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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Interpretable Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings.

Zeda Li1, Scott A Bruce2, Tian Cai3

  • 1Paul H. Chook Department of Information System and Statistics, Baruch College, The City University of New York, New York, NY 10010, USA.

Journal of Machine Learning Research : JMLR
|May 26, 2023
PubMed
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This study presents a new method for classifying categorical time series using spectral envelopes and optimal scalings. The approach accurately identifies group membership and reveals differences in sleep disorder patterns.

Keywords:
categorical time seriesclassificationmultiple time seriesoptimal scalingspectral envelope

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Area of Science:

  • Machine Learning
  • Time Series Analysis
  • Signal Processing

Background:

  • Categorical time series classification is challenging due to the discrete nature of data.
  • Existing methods may not fully capture the complex oscillatory patterns inherent in such data.

Purpose of the Study:

  • To introduce a novel supervised learning approach for categorical time series classification.
  • To develop an interpretable and parsimonious feature-based classifier using spectral properties.
  • To demonstrate the method's accuracy and consistency in identifying group membership.

Main Methods:

  • Utilized the spectral envelope and optimal scalings to extract features from categorical time series.
  • Developed a feature-based classifier combining these two quantities.
  • Investigated classification consistency and performed simulation studies.
  • Applied the method to analyze sleep stage time series for different sleep disorders.

Main Results:

  • The proposed method accurately classifies categorical time series across various underlying group structures.
  • Simulation studies confirmed the classifier's accuracy.
  • The approach successfully identified key differences in oscillatory patterns in sleep stage data.
  • Patients with different sleep disorders were accurately classified based on their sleep stage patterns.

Conclusions:

  • The novel approach using spectral envelope and optimal scalings provides an effective tool for categorical time series classification.
  • The method offers an interpretable and accurate way to determine group membership.
  • This technique has practical applications in analyzing complex biological time series data, such as sleep studies.