardiovascular system, especially on the Heart Rate Variation. Recording of Electroencephalogram (EEG) for detection of the onset of epileptic seizure had been used for constructing automatic seizure detection algorithm. Electrocardiogram (ECG) also can be used to evaluate Heart Rate Variability in patients with epileptic seizures. This paper studies 5 minutes pre-ictal ECG signal acquired from subjects suffering from Frontal Lobe Epilepsy with demonstrated seizures, whose age ranges from 18-28. ECG is acquired for both normal and epileptic subjects with demonstrated seizures. After detection of QRS complex using modified Pan-Tompkins algorithm, we perform three analysis methods to evaluate the features that uniquely help to predict an epileptic seizure. Time domain, Frequency domain and Non linear features provide accurate results in this prediction. The process can be used for implementing an automated prediction of pre-ictal (before seizure) period for seizure using only ECG signal.

KW - Biomedical signal processing

KW - Electrocardiogram

KW - Frontal Lobe Epilepsy

KW - Heart Rate Variation

KW - Linear analysis

KW - Non linear analysis

KW - Pre-ictal epoch

KW - Seizure prediction

U2 - 10.1109/I2CT.2017.8226263

DO - 10.1109/I2CT.2017.8226263

M3 - Paper in conference proceeding

AN - SCOPUS:85046531161

VL - 2017-January

SP - 920

EP - 925

BT - 2017 2nd International Conference for Convergence in Technology, I2CT 2017

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference for Convergence in Technology, I2CT 2017

Y2 - 7 April 2017 through 9 April 2017

ER -

Pre-ictal epileptic seizure prediction based on ECG signal analysis

Abstract

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