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Mania and Antimanic Drugs: Overview

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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...

Bipolar Disorder

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.

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Automated Speech Analysis for Screening and Monitoring Bipolar Depression: Machine Learning Model Development and

Sooyeon Min¹, Tae-Sung Yeum², Daun Shin³

¹Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.

JMIR Medical Informatics

|December 4, 2025

Summary

Speech analysis can detect bipolar depression and recurrence. Multimodal voice analysis offers a scalable method for mental health monitoring and care.

Keywords:

AI artificial intelligence bipolar disorder depression natural language processing speech modalities voice analysis

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Area of Science:

Psychiatry and Mental Health
Computational Linguistics
Digital Health

Background:

Bipolar disorder (BD) depressive episodes cause significant functional impairment and reduced quality of life.
Early and objective detection of bipolar depression is crucial for timely intervention.
Multimodal speech analysis shows potential for identifying psychomotor, cognitive, and affective changes in BD depression.

Purpose of the Study:

Develop classifiers for screening bipolar depression and monitoring longitudinal changes.
Detect depressive recurrence in patients with bipolar disorder using speech markers.
Compare the predictive performance of different speech modalities.

Main Methods:

Collected 304 voice recordings from 92 BD patients over 1 year.
Extracted acoustic features (openSMILE) and linguistic features (LIWC) from speech.
Developed between- and within-person classifiers using extreme gradient boosting and light gradient boosting, validated with bootstrap cross-validation.

Main Results:

Speech analysis identified reduced energy modulation, monotony, and increased use of negative emotion words in depressed patients.
The combined acoustic and linguistic classifier detected moderate to severe depression (AUC=0.76).
The within-person classifier detected depression recurrence (AUC=0.70).

Conclusions:

Speech markers can effectively detect and monitor bipolar depression and recurrence.
Psycholinguistic analysis of transcribed and translated speech is feasible across languages.
Automated voice analysis offers a scalable digital health solution for mental health monitoring.