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Knowledge Engineering and Data Science (KEDS) pISSN 2597-4602 

Vol 4, No 2, December 2021, pp. 145–152 eISSN 2597-4637 
 

 

 

 
https://doi.org/10.17977/um018v4i22021p145-152   

©2021 Knowledge Engineering and Data Science | W : http://journal2.um.ac.id/index.php/keds | E : keds.journal@um.ac.id  

This is an open access article under the CC BY-SA license (https://creativecommons.org/licenses/by-sa/4.0/) 
KEDS is Sinta 2 Journal (https://sinta.kemdikbud.go.id/journals/detail?id=6662) accredited by Indonesian Ministry of Education, Culture, 

Research, and Technology 

Stress Classification using Deep Learning with 1D 

Convolutional Neural Networks 

Abdulrazak Yahya Saleh 1, *, Lau Khai Xian 2 

Faculty of Cognitive Sciences & Human Development (FCSHD), Universiti Malaysia Sarawak (UNIMAS) 

Kota Samarahan, Sarawak, Malaysia 

1 ysahabdulrazak@unimas.my*; 2 lkhixian@hotmail.com 

* corresponding author 

 

 

I. Introduction 

The new COVID-19 has contributed to psychological disorders among people, leading to a higher 
risk of suicide. Stress endurance varies between individuals; some people can handle it, and others 
cannot [1]. Some research has been conducted to analyze COVID19 impact on an individual's stress 
[2][3][4][5][6]. In the new era, stress has been extended related to finances, work, and relationships; 
it is now a familiar feeling people face each day in their lives. People facing stress are increasing at a 
fast rate. WillisTowersWatson [7] reported that "75% of the U.S. employers ranked stress as their top 
health and productivity concern, but employers and employees disagreed on its causes". These 
findings are based on responses from 487 U.S. employers in Willis Towers Watson's 2015/2016 
Global Staying@Work Survey and more than 5000 U.S. employees in Willis Towers Watson's 
2015/2016 Global Benefits Attitudes Survey. American Psychological Association [8] reported that 
the percentage of Americans who experienced at least one symptom of stress over the past month rose 
from 71 percent in August 2016 to 80 percent in January 2017. 

According to Cacioppo, Tassinary, and Berntson [9], chronic psychological stress carries many 
pathophysiological risks, such as cerebrovascular disease, cardiovascular disease, immune 
deficiencies, and diabetes. Francis [10] said that young adults spend more than six hours a day 
"stressed out." It is essential to classify whether someone is stressed before becoming a serious illness 
[11]. This problem called for a solution where stress can be recognized before it worsens.  

Machine Learning is a branch of artificial intelligence that focuses on machines' ability to learn 
from experience [12]. A computer with the ability has rules put into it to allow it to resolve problems 

ARTICLE INFO A B S T R A C T   

Article history: 

Submitted 18 October 2021 

Revised 27 October 2021 

Accepted 29 October2021 

Published online 31 December 2021 

 

Stress has been a major problem impacting people in various ways, and it gets serious 
every day. Identifying whether someone is suffering from stress is crucial before it 
becomes a severe illness. Artificial Intelligence (AI) interprets external data, learns 
from such data, and uses the learning to achieve specific goals and tasks. Deep 
Learning (DL) has created an impact in the field of Artificial Intelligence as it can 
perform tasks with high accuracy. Therefore, the primary purpose of this paper is to 
evaluate the performance of 1D Convolutional Neural Networks (1D CNNs) for stress 
classification. A Psychophysiological stress (PS) dataset is utilized in this paper. The 
PS dataset consists of twelve features obtained from the expert. The 1D CNNs are 
trained and tested using 10-fold cross-validation using the PS dataset. The algorithm 
performance is evaluated based on accuracy and loss matrices. The 1D CNNs outputs 
99.7% in stress classification, which outperforms the Backpropagation (BP), only 
65.57% in stress classification. Therefore, the findings yield a promising outcome that 
the 1D CNNs effectively classify stress compared to BP. Further explanation is 
provided in this paper to prove the efficiency of 1D CNN for the classification of 
stress. 

