Supervised Brain Network Learning Based on Deep Recurrent Neural Networks

Zhao, Shijie; Cui, Yan; Huang, Linwei; Xie, Li; Chen, Yaowu; Han, Junwei; Zhang, Shu; Liu, Tianming; Lv, Jinglei

doi:10.1109/access.2020.2984948

Cited by 3 publications

(2 citation statements)

References 48 publications

(91 reference statements)

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“…Moreover, Zhao and Cui [77] presented a method for learning brain networks using deep RNNs that can capture temporal dependencies. They proposed a supervised learning framework that uses RNNs to learn the functional connectivity patterns in the brain.…”

Section: Rnn Methodsmentioning

confidence: 99%

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

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With the swift pace of the development of artificial intelligence (AI) in diverse spheres, the medical and healthcare fields are utilizing machine learning (ML) methodologies in numerous inventive ways. ML techniques have outstripped formerly state-of-the-art techniques in medical and healthcare practices, yielding faster and more precise outcomes. Healthcare practitioners are increasingly drawn to this technology in their initiatives relating to the Internet of Behavior (IoB). This area of research scrutinizes the rationales, approaches, and timing of human technology adoption, encompassing the domains of the Internet of Things (IoT), behavioral science, and edge analytics. The significance of ML in medical and healthcare applications based on the IoB stems from its ability to analyze and interpret copious amounts of complex data instantly, providing innovative perspectives that can enhance healthcare outcomes and boost the efficiency of IoB-based medical and healthcare procedures and thus aid in diagnoses, treatment protocols, and clinical decision making. As a result of the inadequacy of thorough inquiry into the employment of ML-based approaches in the context of using IoB for healthcare applications, we conducted a study on this subject matter, introducing a novel taxonomy that underscores the need to employ each ML method distinctively. With this objective in mind, we have classified the cutting-edge ML solutions for IoB-based healthcare challenges into five categories, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), multilayer perceptions (MLPs), and hybrid methods. In order to delve deeper, we conducted a systematic literature review (SLR) that examined critical factors, such as the primary concept, benefits, drawbacks, simulation environment, and datasets. Subsequently, we highlighted pioneering studies on ML methodologies for IoB-based medical issues. Moreover, several challenges related to the implementation of ML in healthcare and medicine have been tackled, thereby gradually fostering further research endeavors that can enhance IoB-based health and medical studies. Our findings indicated that Tensorflow was the most commonly utilized simulation setting, accounting for 24% of the proposed methodologies by researchers. Additionally, accuracy was deemed to be the most crucial parameter in the majority of the examined papers.

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Section: Rnn Methodsmentioning

confidence: 99%

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

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“…A boxcar function was used to describe the stimulation paradigm. The corresponding hemodynamic responses were obtained by solving the Balloon model [52][53][54][55]. The generated results are shown in Fig.…”

Section: B Hr Paradigmmentioning

confidence: 99%

Multi-Channel-Based Differential Pathlength Factor Estimation for Continuous-Wave fNIRS

Huang

Hong

2021

IEEE Access

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Functional near-infrared spectroscopy (fNIRS) in brain imaging needs to be robust to subjectwise variability. The use of a fixed differential pathlength factor (DPF) per wavelength for the entire brain will degrade the accuracy of hemodynamic responses. Since the tissue composition varies within the brain, correct DPF values should be used for various emitter-detector distances and brain regions. In this paper, a DPF estimation method combining a state-space model of the modified Beer-Lambert law (mBLL), a parameter model for estimating the reduced scattering coefficients, and dual square-root cubature Kalman filters (SCKFs) is proposed. To validate the proposed method, known light intensities (six channels, two wavelengths) and reference DPFs are generated using NIRFAST (a Matlab toolbox) using a presumed paradigm, known tissue properties, a Balloon model, and a finite element head model consisting of 58,818 mesh elements. Then, the DPF values are estimated using a Jacobian matrix from the head model and the mBLL. The results show that the estimated concentration changes correlate well with the reference data. Also, the estimated DPFs showed relative errors less than 1.33% maximum and 0.75% on average. A onetailed t-test revealed that the estimated DPFs matched the reference DPFs with more than 99.9% confidence. The developed method can efficiently access the actual DPFs even if emitter-detector distances vary significantly and the tissue properties are not uniform. With the developed state-space models for dual SCKFs, real-time estimation of the DPFs from one experiment to another has become plausible.INDEX TERMS Differential pathlength factor (DPF), functional near-infrared spectroscopy (fNIRS), Kalman filter, state-space method, finite element method (FEM)

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Artificial Intelligence based Neurodegenerative Disease Diagnosis and Research Analysis using Functional MRI (FMRI): A Review

Nisha

Rajasekaran

Priya³

et al. 2021

2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)

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Supervised Brain Network Learning Based on Deep Recurrent Neural Networks

Cited by 3 publications

References 48 publications

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Multi-Channel-Based Differential Pathlength Factor Estimation for Continuous-Wave fNIRS

Artificial Intelligence based Neurodegenerative Disease Diagnosis and Research Analysis using Functional MRI (FMRI): A Review

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