  Objectives: This study examines human Photoplethysmogram (PPG) along with
Electrocardiogram (ECG) signals to study cardiac autonomic imbalance in
epileptic seizures. The significance and the prevalence of changes in PPG
morphological parameters have been investigated to find common patterns among
subjects. Alterations in cardiovascular parameters measured by PPG/ECG signals
are used to train a neural network based on LSTM for automatic seizure
detection. Methods: Electroencephalogram (EEG), ECG, and PPG signals from 12
different subjects ( 8 males;4 females;age 34.3$\pm$ 13.8) were recorded
including 57 seizures and 101 hours of inter-ictal data. 12 PPG features
significantly changing due to epileptic seizures were extracted and normalized
based on a proposed z-score metric. 7 feature are heart rate variability
related and 5 features hemodynamic related. Results: A consistent pattern of
ictal change was observed for all the features across the subjects/seziures.
The proposed seizure detector is subject independent and works for both
nocturnal and diurnal seizures. With an average of 0.52 false alarms per hour,
positive predictive value of $43\%$ and sensitivity of $92\%$, the new proposed
hemodynamic based seizure detector shows improvement over the the heart rate
variability based detector. Conclusion: The cardiac autonomic imbalance due to
seizure manifests itself in variations of peripheral hemodynamics measured by
PPG signal, suggesting vasoconstriction in limbs. These variations can be used
on a consumer seizure detecting devices with optical sensors for seizure
detection. Significance: The stereotyped pattern is common among all the
subjects which can help understand the mechanism of cardiac autonomic imbalance
induced by epileptic seizures.