This is an open access article under the CC BY-SA license 

(https://creativecommons.org/licenses/by-sa/4.0/). 

Keywords: 

1D Convolutional Neural Networks 

Artificial Intelligence 

Classification  

Deep Learning  

Stress  

 

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146 A.Y. Saleh and L.K. Xian / Knowledge Engineering and Data Science 2021, 4 (2): 145–152 

without human intervention [13]. Machine learning provides the ability for machines to train on and 
accordingly adapt when there is new data input, enables the prediction from known data, and learns 
from the new unknown data. 

One of the machine learning methods that can be used to recognize stress is Artificial Neural 
Networks (ANNs). ANNs are a non-linear data-driven self-adaptive approach instead of the traditional 
model-based methods" by Solomatine & Ostfeld [14]. "ANNs can determine and study correlated 
patterns between input data and corresponding target values. It can use to predict the outcome of new 
independent input data after training. Neural networks (NNs) "have been used in a wide variety of 
applications where statistical methods are traditionally employed like classifying problems or 
predicting outcomes as said by Solomatine & Ostfeld [14].  

For a very long period, ANNs had to be limited in size due to difficulties in training. However, 
Hinton, Osindero, and Teh [15] suggested a procedure that could be used with much larger networks 
known as Deep Learning (DL). LeCun, Bengio, and Hinton [16] said Deep learning allows 
computational models composed of multiple processing layers to learn the representation of data with 
multiple levels of abstraction. Convolutional Neural Networks (CNNs), one of the most well-known 
DL models, have become the de facto standard for many Computer Vision and Machine Learning 
operations over the last decade. CNNs are feed-forward Artificial Neural Networks (ANNs) with 
convolutional and subsampling layers that alternate. When trained on a large visual database with 
ground-truth labels, deep 2D CNNs with many hidden layers and millions of parameters can learn 
complex objects and patterns. With proper training, this one-of-a-kind ability transforms them into the 
primary tool for various engineering applications involving 2D signals such as images and video 
frames. As an alternative, 1D Convolutional Neural Networks (1D CNNs) have recently been 
developed as modified 2D CNNs. 1D CNNs have been utilized in several classification problems like 
heart sound and Soil Texture [17][18][19]. It is believed that the classification between stress and non-
stress will be more accurate with multiple processing layers compared to the ANNs that only consist 
of a single processing layer. Smets et al. [20] said the previous approach to measure stress does not 
allow for continuous monitoring and often suffers from biases such as demand effects, response, and 
memory biases; therefore, the focus has been shifted towards measuring bodily responses as indicators 
of stress.  

This study aims to use stress datasets to anticipate an accurate output; society will use the 
outcomes. The remaining parts of this paper are organized as follows: methodology and empirical 
studies are presented in section 2; Findings and the analysis of the results are presented in sections 3 
and 4, and finally, section 5 concludes the paper's future works. 

II. Methods 

This section introduces the research methodology that had been used in this research. The 
components in this section include Dataset Preparation, Data pre-processing, Research Design, 
Research Implementation, and Instruments, as in Figure 1. DL has become a growing research interest, 
and the method has shown certain advantages of learning performance. DL can learn from the past 
data to solve complex problems and has been widely used in classification. It allows the computational 
models with various processing layers to learn the data representation with various levels of 
abstraction. The result of the DL method is compared to one of the neural network standards, the 
Backpropagation (BP) algorithm, which was used in previous studies related to stress classification 
[21][22]. This comparison is to analyze the effectiveness of the DL algorithms in this study. The 
research focuses on psychophysiological stress, which looks at heart rate variability (HRV), galvanic 
skin response (GSR), skin temperature (Temp), and respiration. 

