Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 74-86 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 65 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2018 |
| Externally published | Yes |
Fingerprint
Keywords
- Calcium imaging
- Compressive sensing
- Signal deconvolution
- Sleep spindles
- State-space models
ASJC Scopus subject areas
- Biomedical Engineering
Cite this
Fast and stable signal deconvolution via compressible state-space models. / Kazemipour, Abbas; Liu, Ji; Solarana, Krystyna; Nagode, Daniel A.; Kanold, Patrick; Wu, Min; Babadi, Behtash.
In: IEEE Transactions on Biomedical Engineering, Vol. 65, No. 1, 01.01.2018, p. 74-86.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Fast and stable signal deconvolution via compressible state-space models
AU - Kazemipour, Abbas
AU - Liu, Ji
AU - Solarana, Krystyna
AU - Nagode, Daniel A.
AU - Kanold, Patrick
AU - Wu, Min
AU - Babadi, Behtash
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Calcium imaging
KW - Compressive sensing
KW - Signal deconvolution
KW - Sleep spindles
KW - State-space models
UR - http://www.scopus.com/inward/record.url?scp=85043518713&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85043518713&partnerID=8YFLogxK
U2 - 10.1109/TBME.2017.2694339
DO - 10.1109/TBME.2017.2694339
M3 - Article
VL - 65
SP - 74
EP - 86
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 1
ER -