Deep Analysis of EIT Dataset to Classify Apnea and Non-Apnea Cases in Neonatal Patients

Vahabi, Nafiseh; Yerworth, Rebecca J.; Miedema, Martijn; Kaam, Anton van; Bayford, Richard; Demosthenous, Andreas

doi:10.1109/access.2021.3056558

Cited by 7 publications

(1 citation statement)

References 35 publications

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“…An example of a classifier model which is built on limited training images was reported by [28]. Nevertheless, this dataset will be a valuable framework in developing tomographic sensing interpretation using ML [29] [30].…”

Section: Machine Learning Methods Using Mutual Induction Datamentioning

confidence: 99%

Interior Void Classification in Liquid Metal Using Multi-Frequency Magnetic Induction Tomography With a Machine Learning Approach

Muttakin

Soleimani

2021

IEEE Sensors J.

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Identification of gas bubble, void detection and porosity estimation are important factors in many liquid metal processes. In steel casting, the importance of flow condition and phase distribution in crucial parts, such as submerged entry nozzle (SEN) and mould raises the needs to observe the phenomena. Cross-section of flow shapes can be visualised using the magnetic induction tomography (MIT) technique. However, the inversion procedure in the image reconstruction has either limited resolution or postprocessing stages. Additionally, in some cases, the displayed image may not be essential when quantifying the void fraction or porosity. This work proposes an interior void classifier based on multi-frequency mutual induction measurements with eutectic alloy GaInSn as a cold liquid metal model contained in a 3D printed plastic miniature of an SEN. The sensors consist of eight coils arranged in a circle encapsulating the column, providing combinatorial detection on conductive surface and depth. The datasets are induced voltage collections of several non-metallic inclusions (NMI) patterns in liquid metal static test and used to train a machine learning model. The model architectures are a fully connected neural network (FCNN) for 1D; and a convolutional neural network (CNN) for 2D data. The classifier using 1D data has been trained providing 98% accuracy on this dataset. On the other hand, CNN classification using multi-dimensional data produces 96% of test accuracy. Refined with representative flow scenarios, the trained model could be deployed for an intelligent online control system of the liquid metal process.

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Section: Machine Learning Methods Using Mutual Induction Datamentioning

confidence: 99%

Interior Void Classification in Liquid Metal Using Multi-Frequency Magnetic Induction Tomography With a Machine Learning Approach

Muttakin

Soleimani

2021

IEEE Sensors J.

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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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A Correlation Blockchain Matrix Factorization to Enhance the Disease Prediction Accuracy and Security in IoT Medical Data

Renuka

Booba

2021

Lecture Notes on Data Engineering and Communications Technologies

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Deep Analysis of EIT Dataset to Classify Apnea and Non-Apnea Cases in Neonatal Patients

Cited by 7 publications

References 35 publications

Interior Void Classification in Liquid Metal Using Multi-Frequency Magnetic Induction Tomography With a Machine Learning Approach

Interior Void Classification in Liquid Metal Using Multi-Frequency Magnetic Induction Tomography With a Machine Learning Approach

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

A Correlation Blockchain Matrix Factorization to Enhance the Disease Prediction Accuracy and Security in IoT Medical Data

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