A new technique for the prediction of heart failure risk driven by hierarchical neighborhood component-based learning and adaptive multi-layer networks

Samuel, Oluwarotimi Williams; Yang, Bin; Geng, Yanjuan; Asogbon, Mojisola Grace; Pirbhulal, Sandeep; Mzurikwao, Deogratias; Idowu, Oluwagbenga Paul; Ogundele, Tunde Joseph; Li, Xiangxin; Chen, Shixiong; Naik, Ganesh R.; Peng, Fang; Han, Fanghai; Li, Guanglin

doi:10.1016/j.future.2019.10.034

Cited by 29 publications

(17 citation statements)

References 32 publications

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“…Further work in this area would now involve continued exploration of unsupervised learning methods for further effective classification methods, and in particular, methods whose configuration is similar to that of the SC (Samuel et al, 2020), i.e., no cluster shape assumption, to observe the extent which data of this nature can be correctly clustered, and what clustering methods best suit the data source.…”

Section: Discussionmentioning

confidence: 99%

Toward a Self-Supervised Architecture for Semen Quality Prediction Using Environmental and Lifestyle Factors

Nsugbe

2022

AIA

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Male fertility has been seen to be declining, prompting for more effective and accessible means of its assessment. AI methods have been effective towards predicting semen quality through a questionnaire-based information source comprising a selection of factors from the medical literature which have been seen to influence semen quality. Prior work has seen the application of supervised learning towards the prediction of semen quality, but since supervised learning hinges on the provision of data class labels it can be said to depend on an external intelligence intervention, which can translate towards further costs and resources in practical settings. In contrast, unsupervised learning methods partition data into clusters and groups based on an objective function and do not rely on the provision of class labels, and can allow for a fully automated flow of a prediction platform. In this paper we apply three unsupervised learning models with different model architectures, namely: Gaussian mixture model (GMM), K-means and spectral clustering (SC), alongside low dimensional embedding methods which include sparse autoencoder (SAE), principal component analysis (PCA) and robust principal component analysis (RPCA). The best results were obtained with a combination of the sparse autoencoder and the SC algorithm, which was likely due to its nonspecific and arbitrary cluster shape assumption. Further work would now involve the exploration of similar unsupervised learning algorithms with a similar framework to the SC to investigate the extent to which various clusters can be learned with maximal accuracy.

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Section: Discussionmentioning

confidence: 99%

Toward a Self-Supervised Architecture for Semen Quality Prediction Using Environmental and Lifestyle Factors

Nsugbe

2022

AIA

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“…Finally, we have used the HS optical flow-based interpolation method to achieve the best results over the PC-MRI sequence. However, the IE scores and SSIM scores on each selected time frame highlight the insufficiency of the interpolation model’s capacity to deal with the challenging motion regions of the heart; the temporal sampling in medical image sequences are lower than that of natural scene videos ( Guo et al, 2020 ; Samuel et al, 2020 ), and the computation of optical flow is expensive ( Meyer et al, 2015 ; Samuel et al, 2017 ). Improving the optical flow-based interpolation performance, and decreasing the computation time are also key factors for our future research.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Approach for Cardiac Blood Flow Vortex Ring Identification Based on Optical Flow and Lagrangian Averaged Vorticity Deviation

Samuel

Zhang

et al. 2021

Front. Physiol.

