Abstract
In this paper, we investigate the use of heart rate variability (HRV) for automatic newborn seizure detection. The proposed method consists of a sequence of processing steps, namely, obtaining HRV from the ECG, extracting a discriminating HRV feature set, selecting an optimal subset from the full feature set, and, finally, classifying the HRV into seizure/nonseizure using a supervised statistical classifier. Due to the fact that HRV signals are nonstationary, a set of timefrequency features from the newborn HRV is proposed and extracted. In order to achieve efficient HRV-based automatic newborn seizure detection, a two-phase wrapper-based feature selection technique is used to select the feature subset with minimum redundancy and maximum class discriminability. Tested on ECG recordings obtained from eight newborns with identified EEG seizure, the proposed HRV-based neonatal seizure detection algorithm achieved 85.7 sensitivity and 84.6 specificity. These results suggest that the HRV is sensitive to changes in the cardioregulatory system induced by the seizure, and therefore, can be used as a basis for an automatic seizure detection.
| Original language | English |
|---|---|
| Article number | 5170066 |
| Pages (from-to) | 2594-2603 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 56 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - Nov 2009 |
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Keywords
- EEG
- Heart rate variability (HRV)
- Newborn seizure
- Pattern recognition
ASJC Scopus subject areas
- Biomedical Engineering
Cite this
Newborn seizure detection based on heart rate variability. / Malarvili, M. B.; Mesbah, Mostefa.
In: IEEE Transactions on Biomedical Engineering, Vol. 56, No. 11, 5170066, 11.2009, p. 2594-2603.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Newborn seizure detection based on heart rate variability
AU - Malarvili, M. B.
AU - Mesbah, Mostefa
PY - 2009/11
Y1 - 2009/11
N2 - In this paper, we investigate the use of heart rate variability (HRV) for automatic newborn seizure detection. The proposed method consists of a sequence of processing steps, namely, obtaining HRV from the ECG, extracting a discriminating HRV feature set, selecting an optimal subset from the full feature set, and, finally, classifying the HRV into seizure/nonseizure using a supervised statistical classifier. Due to the fact that HRV signals are nonstationary, a set of timefrequency features from the newborn HRV is proposed and extracted. In order to achieve efficient HRV-based automatic newborn seizure detection, a two-phase wrapper-based feature selection technique is used to select the feature subset with minimum redundancy and maximum class discriminability. Tested on ECG recordings obtained from eight newborns with identified EEG seizure, the proposed HRV-based neonatal seizure detection algorithm achieved 85.7 sensitivity and 84.6 specificity. These results suggest that the HRV is sensitive to changes in the cardioregulatory system induced by the seizure, and therefore, can be used as a basis for an automatic seizure detection.
AB - In this paper, we investigate the use of heart rate variability (HRV) for automatic newborn seizure detection. The proposed method consists of a sequence of processing steps, namely, obtaining HRV from the ECG, extracting a discriminating HRV feature set, selecting an optimal subset from the full feature set, and, finally, classifying the HRV into seizure/nonseizure using a supervised statistical classifier. Due to the fact that HRV signals are nonstationary, a set of timefrequency features from the newborn HRV is proposed and extracted. In order to achieve efficient HRV-based automatic newborn seizure detection, a two-phase wrapper-based feature selection technique is used to select the feature subset with minimum redundancy and maximum class discriminability. Tested on ECG recordings obtained from eight newborns with identified EEG seizure, the proposed HRV-based neonatal seizure detection algorithm achieved 85.7 sensitivity and 84.6 specificity. These results suggest that the HRV is sensitive to changes in the cardioregulatory system induced by the seizure, and therefore, can be used as a basis for an automatic seizure detection.
KW - EEG
KW - Heart rate variability (HRV)
KW - Newborn seizure
KW - Pattern recognition
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UR - http://www.scopus.com/inward/citedby.url?scp=70449378583&partnerID=8YFLogxK
U2 - 10.1109/TBME.2009.2026908
DO - 10.1109/TBME.2009.2026908
M3 - Article
VL - 56
SP - 2594
EP - 2603
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 11
M1 - 5170066
ER -