A Machine Learning Model for Automated Classification of Sleep Stages Using Polysomnography Signals

2021 ◽  
pp. 209-222
Author(s):  
Santosh Kumar Satapathy ◽  
Hari Kishan Kondaveeti ◽  
D. Loganathan ◽  
S. Sharathkumar
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Sleep Stages ◽  
Automated Classification ◽  
Machine Learning Model

A Machine Learning Model for Automated Classification of Sleep Stages using Polysomnography Signals

2021 ◽  
pp. 285-296
Author(s):  
Santosh Kumar Satapathy ◽  
D. Loganathan ◽  
S. Sharathkumar ◽  
Praveena Narayanan
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Sleep Stages ◽  
Automated Classification ◽  
Machine Learning Model

Classification of Lathe’s Cutting Tool Wear Based on an Autonomous Machine Learning Model

2021 ◽  
Author(s):  
Thiago E. Fernandes ◽  
Matheus A. M. Ferreira ◽  
Guilherme P. C. de Miranda ◽  
Alexandre F. Dutra ◽  
Matheus P. Antunes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Cutting Tool ◽  
Learning Model ◽  
Cutting Tool Wear ◽  
Machine Learning Model ◽  
Autonomous Machine

A MULTI-LAYER HYBRID MACHINE LEARNING MODEL FOR AUTOMATIC SLEEP STAGE CLASSIFICATION

2018 ◽  
Vol 30 (06) ◽  
pp. 1850041
Author(s):  
Thakerng Wongsirichot ◽  
Anantaporn Hanskunatai
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Learning Model ◽  
Alternative Methods ◽  
Sleep Stages ◽  
Machine Learning Model ◽  
Subject Specific ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Hybrid Machine

Sleep Stage Classification (SSC) is a standard process in the Polysomnography (PSG) for studying sleep patterns and events. The SSC provides sleep stage information of a patient throughout an entire sleep test. A physician uses results from SSCs to diagnose sleep disorder symptoms. However, the SSC data processing is time-consuming and requires trained sleep technicians to complete the task. Over the years, researchers attempted to find alternative methods, which are known as Automatic Sleep Stage Classification (ASSC), to perform the task faster and more efficiently. Proposed ASSC techniques usually derived from existing statistical methods and machine learning (ML) techniques. The objective of this study is to develop a new hybrid ASSC technique, Multi-Layer Hybrid Machine Learning Model (MLHM), for classifying sleep stages. The MLHM blends two baseline ML techniques, Decision Tree (DT) and Support Vector Machine (SVM). It operates on a newly developed multi-layer architecture. The multi-layer architecture consists of three layers for classifying [Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text], [Formula: see text] in different epoch lengths. Our experiment design compares MLHM and baseline ML techniques and other research works. The dataset used in this study was derived from the ISRUC-Sleep database comprising of 100 subjects. The classification performances were thoroughly reviewed using the hold-out and the 10-fold cross-validation method in both subject-specific and subject-independent classifications. The MLHM achieved a certain satisfactory classification results. It gained 0.694[Formula: see text][Formula: see text][Formula: see text]0.22 of accuracy ([Formula: see text]) in subject-specific classification and 0.942[Formula: see text][Formula: see text][Formula: see text]0.02 of accuracy ([Formula: see text]) in subject-independent classification. The pros and cons of the MLHM with the multi-layer architecture were thoroughly discussed. The effect of class imbalance was rationally discussed towards the classification results.

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