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Objective: The measurement of cardiac blood flow vortex characteristics can help to facilitate the analysis of blood flow dynamics that regulates heart function. However, the complexity of cardiac flow along with other physical limitations makes it difficult to adequately identify the dominant vortices in a heart chamber, which play a significant role in regulating the heart function. Although the existing vortex quantification methods can achieve this goal, there are still some shortcomings: such as low precision, and ignoring the center of the vortex without the description of vortex deformation processes. To address these problems, an optical flow Lagrangian averaged vorticity deviation (Optical flow-LAVD) method is proposed.Methodology: We examined the flow within the right atrium (RA) of the participants’ hearts, by using a single set of scans pertaining to a slice at two-chamber short-axis orientation. Toward adequate extraction of the vortex ring characteristics, a novel approach driven by the Lagrangian averaged vorticity deviation (LAVD) was implemented and applied to characterize the trajectory integral associated with vorticity deviation and the spatial mean of rings, by using phase-contrast magnetic resonance imaging (PC-MRI) datasets as a case study. To interpolate the time frames between every larger discrete frame and minimize the error caused by constructing a continuous velocity field for the integral process of LAVD, we implemented the optical flow as an interpolator and introduced the backward warping as an intermediate frame synthesis basis, which is then used to generate higher quality continuous velocity fields.Results: Our analytical study results showed that the proposed Optical flow-LAVD method can accurately identify vortex ring and continuous velocity fields, based on optical flow information, for yielding high reconstruction outcomes. Compared with the linear interpolation and phased-based frame interpolation methods, our proposed algorithm can generate more accurate synthesized PC-MRI.Conclusion: This study has developed a novel Optical flow-LAVD model to accurately identify cardiac vortex rings, and minimize the associated errors caused by the construction of a continuous velocity field. Our paper presents a superior vortex characteristics detection method that may potentially aid the understanding of medical experts on the dynamics of blood flow within the heart.

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“…Next to that, DL driven layered architecture for IoMT is presented. In [24], an effective integrated approach for adequate heart failure risk prediction is presented. This method is based on hierarchical neighborhood component-based-learning (HNCL) and adaptive multi-layer networks (AMLN).…”

Section: Literature Surveymentioning

confidence: 99%

An Effective Training Scheme for Deep Neural Network in Edge Computing Enabled Internet of Medical Things (IoMT) Systems

et al. 2020

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At present times, the real-time requirement on the multiaccess healthcare monitoring system, information mining, and efficient disease diagnosis of health conditions is a difficult process. The recent advances in information technology and the internet of medical things (IoMT) have fostered extensive utilization of the smart system. A complex, 24/7 healthcare service is needed for effective and trustworthy monitoring of patients on a daily basis. To accomplish this need, edge computing and cloud platforms are highly required to satisfy the requirements of smart healthcare systems. This paper presents a new effective training scheme for the deep neural network (DNN), called ETS-DNN model in edge computing enabled IoMT system. The proposed ETS-DNN intends to facilitate timely data collection and processing to make timely decisions using the patterns that exist in the data. Initially, the IoMT devices sense the patient's data and transfer the captured data to edge computing, which executes the ETS-DNN model to diagnose it. The proposed ETS-DNN model incorporates a Hybrid Modified Water Wave Optimization (HMWWO) technique to tune the parameters of the DNN structure, which comprises of several autoencoder layers cascaded to a softmax (SM) layer. The SM classification layer is placed at the end of the DNN to perform the classification task. The HMWWO algorithm integrates the MWWO technique with limited memory Broyden-Fletcher-Goldfarb-Shannon (L-BFGS). Once the ETS-DNN model generates the report in edge computing, then it will be sent to the cloud server, which is then forwarded to the healthcare professionals, hospital database, and concerned patients. The proposed ETS-DNN model intends to facilitate timely data collection and processing to identify the patterns exist in the data. An extensive set of experimental analysis takes place and the results are investigated under several aspects. The simulation outcome pointed out the superior characteristics of the ETS-DNN model over the compared methods.

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A new technique for the prediction of heart failure risk driven by hierarchical neighborhood component-based learning and adaptive multi-layer networks

Cited by 29 publications

References 32 publications

Toward a Self-Supervised Architecture for Semen Quality Prediction Using Environmental and Lifestyle Factors

Toward a Self-Supervised Architecture for Semen Quality Prediction Using Environmental and Lifestyle Factors

A Hybrid Approach for Cardiac Blood Flow Vortex Ring Identification Based on Optical Flow and Lagrangian Averaged Vorticity Deviation

An Effective Training Scheme for Deep Neural Network in Edge Computing Enabled Internet of Medical Things (IoMT) Systems

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