Assess Sleep Stage by Modern Signal Processing Techniques

Wu, Hau‐Tieng; Talmon, Ronen; Lo, Yu‐Lun

doi:10.1109/tbme.2014.2375292

Cited by 112 publications

(55 citation statements)

References 29 publications

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“…Another study has proposed the use of accelerometer plus oximeter implanted in smart-phone device to detect obstructive sleep apnea [37]. Additionally, respiratory signal can be derived from EEG and it has been suggested the use of this method as a promising application in medical field [38]. …”

Section: Discussionmentioning

confidence: 99%

Predictability of arousal in mouse slow wave sleep by accelerometer data

et al. 2017

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Arousals can be roughly characterized by punctual intrusions of wakefulness into sleep. In a standard perspective, using human electroencephalography (EEG) data, arousals are associated to slow-wave rhythms and K-complex brain activity. The physiological mechanisms that give rise to arousals during sleep are not yet fully understood. Moreover, subtle body movement patterns, which may characterize arousals both in human and in animals, are usually not detectable by eye perception and are not in general present in sleep studies. In this paper, we focus attention on accelerometer records (AR) to characterize and predict arousal during slow wave sleep (SWS) stage of mice. Furthermore, we recorded the local field potentials (LFP) from the CA1 region in the hippocampus and paired with accelerometer data. The hippocampus signal was also used here to identify the SWS stage. We analyzed the AR dynamics of consecutive arousals using recurrence technique and the determinism (DET) quantifier. Recurrence is a fundamental property of dynamical systems, which can be exploited to characterize time series properties. The DET index evaluates how similar are the evolution of close trajectories: in this sense, it computes how accurate are predictions based on past trajectories. For all analyzed mice in this work, we observed, for the first time, the occurrence of a universal dynamic pattern a few seconds that precedes the arousals during SWS sleep stage based only on the AR signal. The predictability success of an arousal using DET from AR is nearly 90%, while similar analysis using LFP of hippocampus brain region reveal 88% of success. Noteworthy, our findings suggest an unique dynamical behavior pattern preceding an arousal of AR data during sleep. Thus, the employment of this technique applied to AR data may provide useful information about the dynamics of neuronal activities that control sleep-waking switch during SWS sleep period. We argue that the predictability of arousals observed through DET(AR) can be functionally explained by a respiratory-driven modification of neural states. Finally, we believe that the method associating AR data with other physiologic events such as neural rhythms can become an accurate, convenient and non-invasive way of studying the physiology and physiopathology of movement and respiratory processes during sleep.

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Section: Discussionmentioning

confidence: 99%

Predictability of arousal in mouse slow wave sleep by accelerometer data

et al. 2017

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“…Therefore, the combination of S4 and S3 as 5-stage classification is of interest. Again, S1 and S2 is combinedly termed as shallow sleep [51]. Thus, combining S1 and S2 of 5-stage classification, we get the 4-stage classification case considered in Table 4.…”

Section: Resultsmentioning

confidence: 99%

An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting

2017

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“…We use the Rényi entropy-based adaptive FSST and our proposed adaptive FSST with σ = σ est (t) to process the two-component linear chirp signal in (56). The different time-varying parameters are shown in the top-left panel of Fig.2, where σ 1 (t), σ 2 (t), σ u (t), σ est (t), σ Re (t) and σ Re2 (t) are defined by (43), (55), (63), (64) and (65), respectively.…”

Section: Selecting the Time-varying Parameter Automaticallymentioning

confidence: 99%

Adaptive short-time Fourier transform and synchrosqueezing transform for non-stationary signal separation

et al. 2020

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The synchrosqueezing transform, a kind of reassignment method, aims to sharpen the timefrequency representation and to separate the components of a multicomponent non-stationary signal. In this paper, we consider the short-time Fourier transform (STFT) with a time-varying parameter, called the adaptive STFT. Based on the local approximation of linear frequency modulation mode, we analyze the well-separated condition of non-stationary multicomponent signals using the adaptive STFT with the Gaussian window function. We propose the STFT-based synchrosqueezing transform (FSST) with a time-varying parameter, named the adaptive FSST, to enhance the time-frequency concentration and resolution of a multicomponent signal, and to separate its components more accurately. In addition, we also propose the 2nd-order adaptive FSST to further improve the adaptive FSST for the non-stationary signals with fast-varying frequencies. Furthermore, we present a localized optimization algorithm based on our well-separated condition to estimate the time-varying parameter adaptively and automatically. Simulation results on synthetic signals and the bat echolocation signal are provided to demonstrate the effectiveness and robustness of the proposed method.where A k (t), φ k (t) > 0, has been a very active research area over the past few years. Note that the number of component K may change with time t, but it should be constant for long enough time intervals. The representation of x(t) in (1) with A k (t) and φ k (t) varying slowly or more slowly than φ k (t) is called an adaptive harmonic model (AHM) representation of x(t), where A k (t) are called the instantaneous amplitudes and φ k (t) the instantaneous frequencies (IFs). To decompose x(t) as an AHM representation (1) is important to extract information, such as the underlying dynamics, hidden in x(t). Time-frequency (TF) analysis is widely used in engineering fields such as communication, radar and sonar as a powerful tool for analyzing time-varying non-stationary signals [1]. Time-frequency analysis is especially useful for signals containing many oscillatory components with slowly timevarying amplitudes and instantaneous frequencies. The short-time Fourier transform (STFT), the continuous wavelet transform (CWT) and the Wigner-Ville distribution are the most typical TF analysis, see details in [1]-[6]. Other TF distributions of Cohen's class include the exponential distribution [7], a smoothed pseudo Wigner distribution [8] and the complex-lag distribution [9].In addition, the TF signal analysis and synthesis using the eigenvalue decomposition method has been studied [10,11]. In particular, an eigenvalue decomposition-based approach which enables the separation of non-stationary components with overlapped supports in the TF plane has been proposed in [12].Recently a number of new TF analysis methods such as the Hilbert spectrum analysis with empirical mode decomposition (EMD) [13], the reassignment method [14] and synchrosqueezed wavelet transform (SST) [15] have also been proposed ...

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Assess Sleep Stage by Modern Signal Processing Techniques

Cited by 112 publications

References 29 publications

Predictability of arousal in mouse slow wave sleep by accelerometer data

Predictability of arousal in mouse slow wave sleep by accelerometer data

An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting

Adaptive short-time Fourier transform and synchrosqueezing transform for non-stationary signal separation

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