Artifact Removal from Sleep Hipnograms using Complete Ensemble Empirical Decomposition with Adaptive Noise

Gurrala, Vijaya Kumar; Yarlagadda, Prasad K.D.V.; Koppireddi, Padmaraju

doi:10.17485/ijst/2017/v10i47/108645

Cited by 2 publications

(1 citation statement)

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“…Next is the data pre-processing step. Pre-processing involves either artefact removal [19] or filtering the data. Generally during the process of acquisition the data mixed with the body movements signals called as artefacts or with the power line noise signal, since EEG signal is from 0.5 Hz to 100 Hz.…”

Section: Figure 2 Block Diagram Of Sleep Apnea Detectionmentioning

confidence: 99%

Detection of Sleep Apnea Based on the Analysis of Sleep Stages Data Using Single Channel EEG

Gurrala¹,

Yarlagadda²,

Koppireddi³

2021

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Sleep is a basic need for a human being’s intellectual and physiological restoration and overlaying nearly one 1/3 length of a daytime. A first-rate and deep sleep is required for green regeneration of the body. Sleep disorders hamper the performance of an individual. Sleep Apnea is the one amongst the disorders that affect many. Most of Apnea related works consider Electrocardiogram (ECG) and respiratory signals /or combinations, instead of considering all Polysomnographic signals (PSG). It is evident that for the detection of Apnea related sleep disorders it is required to consider one or few signals rather considering all PSG signals. In this work, we advocate a way that might be carried out to perceive the information of sleep stages which might be crucial in diagnosing and treating sleep disorders. It differentiates sleep stages and derives new features from the sleep EEG that allows helping physicians with the analysis and treatment of associated sleep issues. This theory depends on exclusive EEG datasets from Physionet with the use of MIT-BIH polysomnographic database that have been received and described through scientists for the analysis and prognosis of sleep ranges. Experimental results on 18 records with 10197 epochs show that an Apnea detection accuracy of 95.9% obtained for Machine learning classifier with Ensemble Bagged Tree classifier.

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Section: Figure 2 Block Diagram Of Sleep Apnea Detectionmentioning

confidence: 99%