Anxiety / DepressionHealth Research News

New Technology To Detect Depression Through Voice

Recently a new technology has come into spot light like a machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people,

New Technology To Detect Depression Through Voice

There are various emotions in our day to day activities and they include up and downs like sadness and feeling down etc and these are common to all humans.

According to the Centers for Disease Control and Prevention (CDC), 7.6 percent of people over the age of 12 have depression in any 2-week period. This is substantial and shows the scale of the issue.
According to the World Health Organization (WHO), depression is the most common illness worldwide and the leading cause of disability. They estimate that 350 million people are affected by depression, globally.

Few facts that you have to know about depression:

  • Depression seems to be more common among women than men.
  • The causes of depression are not fully understood but are likely to be a complex combination of genetic, biological, environmental, and psycho-social factors.
  • Life events, such as bereavement, produce mood changes that can usually be distinguished from the features of depression.
  • Symptoms include lack of joy and a reduced interest in things that used to bring a person happiness.

Signs and symptoms

  • Depressed mood
  • Psycho-motor agitation, for example, restlessness, pacing up and down
  • Delayed psycho-motor skills, for example, slowed movement and speech
  • Fatigue or loss of energy
  • Feelings of worthlessness or guilt
  • Impaired ability to think, concentrate, or make decisions
  • Recurrent thoughts of death or suicide, or attempt at suicide
  • Reduced interest or pleasure in activities previously enjoyed, loss of sexual desire
  • Unintentional weight loss (without dieting) or low appetite
  • Insomnia (difficulty sleeping) or hyper-somnia (excessive sleeping)

Recently a new technology has come into spotlight like a machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people, according to new research published in the Journal of Biomedical and Health Informatics.

Around one in five children suffer from anxiety and depression, collectively known as “internalizing disorders.” But because children under the age of eight can’t reliably articulate their emotional suffering, adults need to be able to infer their mental state and recognize potential mental health problems.

“We need quick, objective tests to catch kids when they are suffering,” says Ellen McGinnis, a clinical psychologist at the University of Vermont Medical Center’s Vermont Center for Children, Youth and Families and lead author of the study. “The majority of kids under eight are un-diagnosed.”

Early diagnosis is critical because children respond well to treatment while their brains are still developing, but if they are left untreated they are at greater risk of substance abuse and suicide later in life.

McGinnis, along with the University of Vermont biomedical engineer and study senior author Ryan McGinnis, has been looking for ways to use artificial intelligence and machine learning to make diagnosis faster and more reliable.

The researchers used an adapted version of a mood induction task called the Trier-Social Stress Task, which is intended to cause feelings of stress and anxiety in the subject.

A group of 71 children between the ages of three and eight were asked to improvise a three-minute story and told that they would be judged based on how interesting it was.

The researcher acting as the judge remained stern throughout the speech and gave only neutral or negative feedback.

After 90 seconds, and again with 30 seconds left, a buzzer would sound and the judge would tell them how much time was left.

“The task is designed to be stressful, and to put them in the mindset that someone was judging them,” says Ellen McGinnis.

The children were also diagnosed using a structured clinical interview and parent questionnaire, both well-established ways of identifying internalizing disorders in children.

The researchers used a machine-learning algorithm to analyze the statistical features of the audio recordings of each kid’s story and relate them to the child’s diagnosis. They found the algorithm was highly successful at diagnosing children, and that the middle phase of the recordings, between the two buzzers, was the most predictive of diagnosis.

The algorithm was able to identify children with a diagnosis of an internalizing disorder with 80% accuracy, and in most cases that compared really well to the accuracy of the parent checklist,” says Ryan McGinnis.

It can also give the results much more quickly — the algorithm requires just a few seconds of processing time once the task is complete to provide a diagnosis.

The algorithm identified eight different audio features of the children’s speech, but three, in particular, stood out as highly indicative of internalizing disorders: low-pitched voices, with repeatable speech inflections and content, and a higher-pitched response to the surprising buzzer.

Ellen McGinnis says these features fit well with what you might expect from someone suffering from depression. “A low-pitched voice and repeatable speech elements mirror what we think about when we think about depression: speaking in a monotone voice, repeating what you’re saying,” says Ellen McGinnis.

children with internalizing disorders were found to exhibit a larger turning-away response from a fearful stimulus in a fear induction task.

The voice analysis has a similar accuracy in diagnosis to the motion analysis in that earlier work, but Ryan McGinnis thinks it would be much easier to use in a clinical setting.

The fear task requires a darkened room, toy snake, motion sensors attached to the child and a guide, while the voice task only needs a judge, a way to record speech and a buzzer to interrupt. “This would be more feasible to deploy,” he says.

Ellen McGinnis says the next step will be to develop the speech analysis algorithm into a universal screening tool for clinical use, perhaps via a smartphone app that could record and analyze results immediately.

The voice analysis could also be combined with the motion analysis into a battery of technology-assisted diagnostic tools, to help identify children at risk of anxiety and depression before even their parents suspect that anything is wrong. (source)

CONCLUSION:

According to the World Health Organization (WHO), depression is the most common illness worldwide and the leading cause of disability. They estimate that 350 million people are affected by depression, globally.

Around one in five children suffer from anxiety and depression, collectively known as “internalizing disorders.” But because children under the age of eight can’t reliably articulate their emotional suffering, adults need to be able to infer their mental state and recognize potential mental health problems.

Recently a new technology has come into spotlight like a machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people, according to new research published in the Journal of Biomedical and Health Informatics.

This voice analysis could also be combined with the motion analysis into a battery of technology-assisted diagnostic tools, to help identify children at risk of anxiety and depression before even their parents suspect that anything is wrong.

For more related articles regarding Mental and physical health :

Past trauma affects your relationships
Allergy is associated with depression and anxiety – Research proven
Wrist band that can track your emotions
WhatsApp is good for your mental health- Research proven
11 ways to get rid of over thinking!! (VIDEO)
what anxiety can do to your body?-Research proven (VIDEO)

 

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