Abstract
Sleep quantification and automatic scoring of sleep stages via electroencephalogram (EEG) signals has been a challenge for years. It is crucial to investigate the correlation of brain waves by sleep EEG analysis due to the association between rhythmic oscillations of neuronal activity and neocortical synchronization. Multiscale joint permutation entropy (MJPE) had been proven to be capable of measuring the correlation between time series from the view of multiple time scales and thus can be a promising approach to address the challenge. Instead of simulation, we tested the MJPE method on a widely used open dataset of sleep EEG time series from healthy subjects and found that the correlation index obtained by MJPE had the capability of quantifying the brain wave correlations under different sleep stages, reflecting the typical sleep patterns and indicating the weakened correlation with aging. A higher level of correlation was found as the sleep stage advanced. The findings based on the MJPE results were in accordance with previous studies and existing knowledge in sleep medicine. In the second part of the study, we applied MJPE on sleep EEGs from subjects under pathological conditions (sleep apnea and sleep at high altitude). Likewise, the correlation index also properly revealed their sleep architectures, with consistent trends of the correlation through the nights. The effectiveness and practicability of the MJPE method had been verified on sleep EEGs. Therefore, the MJPE method should be encouraged to be widely used for analyzing large-scale sleep EEGs under various pathological conditions to provide insight into the mechanisms of the sleep process and neuron synchronization.
Highlights
Entropy-based methods derived from information theory have had a wide application on analyzing complex time series in various areas.1–19 With the aim of measuring the complexity of time series, permutation entropy (PE) was proposed,20 which mapped the neighboring values into ordinal patterns
We introduced the Multiscale joint permutation entropy (MJPE) method and defined the correlation index, which can be used in physiological signals such as EEG data
The validity and applicability of the MJPE method on sleep EEGs can be demonstrated by MJPE results for the healthy subjects
Summary
Entropy-based methods derived from information theory have had a wide application on analyzing complex time series in various areas. With the aim of measuring the complexity of time series, permutation entropy (PE) was proposed, which mapped the neighboring values into ordinal patterns. Entropy-based methods derived from information theory have had a wide application on analyzing complex time series in various areas.. With the aim of measuring the complexity of time series, permutation entropy (PE) was proposed, which mapped the neighboring values into ordinal patterns. Joint permutation entropy (JPE) is developed from PE to investigate the correlation between time series. It can be gathered from previous studies that the time series in the complex systems show multiscale properties. The concept of multiscale accounts for the multiple time scales inherent in the complex systems and has had successful applications in different fields.. Multiscale joint permutation entropy (MJPE) is proposed to study the correlation between two complex time series on different time scales. The concept of multiscale accounts for the multiple time scales inherent in the complex systems and has had successful applications in different fields. multiscale joint permutation entropy (MJPE) is proposed to study the correlation between two complex time series on different time scales.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
