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Visualization and Classification of Neurological Status with Tensor Decomposition and Machine Learning
Linköping University, Department of Computer and Information Science.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Recognition of physical and mental responses to stress is important for stress assessment and management as its negative effects in health can be prevented or reduced. Wearable technology, mainly using electroencephalogram (EEG), provides information such as tracking fitness activity, disease detection, and monitoring neurologicalstates of individuals. However, the recording of EEG signals from a wearable device is inconvenient, expensive, and uncomfortable during normal daily activities. This study introduces the application of tensor decomposition of non-EEG data for visualizing and classifying neurological statuses with application to human stress recognition. The multimodal dataset of non-EEG physiological signals publicly available from the PhysioNet database was used for testing the proposed method. To visualize the biosignals in a low dimensional feature space, the multi-way factorization technique known as the PARAFAC was applied for feature extraction. Results show visualizations that well separate the four groups of neurological statuses obtained from twenty healthy subjects. The extracted features were then used for pattern classification. Two statistical classifiers, which are the multinomial logit regression(MLR) and linear discriminant analysis (LDA), were implemented. The results show that the MLR and LDA can identify the four neurological statuses with accuracies of 95% and 98.8%, respectively. This study suggests the potential application of tensor decomposition for the analysis of physiological measurements collected from multiple sensors. Moreover, the proposed study contributes to the advancement of wearable technology for human stress monitoring. With tensor decomposition of complex multi-sensor or multi-channel data, simple classification techniques can be employed to achieve similar results obtained using sophisticated machine-learning techniques.

Place, publisher, year, edition, pages
2019. , p. 58
Keywords [en]
Stress assessment; physiological signals, biosensors, tensor decomposition, feature visualization, machine learning
National Category
Other Medical Engineering Probability Theory and Statistics Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-158497ISRN: ISRN: LIU-IDA/STAT-A--19/020--SEOAI: oai:DiVA.org:liu-158497DiVA, id: diva2:1335454
Subject / course
Statistics
Presentation
2019-06-04, Alan Turing, Linköping University SE-581 83 Linköping, Sweden, Linköping, 13:45 (English)
Examiners
Available from: 2019-08-12 Created: 2019-07-05 Last updated: 2019-08-12Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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