Automatic Sleep Stage Detection Based on HRV Spectrum Analysis

Tanno, Koichi; Tamura, H.; Widasari, Edita Rosana

doi:10.1109/smc.2018.00155

Cited by 5 publications

(1 citation statement)

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“…They have used Supervised method based on features calculated from spectral domain of the EEG signals. Other studies consider only the ECG features and obtain an accuracy ranging from 76% to 89.2%, or only BCG features and obtain an accuracy around 75% [17], [22], [24], [25], [14], [15]. EOG and EMG features were also considered with similar accuracy results.…”

Section: Introductionmentioning

confidence: 99%

Supervised Classification with Short-Term Memory of Sleep Stages using Cardio-respiratory and Body Movement Variables

Gasmi

Augusto

Beaudet³

et al. 2021

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

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In the context of the Internet of Things (IoT) healthcare, biophysical features collected during sleep needs robust analysis methods to be efficiently used to detect sleep disorders. In this paper, analysis methods using a limited number of input variables (cardiac, respiratory, and body movement) have been used to perform the classification of sleep stages. The efficiency of each classification method has been compared to a reference method that combines a large number of biophysical features referred to as PolySomnoGraphy (PSG). Five classical machine learning methods were evaluated by testing their accuracy on the same collected data. Finally, using a neural network with a short memory method, the classification task fitted 91.34% of the PSG classification.Sleep stages, machine learning, supervised classification, sleep architecture, polysomnography 1) Wake stage, characterised by slow muscle activity and slow eye movement. It represents less than 2% of the total sleep duration in standard sleep conditions.

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