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    Detecting Bipolar Disorder Mood Swings: There's An App For That
    By News Staff | May 8th 2014 08:26 AM | Print | E-mail | Track Comments

     The PRIORI project at University of Michigan says they have created a smartphone app that monitors subtle qualities of a person's voice during everyday phone conversations - and can detect early signs of mood changes in people with bipolar disorder. 

    The app still needs a lot of testing before it can be used outside controlled conditions, but the creators say early results from a small group of patients show its potential to monitor moods while protecting privacy.  

    The project is
    led by computer scientists Zahi Karam, Ph.D. and Emily Mower Provost, Ph.D., and psychiatrist Melvin McInnis, M.D.  They presented first findings on PRIORI
    at the International Conference on Acoustics, Speech and Signal Processing in Italy. 

    PRIORI got its name because they hope it will yield a biological marker to prioritize bipolar disorder care to those who need it most urgently to stabilize their moods. Bipolar disorder affects tens of millions of people worldwide but 60 people are trying this first. The technology and algorithms are being developing using patients who receive treatment from University of Michigan teams. 

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

    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 kept 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.