PURPOSE: To characterize a biomarker for epileptogenesis based on cardiac interbeat interval characteristics. METHODS: Electrocardiograph (ECG) and electroencephalogram (EEG) signals were recorded from freely moving rats (n = 23) before status epilepticus (SE) induced by i.p. pilocarpine (PILO) injection as baseline, and on days 1, 3 and 7 after SE. We assessed several features from cardiac interbeat intervals, including linear, non-linear and frequency parameters of interbeat intervals, and power spectra of interpolated intervals during epileptogenesis. After thresholding, the altered values were applied to a non-linear classifier. The non-linear classifier divided animals into two groups; with and without epilepsy, based on all collected data. RESULTS: We found that none of the single altered parameters in cardiac activity emerged as a sole biomarker for epileptogenesis. However, the non-linear classifier distinguished animals that later developed from those and did not develop epilepsy. The non-linear classification was performed on preliminary findings from 23 animals; six did not develop epilepsy and the rest did. The average positive predictive value (precision rate) was 78%. This was calculated based on the average sensitivity and specificity, which were 80.6% and 35.2% respectively, for the 100 classification passes. We also showed that these numbers would have increased as the number of subjects increased. CONCLUSION: Changes to the brain caused by status epilepticus that lead to epileptogenesis have systemic effects, and alter cardiac activity. A non-linear classifier performed on several extracted features of cardiac interbeat intervals may be useful as a biomarker to identify animals with low and high probability of developing epilepsy after status epilepticus. Published by Elsevier B.V.
PURPOSE: To characterize a biomarker for epileptogenesis based on cardiac interbeat interval characteristics. METHODS: Electrocardiograph (ECG) and electroencephalogram (EEG) signals were recorded from freely moving rats (n = 23) before status epilepticus (SE) induced by i.p. pilocarpine (PILO) injection as baseline, and on days 1, 3 and 7 after SE. We assessed several features from cardiac interbeat intervals, including linear, non-linear and frequency parameters of interbeat intervals, and power spectra of interpolated intervals during epileptogenesis. After thresholding, the altered values were applied to a non-linear classifier. The non-linear classifier divided animals into two groups; with and without epilepsy, based on all collected data. RESULTS: We found that none of the single altered parameters in cardiac activity emerged as a sole biomarker for epileptogenesis. However, the non-linear classifier distinguished animals that later developed from those and did not develop epilepsy. The non-linear classification was performed on preliminary findings from 23 animals; six did not develop epilepsy and the rest did. The average positive predictive value (precision rate) was 78%. This was calculated based on the average sensitivity and specificity, which were 80.6% and 35.2% respectively, for the 100 classification passes. We also showed that these numbers would have increased as the number of subjects increased. CONCLUSION: Changes to the brain caused by status epilepticus that lead to epileptogenesis have systemic effects, and alter cardiac activity. A non-linear classifier performed on several extracted features of cardiac interbeat intervals may be useful as a biomarker to identify animals with low and high probability of developing epilepsy after status epilepticus. Published by Elsevier B.V.
Authors: H Stefan; F H Lopes da Silva; W Löscher; D Schmidt; E Perucca; M J Brodie; P A J M Boon; W H Theodore; S L Moshé Journal: Acta Neurol Scand Date: 2006-03 Impact factor: 3.209
Authors: G G Berntson; J T Bigger; D L Eckberg; P Grossman; P G Kaufmann; M Malik; H N Nagaraja; S W Porges; J P Saul; P H Stone; M W van der Molen Journal: Psychophysiology Date: 1997-11 Impact factor: 4.016
Authors: Barry R Greene; Geraldine B Boylan; Richard B Reilly; Philip de Chazal; Sean Connolly Journal: Clin Neurophysiol Date: 2007-03-29 Impact factor: 3.708
Authors: Cameron S Metcalf; Steven Poelzing; Jason G Little; Steven L Bealer Journal: Am J Physiol Heart Circ Physiol Date: 2009-10-09 Impact factor: 4.733