A. 1D Convolutional Neural Networks (1D CNNs) 

1D CNNs is very similar to CNNs where the input of 1D CNNs is dimensional, whereas an 
ordinary CNNs is two-dimensional. A Convolutional Neural Network (CNN) is very similar to 
ordinary neural networks as they are made up of neurons that have learnable weight and biases. Each 
neuron receives some inputs, performs a dot product, and optionally follows it with a non-linearity. 
Loss function still can be found in the last layer or fully connected layer, and all the tips developed 
for learning regular neural networks still apply. Figure 1 shows the schematic representation of 1D 
CNNs. The first hidden layer is always a convolutional layer, followed by a pooling layer. The 
convolutional and pooling layers can be present multiple times in the network until the final hidden 



 A.Y. Saleh and L.K. Xian / Knowledge Engineering and Data Science 2021, 4 (2): 145–152 147 

 

layer is the full-connected layer. The Schematic Representation of 1D Convolutional Neural Networks 
is as shown in Figure 2. 

The convolution layer is where the previous layer's feature maps are folded with learnable kernels 
and execute the activation function to form the output feature map. Each output map may combine 
convolutions with multiple input maps. The mathematical formula for constructing the convolution 
layer is used (1). 

𝑥𝑗
ℓ = 𝑓 (∑ 𝑥𝑖

ℓ−1
𝑖∈𝑀𝑗

∗ 𝑘𝑖𝑗
ℓ + 𝑏𝑗

ℓ) (1) 

where 𝑀𝑗 is the selection of input maps, each output map is given an additive bias 𝑏, for an output 
map, the input maps will be folded with different kernels. So, if output map 𝑗 and map 𝑘 both sums 
over input map 𝑖, the kernels applied to map 𝑖 are different for output maps 𝑗 and 𝑘. 

The pooling layer produces downsampled versions of the input maps. If there are N input maps, 
there will be exactly N output maps, although the output map is smaller (2). 

 
Fig. 1. An overview flow of the research methodology 

 
 

 
Fig. 2. Schematic representation of 1D convolutional neural networks 

 



148 A.Y. Saleh and L.K. Xian / Knowledge Engineering and Data Science 2021, 4 (2): 145–152 

𝑥𝑗
ℓ = 𝑓(𝛽𝑗

ℓ𝑑𝑜𝑤𝑛(𝑥𝑗
ℓ−1) + 𝑏𝑗

ℓ (2) 

where 𝑑𝑜𝑤𝑛(𝑥𝑗
ℓ−1) represents a sub-sampling function. This function will sum over each distinct n-

by-n block in the input so that the output is n-times smaller along both spatial dimensions. Each output 
map is given its multiplicative bias 𝛽 and an additive bias 𝑏. 

The fully connected layer will combine features learned by different convolution kernels so that 
the network can build a global representation of the holistic input. Let 𝛼𝑖𝑗 Denote the weight given to 
input map 𝑖 when forming the output map 𝑗. The output map 𝑗 is (3). 

𝑥𝑗
ℓ = 𝑓(∑ 𝛼𝑖𝑗

𝑁𝑖𝑛
𝑖=1 (𝑥𝑖

ℓ−1 ∗ 𝑘𝑖
ℓ) + 𝑏𝑗

ℓ (3) 

Subject to the constraints (4). 

∑ 𝛼𝑖𝑗𝑖 = 1, and 0 ≤ 𝛼𝑖𝑗 ≤ 1 (4) 

These constraints can be enforced by setting the 𝛼𝑖𝑗 variables equal to the softmax over a set of 
unconstrained, underlying weight 𝑐𝑖𝑗 use (5). 

𝑎𝑖𝑗 =
𝑒𝑥𝑝⁡(𝑐𝑖𝑗)

∑ 𝑒𝑥𝑝⁡(𝑐𝑘𝑗)𝑘
 (5) 

Each set of weights 𝑐𝑖𝑗 for fixed j are independent of all other such sets for any other 𝑗, the single 
map is updated, and the subscript 𝑗. Each map is updated in the same way except with different 𝑗 
indices. 

