Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings

Martini, Michael L; Valliani, Aly; Sun, Claire; Costa, Anthony; Zhao, Shan; Panov, Fedor; Ghatan, Saadi; Rajan, Kanaka; Oermann, Eric K.

doi:10.1038/s41598-021-86891-y

Cited by 11 publications

(9 citation statements)

References 27 publications

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“…is method makes the quantitative analysis of risk assessment more accurate and objective, and the model has achieved good results with five cases. e bidding cost of EPC projects is high, accounting for about 5% of the total contract cost, and good risk management in the bidding stage can help the general contractor save and reduce the pressure of risk management in the later contract negotiation and performance stages [8]. Zhang et al pointed out that the study of organizational structure mainly refers to the aspects of the composition of the organization and the relationship between the members of the organization [9].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm

Dang

Liu

et al. 2021

Computational Intelligence and Neuroscience

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In this paper, a deep confidence neural network algorithm is used to design and deeply analyze the risk warning model for stadium operation. Many factors, such as video shooting angle, background brightness, diversity of features, and the relationship between human behaviors, make feature attribute-based behavior detection a focus of researchers’ attention. To address these factors, researchers have proposed a method to extract human behavior skeleton and optical flow feature information from videos. The key of the deep confidence neural network-based recognition method is the extraction of the human skeleton, which extracts the skeleton sequence of human behavior from a surveillance video, where each frame of the skeleton contains 18 joints of the human skeleton and the confidence value estimated for each frame of the skeleton, and builds a deep confidence neural network model to classify the dangerous behavior based on the obtained skeleton feature information combined with the time vector in the skeleton sequence and determine the danger level of the behavior by setting the corresponding threshold value. The deep confidence neural network uses different feature information compared with the spatiotemporal graph convolutional network. The deep confidence neural network establishes the deep confidence neural network model based on the human optical flow information, combined with the temporal relational inference of video frames. The key of the temporal relationship network-based recognition method is to extract some frames from the video in an orderly or random way into the temporal relationship network. In this paper, we use several methods for comparison experiments, and the results show that the recognition method based on skeleton and optical flow features is significantly better than the algorithm of manual feature extraction.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm

Dang

Liu

et al. 2021

Computational Intelligence and Neuroscience

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“…In the medical domain, multimodal data are often complementary with each other, meaning that each type of data (e.g. images, sensor data, text report) can be used to extract unique latent representations to allow a better understanding of a pathology [160] 2021 Predictive Seizure Forecasting Private 3 Saeed et al [161] 2021 Multiple Multiple HHAR [144], MobiAct [147], MotionSense [149], UCI HAR [146], HAPT [162], Sleep-EDF [131], MIT Driver DB [163], WiFi CSI [164] 4 Spathis et al [165] 2021 Predictive (HR forecasting) Subject Health Private 5 Thiam et al [166] 2021 Generative Pain Classification BioVid heat pain [167], SenseEmotion [168] 6 Das et al [169] 2022 Predictive Stuttering prediction Private 7 Deldari et al [170] 2022 Contrastive (COCOA) Multiple UCI HAR [146], SLEEP-EDF, PAMAP2 [171], WE-SAD [87], Opportunity [172] 8 Lemkhenter et al [173] 2022 Predictive (PhaseSwap)…”

Section: Discussion and Open Challengesmentioning

confidence: 99%

“…Leveraging a combination of EEG and facial activity data extracted from video, Das et al [169] trained an explainable AI model to predict upcoming speech stuttering. Also, Martini et al [160] showed the potentiality of multimodal self-supervised learning by combining stereoencephalography (SEEG) and video data to forecast seizure events in drug resistant epileptic subjects.…”

Section: Multimodal Self-supervised Learning With Biosignalsmentioning

confidence: 99%

Applications of Self-Supervised Learning to Biomedical Signals: where are we now

Pup¹,

Atzori²

2023

Preprint

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<p>Over the last decade, deep learning applications in biomedical research have exploded, demonstrating the ability to often outperform previous machine learning approaches in various tasks. However, training deep learning models requires large amounts of data annotated by experts, whose collection is often time- and cost- prohibitive in the biomedical domain. Self-Supervised Learning (SSL) has emerged as a prominent solution for these problems, as it allows to learn powerful data representations in an unsupervised manner. Despite most applications in biomedical research targeted images, the high amount of recent works targeting biosignals can make it difficult for researchers to have a complete picture of the current situation. The aim of this paper is to outline and clarify the state of the art in the domain. The article briefly summarizes the nature and acquisition modality of biomedical signals, introduces the SSL method, and provides a complete but synthetic overview of the main works applying SSL for the analysis of biomedical signals. The analysis of the scientific literature highlights the importance of SSL, confirming its potential to improve the integration of deep learning into clinical tasks. </p>

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“…A popular type of RNN is LSTM, which is better at learning over longer periods of time than a vanilla RNN and has seen use in many CV algorithms. 26,30,44,59,64,66,67,68,69,70,71,72 (Continues)…”

Section: Rnnsmentioning

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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Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings

Cited by 11 publications

References 27 publications

[Retracted] Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm

[Retracted] Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm

Applications of Self-Supervised Learning to Biomedical Signals: where are we now

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

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