Background Epileptic seizures disrupt normal brain activity, with pathological activity detectable on electroencephalograms. While seizure classification guides diagnosis and treatment, current systems often fail to capture individual variability in drug response, even among patients with similar clinical phenotypes. This dissertation applies dynamotype classification—a framework grounded in bifurcation theory and dynamical systems—to quantify seizure onset and offset patterns and their modulation by antiseizure medications (ASMs). Methods Data from over 7500 seizures was analyzed from 100+ mice from two models: the intra-amygdala kainate (IAK) model of temporal lobe epilepsy and the genetic Scn1a+/- model of Dravet syndrome. In the IAK model, spontaneous seizures were tracked over a 90-day period. Dynamotypes were visually scored in a randomized, blinded manner by two human raters and a custom DynamoSort machine learning tool we developed. Seizure onsets were categorized into three dynamotypes: (1) SupH, characterized by increasing spike amplitude (supercritical Hopf); (2) SNIC, by increasing frequency (saddle-node on invariant circle); and (3) SubH, by arbitrary or disorganized features. Seizure offsets were similarly categorized: SupH (decreasing amplitude), SNIC (decreasing frequency), and FLC (fold limit cycle, showing arbitrary declines). Results Longitudinal analysis of the IAK model revealed a temporal shift in dynamotype prevalence, with early seizures dominated by arbitrary transitions (SubH, FLC) and later seizures showing more structured bifurcation types (SupH, SNIC). Dynamotypes also predicted seizure duration and severity, with SNIC onset seizures being more severe and FLC offset seizures being longer in duration. Drug trials demonstrated that ASMs selectively modulated bifurcation types in a mechanism-dependent manner. GABAergic drugs (diazepam, phenobarbital) predominantly suppressed seizure onsets, indicating preferential disruption of initiation dynamics. In contrast, sodium channel blockers (phenytoin, valproate) exerted stronger effects on seizure terminations: phenytoin reduced SupH offsets, while valproate increased SNIC offsets in treatment responders, indicating SNIC offset seizures may be pharmacoresistant to VPA. Baseline dynamotype distributions also differed across models. Dravet mice displayed elevated SubH onset proportions relative to IAK, but GABAergic drugs caused similar dynamotype changes, showing conserved responses to ASMs across models. Conclusion Overall, this work demonstrates that bifurcation-informed seizure classification captures dynamic features of epileptogenesis and drug response, revealing mechanistically distinct signatures across ASM classes and models. These findings support the use of dynamotypes as a model-driven framework for evaluating treatment efficacy and tailoring therapies in epilepsy.