Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction Techniques
One of the most essential components of human life is sleep. One of the first steps in spotting abnormalities connected to sleep is classifying sleep stages. Based on the kind and frequency of signals obtained during a polysomnography test, sleep phases can be separated into groups. Accurate classif...
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MDPI AG
2024-05-01
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Online Access: | https://www.mdpi.com/1999-4893/17/6/229 |
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author | Maadh Rajaa Mohammed Ali Makki Sagheer |
author_facet | Maadh Rajaa Mohammed Ali Makki Sagheer |
author_sort | Maadh Rajaa Mohammed |
collection | DOAJ |
description | One of the most essential components of human life is sleep. One of the first steps in spotting abnormalities connected to sleep is classifying sleep stages. Based on the kind and frequency of signals obtained during a polysomnography test, sleep phases can be separated into groups. Accurate classification of sleep stages from electroencephalogram (EEG) signals plays a crucial role in sleep disorder diagnosis and treatment. This study proposes a novel approach that combines feature selection techniques with convolutional neural networks (CNNs) to enhance the classification performance of sleep stages using EEG signals. Firstly, a comprehensive feature selection process was employed to extract discriminative features from raw EEG data, aiming to reduce dimensionality and enhance the efficiency of subsequent classification using mutual information (MI) and analysis of variance (ANOVA) after splitting the dataset into two sets—the training set (70%) and testing set (30%)—then processing it using the standard scalar method. Subsequently, a 1D-CNN architecture was designed to automatically learn hierarchical representations of the selected features, capturing complex patterns indicative of different sleep stages. The proposed method was evaluated on a publicly available EDF-Sleep dataset, demonstrating superior performance compared to traditional approaches. The results highlight the effectiveness of integrating feature selection with CNNs in improving the accuracy and reliability of sleep stage classification from EEG signals, which reached 99.84% with MI-50. This approach not only contributes to advancing the field of sleep disorder diagnosis, but also holds promise for developing more efficient and robust clinical decision support systems. |
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issn | 1999-4893 |
language | English |
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spelling | doaj.art-a315747c34eb449d8a5e697818a0ac732024-06-26T13:42:34ZengMDPI AGAlgorithms1999-48932024-05-0117622910.3390/a17060229Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction TechniquesMaadh Rajaa Mohammed0Ali Makki Sagheer1Department of Computer Science, College of Computer Science and Information Technology, Anbar University, Ramadi 31001, IraqDepartment of Computer Science, College of Computer Science and Information Technology, Anbar University, Ramadi 31001, IraqOne of the most essential components of human life is sleep. One of the first steps in spotting abnormalities connected to sleep is classifying sleep stages. Based on the kind and frequency of signals obtained during a polysomnography test, sleep phases can be separated into groups. Accurate classification of sleep stages from electroencephalogram (EEG) signals plays a crucial role in sleep disorder diagnosis and treatment. This study proposes a novel approach that combines feature selection techniques with convolutional neural networks (CNNs) to enhance the classification performance of sleep stages using EEG signals. Firstly, a comprehensive feature selection process was employed to extract discriminative features from raw EEG data, aiming to reduce dimensionality and enhance the efficiency of subsequent classification using mutual information (MI) and analysis of variance (ANOVA) after splitting the dataset into two sets—the training set (70%) and testing set (30%)—then processing it using the standard scalar method. Subsequently, a 1D-CNN architecture was designed to automatically learn hierarchical representations of the selected features, capturing complex patterns indicative of different sleep stages. The proposed method was evaluated on a publicly available EDF-Sleep dataset, demonstrating superior performance compared to traditional approaches. The results highlight the effectiveness of integrating feature selection with CNNs in improving the accuracy and reliability of sleep stage classification from EEG signals, which reached 99.84% with MI-50. This approach not only contributes to advancing the field of sleep disorder diagnosis, but also holds promise for developing more efficient and robust clinical decision support systems.https://www.mdpi.com/1999-4893/17/6/229deep learning (DL)convolution neural network (CNN)electroencephalography (EEG)feature selection (FS)sleep stages |
spellingShingle | Maadh Rajaa Mohammed Ali Makki Sagheer Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction Techniques Algorithms deep learning (DL) convolution neural network (CNN) electroencephalography (EEG) feature selection (FS) sleep stages |
title | Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction Techniques |
title_full | Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction Techniques |
title_fullStr | Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction Techniques |
title_full_unstemmed | Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction Techniques |
title_short | Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction Techniques |
title_sort | employing a convolutional neural network to classify sleep stages from eeg signals using feature reduction techniques |
topic | deep learning (DL) convolution neural network (CNN) electroencephalography (EEG) feature selection (FS) sleep stages |
url | https://www.mdpi.com/1999-4893/17/6/229 |
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