Abstract
A machine learning-based technique for the detection of unipolar and bipolar depression disorders is developed and implemented in this paper. Unipolar depression and bipolar depression share almost similar clinical symptom profile, and hence, the diagnosis of the type of depression is a great challenge. Disturbances in motoric activity may be a useful way to detect pathological mental states. Hence, a unique motor activity database (Depresjon) which is collected from patients suffering from unipolar depression, bipolar depression and healthy subjects is employed in this work. Performance of the proposed method is evaluated using sensitivity and specificity metrics. Results show a sensitivity value of 0.98 and specificity value of 0.88 for unipolar depression and sensitivity value of 0.88 and specificity of 0.98 for bipolar depression.
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Acknowledgements
This paper and the research behind it would not have been possible without the exceptional support of Dr. Vikas Menon, Additional Professor, Department of Psychiatry, JIPMER, Puducherry, India. His enthusiasm and knowledge have been an inspiration and kept our work on track from the first encounter to the final draft of this paper.
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Singh, P.M., Sathidevi, P.S. (2022). Design and Implementation of a Machine Learning-Based Technique to Detect Unipolar and Bipolar Depression Using Motor Activity Data. In: Zhang, YD., Senjyu, T., So-In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. Lecture Notes in Networks and Systems, vol 286. Springer, Singapore. https://doi.org/10.1007/978-981-16-4016-2_10
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