A Novel Rapid Assessment of Mental Stress by Using PPG Signals Based on Deep Learning

Wang, Zhi-Hao; Wu, Yu-Chan

doi:10.1109/jsen.2022.3208427

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…In any case, with the number of variables, the computation time also increases, which jeopardizes its application potential in real-time environments. The characteristics usually provided to classification models attend to linear aspects, both in the time domain and frequency domain and to non-linear aspects of biological signals [62][63][64][65].…”

Section: Discussionmentioning

confidence: 99%

Photoplethysmographic Signal-Diffusive Dynamics as a Mental-Stress Physiological Indicator Using Convolutional Neural Networks

et al. 2023

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Stress is one of the primary triggers of serious pathologies (e.g., depression, obesity, heart attack). Prolonged exposure to it can lead to addictive substance consumption and even suicide, without ignoring other adverse side effects in the economic, work and family spheres. Early detection of stress would relax the pressure of medical practice exercised by the population affected and result in a healthier society with a more satisfying quality of life. In this work, a convolutional-neural-network (CNN) model is proposed to detect an individual’s stress state by analyzing the diffusive dynamics of the photoplethysmographic (PPG) signal. The characteristic (p,q)-planes of the 0–1 test serve as a framework to preprocess the PPG signals and feed the CNN with the dynamic information they supply to typify an individual’s stress level. The methodology follows CRISP-DM (Cross Industry Standard Process for Data Mining), which provides the typical steps in developing data-mining models. An adaptation of CRIPS-DM is applied, adding specific transitions between the usual stages of deep-learning models. The result is a CNN model whose performance amounts to 97% accuracy in diagnosing the stress level; it compares with other published results.

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Section: Discussionmentioning

confidence: 99%

Photoplethysmographic Signal-Diffusive Dynamics as a Mental-Stress Physiological Indicator Using Convolutional Neural Networks

et al. 2023

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“…However, an ELM based on local receptive fields (ELM-LRFs) is among the fastest techniques employed in the segmentation and classification of time series signals. DELM-LRF-BLSTM [2] is a faster and accurate hybrid deep learning model for ECG signal recognition proposed by FENGJUAN QIAO & Co. It yielded very high levels of accuracy and sensitivity of about 99.32% and 97.15%, respectively when applied to the MIT-BIH Arrhythmia dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Detecting HVD at an early stage can significantly reduce the mortality rate associated with various cardiac failures, potentially by up to one-third [1]. The prevalence of cardiovascular diseases is alarmingly increasing worldwide, with projected costs of treating such conditions in the USA alone expected to reach $1 trillion per year by 2030 [2]. In addition to irregular heartbeat, morphological parameters such as shortness of breath, dizziness, palpitations, fainting, and other symptoms may indicate the presence of arrhythmias or HVD.…”

Section: Introductionmentioning

confidence: 99%

“…Among the complications associated with CVD, cardiovascular arrhythmias stand out, characterized by abnormal electrical impulses in the myocardium, leading to irregular heart rhythms. These arrhythmias contribute significantly to the occurrence of cardiac arrests [2]. Hence, the prompt and accurate identification of cardiac arrhythmias becomes paramount in order to save lives and mitigate the impact of these lifethreatening conditions.…”

Section: Introductionmentioning

confidence: 99%

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An RNN-Bi LSTM Based Multi Decision GAN Approach for the Recognition of Cardiovascular Disease (CVD) From Heart Beat Sound: A Feature Optimization Process

Vinay,

Vidyasagar,

Rohith

et al. 2024

IEEE Access

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The cardiovascular system is responsible for carrying the blood along with nutrition and oxygen throughout the body. This system consists of heart, blood, and blood vessels. The experts, or doctors called as cardiologists, analyze the sounds of heart's (lub-dub) beat and flow of blood to diagnose Cardio Vascular Disease (CVD) using a traditional stethoscope and phonological cardiogram technique. Through the stethoscope, the cardiologist will listen to vibration produced by heart beat and heart beat sound and murmur sound are popularly known as phonocardiogram (PCG) signals, which are being recorded for medical diagnosis purposes using a stethoscope. The development of a technique for the automatic recognition of HVD's assists the experts in recognizing the CVD effectively in the initial stage itself from PCG signals.There are many tools available to help doctors in a clinical setting for the accurate diagnose the CVD in a less time. The main aim of this proposed work is to provide an Artificial Intelligence (AI) based PCG signal analysis for the automatic and early detection of various cardiac conditions using supervised and unsupervised Recurrent Neural Network (RNN) based Bidirectional Long Short-Term Memory (Bi-LSTM) Machine Learning (ML) algorithm. Along with this algorithm, Generative Adversarial Networks (GAN's) is considered because they can create fresh, high-quality, pseudo-real data that resembles their training set which has been demonstrated by using their two unique networks: Discriminator Network (DN) and the Generator Network (GN). The proposed method is tested using heart sound signals from the well-known, freely accessible PhysioNet and Kaggle datasets. The Experimental results are validated based accuracy, precision, F1-score, sensitivity, and specificity.

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“…Other researchers have taken into account the properties of convolutional neural networks and transformed 1D signals into 2D maps to facilitate network learning. Among them, methods such as Poincaré plots and continuous wavelet variation (CWT) have been used quite extensively [10].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Mental Workload Level Based on PPG Signal Fusion Continuous Wavelet Transform and Cardiopulmonary Coupling Technology

Zhang,

Wang,

Zhuang

et al. 2024

Electronics

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Mental workload is an important predisposing factor for mental illnesses such as depression and is closely related to individual mental health. However, the suboptimal accuracy of utilizing photoplethysmography (PPG) exclusively for mental workload classification has constrained its application within pertinent professional domains. To this end, this paper proposes a signal processing method that combines continuous wavelet transform (CWT) and cardiopulmonary coupling mapping (CPC) to classify mental load via a convolutional neural network (ResAttNet). The method reflects changes in mental workload, as assessed by changes in the association between heart rate variability and respiration. In this paper, the strengths and weaknesses of this method are compared with other traditional psychological workload monitoring methods, such as heart rate variability (HRV), and its validation is performed on the publicly available dataset MAUS. The experiments show that the method is significantly better than previous machine learning methods based on heart rate variability correlation. Meanwhile, the accuracy of the method proposed in this paper reaches 80.5%, which is 6.2% higher than in previous studies. It is comparable to the result of 82.4% for the ECG-based mental workload monitoring system. Therefore, the method of combining CWT and CPC has considerable potential and provides new ideas for mental workload classification.

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A Novel Rapid Assessment of Mental Stress by Using PPG Signals Based on Deep Learning

Cited by 6 publications

References 31 publications

Photoplethysmographic Signal-Diffusive Dynamics as a Mental-Stress Physiological Indicator Using Convolutional Neural Networks

Photoplethysmographic Signal-Diffusive Dynamics as a Mental-Stress Physiological Indicator Using Convolutional Neural Networks

An RNN-Bi LSTM Based Multi Decision GAN Approach for the Recognition of Cardiovascular Disease (CVD) From Heart Beat Sound: A Feature Optimization Process

Assessment of Mental Workload Level Based on PPG Signal Fusion Continuous Wavelet Transform and Cardiopulmonary Coupling Technology

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