Objectives.-Rhythmic, stereotyped movements occur in some epileptic seizures. We aimed to document time-evolving frequencies of antero-posterior rocking occurring during prefrontal seizures, using a quantitative video analysis. Methods.-Six seizures from 3 patients with prefrontal epilepsy yet different sublobar localizations were analyzed using a deep learning-based head-tracking method. Results.-Mean rocking frequency varied between patients and seizures (0.37-1.0 Hz). Coefficient of variation of frequency was low (≤ 12%). Discussion.-Regularity of body rocking movements suggests a mechanism involving intrinsic oscillatory generators. Since localization of seizure onset varied within prefrontal cortex across patients, altered dynamics converging on a ''final common pathway'' of seizure propagation involving cortico-subcortical circuits is hypothesized.
In this work, we propose a multi-stream approach with knowledge distillation to classify epileptic seizures and psychogenic non-epileptic seizures. The proposed framework utilizes multi-stream information from keypoints and appearance from both body and face. We take the detected keypoints through time as spatio-temporal graph and train it with an adaptive graph convolutional networks to model the spatio-temporal dynamics throughout the seizure event. Besides, we regularize the keypoint features with complementary information from the appearance stream by imposing a knowledge distillation mechanism. We demonstrate the effectiveness of our approach by conducting experiments on real-world seizure videos. The experiments are conducted by both seizure-wise cross validation and leaveone-subject-out validation, and with the proposed model, the performances of the F1-score/accuracy are 0.89/0.87 for seizure-wise cross validation, and 0.75/0.72 for leaveone-subject-out validation.
Objectives.-Rhythmic body rocking movements may occur in prefrontal epileptic seizures. Here, we compare quantified time-evolving frequency of stereotyped rocking with signal analysis of intracerebral electroencephalographic data. Methods.-In a single patient, prefrontal seizures with rhythmic anteroposterior body rocking recorded on stereoelectroencephalography (SEEG) were analyzed using fast Fourier transform, time-frequency decomposition and phase amplitude coupling, with regards to quantified video data. Comparison was made with seizures without rocking in the same patient, as well as resting state data. Results.-Rocking movements in the delta (∼1 Hz) range began a few seconds after SEEG onset of low voltage fast discharge. During rocking movements: (1) presence of a peak of delta band activity was visible in bipolar montage, with maximal power in epileptogenic zone and corresponding to mean rocking frequency; (2) correlation, using phase amplitude coupling, was shown between the phase of this delta activity and high-gamma power in the epileptogenic zone and the anterior cingulate region. Conclusions.-Here, delta range rhythmic body rocking was associated with cortical delta oscillatory activity and phase-coupled high-gamma energy. These results suggest a neural signature during expression of motor semiology incorporating both temporal features associated with rhythmic movements and spatial features of seizure discharge.
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