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Related Concept Videos

Bipolar Disorder01:30

Bipolar Disorder

411
Bipolar disorder is a chronic mental health condition marked by significant mood fluctuations, including episodes of mania and depression. Elevated energy levels, heightened mood or irritability, impulsive behavior, reduced sleep needs, rapid speech, racing thoughts, inflated self-esteem, and distractibility characterize mania. Individuals with bipolar disorder often alternate between depressive and manic states, with periods of emotional stability lasting an average of six months to a year.
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Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study.

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  • 1Technological Institute of Informatics, Alcoi Campus, Universitat Politècnica de València, 46022 Valencia, Spain.

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|December 8, 2020
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Summary

This study explores using wearable sensor data and entropy measures to classify Bipolar Disorder (BD) episodes. The findings show this method can effectively distinguish between depression, mania, and remission states in patients.

Keywords:
actigraphybipolar disorderpermutation entropysample entropyslope entropytime series classification

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Area of Science:

  • Biomedical Engineering
  • Computational Psychiatry
  • Wearable Technology

Background:

  • Bipolar Disorder (BD) is a prevalent, recurrent illness with significant socioeconomic impact.
  • Effective management of BD relies on early treatment and continuous patient monitoring.
  • Current methods lack tools for large-scale, semi-automatic monitoring of BD patients.

Purpose of the Study:

  • To investigate the feasibility of classifying Bipolar Disorder episodes using wearable sensor data.
  • To evaluate the efficacy of entropy measures for analyzing actigraphy records in BD patients.
  • To develop a method for semi-automatic monitoring and control of BD patients.

Main Methods:

  • Utilized wearable technology to collect actigraphy data from BD patients.
  • Developed a preprocessing stage to extract relevant activity epochs from non-stationary and artifact-corrupted data.
  • Applied Slope Entropy, a novel quantification measure, to analyze time series data and classify patient states.

Main Results:

  • Successfully distinguished between the three states of Bipolar Disorder: depression, mania, and remission.
  • Demonstrated that Slope Entropy outperforms commonly used entropy measures for biomedical time series analysis in this context.
  • Confirmed the feasibility of using entropy measures on wearable data for BD episode classification.

Conclusions:

  • The proposed method using entropy measures on wearable data is a feasible approach for classifying Bipolar Disorder episodes.
  • This technology offers a potential solution for massive and semi-automatic monitoring of BD patients.
  • Further development could significantly improve the management and treatment of Bipolar Disorder.