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.