Automated video analysis of emotion and dystonia in epileptic seizures

Hou, Jen-Cheng; Thonnat, Monique; Bartolomei, Fabricē; McGonigal, Aileen

doi:10.1016/j.eplepsyres.2022.106953

Cited by 17 publications

(6 citation statements)

References 22 publications

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“…Additionally, CNNs are often harder to interpret and modify when an error occurs, as their internal representations are less easily understood compared to traditional algorithms. Overall, the advantages of CNNs outweigh their disadvantages, as they have been used successfully to detect poses in videos of seizures 26,27,30,51,62,63,66,67,70 …”

Section: Movement‐based Methodologies For Seizure Detectionmentioning

confidence: 99%

“…Another OF‐based study (combined with audio) for BTC seizures reported an 80% accuracy for detecting hyperkinetic seizures 33 . Two deep learning models specifically examining hyperkinetic seizures were able to detect and classify certain semiologic patterns such as limb dystonia and emotional expressions with accuracies of approximately 80% 62,63 . Sleep‐related hypermotor epilepsy could be distinguished from sleep disorders of arousal with an accuracy of 90% 63 in another study.…”

Section: Clinical Application In Epilepsymentioning

confidence: 97%

“…CNNs excel on pattern recognition in images and have been widely used in seizure analysis. 13,26,27,30,44,51,59,60,62,64,66,68,69,70,71,72,73,81 In contrast to simple FFNNs, a CNN uses multiple layers of convolution operations, which pool information from local regions of image space (Figure 4). With sufficient layers and spatial extent of pooling, information is aggregated from the entire image.…”

Section: Cnnsmentioning

confidence: 99%

“…Overall, the advantages of CNNs outweigh their disadvantages, as they have been used successfully to detect poses in videos of seizures. 26,27,30,51,62,63,66,67,70 PNNs PNNs are a subtype of FFNNs that estimate the probability of new data in the same category as previously learned data. 85 The probability is estimated by comparing the various parameters of the new data to the parameters of each previous case of training data to compute a Euclidean distance-a numeric value of similarity.…”

Section: Cnnsmentioning

confidence: 99%

“…33 Two deep learning models specifically examining hyperkinetic seizures were able to detect and classify certain semiologic patterns such as limb dystonia and emotional expressions with accuracies of approximately 80%. 62,63 Sleep-related hypermotor epilepsy could be distinguished from sleep disorders of arousal with an accuracy of 90% • Landmark data was more useful for the body. Total appearance was more useful for the face.…”

Section: Hyperkinetic Seizuresmentioning

confidence: 99%

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Computer vision for automated seizure detection and classification: A systematic review

Brown,

Boyne,

Hassan

et al. 2024

Epilepsia

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Computer vision (CV) shows increasing promise as an efficient, low‐cost tool for video seizure detection and classification. Here, we provide an overview of the fundamental concepts needed to understand CV and summarize the structure and performance of various model architectures used in video seizure analysis. We conduct a systematic literature review of the PubMed, Embase, and Web of Science databases from January 1, 2000 to September 15, 2023, to identify the strengths and limitations of CV seizure analysis methods and discuss the utility of these models when applied to different clinical seizure phenotypes. Reviews, nonhuman studies, and those with insufficient or poor quality data are excluded from the review. Of the 1942 records identified, 45 meet inclusion criteria and are analyzed. We conclude that the field has shown tremendous growth over the past 2 decades, leading to several model architectures with impressive accuracy and efficiency. The rapid and scalable detection offered by CV models holds the potential to reduce sudden unexpected death in epilepsy and help alleviate resource limitations in epilepsy monitoring units. However, a lack of standardized, thorough validation measures and concerns about patient privacy remain important obstacles for widespread acceptance and adoption. Investigation into the performance of models across varied datasets from clinical and nonclinical environments is an essential area for further research.

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Section: Movement‐based Methodologies For Seizure Detectionmentioning

confidence: 99%

Section: Clinical Application In Epilepsymentioning

confidence: 97%

Section: Cnnsmentioning

confidence: 99%

Section: Cnnsmentioning

confidence: 99%

Section: Hyperkinetic Seizuresmentioning

confidence: 99%

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Computer vision for automated seizure detection and classification: A systematic review

Brown,

Boyne,

Hassan

et al. 2024

Epilepsia

View full text Add to dashboard Cite

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Characteristics of motion signal profiles of tonic–clonic, tonic, hyperkinetic, and motor seizures extracted from nocturnal video recordings

Ojanen,

Kertész,

Peltola

2024

Epileptic Disorders

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ObjectiveIn this study, characteristics of signal profiles formed by motion, oscillation, and sound signals were analyzed to evaluate generalizability and variability in a single patient setting (intra‐patient variability) and between patients (inter‐patient variability). As a secondary objective, the effect of brivaracetam intervention on signal profiles was explored.MethodsPatient data included 13 hyperkinetic seizures, 65 tonic seizures, 13 tonic–clonic seizures, and 138 motor seizures from 11 patients. All patients underwent an 8‐week monitoring, and after a 3‐week baseline, brivaracetam was initiated. Motion, oscillation, and sound features extracted from the video were used to form signal profiles. Variance of signals was calculated, and combined median and quartile visualizations were used to visualize the results. Similarly, the effect of intervention was visualized.ResultsHyperkinetic motion signals showed a rapid increase in motion and sound signals without oscillations and achieved low intra‐patient variance. Tonic component created a recognizable peak in motion signal typical for tonic and tonic–clonic seizures. For tonic seizures, inter‐patient variance was low. Motor signal profiles were varying, and they did not form a generalizable signal profile. Visually recognizable changes were observed in the signal profiles of two patients.SignificanceVideo‐based motion signal analysis enabled the extraction of motion features characteristic for different motor seizure types which might be useful in further development of this system. Tonic component formed a recognizable seizure signature in the motion signal. Hyperkinetic and motor seizures may have not only significantly different motion signal amplitude but also overlapping signal profile characteristics which might hamper their automatic differentiation. Motion signals might be useful in the assessment of movement intensity changes to evaluate the treatment effect. Further research is needed to test generalizability and to increase reliability of the results.

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