B. Empirical Studies 

Several experiments are conducted on the psychophysiological stress dataset. The implementation 
is multivariate analysis to evaluate 1D CNNs deep learning model. There is a total of twelve features 
in the raw data given by Dr. Elena Smets [20], which include Skin Conductance / Galvanic Skin 
Response (SC/GSR), Skin Temperature (Temp), Blood Volume Pulse (BVP), Respiration Changes 
(RSP), Heart Rate (HR), Electromyography amplitude (EMG amp), Blood Volume Pulse amplitude 
(BVP amplitude), Respiration Changes rate (RSP-Rate), Respiration Changes amplitude (RSP-
amplitude), Heart Rate Variability amplitude (HRV amplitude), Respiration Changes + Heart Rate 
Coherence (RSP+HR Coherence), and Segments. The segments will display the state of the 
respondents at that moment. For each sensing modality, features have been calculated on a sliding 
window of 30 s with 29 s overlap. The illustration of the datasets is shown in Figure 3. 

The dataset had been separated into two parts: a training set and a testing set. The training set 
usually consists of input vectors and corresponding output vectors that fit into the model to produce a 
result compared with the target for each input vector in the training dataset. The testing set is utilized 

 

 

Fig. 3. Psychophysiological stress dataset 
 



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to provide an unbiased evaluation of a final model fit on the training dataset. The dataset had been 
normalized using the Z-Score normalization technique to scale the dataset value from 0 to 1. The 
normalized data then fits into the proposed deep learning model for classification. The classification 
performance of the deep learning model will be evaluated by utilizing the k-fold cross-validation. 
Brownlee [23] said that Cross-validation is a resampling procedure used to evaluate a limited data 
sample of machine learning models. In k-fold cross-validation, the dataset is divided into k subsets of 
equal size. The model is built fork times, leaving one of the subsets from training and using it as the 
test set. 

Python is a hugely popular general-purpose programming language, so it is beneficial to apply 
solidly to AI and profound learning principles. Keras is a high-level neural networks API, written in 
Python and capable of running on top of TensorFlow, CNTK, or Theano. TensorFlow serves as the 
backend engine for the Keras API to handle the low-level operations such as tensor products and 
convolutions. The machine used is Intel® Core™ i7-4720HQ CPU @ 2.60GHz 2.59GHz with 
8.00GB RAM. The proposed method uses the 1D CNNs deep learning model to classify. 1D CNNs 
works precisely like normal CNNs but with different input dimension. The input for normal CNNs 
usually be two-dimensional or image datasets, but the input for 1D CNNs is one-dimensional or signal 
datasets. Table 1 shows the dataset, batch size, epochs, activation function, and optimizer used in this 
study. 

The Batch Size used is 256. Batch Size is the number of training examples utilized in one iteration. 
The data was run for 15 epochs to see the model's performance. Epoch is one forward pass and one 
backward pass of all the training examples. The activation functions are ReLU and Softmax. ReLU 
or Rectified-Linear Unit Layer will provide a threshold at zero. Softmax function or normalized 
exponential function is used to represent categorical distribution. The optimizer function is SGD. SGD 
or Stochastic Gradient Descent is a simple yet efficient approach to discriminative learning of linear 
classifiers under convex loss functions such as Support Vector Machine or Linear Regression. 
Figure 4 shows the schematic representation of the 1D CNNs model.  

The classification accuracy of the output is calculated according to the use of equation (6). 

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑁𝑢𝑚𝑏𝑒𝑟⁡𝑜𝑓⁡𝑐𝑜𝑟𝑟𝑒𝑐𝑡⁡𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛

𝑇𝑜𝑡𝑎𝑙⁡𝑁𝑢𝑚𝑏𝑒𝑟⁡𝑜𝑓⁡𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠
 (6) 

 

 
Fig. 4. Schematic representation of 1D CNNs 

 

Table 1. Hyper parameter of 1D CNNs 

Dataset Batch Size Epochs Activation Optimizer 

Psychophysiological Stress Dataset 256 15 ReLU Softmax SGD 

 
 



150 A.Y. Saleh and L.K. Xian / Knowledge Engineering and Data Science 2021, 4 (2): 145–152 

III. Results and Discussions 

Table 2 shows the results of the 1D CNNs deep learning model with 10-fold cross-validation. In 
each fold, the data had been divided into ten groups. The first group will be used as testing in the first 
fold, and the other groups will be used as training. In the second fold, the second group will be used 
as testing, and the other groups will be used as training, followed by the third group for testing in the 
third fold. This will recursively continue until the 10th fold is completed. Besides that, the plotting of 
accuracy and loss function for each training process and testing process also can be seen in Table 2. 
The Final accuracy for 1D CNNs is 99.97%.  

