Pre-ictal epileptic seizure prediction based on ECG signal analysis
Ghosh, Arijit ; Sarkar, Anasua LU
; Das, Tarak
and Basak, Piyali
(2017)
2nd International Conference for Convergence in Technology, I2CT 2017
2017-January.
p.920-925
- 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... (More)
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.
(Less)
- author
- Ghosh, Arijit
; Sarkar, Anasua
LU
; Das, Tarak
and Basak, Piyali
- publishing date
- 2017-12-18
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- keywords
- Biomedical signal processing, Electrocardiogram, Frontal Lobe Epilepsy, Heart Rate Variation, Linear analysis, Non linear analysis, Pre-ictal epoch, Seizure prediction
- host publication
- 2017 2nd International Conference for Convergence in Technology, I2CT 2017
- volume
- 2017-January
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2nd International Conference for Convergence in Technology, I2CT 2017
- conference location
- Pune, India
- conference dates
- 2017-04-07 - 2017-04-09
- external identifiers
-
- scopus:85046531161
- ISBN
- 9781509043071
- DOI
- 10.1109/I2CT.2017.8226263
- language
- English
- LU publication?
- no
- id
- 9b834dc2-20fc-4942-a10e-ca42b8d3c120
- date added to LUP
- 2018-09-13 10:15:25
- date last changed
- 2022-04-25 17:11:16
@inproceedings{9b834dc2-20fc-4942-a10e-ca42b8d3c120,
abstract = {{<p>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.</p>}},
author = {{Ghosh, Arijit and Sarkar, Anasua and Das, Tarak and Basak, Piyali}},
booktitle = {{2017 2nd International Conference for Convergence in Technology, I2CT 2017}},
isbn = {{9781509043071}},
keywords = {{Biomedical signal processing; Electrocardiogram; Frontal Lobe Epilepsy; Heart Rate Variation; Linear analysis; Non linear analysis; Pre-ictal epoch; Seizure prediction}},
language = {{eng}},
month = {{12}},
pages = {{920--925}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Pre-ictal epileptic seizure prediction based on ECG signal analysis}},
url = {{http://dx.doi.org/10.1109/I2CT.2017.8226263}},
doi = {{10.1109/I2CT.2017.8226263}},
volume = {{2017-January}},
year = {{2017}},
}