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

These pilot study results give us preliminary proof of the concept that we can detect mood states in regular phone calls by analyzing broad features and properties of speech, without violating the privacy of those conversations," says Karam, a postdoctoral fellow and specialist in machine learning and speech analysis. "As we collect more data the model will become better, and our ultimate goal is to be able to anticipate swings, so that it may be possible to intervene early."

Adds McInnis, a bipolar specialist, "This is tremendously exciting not only as a technical achievement, but also as an illustration of what the marriage of mental health research, engineering and innovative research funding can make possible."

He adds, "The ability to predict mood changes with sufficient advance time to intervene would be an enormously valuable biomarker for bipolar disorder."

He notes that the initial seed funding for the voice technology research came from the Michigan Institute for Clinical and Health Research. The ready source of patient-volunteers came from a Prechter Fund-supported registry, and the new early results were made possible by NIMH funding.

The research team hails from the Department of Psychiatry at the U-M Medical School and the Division of Computer Science and Engineering in the Department of Electrical and Computer Engineering at the U-M College of Engineering. It includes Satinder Singh, Ph.D. an artificial intelligence and machine learning expert.

How it works

The app runs in the background on an ordinary smartphone, and automatically monitors the patients' voice patterns during any calls made as well as during weekly conversations with a member of the patient's care team. The computer program analyzes many characteristics of the sounds and silences of each conversation.

Only the patient's side of everyday phone calls is recorded and the recordings themselves are encrypted and ke
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Contact: Kara Gavin
kegavin@umich.edu
734-764-2220
University of Michigan Health System
Source:Eurekalert

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