Article: A machine learning approach to detect depression in an individual Journal: International Journal of Creative Computing (IJCRC) 2023 Vol.2 No.1 pp.31 - 40 Abstract: According to a population-based study in India, the prevalence of depression stands at 15.1%, affecting an estimated 57 million individuals in the country. This accounts for roughly 18% of the global population. However, a significant obstacle to timely detection of depression cases is the severe scarcity of psychiatrists, amounting to a staggering 77%. To address this issue, a self-assessment method has been developed, allowing individuals to assess whether they might be experiencing depression without the need for a psychiatrist's visit. This method can also be employed by psychiatrists to augment their diagnostic process and ensure prompt patient care. The self-assessment process involves a user interface that presents a series of questions. The responses are captured through speech acoustics, eye blink rate, and EEG signals. These data are then pre-processed and analysed individually using trained machine learning (ML) and convolutional neural network (CNN) models. The final outcome determines if an individual is experiencing depression. Additionally, the system generates a comprehensive report that includes acoustic spectrogram features, components, individual analyses, and outputs. Inderscience Publishers - linking academia, business and industry through research

Title: A machine learning approach to detect depression in an individual

Authors: Tanya Garg; Gurjinder Kaur; Manish Kumar; Chandramohan Dhasarathan

Addresses: CSE, Thapar Institute of Engineering and Technology, Patiala, Punjab, India ' Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, India ' CSE, Thapar Institute of Engineering and Technology, Patiala, Punjab, India ' CSE, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

Abstract: According to a population-based study in India, the prevalence of depression stands at 15.1%, affecting an estimated 57 million individuals in the country. This accounts for roughly 18% of the global population. However, a significant obstacle to timely detection of depression cases is the severe scarcity of psychiatrists, amounting to a staggering 77%. To address this issue, a self-assessment method has been developed, allowing individuals to assess whether they might be experiencing depression without the need for a psychiatrist's visit. This method can also be employed by psychiatrists to augment their diagnostic process and ensure prompt patient care. The self-assessment process involves a user interface that presents a series of questions. The responses are captured through speech acoustics, eye blink rate, and EEG signals. These data are then pre-processed and analysed individually using trained machine learning (ML) and convolutional neural network (CNN) models. The final outcome determines if an individual is experiencing depression. Additionally, the system generates a comprehensive report that includes acoustic spectrogram features, components, individual analyses, and outputs.

Keywords: depression; electroencephalogram; EEG; feature extraction; machine learning; emotions; deep learning.

DOI: 10.1504/IJCRC.2023.133552

International Journal of Creative Computing, 2023 Vol.2 No.1, pp.31 - 40

Received: 13 Dec 2022
Accepted: 15 Apr 2023
Published online: 20 Sep 2023 *

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Title: A machine learning approach to detect depression in an individual

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