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Listening to bipolar disorder: Smartphone app detects mood swings via voice analysis
Date:5/8/2014

pt off-limits to the research team. They can see only the results of computer analysis of the recordings, which are stored in secure servers that comply with patient privacy laws.

Standardized weekly mood assessments with a trained clinician provide a benchmark for the patient's mood, and are used to correlate the acoustic features of speech with their mood state.

Because other mental health conditions also cause changes in a person's voice, the same technology framework developed for bipolar disorder could prove useful in everything from schizophrenia and post-traumatic stress disorder to Parkinson's disease, the researchers say.

Results so far

The first six patients all have a rapid-cycling form of Type 1 bipolar disorder and a history of being prone to frequent depressive and manic episodes. The researchers showed that their analysis of voice characteristics from everyday conversations could detect elevated and depressed moods.

The detection of mood states will improve over time as the software gets trained based on more conversations and data from more patients.

The researchers study patients as they experience all aspects of bipolar disorder mood changes, from mild depressions and hypomania (mild mania) to full-blown depressed and manic states. Over time, they hope to develop software that will learn to detect the changes that precede the transitions to each of these states. They also need to develop and explore strategies for notifying the app user and care providers about mood changes, so that appropriate intervention can take place.

The app currently runs on Android operating system phones, and complies with laws about recording conversations because only one side of the conversation actually gets recorded. The University of Michigan has applied for patent protection for the intellectual property involved.


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Contact: Kara Gavin
kegavin@umich.edu
734-764-2220
University of Michigan Health System
Source:Eurekalert

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