Epileptic seizure detection based on video and EEG recordings

Aghaei, Hoda; Kiani, Mohammad Mahdi; Aghajan, Hamid

doi:10.1109/biocas.2017.8325156

Cited by 6 publications

(11 citation statements)

References 14 publications

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“…Segmentation accuracy is excellent, which indicates that this method is suitable for automatic identification of seizure episodes in video‐EEG sessions, reducing review workload for video‐EEG technicians. Latency is roughly in line with the state of the art (between 5 and 35 s), although most markerless video‐based methods do not specify latency 24,41–44 …”

Section: Discussionmentioning

confidence: 81%

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A novel approach to automatic seizure detection using computer vision and independent component analysis

Garção,

Abreu,

Peralta

et al. 2023

Epilepsia

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ObjectiveEpilepsy is a neurological disease that affects ~50 million people worldwide, 30% of which have refractory epilepsy and recurring seizures, which may contribute to higher anxiety levels and poorer quality of life. Seizure detection may contribute to addressing some of the challenges associated with this condition, by providing information to health professionals regarding seizure frequency, type, and/or location in the brain, thereby improving diagnostic accuracy and medication adjustment, and alerting caregivers or emergency services of dangerous seizure episodes. The main focus of this work was the development of an accurate video‐based seizure‐detection method that ensured unobtrusiveness and privacy preservation, and provided novel approaches to reduce confounds and increase reliability.MethodsThe proposed approach is a video‐based seizure‐detection method based on optical flow, principal component analysis, independent component analysis, and machine learning classification. This method was tested on a set of 21 tonic–clonic seizure videos (5–30 min each, total of 4 h and 36 min of recordings) from 12 patients using leave‐one‐subject‐out cross‐validation.ResultsHigh accuracy levels were observed, namely a sensitivity and specificity of 99.06% ± 1.65% at the equal error rate and an average latency of 37.45 ± 1.31 s. When compared to annotations by health care professionals, the beginning and ending of seizures was detected with an average offset of 9.69 ± 0.97 s.SignificanceThe video‐based seizure‐detection method described herein is highly accurate. Moreover, it is intrinsically privacy preserving, due to the use of optical flow motion quantification. In addition, given our novel independence‐based approach, this method is robust to different lighting conditions, partial occlusions of the patient, and other movements in the video frame, thereby setting the base for accurate and unobtrusive seizure detection.

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Section: Discussionmentioning

confidence: 81%

“…¶ Reported sensitivity and specificity of most video-based motor seizure-detection methods varies between 78% and 95%, with no method delivering both sensitivity and specificity above 93%. 23,24,[38][39][40][41][42][43][44]…”

Section: Funding Informationmentioning

confidence: 99%

A novel approach to automatic seizure detection using computer vision and independent component analysis

Garção,

Abreu,

Peralta

et al. 2023

Epilepsia

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“…22 OF captures motion patterns and movement periodicity of the body during seizures, providing an information-dense account of seizure semiology that forms an integral part of many seizure detection models. 14,23,24,25,26,27,28,29,30,31,32,33,34 Like FD, OF algorithms typically result in privacy preserving output, in this case by removing identifying facial information. Researchers often combine OF with other motion-based or appearance-based features to improve the overall performance of seizure detection algorithms.…”

Section: Optical Flowmentioning

confidence: 99%

“…50 They have found use as robust classifiers within the field of seizure analysis, though they lack a built-in method of dealing with unknown values or temporal data. 23,32,51,52,53,55,56,57 Tree-based methods Figure 3C Tree-based alogrithms are simple iterative methods often used in seizure analysis. 14,76 At each node of the tree, a feature is used to split the dataset into two groups based on whether they are above or below a threshold.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

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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“…By analyzing the information on the recorded brain activity of the EEG signals, it is possible to detect epileptic seizures [6]. However, these types of procedures are exhaustive and complex, mainly carried out under appropriate clinical settings [7].…”

Section: Related Workmentioning

confidence: 99%

Automated Epileptic Seizure Detection System Based on a Wearable Prototype and Cloud Computing to Assist People with Epilepsy

Cruz

Solarte

Gonzalez-Vargas

2018

Communications in Computer and Information Science

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Epilepsy is characterized by the recurrence of epileptic seizures that affect secondary physiological changes in the patient. This leads to a series of adverse events in the manifestation of convulsions in an uncontrolled environment and without medical help, resulting in risk to the patient, especially in people with refractory epilepsy where modern pharmacology is not able to control seizures. The traditional methods of detection based on wired hospital monitoring systems are not suitable for the detection of long-term monitoring in outdoors. For these reasons, this paper proposes a system that can detect generalized tonic-clonic seizures on patients to alert family members or medical personnel for prompt assistance, based on a wearable device (glove), a mobile application and a Support Vector Machine classifier deployed in a system based on cloud computing. In the proposed approach we use Accelerometry (ACC), Electromyography (ECG) as measurement signals for the development of the glove, a machine learning algorithm (SVM) is used to discriminate between simulated tonic-clonic seizures and non-seizure activities that may be confused with convulsions. In this paper, the high level architecture of the system and its implementation based on Cloud Computing are described. Considering the traditional methods of measurement, the detection system proposed in this paper could mean an alternative solution that allows a prompt response and assistance that could be lifesaving in many situations.

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Epileptic seizure detection based on video and EEG recordings

Cited by 6 publications

References 14 publications

A novel approach to automatic seizure detection using computer vision and independent component analysis

A novel approach to automatic seizure detection using computer vision and independent component analysis

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

Automated Epileptic Seizure Detection System Based on a Wearable Prototype and Cloud Computing to Assist People with Epilepsy

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