Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Neuroinformatics Pub Date : 2024-03-15 DOI:10.3389/fninf.2024.1324981
Pragya Rai, Andrew Knight, Matias Hiillos, Csaba Kertész, Elizabeth Morales, Daniella Terney, Sidsel Armand Larsen, Tim Østerkjerhuus, Jukka Peltola, Sándor Beniczky
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Abstract

IntroductionAutomated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment.MethodsIn this phase 2 exploratory study, the performance of a contactless, marker-free, video-based motor seizure detection system is assessed, considering video recordings of patients (age 0–80 years), in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to video-electroencephalographic monitoring (VEM) as the medical gold standard. Detection performances of five categories of motor epileptic seizures (tonic–clonic, hyperkinetic, tonic, unclassified motor, automatisms) and psychogenic non-epileptic seizures (PNES) with a motor behavioral component lasting for &gt;10 s were assessed independently at different detection thresholds (rather than as a categorical classification problem). A total of 230 patients were recruited in the study, of which 334 in-scope (&gt;10 s) motor seizures (out of 1,114 total seizures) were identified by VEM reported from 81 patients. We analyzed both daytime and nocturnal recordings. The control threshold was evaluated at a range of values to compare the sensitivity (n = 81 subjects with seizures) and false detection rate (FDR) (n = all 230 subjects).ResultsAt optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/h (CI): tonic–clonic- 95.2% (82.4, 100%); 0.09 (0.077, 0.103), hyperkinetic- 92.9% (68.5, 98.7%); 0.64 (0.59, 0.69), tonic- 78.3% (64.4, 87.7%); 5.87 (5.51, 6.23), automatism- 86.7% (73.5, 97.7%); 3.34 (3.12, 3.58), unclassified motor seizures- 78% (65.4, 90.4%); 4.81 (4.50, 5.14), and PNES- 97.7% (97.7, 100%); 1.73 (1.61, 1.86). A generic threshold recommended for all motor seizures under study asserted 88% sensitivity and 6.48 FDR/h.DiscussionThese results indicate an achievable performance for major motor seizure detection that is clinically applicable for use as a seizure screening solution in diagnostic workflows.
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利用人工智能自动分析和检测视频记录中的癫痫发作
方法在这项第二阶段探索性研究中,通过对患者(0-80 岁)的视频记录,从灵敏度、特异性和接收者工作特征曲线 (ROC) 等方面评估了非接触式、无标记、基于视频的运动性癫痫发作检测系统的性能,并将视频脑电图监测 (VEM) 作为医疗金标准。在不同的检测阈值下(而不是作为分类问题),对五类运动性癫痫发作(强直-阵挛、运动过度、强直、未分类运动、自动症)和具有运动行为成分且持续时间为 &gt;10 秒的精神性非癫痫发作(PNES)的检测性能进行了独立评估。研究共招募了 230 名患者,其中 81 名患者的 VEM 报告确定了 334 次范围内(&gt;10 秒)运动性发作(总发作次数为 1,114 次)。我们分析了白天和夜间的记录。结果在最佳阈值下,各发作组的灵敏度(CI)和误检率/h(CI)分别为:强直-阵挛-95.2%(82.4, 100%); 0.09 (0.077, 0.103), hyperkinetic- 92.9% (68.5, 98.7%); 0.64 (0.59, 0.69), tonic- 78.3% (64.4, 87.7%); 5.87 (5.51, 6.23), automatism- 86.7%(73.5,97.7%);3.34(3.12,3.58);未分类运动性发作- 78%(65.4,90.4%);4.81(4.50,5.14);PNES- 97.7%(97.7,100%);1.73(1.61,1.86)。这些结果表明,大运动性癫痫发作检测具有可实现的性能,在临床上可用作诊断工作流程中的癫痫发作筛查解决方案。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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