Depression Signs Detection through Computer Software Analysis

New technologies are investigating new methods of the Depression signs identification without visiting a therapist by the potential patient. No matter how much successful these approaches appear, the computerized system absolutely cannot replace the personal communication. It is a common knowledge that only 10% of the communication is verbal. Therapist reviews multiple signs and makes the conclusion based on multiple factors, and what patient is saying, is just one of them. However, every possibility to give a heads-up and detect dangerous signs in the individual’s mental well-being should be encouraged. Yes, that should be a first screening test, which will require more detailed therapist assessment, but for some people it might catch the disease before it becomes major health impairment.

In this post, we will review two new techniques, both under development, for early detection of the depression signs: one through voice recognition, another – through posted text analysis (which might be very useful for fellow bloggers).

Software detecting depression signs through Voice Recognition

It's a common complaint in any communication breakdown: "It's not what you said, it's how you said it." For Professor Sandy Pentland and his group at MIT's Media Lab, the tone and pitch of a person's voice, the length and frequency of pauses and speed of speech can reveal much about his or her mood.

While most speech recognition software concentrates on turning words and phrases into text, Pentland's group is developing algorithms that analyze subtle cues in speech to determine whether someone is feeling awkward, anxious, disconnected or depressed.

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Cogito Health, a company spun out of MIT based in Charlestown, MA, is building on Pentland's research by developing voice-analysis software to screen for depression over the phone.

For years, psychiatrists have recognized a characteristic pattern in the way that many people with clinical depression speak: slowly, quietly and often in a halting monotone. Company CEO Joshua Feast and his colleagues are training computers to recognize such vocal patterns in audio samples.

Tool Could Help Manage People with Chronic Disease

Feast says the software could be a valuable tool in managing patients with chronic diseases, which often lead to depression.

As part of certain disease-management programs, nurses routinely call patients between visits to ask if they are taking their medication. However, symptoms of depression are more difficult for nurses to identify. Feast says voice analysis software could provide a natural and noninvasive way for nurses to screen for depression during routine phone calls.

"If you're a nurse and you're trying to deal with a patient with long-term diabetes, it's very hard to tell if a person is depressed," says Feast. "We try to help nurses detect possible mood disorders in patients that have chronic disease."

A few years ago, the pharmaceutical giant Pfizer developed voice-analysis software to detect early signs of Parkinson's disease. Pfizer scientists designed the software to recognize tiny tremors in speech. Such tremors offered clues to help gauge patients' response to various medications.

Software Detects Patterns in Vocal Recordings

In much the same way, Cogito Health's software detects specific patterns in vocal recordings. For example, the researchers have developed mathematical models to measure a speaker's consistency in tone, fluidity of speech, level of vocal energy, and level of engagement in the conversation (for example, whether someone responds with "uh-huh's" or with silence).

"It listens to the pattern of speech, not the words," says Pentland, a scientific advisor to the company. "By measuring those signals in the background, you can tell what's going on."

The company is conducting a large-scale trial of the software by collecting hundreds of routine phone conversations between nurses and patients, with consent from both parties. After performing follow-up questionnaires to see which patients are depressed, the researchers tested the software, to see if it could accurately identify these patients.

Vocal Cues Can ID Deception, Anger, Signs of Intoxication

Mark Clements, a professor of electrical and computer engineering at the Georgia Institute of Technology, has analyzed vocal patterns associated with clinical depression. His lab also uses vocal cues to identify deception and anger, as well as early signs of intoxication.

Clements says the benefit of Cogito Health's approach is that it could help untrained professionals detect signs of depression.

"A trained listener could detect these types of things in a person's voice, but it's difficult to teach a novice," he says. "But things that are hard to hear can be detected by a computer, and have correlations with various emotional and even physical states."

Carl Marci, director of Social Neuroscience at the Massachusetts General Hospital's Department of Psychiatry, and another a scientific advisor to the company, says such technology could help monitor a patient's long-term progress.

Software detecting depression signs through blogs and websites postings

Israeli researchers have developed a software program that can detect depression in blogs and online texts. The software is capable of identifying language that can indicate a writer's psychological state, which could serve as a screening tool.

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Developed by a team headed by Yair Neuman, associate professor of education at Ben-Gurion University (BGU) of the Negev, Israel, the software was used to scan more than 300,000 English language blogs posted on mental health websites. The program identified what it perceived to be the 100 "most depressed" and 100 "least depressed" bloggers.  A panel of four clinical psychologists reviewed the samples and concluded that there was a 78 per cent correlation between the computer's and the panel's findings.

"The software program was designed to find depressive content hidden in language that did not mention the obvious terms like depression or suicide," Neuman said.  "A psychologist knows how to spot various emotional states through intuition. Here, we have a program that does this methodically through the innovative use of 'web intelligence'."

For example, the program spots words that express various emotions, like coolers that the writer employs to metaphorically describe certain situations. Words like "black" combined with other terms that describe symptoms of depression, such as sleep deprivation or loneliness, will be recognized by the software as "depressive" texts.

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Originally conducted for academic purposes, the findings could potentially be used to screen for would-be suicides. The software provides a screening process that raises an individual's awareness of his or her condition, enables mental health workers to identify individuals in need of treatment and can recommend they seek professional help.

The software isn’t designed to replace human judgment, said Neuman. And it’s not sophisticated enough to analyze intention or detect those who may be more likely to write when they’re feeling sad. But given the large number of people suffering from depression, it can be an effective screening tool, especially if combined with other technologies, such as voice recognition or new algorithms that can detect sarcasm, Neuman said. “It has the power to screen for depression in an economical, proactive and quick way,” said Neuman. “The language we use tends to shape our thoughts in a very deep sense.”



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