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

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

doi:10.1109/jbhi.2019.2895855

Cited by 35 publications

(33 citation statements)

References 38 publications

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“…As a result of this focus, the areas of patient behaviour assessment have received less attention. While several in-clinic systems using CNN and RNN-based models have been introduced to enable comprehensive data analysis through accurate and granular quantification of a patient’s movements [ 231 , 232 , 233 ], these methods are not yet sufficiently accurate for widespread clinical use, yet we argue that graph neural networks have a great potential in these application areas.…”

Section: Research Challenges and Future Directionsmentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

Self Cite

136

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With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

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Section: Research Challenges and Future Directionsmentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

Self Cite

136

View full text Add to dashboard Cite

show abstract

“…These baselines ( [30], [1], [4], and [40]) generally represent the current state-of-the-art approaches currently in use for this classification task. In line with several related works from the literature [6], [13], [16], [27], we report the best average classification accuracy, sensitivity and specificity for each method, using a leave-one-out data split.…”

Section: Classification Results and Discussionmentioning

confidence: 57%

Identification of Abnormal Movements in Infants: A Deep Neural Network for Body Part-Based Prediction of Cerebral Palsy

et al. 2021

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The early diagnosis of cerebral palsy is an area which has recently seen significant multidisciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results, however these manual methods can be laborious. The prospect of automating these processes is seen as key in advancing this field of study. In our previous works, we examined the viability of using pose-based features extracted from RGB video sequences to undertake classification of infant body movements based upon the GMA. In this paper, we propose a new deep learning framework for this classification task. We also propose a visualization framework which identifies body-parts with the greatest contribution towards a classification decision. The inclusion of a visualization framework is an important step towards automation as it helps make the decisions made by the machine learning framework interpretable. We directly compare the proposed framework's classification with several other methods from the literature using two independent datasets. Our experimental results show that the proposed method performs more consistently and more robustly than our previous pose-based techniques as well as other features from related works in this setting. We also find that our visualization framework helps provide greater interpretability, enhancing the likelihood of the adoption of these technologies within the medical domain.

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“…Key frame-based saliency detection and real-time action with 3D deep learning are also part of HAR [41,42]. The arrangement of HAR is created as structures to enable the constant checking and examination of human practices in various zones, for example, clever human action and conduct investigation intelligent human activity and behavior analysis [33,[43][44][45][46], sports injury detection [47], patient rehabilitation [33], monitor activity shifts amid elderly citizens that might be helpful to detect and diagnose serious illness [48,49], monitoring children's surveillance, hospital/patient monitoring [50,51], recognition and classification of the human usual and unusual activities [26,[52][53][54][55][56], human behavior recognition and human activity detection [53,57,58], criminal tracking system [59,60], automatic attendance system [61,62], etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization

et al. 2021

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Intelligent visual surveillance systems are attracting much attention from research and industry. The invention of smart surveillance cameras with greater processing power has now been the leading stakeholder, making it conceivable to design intelligent visual surveillance systems. It is possible to assure the safety of people in both homes and public places. This work aims to distinguish the suspicious activities for surveillance environments. For this, a 63 layers deep CNN model is suggested and named "L4-Branched-ActionNet". The suggested CNN structure is centered on the alteration of AlexNet with added four blanched sub-structures. The developed framework is first transformed into a pre-trained framework by conducting its training on an object detection dataset called CIFAR-100 with the SoftMax function. The dataset for suspicious activity recognition is then forwarded to this pretrained model for feature acquisition. The acquired deep features are subjected to feature subset optimization. These extracted features are first coded by applying entropy and then an ant colony system (ACS) is utilized on the entropy-based coded features for optimization. The configured features are then fed into numerous SVM and KNN based classification models. The cubic SVM has the highest efficiency scores, with a performance of 0.9924 in terms of accuracy. The proposed model is also validated on the Weizmann action dataset and attained an accuracy of 0.9796. The successful findings indicate the suggested work's soundness.

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

Cited by 35 publications

References 38 publications

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Identification of Abnormal Movements in Infants: A Deep Neural Network for Body Part-Based Prediction of Cerebral Palsy

Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization

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