Time-Frequency Ridge Analysis of Sleep Stage Transitions

TY - GEN

T1 - Time-Frequency Ridge Analysis of Sleep Stage Transitions

AU - McCausland, C.

AU - Biglarbeigi, P.

AU - Bond, R.

AU - Yadollahikhales, G.

AU - Finlay, D.

N1 - Copyright: © 2022 IEEE.

PY - 2022/12/3

Y1 - 2022/12/3

N2 - The development of automated sleep apnea detection algorithms is an emerging topic of interest [1], [2]. The main aim of automation is to reduce the time and cost associated with manually scoring polysomnogram (PSG) tests [3]. To automate the process, traditional algorithms attempt to mimic the human observer by implementing a series of predefined rules, such as the American Academy of Sleep Medicine's (AASM) scoring guidelines [4]. Recently, data driven methods have emerged [5]. Electroencephalogram (EEG) frequency is known to be an important feature for both the human observer and data driven methods for sleep staging classification. This study presents the initial findings for a novel approach to sleep stage analysis. EEG time-frequency analysis is used to characterise the dominant frequency with respect to time, specifically at the point of sleep stage transition. Poor inter-scorer agreement at sleep stage transitions is a noted limitation of current manual and automated methods as the point of transition is poorly defined [6]. The goal of this study is to further discuss on the topic of sleep staging automation and explore alternative and novel features to improve the inter-scorer reliability of sleep staging.

AB - The development of automated sleep apnea detection algorithms is an emerging topic of interest [1], [2]. The main aim of automation is to reduce the time and cost associated with manually scoring polysomnogram (PSG) tests [3]. To automate the process, traditional algorithms attempt to mimic the human observer by implementing a series of predefined rules, such as the American Academy of Sleep Medicine's (AASM) scoring guidelines [4]. Recently, data driven methods have emerged [5]. Electroencephalogram (EEG) frequency is known to be an important feature for both the human observer and data driven methods for sleep staging classification. This study presents the initial findings for a novel approach to sleep stage analysis. EEG time-frequency analysis is used to characterise the dominant frequency with respect to time, specifically at the point of sleep stage transition. Poor inter-scorer agreement at sleep stage transitions is a noted limitation of current manual and automated methods as the point of transition is poorly defined [6]. The goal of this study is to further discuss on the topic of sleep staging automation and explore alternative and novel features to improve the inter-scorer reliability of sleep staging.

KW - Observers

KW - Signal processing

KW - Time-frequency analysis

KW - Automation

KW - Sleep

KW - Signal processing algorithms

KW - Manuals

U2 - 10.1109/SPMB55497.2022.10014897

DO - 10.1109/SPMB55497.2022.10014897

M3 - Conference contribution

AN - SCOPUS:85147662897

SN - 9781665470308

T3 - 2022 IEEE Signal Processing in Medicine and Biology Symposium Proceedings

SP - 1

EP - 4

BT - 2022 IEEE Signal Processing in Medicine and Biology Symposium

PB - IEEE

CY - Philadelphia, PA

T2 - 2022 IEEE Signal Processing in Medicine and Biology Symposium

Y2 - 3 December 2022 through 3 December 2022

ER -