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Related Concept Videos

Bipolar Disorder01:30

Bipolar Disorder

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.
Mania and Antimanic Drugs: Overview01:24

Mania and Antimanic Drugs: Overview

Mania, a psychological condition characterized by elevated mood, increased energy, and reduced sleep need, is part of the bipolar disorder cycle. The exact cause of mania isn't entirely known, but it is thought to be a combination of genetic, environmental, and neurological factors. Bipolar disorder involves alternating manic and depressive episodes. Mood stabilizers like lithium, antipsychotics, and anticonvulsants help manage these episodes. Lithium carbonate is particularly effective as a...
Traits, Mood, and Subjective Wellbeing01:22

Traits, Mood, and Subjective Wellbeing

Subjective well-being (SWB) refers to an individual's self-evaluation of their overall life satisfaction, happiness, and fulfillment. This multifaceted construct is typically assessed by analyzing the balance of positive and negative emotions alongside perceptions of life satisfaction. Personality traits such as neuroticism and extraversion are strongly associated with variations in SWB, offering critical insights into the underlying mechanisms of emotional well-being.
Neuroticism and Emotional...
Traits and States01:17

Traits and States

Personality traits represent consistent patterns in behavior, thoughts, and emotions, reflecting an individual's tendencies across various situations. For example, extraversion, a well-known trait, manifests in individuals as talkative, energetic, and enthusiastic behaviors. These traits are stable over time, offering a reliable framework for predicting how people might act in different contexts. However, they do not define every moment of an individual's life. In contrast to traits, states are...
Negative and Cognitive Symptoms of Schizophrenia01:30

Negative and Cognitive Symptoms of Schizophrenia

Negative symptoms of schizophrenia indicate a reduction or absence of typical behaviors and emotional responses found in healthy individuals, while positive symptoms reflect an excess or distortion of normal functioning.
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Negative symptoms of schizophrenia manifest as deficits in normal emotional and behavioral functioning, profoundly impacting daily life. Individuals with schizophrenia often display a flat affect, characterized by a near-total absence of emotional expression,...
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Speech analysis for mood state characterization in bipolar patients.

Nicola Vanello1, Andrea Guidi, Claudio Gentili

  • 1Department of Information Engineering, University of Pisa, Pisa, Italy. nicola.vanello@iet.unipi.it

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary

This study introduces an algorithm to analyze speech features for characterizing mood states in bipolar disorder patients. The method segments speech to estimate pitch and its variations, aiding in mood state detection.

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Area of Science:

  • Psychiatry and Neuroscience
  • Speech Signal Processing
  • Biomedical Engineering

Background:

  • Bipolar disorders involve unpredictable mood swings, including depressive, hypomanic, and manic episodes.
  • Accurate mood state estimation is crucial for effective bipolar disorder management.
  • Integrating physiological signals and voice analysis offers a multi-parametric approach to mood state assessment.

Purpose of the Study:

  • To develop and evaluate an algorithm for estimating speech features from running speech to characterize mood states in bipolar patients.
  • To assess the algorithm's performance using a speech database with electroglottographic signals.

Main Methods:

  • Automatic segmentation of speech signals to identify voiced segments.
  • Spectral matching techniques to estimate pitch and pitch variations (jitter, pitch standard deviation).
  • Estimation of average pitch, jitter, and pitch standard deviation within voiced segments.

Main Results:

  • The algorithm successfully estimates key speech features relevant to mood state characterization.
  • Preliminary analysis on bipolar disorder subjects demonstrates the potential of the proposed method.
  • Performance evaluation on a speech database including electroglottographic signals was conducted.

Conclusions:

  • The developed algorithm shows promise for objective mood state assessment in bipolar disorder through speech analysis.
  • Speech feature estimation provides a valuable, non-invasive tool for monitoring bipolar disorder.
  • Further research and validation are warranted to integrate this technology into clinical practice.