The dataset is tested in a second experiment with The Backpropagation (BP) algorithm for further 
proof. BP is fit into the same data to compare the performance of both models. In Table 2, it is clearly 
stated that the training classification performance of 1D CNNs is 100% and the testing classification 
performance of 1D CNNs is 99.97%. However, the training classification performance of BP is 
65.85%, and the testing classification performance of BP is 65.57%. Figure 5 clearly shows that the 
1D CNNs deep learning model outperforms the BP algorithms. Moreover, our algorithm achieved 
higher accuracy than the methods mentioned in [24]. 

Table 3 compares the implementation of the CNN and other recent works based on different 
machine and deep learning algorithms. As shown in Table 3, our work achieves better accuracy than 
most of the other recent works (which have been reported in this paper) except that of [20], which 
achieved 99.8% using the physiological signals dataset. However, our work's accuracy gains better 
results than the same dataset.  

The datasets must be structured appropriately to fit perfectly into the model and provide a good 
analysis of the results. Every step, from loading the dataset from a text file or CSV file to evaluating 
the trained model's performance, is critical because simple errors can cause errors when the code is 
run. The parameters used to build the model must be tested repeatedly to find the best parameter. For 
example, a sigmoid may be better when ReLU is a terrible choice and vice versa. Building the best 
model requires a lot of trial and error. The deep learning model is well-known for its high accuracy, 
regardless of whether it can solve a classification problem or a regression problem. 

Table 2. Summary of the accuracy 1D CNNs 

Fold Accuracy 

1st 99.98 

2nd  100.00 

3rd  99.99 

4th  99.98 

5th  99.97 

6th  99.93 

7th  99.99 

8th  99.97 

9th  99.94 

10th  99.98 

Final Accuracy 99.97 

 

Table 3. Comparison with other published works 

Published Work Dataset Accuracy 

Deep neural networks [25]  Physiological signals  99.80% 

ANN [26]  WESAD dataset 95.21% 

SVM [20]  Psychophysiological Stress 93.4 ± 3.2% 

Convolutional Neural Network (CNN) [27] The heart rate signal 98.69 ± 0.45% 

Machine learning [28]  The mind-brain-body dataset 81.33% 

Machine learning [29] Questionnaire (DASS 21) Between 0.628 - 0.798 

Hybrid deep learning technique [30] Employee Data 96.2 % 

This work Psychophysiological Stress 99.7% 

 



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IV. Conclusion 

 In this study, the 1D CNNs model was utilized to classify stress and compared with BP and various 
techniques from the Literature review. A psychophysiological stress dataset was used in this study. 
The performance of the developed model is evaluated by comparing it with the BP model and other 
works. The result obtained in this study shows that the 1D CNNs model is reliable in classifying stress 
with low lost function, which is 0.001, and high accuracy of 99.97%. More datasets are required to 
enhance the result of stress classification in the future. Skill and experience are essential in assisting 
the person in selecting and checking the best method in the modeling process. Spiking Neural Network 
as the latest generation of neural network and other areas of study will be applied to do stress 
classification. 

Acknowledgment 

The authors wish to thank Dr. Elena Smets for sharing the datasets to complete this study. 
Universiti Malaysia Sarawak (UNIMAS) supported and funded this work under the CDRG Cross-
Disciplinary Research (F04/CDRG/1839/2019). 

Declarations  

Author contribution  

All authors contributed equally as the main contributor of this paper. All authors read and approved the final paper. 

Funding statement  

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  

Conflict of interest  

The authors declare no known conflict of financial interest or personal relationships that could have appeared to influence 
the work reported in this paper.  

Additional information  

Reprints and permission information are available at http://journal2.um.ac.id/index.php/keds. 

Publisher’s Note: Department of Electrical Engineering - Universitas Negeri Malang remains neutral with regard to 
jurisdictional claims and institutional affiliations. 

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Fig. 5. Comparison in training and testing accuracy of 1D CNNs 

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