Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Savareh, Behrouz Alizadeh; Bashiri, Azadeh; Behmanesh, Ali; Meftahi, Gholam Hossein; Hatef, Boshra

doi:10.7717/peerj.5247

Cited by 17 publications

(5 citation statements)

References 60 publications

(79 reference statements)

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“…Radha et al [47] considered multiple EEG signals and extracted features from different domains like time and frequency. The author in this work used SVM and Random Forests (RF) were considered for the classification of different sleep stages.…”

Section: Sn Computer Sciencementioning

confidence: 99%

A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques

Satapathy

Loganathan

2021

SN COMPUT. SCI.

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Sleep staging is one of the important methods for the diagnosis of the different types of sleep-related diseases. Manual inspection of sleep scoring is a very time-consuming process, labor-intensive, and requires more human interpretations, which may produce biased results. Therefore, in this paper, we propose an efficient automated sleep staging system to improve sleep staging accuracy. In this work, we extracted both linear and non-linear properties from the input signal. Next to that, a set of optimal features was selected from the extracted feature vector by using a feature reduction technique based on the ReliefF weight algorithm. Finally, the selected features were classified through four machine learning techniques like support vector machine, K-nearest neighbor, decision tree, and random forest. The proposed methodology performed using dualchannel EEG signals from the ISRUC-Sleep dataset under the AASM sleep scoring rules. The performance of the proposed methodology compared with the existing similar methods. In this work, we considered the 10-Fold cross validation strategy; our proposed methods reported the highest classification accuracy of 91.67% with the C4-A1 channel, and 93.8% with the O2-A1 channel using the Random forest classification model. The result of the proposed methodology outperformed the earlier contribution for two-class sleep states classification. The proposed dual-channel sleep staging method can be helpful for the clinicians during the sleep scoring and treatment for the different sleep-related diseases.

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Section: Sn Computer Sciencementioning

confidence: 99%

A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques

Satapathy

Loganathan

2021

SN COMPUT. SCI.

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“…ANNs and SVM can integrate and enhance WA, whereas WA-based state-of-art methods can be effectively utilized in feature generation and dimension reduction together with next-generation tools, such as the neighboring component analysis (NCA) and other techniques combining both linear and non-linear feature selection methods. ANNs and SVM can achieve accuracy up to 90.30% and 89.93%, respectively, in properly classifying EEG patterns and sleep scoring [40].…”

Section: Sleep and Signaling-based Big Datamentioning

confidence: 99%

SleepOMICS: How Big Data Can Revolutionize Sleep Science

Bragazzi

Guglielmi

Garbarino

2019

IJERPH

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Sleep disorders have reached epidemic proportions worldwide, affecting the youth as well as the elderly, crossing the entire lifespan in both developed and developing countries. “Real-life” behavioral (sensor-based), molecular, digital, and epidemiological big data represent a source of an impressive wealth of information that can be exploited in order to advance the field of sleep research. It can be anticipated that big data will have a profound impact, potentially enabling the dissection of differences and oscillations in sleep dynamics and architecture at the individual level (“sleepOMICS”), thus paving the way for a targeted, “one-size-does-not-fit-all” management of sleep disorders (“precision sleep medicine”).

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“…In another study conducted with the sleep-EDF dataset, Savarek et.al. utilized from wavelet tree, neighboring component analysis (NCA), SVM, and ANN [23] to reach a classification accuracy of 90% and 89% for the ANN and SVM respectively. Wei et.…”

Section: Introductionmentioning

confidence: 99%

Automatic Sleep Stage Classification for the Obstructive Sleep Apnea Patients with Feature Mining

Özşen

Koca

Tezel

et al. 2023

JBBBE

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Automatic sleep scoring systems have being much more attention in last decades. Whereas a wide variety of studies have been used in this subject area, the accuracies are still under acceptable limits to apply these methods in real life data. One can find many high accuracy studies in literature using standard database but when it comes to the using real data reaching such a high performances is not straightforward. In this study, five distinct datasets were prepared using 124 persons including 93 unhealthy and 31 healthy persons. These datasets consist of time-, nonlinear-, welch-, discrete wavelet transform-and Hilbert-Huang transform-features. By applying k-NN, Decision Trees, ANN, SVM and Bagged Tree classifiers to these feature sets in various manners by using feature-selection highest classification accuracy was searched. The maximum classification accuracy was detected in case of Bagged Tree classifier as 95.06% with the use of 14 features among a total of 136 features. This accuracy is relatively high compared with literature for a real-data application.

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Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Cited by 17 publications

References 60 publications

A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques

A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques

SleepOMICS: How Big Data Can Revolutionize Sleep Science

Automatic Sleep Stage Classification for the Obstructive Sleep Apnea Patients with Feature Mining

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