Hybridization of soft-computing algorithms with neural network for prediction obstructive sleep apnea using biomedical sensor measurements

Chyad, Mustafa Habeeb; Gharghan, Sadik Kamel; Hamood, Haider Qasim; Altayyar, Ahmed Saleh Hameed; Zubaidi, Salah L.; Ridha, Hussein Mohammed

doi:10.1007/s00521-022-06919-w

Cited by 10 publications

(6 citation statements)

References 63 publications

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“…In this study, for each variable, 70% of the data set (92 out of 132 data points) was used for training, 15% was used as the test set (20 out of 132 data points), and 15% was used for validation (20 out of 132 data points). Choosing these percentages followed earlier studies, (e.g., Chyad et al 2022;Zubaidi et al 2020b, c).…”

Section: Artificial Neural Networkmentioning

confidence: 95%

Assessing the Benefits of Nature-Inspired Algorithms for the Parameterization of ANN in the Prediction of Water Demand

Zubaidi

Al-Bdairi

Ortega‐Martorell

et al. 2023

J. Water Resour. Plann. Manage.

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Accurate forecasting techniques for a stochastic pattern of water demand are essential for any city that faces high variability in climate factors and a shortage of water resources. This study was the first research to assess the impact of climatic factors on urban water demand in Iraq, which is one of the hottest countries in the world. We developed a novel forecasting methodology that includes data preprocessing and an artificial neural network (ANN) model, which we integrated with a recent nature-inspired metaheuristic algorithm [marine predators algorithm (MPA)]. The MPA-ANN algorithm was compared with four nature-inspired metaheuristic algorithms. Nine climatic factors were examined with different scenarios to simulate the monthly stochastic urban water demand over 11 years for Baghdad City, Iraq. The results revealed that (1) precipitation, solar radiation, and dew point temperature are the most relevant factors; (2) the ANN model becomes more accurate when it is used in combination with the MPA; and (3) this methodology can accurately forecast water demand considering the variability in climatic factors. These findings are of considerable significance to water utilities in planning, reviewing, and comparing the availability of freshwater resources and increasing water requests (i.e., adaptation variability of climatic factors).

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Section: Artificial Neural Networkmentioning

confidence: 95%

Assessing the Benefits of Nature-Inspired Algorithms for the Parameterization of ANN in the Prediction of Water Demand

Zubaidi

Al-Bdairi

Ortega‐Martorell

et al. 2023

J. Water Resour. Plann. Manage.

Self Cite

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“…The recording of biological signals is noninvasive and they are used to diagnose various diseases including epileptic seizures (Ghassemi et al, 2019; Shoeibi, Ghassemi, Alizadehsani, et al, 2021), schizophrenia (Ahmedt‐Aristizabal et al, 2020; Aslan & Akin, 2021), and sleep apnea (Chyad et al, 2022). Since the biological signals in the time domain demonstrate the body activity during the apnea, the time‐domain and statistical features are powerful tools in analyzing these signals.…”

Section: Cads Based On Ai Methods For Sleep Apnea Syndrome Detectionmentioning

confidence: 99%

Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

Moridian

Shoeibi

Khodatars

et al. 2022

WIREs Data Min & Knowl

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Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep apnea may last for a few seconds and happen for many while sleeping. This reduction in breathing is associated with loud snoring, which may awaken the person with a feeling of suffocation. So far, a variety of methods have been introduced by researchers to diagnose sleep apnea, among which the polysomnography (PSG) method is known to be the best. Analysis of PSG signals is very complicated. Many studies have been conducted on the automatic diagnosis of sleep apnea from biological signals using artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods. This research reviews and investigates the studies on the diagnosis of sleep apnea using AI methods. First, computer aided diagnosis system (CADS) for sleep apnea using ML and DL techniques along with its parts including dataset, preprocessing, and ML and DL methods are introduced. This research also summarizes the important specifications of the studies on the diagnosis of sleep apnea using ML and DL methods in a table. In the following, a comprehensive discussion is made on the studies carried out in this field. The challenges in the diagnosis of sleep apnea using AI methods are of paramount importance for researchers. Accordingly, these obstacles are elaborately addressed. In another section, the most important future works for studies on sleep apnea detection from PSG signals and AI techniques are presented. Ultimately, the essential findings of this study are provided in the conclusion section. This article is categorized under: Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools Algorithmic Development > Biological Data Mining

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“…While many previous studies used a trial-and-error approach to select these crucial factors [46][47][48][49][50][51][52], it is not always efficient [53] because it involves a significant amount of computational sophistication and is subject to error. Furthermore, the hidden layer may contain a sizable number of neurons due to the trial-and-error approach, which could overfit the data [54]. Therefore, the trial-and-error approach cannot be considered an ideal solution.…”

Section: Hybridizing Pso With Ann Modelmentioning

confidence: 99%

Hybridization of particle swarm optimization algorithm with neural network for COVID‐19 using computerized tomography scan and clinical parameters

Sameer

Gharghan

Mutlag

2023

The Journal of Engineering

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The 2019 coronavirus disease began in Wuhan, China, and spread worldwide. This pandemic was concerning, given its significant and worrying impact on human health. Strategies to manage the disease begin with diagnosing the infection, often using the real-time reverse transcription polymerase chain reaction (RT-PCR) assay. However, this process is time intensive. Therefore, alternative rapid methods to diagnose the coronavirus with high accuracy are needed. X-ray and computerized tomography (CT) scans are reasonable solutions for rapid coronavirus diagnosis. The dataset of 500 patients was tested, including 286 uninfected patients and 214 infected with COVID-19. Clinical parameters, including heart rate (HR), temperature (T), blood oxygen level, D-dimer, and CT scan, including red-green-blue (RGB) pixel values of the left and right lungs, were collected from 500 patients and used to train an artificial neural network (ANN) to diagnose coronavirus. The ANN was hybridized with a particle swarm optimization (PSO) algorithm to improve diagnosis accuracy. The results show that the proposed PSO-ANN method significantly improved diagnosis accuracy (98.93%), sensitivity (100%), and specificity (98.13%). The effectiveness of the proposed method was confirmed by comparing the findings with those of previous studies.

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Hybridization of soft-computing algorithms with neural network for prediction obstructive sleep apnea using biomedical sensor measurements

Cited by 10 publications

References 63 publications

Assessing the Benefits of Nature-Inspired Algorithms for the Parameterization of ANN in the Prediction of Water Demand

Assessing the Benefits of Nature-Inspired Algorithms for the Parameterization of ANN in the Prediction of Water Demand

Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

Hybridization of particle swarm optimization algorithm with neural network for COVID‐19 using computerized tomography scan and clinical parameters

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