An ANN based SpO2 Measurement for Clinical Management Systems

Gupta, Garima P; Nair, Rajashree R; Jeyanthi, R.

doi:10.1016/j.egypro.2017.05.154

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…For better validation and to solve complex interdependency, stochastic tools like Artificial Neural Network (ANN) were used. These mathematical models were used in many fields for modelling, prediction, optimization, validation, and data mining …”

Section: Introductionmentioning

confidence: 99%

“…These mathematical models were used in many fields for modelling, prediction, optimization, validation, and data mining. 27,28 Here, we have demonstrated an in silico approach for screening functional SNPs from LEPR clinical variants from the intact group of missense mutations and identified novel variants. The identified deleterious SNPs were further validated using ANN using backpropagation neural network algorithm (BPNN).…”

Section: Introductionmentioning

confidence: 99%

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Computational and artificial neural network based study of functional SNPs of human LEPR protein associated with reproductive function

Muruganandhan

Manian

2019

J of Cellular Biochemistry

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Genetic polymorphisms are mostly associated with inherited diseases, detecting and analyzing the biological significance of functional single‐nucleotide polymorphisms (SNPs) using wet laboratory experiments is an arduous task hence the computational analysis of putative SNPs is essential before conducting a study on a large population. SNP in the leptin receptor (LEPR) could result in the retention of intracellular signalling due to the structural and functional instability of the receptor causing abnormal reproductive function in human. In this first comprehensive computational analysis of LEPR gene mutation, we have identified and analyzed the functional consequence and structural significance of the SNPs in LEPR using recently developed several computational algorithms. Thirteen deleterious mutations such as W13C, S93G, I232R, Q307H, Y354C, E497A, Q571H, R612H, K656N, T690A, T699M V741M, and L760R were identified in the LEPR gene coding region. Backpropagation algorithm has been developed to forestall the deleterious nature of SNP and to validate the outcome of the tested computational tools. From ConSurf prediction three SNPs (Q571H, R612H, and T699M) were highly conserved on LEPR protein and the most deleterious variant R612H had one hydrogen bond abolished and severely reduced protein stability. Molecular docking suggested that the mutant (R612H) LEPR had lowest binding energy than native LEPR with the ligand molecule. Thus the energetically destructive changeover of ARG to HIS in R612H could possibly affect the LEPR protein structural stability and functional constancy due to interruption in the amino acid interactions and could result in reproductive disorders in human and increases the complication in obstetric and pregnancy outcome.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational and artificial neural network based study of functional SNPs of human LEPR protein associated with reproductive function

Muruganandhan

Manian

2019

J of Cellular Biochemistry

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“…GP Gupta et al developed an ANN for predicting the blood oxygen saturation value. But it is only a predictive system for SpO 2 of patients in ventilator [11] . Pan et al proposed transfer learning to identify patient ventilator asynchrony by changing convolution neural network models.…”

Section: Literature Reviewmentioning

confidence: 99%

Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning

Radhakrishnan

Nair

Isaac

2022

Biomedical Signal Processing and Control

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Background and objective In pandemic situations like COVID 19, real time monitoring of patient condition and continuous delivery of inspired oxygen can be made possible only through artificial intelligence-based system modeling. Even now manual control of mechanical ventilator parameters is continuing despite the ever-increasing number of patients in critical epidemic conditions. Here a suggestive multi-layer perceptron neural network model is developed to predict the level of inspired oxygen delivered by the mechanical ventilator along with mode and positive end expiratory pressure (PEEP) changes for reducing the effort of health care professionals. Methods The artificial neural network model is developed by Python programming using real time data. Parameter identification for model inputs and outputs is done by in corporating consistent real time patient data including periodical arterial blood gas analysis, continuous pulse oximetry readings and mechanical ventilator settings using statistical pairwise analysis using R programming. Results Mean square error values and R values of the model are calculated and found to be an average of 0.093 and 0.81 respectively for various data sets. Accuracy loss will be in good fit with validation loss for a comparable number of epochs. Conclusions Comparison of the model output is undertaken with physician’s prediction using statistical analysis and shows an accuracy error of 4.11 percentages which is permissible for a good predictive system.

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Remote Patient Monitoring During the COVID-19 Pandemic in the Framework of Home Device Manufacturing for IoT-Based BPM and SPO2 Measurements

Altıparmak¹,

Kaba²

2022

Forthcoming Networks and Sustainability in the IoT Era

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An ANN based SpO2 Measurement for Clinical Management Systems

Cited by 10 publications

References 7 publications

Computational and artificial neural network based study of functional SNPs of human LEPR protein associated with reproductive function

Computational and artificial neural network based study of functional SNPs of human LEPR protein associated with reproductive function

Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning

Remote Patient Monitoring During the COVID-19 Pandemic in the Framework of Home Device Manufacturing for IoT-Based BPM and SPO2 Measurements

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