Understanding Patients' Behavior: Vision-Based Analysis of Seizure Disorders

• Published in: IEEE journal of biomedical and health informatics Vol. 23; no. 6; pp. 2583 - 2591
• Main Authors: Ahmedt-Aristizabal, David, Denman, Simon, Nguyen, Kien, Sridharan, Sridha, Dionisio, Sasha, Fookes, Clinton
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cameras; Complications; Computer architecture; Computer vision; Convulsions & seizures; Deep Learning; Diagnostic systems; Disorders; Epilepsy; Epilepsy - diagnostic imaging; Epileptic seizures; Feature extraction; functional neurological disorder; Humans; Image Interpretation, Computer-Assisted - methods; Machine learning; Markers; Monitoring; Monitoring, Physiologic - methods; Neurological diseases; Patients; quantitative movement analysis; Seizures; Semiotics; Video Recording - methods
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2019.2895855

A substantial proportion of patients with functional neurological disorders (FND) are being incorrectly diagnosed with epilepsy because their semiology resembles that of epileptic seizures (ES). Misdiagnosis may lead to unnecessary treatment and its associated complications. Diagnostic errors often result from an overreliance on specific clinical features. Furthermore, the lack of electrophysiological changes in patients with FND can also be seen in some forms of epilepsy, making diagnosis extremely challenging. Therefore, understanding semiology is an essential step for differentiating between ES and FND. Existing sensor-based and marker-based systems require physical contact with the body and are vulnerable to clinical situations such as patient positions, illumination changes, and motion discontinuities. Computer vision and deep learning are advancing to overcome these limitations encountered in the assessment of diseases and patient monitoring; however, they have not been investigated for seizure disorder scenarios. Here, we propose and compare two marker-free deep learning models, a landmark-based and a region-based model, both of which are capable of distinguishing between seizures from video recordings. We quantify semiology by using either a fusion of reference points and flow fields, or through the complete analysis of the body. Average leave-one-subject-out cross-validation accuracies for the landmark-based and region-based approaches of 68.1% and 79.6% in our dataset collected from 35 patients, reveal the benefit of video analytics to support automated identification of semiology in the challenging conditions of a hospital setting.