ArticlePeerJ2018
Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis.
Article in PeerJ, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions.PeerJ. Computer science · 2022Article
- Comparison of wavelet transformations to enhance convolutional neural network performance in brain tumor segmentation.BMC medical informatics and decision making · 2021Article
- Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals.Diagnostics (Basel, Switzerland) · 2021Article
- SleepOMICS: How Big Data Can Revolutionize Sleep Science.International journal of environmental research and public health · 2019Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionSleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques.
methodsSleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis.
resultsNeighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy, respectively. DISCUSSION AND
conclusionSimilar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.
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