Affiliations 

  • 1 Center for Intelligent Signal and Imaging Research (CISIR), Electrical and Electronics Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia
  • 2 Department of Psychiatry, Universiti Sains Malaysia Health Campus, Kota Bharu 16150, Malaysia
  • 3 Department of Computer Science, Iqra University, Karachi 75500, Pakistan
  • 4 Center of Excellence in Intelligent Engineering Systems, Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudia Arabia
Sensors (Basel), 2020 Aug 07;20(16).
PMID: 32784531 DOI: 10.3390/s20164400

Abstract

Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications. This paper presents a real-time mental stress assessment approach based on convolutional neural networks (CNNs). The CNN-based approach afforded real-time mental stress assessment with an accuracy as high as 96%, the sensitivity of 95%, and specificity of 97%. The proposed approach is compared with state-of-the-art ML techniques in terms of accuracy, time utilisation, and quality of features.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.