Computer Detection Of Depression

Computers Can Now Spot Symptoms Of Depression Psychiatrists May Have Missed

Computer Diagnoses Depression with 85 Percent Accuracy.

Dision | November 19, 2015

“Clinical depression is, simply put, a dreadful disease. Diagnosing it is anything but simple, however. Its symptoms vary, can shift with the ups and downs of everyday life, and sometimes overlap with those of other diseases. For these reasons, it is common for depression to go unidentified for months, or even to be missed altogether.
Stefan Scherer of the University of Southern California and Louis-Philippe Morency of Carnegie Mellon University, in Pittsburgh, hope to change this. They are trying to develop a reliable way of diagnosing depression by using a computer to record and analyse aspects of a putative sufferer’s behaviour. They are, they think, 85% of the way there.

“The extra 10% of reliability has come from quantifying what was previously a qualitative observation, which is that depressed people tend to run their vowels together when they speak. Dr Scherer and Dr Morency programmed their software to record patterns of vowel-spacing (known as vowel-space ratios) and then tested the system on more than 250 people, some of whom had been diagnosed independently as depressed and some of whom had not.

Source: http://3tags.org/article/computers-can-now-spot-symptoms-of-depression-psychiatrists-may-have-missed

<more at http://3tags.org/article/computers-can-now-spot-symptoms-of-depression-psychiatrists-may-have-missed; http://www.npr.org/sections/money/2015/05/20/407978049/how-a-machine-learned-to-spot-depression (How A Machine Learned To Spot Depression. May 21, 2015) and https://www.karger.com/Article/FullText/381950 (Computer-Aided Diagnosis of Depression Using EEG Signals. Eur Neurol 2015;73:329-336. (DOI:10.1159/000381950) U.R. Acharya, V. K. Sudarshan, H. Adeli, J. Santhosh J, J.E.W. Koh, and A. Adeli. [Abstract: The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression.]) >