Deep facial analysis: A new phase I epilepsy evaluation using computer vision

Ahmedt-Aristizabal, David; Fookes, Clinton; Nguyen, Kien; Denman, Simon; Sridharan, Sridha; Dionisio, Sasha

doi:10.1016/j.yebeh.2018.02.010

Cited by 48 publications

(31 citation statements)

References 23 publications

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“…Our study shows that the analysis of patients considering all body motions simultaneously is viable using the existing monitoring technology in the hospital. As it was confirmed in previous studies [5,6], the performance of LOSO-CV schemes is strictly related to semiological patterns contained in the dataset. This is evident from the difference in classification performance between patients based on the deep learning architecture.…”

Section: Discussionsupporting

confidence: 79%

“…The CNN output is subsequently fed to a stacked LSTM architecture. We adopt an LSTM with 2 hidden layers of 128 and 64 units respectively [5]. Finally, the output of the second hidden recurrent layer is fed into a densely connected layer with a sigmoid activation function to describe the probability of each sequence having MTLE or ETLE behavior.…”

Section: Deep Learning Architecture and Trainingmentioning

confidence: 99%

“…Given the importance of body motion patterns in the assessment of epilepsy, prior works have demonstrated that automated analysis of semiological patterns based on computer vision can support diagnosis by standard and objective assessment methods among evaluators [3,4]. Previously, we proposed a facial semiology analysis approach to identify patients with mesial temporal lobe epilepsy (MTLE) [5], and a hierarchical multi-modal system to quantify and classify patients with MTLE and extra-temporal lobe epilepsy (ETLE) based on face, body and hand motions [6].…”

Section: Introductionmentioning

confidence: 99%

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Aberrant epileptic seizure identification: A computer vision perspective

Ahmedt-Aristizabal

Fookes

Denman

et al. 2019

Seizure

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A B S T R A C TPurpose: The recent explosion of artificial intelligence techniques in video analytics has highlighted the clinical relevance in capturing and quantifying semiology during epileptic seizures; however, we lack an automated anomaly identification system for aberrant behaviors. In this paper, we describe a novel system that is trained with known clinical manifestations from patients with mesial temporal and extra-temporal lobe epilepsy and presents aberrant semiology to physicians. Methods: We propose a simple end-to-end-architecture based on convolutional and recurrent neural networks to extract spatiotemporal representations and to create motion capture libraries from 119 seizures of 28 patients. The cosine similarity distance between a test representation and the libraries from five aberrant seizures separate to the main dataset is subsequently used to identify test seizures with unusual patterns that do not conform to known behavior. Results: Cross-validation evaluations are performed to validate the quantification of motion features and to demonstrate the robustness of the motion capture libraries for identifying epilepsy types. The system to identify unusual epileptic seizures successfully detects out of the five seizures categorized as aberrant cases. Conclusions: The proposed approach is capable of modeling clinical manifestations of known behaviors in natural clinical settings, and effectively identify aberrant seizures using a simple strategy based on motion capture libraries of spatiotemporal representations and similarities between hidden states. Detecting anomalies is essential to alert clinicians to the occurrence of unusual events, and we show how this can be achieved using prelearned database of semiology stored in health records.

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Section: Discussionsupporting

confidence: 79%

Section: Deep Learning Architecture and Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Aberrant epileptic seizure identification: A computer vision perspective

Ahmedt-Aristizabal

Fookes

Denman

et al. 2019

Seizure

Self Cite

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“…This method obtained specificity, sensitivity and accuracy of 90.00, 95.00 and 88.67% respectively. Whereas in [16], developed a method to attempt to rxtract automatically and categorize the semiological patterns via facial expressions. Afterwards, they address the limitations of computer-based-analytical methods of the epilepsy-monitoring, where the movements of facial have been ignored.…”

Section: Literature Surveymentioning

confidence: 99%

Improved feature exctraction process to detect seizure using CHBMIT-dataset

Kumar¹,

Narayanappa

2021

IJECE

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One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.

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“…Specifically, a framework known as task-constrained deep convolutional network (TCDCN) framework was used first, which is a facial These two models were then combined to improve the accuracy of the facial landmark detection and tracking across videos. This cascade framework follows previously established approaches (Jin, Su et al 2017, Ahmedt-Aristizabal, Fookes et al 2018). First, model 1 is used to detect the landmarks.…”

Section: Facial Landmark Estimationmentioning

confidence: 99%

Body and brain responses underlying salient emotional information processing

Sonkusare¹

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Cognitive neuroscience has traditionally focused on simple tasks, presented sparsely and with abstract stimuli. While this approach has yielded fundamental insights into functional specialization in the brain, its ecological validity remains uncertain: Do these tasks fully capture how our brains function in the real world, where stimuli are dynamic, multimodal and crowded? Ecologically valid paradigms that approximate real life scenarios, using stimuli such as films, spoken narratives, music and multiperson games emerged in response to these concerns over a decade ago. We critically appraise whether this approach has delivered on its promise to deliver new insights into brain function. We highlight the challenges, technological innovations and clinical opportunities that are required should this field meet its full potential.

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Deep facial analysis: A new phase I epilepsy evaluation using computer vision

Cited by 48 publications

References 23 publications

Aberrant epileptic seizure identification: A computer vision perspective

Aberrant epileptic seizure identification: A computer vision perspective

Improved feature exctraction process to detect seizure using CHBMIT-dataset

Body and brain responses underlying salient emotional information processing

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