Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal

Abstract

In the modern world, wearable smart devices are continuously used to monitor people's health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and normal) and inter-subject classification. The DT leave-one-out model has better performance in terms of recall (93.30%), specificity (96.70%), precision (94.40%), accuracy (93.30%), and F1 (93.50%) in the intra-subject classification. Additionally, The classification accuracy of the system in classifying inter-subjects is 94.10% when using a DT classifier. However, our findings suggest that the wearable smart T-shirt based on the DT classifier may be used in big data applications and health monitoring. Mental stress can lead to mitochondrial dysfunction, oxidative stress, blood pressure, cardiovascular disease, and various health problems. Therefore, real-time ECG signals help assess cardiovascular and related risk factors in the initial stage based on machine learning techniques.

Keywords: decision tree; diagnosis; electrode; flexible electronics; machine learning; mitochondria; overwork; oxidative stress; smart device; stress.

Figure 1

ECG acquisition system of the wearable smart T-shirt used in this study. A wearable smart T-shirt has three silver-coated dry electrodes, one smart textile, one recorder base, and one ECG collector [56,57]. The ECG signal is transferred through Bluetooth in display devices, such as smartphones, computers, laptops, and tablets.

Figure 2

Circuit module diagram of the wearable smart T-shirt.

Figure 3

Block diagram of the proposed study.

Figure 4

One minute signal representation of the single-lead raw ECG signal from (A) mental stress and (B) normal subjects.

Figure 5

One minute signal representation of the single-lead filtered ECG signal from (A) mental stress and (B) normal subjects.

Figure 6

Heat map of the (A) AHR, MRR, and seven HRV features, and (B) AHR, MRR, seven HRV, and four demographics from mental stress and normal subjects. It showed the relationship between the two features of the subjects, including mental stress and normal.

Figure 7

ROC curves of the intra-subject (mental stress and normal) classification with models such as (A) leave one out, (B) 10-fold, (C) 3-fold, and (D) 2-fold on nine features extracted by the ECG signal.

Figure 8

ROC curves of the intra-subject (mental stress and normal) classification with models such as (A) leave one out, (B) 10-fold, (C) 3-fold, and (D) 2-fold on thirteen features, including demographic and extracted through ECG signal.

Figure 9

Comparison between previously published and proposed accuracy of the system.