Smoking is widely recognized as a significant risk factor in the progression of arterial stiffness and cardiovascular diseases. Valuable information related to cardiac arrhythmias and heart function can be obtained by analyzing biosignals such as the electrocardiogram (ECG) and the photoplethysmogram (PPG). The PPG signal is a non-invasive optical technique that can be used to evaluate the changes in blood volume, and thus it can be linked to the health of the vascular system.. In this study, the impact of three smoking habits-cigarettes, shisha, and electronic cigarettes (e-cigarettes)-on the features of the PPG signal were investigated.. The PPG signals are measured for 45 healthy smokers before, during, and after the smoking session and then processed to extract the morphological features. Quantitative statistical techniques were used to analyze the PPG features and provide the most significant features of the three smoking habits. The impact of smoking is observed through significant changes in the features of the PPG signal, indicating blood volume instability.. The results revealed that the three smoking habits influence the characteristics of the PPG signal significantly, which presentseven after 15 min of smoking. Among them, shisha has the greatest impact on PPG features, particularly on heart rate, systolic time, augmentation index, and peak pulse interval change. In contrast, e-cigarettes have the least effect on PPG features. Interestingly, smoking electronic cigarettes, which many participants use as a substitute for traditional cigarettes when attempting to quit smoking, has nearly a comparable effect to regular smoking.. The findings suggest that individuals who smoke shisha are more likely to develop cardiovascular diseases at an earlier age compared to those who have other smoking habits. Understanding the variations in the PPG signal caused by smoking can aid in the early detection of cardiovascular disorders and provide insight into cardiac conditions. This ultimately contributes to the prevention of the development of cardiovascular diseases and the development of a health screening system.
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http://dx.doi.org/10.1088/1361-6579/ad1b10 | DOI Listing |
Physiol Meas
January 2025
Faculty of Sciences, University of Coimbra, Palacio de las Escuelas 3004-531, Coimbra, 3004-504, PORTUGAL.
Objective: The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction.
Approach: A generative adversarial network with fully connected layers (FC-GAN) is proposed for the reconstruction of distorted PPG signals.
Physiol Meas
January 2025
Faculty of Sciences, University of Coimbra, Palacio de las Escuelas 3004-531, Coimbra, 3004-504, PORTUGAL.
Objective: The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction.
Approach: A generative adversarial network with fully connected layers (FC-GAN) is proposed for the reconstruction of distorted PPG signals.
Sensors (Basel)
December 2024
Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium.
Photoplethysmography is a widely used optical technique to extract physiological information non-invasively. Despite its large use and adoption, multiple factors influence the signal shape and quality, including the instrumentation used. This work analyzes the variability of the DC component of the PPG signal at three source-detector distances (6 mm, 9 mm, and 12 mm) using green, red, and infrared light and four photodiodes per distance.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Biomedical Engineering, Lebanese International University, Beirut P.O. Box 146404, Lebanon.
The integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over traditional biometric techniques. PPG signals are non-invasive, inherently contain liveness information that is highly resistant to spoofing, and are cost-efficient, making them a superior alternative for biometric authentication.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of AI Convergence, Sungshin Women's University, 34 da-gil 2, Bomun-ro, Seongbuk-gu, Seoul 02844, Republic of Korea.
This paper proposes a machine learning approach to detect threats using short-term PPG (photoplethysmogram) signals from a commercial smartwatch. In supervised learning, having accurately annotated training data is essential. However, a key challenge in the threat detection problem is the uncertainty regarding how accurately data labeled as 'threat' reflect actual threat responses since participants may react differently to the same experiments.
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