An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment

An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment

Autonomous sleep tracking at home has become inevitable in today’s fast-paced world. A crucial aspect of addressing sleep-related issues involves accurately classifying sleep stages. This paper introduces a novel approach PSO–XGBoost, combining particle swarm optimisation (PSO) with extreme gradient...

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Bibliographic Details
Main Authors: Baskaran Lizzie Radhakrishnan, Kirubakaran Ezra, Immanuel Johnraja Jebadurai, Immanuel Selvakumar, Periyasami Karthikeyan
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Sensors
Subjects:
single-channel EEG
machine learning
sleep-stage classification
particle swarm optimisation (PSO)
XGBoost
sleep monitoring
Online Access:https://www.mdpi.com/1424-8220/24/4/1197

Internet

https://www.mdpi.com/1424-8220/24/4/1197

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