Detection of Spindles in Sleep EEGs Using a Novel Algorithm Based on the Hilbert-Huang Transform

Yang, Zhihua; Yang, Lihua; Qi, Dongxu

doi:10.1007/978-3-7643-7778-6_40

Cited by 15 publications

(14 citation statements)

References 8 publications

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“…Moreover most of these algorithms require significant pre- and post-processing and manual tuning. Examples include algorithms based on Empirical Mode Decomposition (EMD) [47], [48], [49], data-driven Bayesian methods [50], and machine learning approaches [51], [52]. Unlike our approach, none of the existing methods consider modeling the generative dynamics of spindles, as transient sparse events in time, in the detection procedure.…”

Section: Resultsmentioning

confidence: 99%

Fast and Stable Signal Deconvolution via Compressible State-Space Models

Kazemipour

Liu

Solarana

et al. 2018

IEEE Trans. Biomed. Eng.

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Objective-Common biological measurements are in the form of noisy convolutions of signals of interest with possibly unknown and transient blurring kernels. Examples include EEG and calcium imaging data. Thus, signal deconvolution of these measurements is crucial in understanding the underlying biological processes. The objective of this paper is to develop fast and stable solutions for signal deconvolution from noisy, blurred and undersampled data, where the signals are in the form of discrete events distributed in time and space.Methods-We introduce compressible state-space models as a framework to model and estimate such discrete events. These state-space models admit abrupt changes in the states and have a convergent transition matrix, and are coupled with compressive linear measurements. We consider a dynamic compressive sensing optimization problem and develop a fast solution, using two nested Expectation Maximization algorithms, to jointly estimate the states as well as their transition matrices. Under suitable sparsity assumptions on the dynamics, we prove optimal HHS Public Access Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript stability guarantees for the recovery of the states and present a method for the identification of the underlying discrete events with precise confidence bounds.Results-We present simulation studies as well as application to calcium deconvolution and sleep spindle detection, which verify our theoretical results and show significant improvement over existing techniques.Conclusion-Our results show that by explicitly modeling the dynamics of the underlying signals, it is possible to construct signal deconvolution solutions that are scalable, statistically robust, and achieve high temporal resolution.Significance-Our proposed methodology provides a framework for modeling and deconvolution of noisy, blurred, and undersampled measurements in a fast and stable fashion, with potential application to a wide range of biological data.

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Section: Resultsmentioning

confidence: 99%

Fast and Stable Signal Deconvolution via Compressible State-Space Models

Kazemipour

Liu

Solarana

et al. 2018

IEEE Trans. Biomed. Eng.

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“…(14) Since f ε (t) is continuous respect to ε and for fixed t ∈ ( π 4ω , π 2ω ), f ε (t) is strictly monotone increasing respect to ε, from the definition of ε 0 it is easy to check that f ε 0 (t) 0, t ∈ π 4ω , π 2ω . (15) Therefore f ε (t) 0 0 ε ε 0 , t ∈ π 4ω , π 2ω . (16) Combining inequality (14) and inequality (16), the following inequality holds f ε (t) 0 0 ε ε 0 , t ∈ 0, π 2ω .…”

Section: Discussionmentioning

confidence: 95%

“…Recently, the HHT has received more attention in terms of interpretations [8][9][10][11] and applications in many disciplines such as ocean science [12], biomedicine [13][14][15], speech signal processing [16], image processing [17], pattern recognition [18][19][20] and so on. Though EMD is effective for detecting large and apparent oscillations, it may miss the gentle humps characterized as hidden scales [12,21].…”

Section: Introductionmentioning

confidence: 99%

An oblique-extrema-based approach for empirical mode decomposition

Yang

Qing

2010

Digital Signal Processing

Self Cite

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“…Moreover most of these algorithms require significant pre-and post-processing and manual tuning. Examples include algorithms based on Empirical Mode Decom-position (EMD) [46], [47], [48], data-driven Bayesian methods [49], and machine learning approaches [50], [51]. Unlike our approach, none of the existing methods consider modeling the generative dynamics of spindles, as transient sparse events in time, in the detection procedure.…”

Section: Estimated Eventsmentioning

confidence: 99%

Fast and Stable Signal Deconvolution via Compressible State-Space Models

Kazemipour

Liu

Solarana

et al. 2016

Preprint

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Objective: Common biological measurements are in the form of noisy convolutions of signals of interest with possibly unknown and transient blurring kernels. Examples include EEG and calcium imaging data. Thus, signal deconvolution of these measurements is crucial in understanding the underlying biological processes. The objective of this paper is to develop fast and stable solutions for signal deconvolution from noisy, blurred and undersampled data, where the signals are in the form of discrete events distributed in time and space. Methods: We introduce compressible state-space models as a framework to model and estimate such discrete events. These state-space models admit abrupt changes in the states and have a convergent transition matrix, and are coupled with compressive linear measurements. We consider a dynamic compressive sensing optimization problem and develop a fast solution, using two nested Expectation Maximization algorithms, to jointly estimate the states as well as their transition matrices. Under suitable sparsity assumptions on the dynamics, we prove optimal stability guarantees for the recovery of the states and present a method for the identification of the underlying discrete events with precise confidence bounds. Results: We present simulation studies as well as application to calcium deconvolution and sleep spindle detection, which verify our theoretical results and show significant improvement over existing techniques. Conclusion: Our results show that by explicitly modeling the dynamics of the underlying signals, it is possible to construct signal deconvolution solutions that are scalable, statistically robust, and achieve high temporal resolution. Significance: Our proposed methodology provides a framework for modeling and deconvolution of noisy, blurred, and undersampled measurements in a fast and stable fashion, with potential application to a wide range of biological data.

show abstract

Detection of Spindles in Sleep EEGs Using a Novel Algorithm Based on the Hilbert-Huang Transform

Cited by 15 publications

References 8 publications

Fast and Stable Signal Deconvolution via Compressible State-Space Models

Fast and Stable Signal Deconvolution via Compressible State-Space Models

An oblique-extrema-based approach for empirical mode decomposition

Fast and Stable Signal Deconvolution via Compressible State-Space Models

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