Exercise Prescription Using a Group‐Normalized Rating of Perceived Exertion in Adolescents and Adults With Spina Bifida

Crytzer, Theresa M.; Keramati, Mariam; Anthony, Steven J.; Cheng, Yu‐Ting; Robertson, Robert J.; Dicianno, Brad E.

doi:10.1016/j.pmrj.2018.01.004

Cited by 4 publications

(2 citation statements)

References 36 publications

(62 reference statements)

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“…Dropout strategy makes results in the dysfunction of the weight of some hidden layer nodes. Thirdly, considering the size of the epoch, group normalization proposed by He [ 33 ] have replaced batch normalization algorithm [ 34 ] in 3D CNN. Group normalization can divide the data into groups, then calculate the mean and variance in each group.…”

Section: Methodsmentioning

confidence: 99%

Automatic seizure detection using three-dimensional CNN based on multi-channel EEG

Wei

Zhou

Chen

et al. 2018

BMC Med Inform Decis Mak

107

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BackgroundAutomated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain experts, which are inflexible for the detection of a variety of patterns in a large amount of patients’ EEG data. Moreover, conventional machine learning algorithms for seizure detection cannot accommodate multi-channel Electroencephalogram (EEG) data effectively, which contains both temporal and spatial information. Recently, deep learning technology has been widely applied to perform image processing tasks, which could learns useful features from data and process multi-channel data automatically. To provide an effective system for automatic seizure detection, we proposed a new three-dimensional (3D) convolutional neural network (CNN) structure, whose inputs are multi-channel EEG signals.MethodsEEG data of 13 patients were collected from one center hospital, which has already been inspected by experts. To represent EEG data in CNN, firstly time series of each channel of EEG data was converted into the two-dimensional image. Then all channel images were combined into 3D images according to the mutual correlation intensity between different electrodes. Finally, a CNN was constructed using 3D kernels to predict different stages of EEG data, including inter-ictal, pre-ictal, and ictal stages. The system performance was evaluated and compared with the traditional feature-based classifier and the two-dimensional (2D) deep learning method.ResultsIt demonstrated that multi-channel EEG data could provide more information for increasing the specificity and sensitivity in cpmparison result between the single and multi-channel. And the 3D CNN based on multi-channel outperformed the 2D CNN and traditional signal processing methods with an accuracy of more than 90%, an sensitivity of 88.90% and an specificity of 93.78%.ConclusionsThis is the first effort to apply 3D CNN in detecting seizures from EEG. It provides a new way of learning patterns simultaneously from multi-channel EEG signals, and demonstrates that deep neural networks in combination with 3D kernels can establish an effective system for seizure detection.

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Section: Methodsmentioning

confidence: 99%

Automatic seizure detection using three-dimensional CNN based on multi-channel EEG

Wei

Zhou

Chen

et al. 2018

BMC Med Inform Decis Mak

107

View full text Add to dashboard Cite

show abstract

“…It adopts the encoder and decoder structures, where i {1,2,3,4,5,6}, for semantic segmentation, equipped with MC2F and MCAF modules. The encoder and decoder components consist of six convolution blocks, , each containing two convolution layers followed by a ReLU activation function, group normalization [ 20 ], while the seventh convolution block is a transitional block between encoder and decoder. The output shape of each convolutional group is shown in Table 1 .…”

Section: Proposed Approachmentioning

confidence: 99%

MLCA2F: Multi-Level Context Attentional Feature Fusion for COVID-19 lesion segmentation from CT scans

Bakkouri

Afdel

2022

SIViP

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In the field of diagnosis and treatment planning of Coronavirus disease 2019 (COVID-19), accurate infected area segmentation is challenging due to the significant variations in the COVID-19 lesion size, shape, and position, boundary ambiguity, as well as complex structure. To bridge these gaps, this study presents a robust deep learning model based on a novel multi-scale contextual information fusion strategy, called Multi-Level Context Attentional Feature Fusion (MLCA2F), which consists of the Multi-Scale Context-Attention Network (MSCA-Net) blocks for segmenting COVID-19 lesions from Computed Tomography (CT) images. Unlike the previous classical deep learning models, the MSCA-Net integrates Multi-Scale Contextual Feature Fusion (MC2F) and Multi-Context Attentional Feature (MCAF) to learn more lesion details and guide the model to estimate the position of the boundary of infected regions, respectively. Practically, extensive experiments are performed on the Kaggle CT dataset to explore the optimal structure of MLCA2F. In comparison with the current state-of-the-art methods, the experiments show that the proposed methodology provides efficient results. Therefore, we can conclude that the MLCA2F framework has the potential to dramatically improve the conventional segmentation methods for assisting clinical decision-making.

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Cross-cultural adaptation and concurrent validity of the children’s OMNI scale of perceived exertion for Arm-Crank activity in Spina Bifida

Bertapelli,

Robertson,

Leonardi-Figueiredo

et al. 2023

Disability and Rehabilitation

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Exercise Prescription Using a Group‐Normalized Rating of Perceived Exertion in Adolescents and Adults With Spina Bifida

Abstract: III.

Cited by 4 publications

References 36 publications

Automatic seizure detection using three-dimensional CNN based on multi-channel EEG

Automatic seizure detection using three-dimensional CNN based on multi-channel EEG

MLCA2F: Multi-Level Context Attentional Feature Fusion for COVID-19 lesion segmentation from CT scans

Cross-cultural adaptation and concurrent validity of the children’s OMNI scale of perceived exertion for Arm-Crank activity in Spina Bifida

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