Kelsey, Malia
(2017)
Applications of Sparse Recovery and Dictionary Learning towards Analysis of Electrodermal Activity.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Electrodermal Activity (EDA) – a measure of sympathetic nervous system arousal – is one of the primary methods used in psychophysiology and has been used to investigate a variety of physiological topics. EDA signals can be separated into two distinct parts, skin conductance level (SCL) and skin conductance response (SCR). The SCL is a slowly fluctuating response that reflects general trends in activity and level of activation. In contrast SCRs are quick responses which can be seen as peaks in the signal and typically reflect responses to a specific stimulus from the nervous system. While many existing studies collected EDA data in short, laboratory-based experiments, recent developments in wireless biosensing have allowed for out-of-lab studies to become more common. The transition to ambulatory data has introduced challenges in SCR and artifact identification and may hinder analysis of ambulatory data. Therefore, the interest in developing automated systems that can facilitate the analysis of EDA signals has increased in the recent years. Ledalab, a current gold standard, is one such system which can be used to identify SCRs. However, Ledalab is computationally inefficient when applied to long, ambulatory data and does not have the ability to distinguish between SCRs and artifacts. This thesis presents a novel technique that can be used to accurately and efficiently identify SCRs using curve fitting and sparse recovery methods, namely orthogonal matching pursuit. Sparse recovery can also be easily extended to include identification of both SCRs and artifacts given seperability between the two. In this work we apply discriminant analysis to determine if seperability exists between SCRs and artifacts. We have shown that our novel approach was able to detect 69% of the SCRs in an EDA signal compared to the 45% detection of Ledalab. Our method also exponentially decreased the computational complexity compared to the Ledalab. Finally, our system begins addressing the issue of artifact detection by determining that the difference between artifacts and SCR shapes could be distinguished with an accuracy of 74%.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
1 February 2017 |
| Date Type: |
Publication |
| Defense Date: |
28 October 2016 |
| Approval Date: |
1 February 2017 |
| Submission Date: |
11 November 2016 |
| Access Restriction: |
5 year -- Restrict access to University of Pittsburgh for a period of 5 years. |
| Number of Pages: |
101 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical Engineering |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Sparse Recovery, Orthogonal Matching Pursuit, Dictionary Learning, Electrodermal Activity, Skin Conductance Response, Artifact Detection |
| Date Deposited: |
01 Feb 2017 17:03 |
| Last Modified: |
01 Feb 2022 06:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/30291 |
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