Investigating the correlation between smoking and blood pressure via photoplethysmography

Abstract Smoking has been widely identified for its detrimental effects on human health, particularly on the cardiovascular health. The prediction of these effects can be anticipated by monitoring the dynamic changes in vital signs and other physiological signals or parameters such as heart rate, bl...

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Main Authors: Q. Qananwah, H. Quran, A. Dagamseh, V. Blazek, S. Leonhardt
Format: Article
Language:English
Published: BMC 2025-05-01
Series:BioMedical Engineering OnLine
Subjects:
Online Access:https://doi.org/10.1186/s12938-025-01373-w
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author Q. Qananwah
H. Quran
A. Dagamseh
V. Blazek
S. Leonhardt
author_facet Q. Qananwah
H. Quran
A. Dagamseh
V. Blazek
S. Leonhardt
author_sort Q. Qananwah
collection DOAJ
description Abstract Smoking has been widely identified for its detrimental effects on human health, particularly on the cardiovascular health. The prediction of these effects can be anticipated by monitoring the dynamic changes in vital signs and other physiological signals or parameters such as heart rate, blood pressure (BP), Electrocardiogram (ECG), and Photoplethysmogram (PPG), which subtly encode smoking-related effects. We investigated the influence of different smoking habits—normal cigarettes (NC), electronic cigarettes (EC), and shisha (SH)—on BP through analysis of ECG and PPG signals. The measurements of these physiological signals were taken across three distinct smoking phases: "before", "during", and "after" smoking. The study assessed changes in heart rate, as well as morphological and statistical characteristics of ECG and PPG signals, induced by smoking. A machine learning (ML) model was developed to predict BP values with different smoking habits and smoking phases, while also evaluating the temporal effects of smoking phases. Results show that smoking markedly alters PPG features in such it significantly affects systolic time, heart rate, peak pulse interval variability, and augmentation index. BP variations were evident across all smoking habits and phases. The ML model demonstrated strong accuracy in estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) during and post-smoking, with a mean error of 0.01 ± 0.29 mmHg and a root mean square error (RMSE) of 0.2924 mmHg for DBP and RMSE of 0.0082 mmHg for SBP. Such a study underscores the pronounced effect of smoking on BP and its potential role in cardiovascular system alterations, offering insights into the development of related diseases.
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spelling doaj-art-58a34d38a086497987e5cf6cb13c6ecb2025-08-20T02:25:11ZengBMCBioMedical Engineering OnLine1475-925X2025-05-0124112210.1186/s12938-025-01373-wInvestigating the correlation between smoking and blood pressure via photoplethysmographyQ. Qananwah0H. Quran1A. Dagamseh2V. Blazek3S. Leonhardt4Department of Biomedical Systems and Informatics Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk UniversityDepartment of Biomedical Systems and Informatics Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk UniversityDepartment of Electronics Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk UniversityHelmholtz-Institute for Biomedical Engineering, RWTH Aachen UniversityHelmholtz-Institute for Biomedical Engineering, RWTH Aachen UniversityAbstract Smoking has been widely identified for its detrimental effects on human health, particularly on the cardiovascular health. The prediction of these effects can be anticipated by monitoring the dynamic changes in vital signs and other physiological signals or parameters such as heart rate, blood pressure (BP), Electrocardiogram (ECG), and Photoplethysmogram (PPG), which subtly encode smoking-related effects. We investigated the influence of different smoking habits—normal cigarettes (NC), electronic cigarettes (EC), and shisha (SH)—on BP through analysis of ECG and PPG signals. The measurements of these physiological signals were taken across three distinct smoking phases: "before", "during", and "after" smoking. The study assessed changes in heart rate, as well as morphological and statistical characteristics of ECG and PPG signals, induced by smoking. A machine learning (ML) model was developed to predict BP values with different smoking habits and smoking phases, while also evaluating the temporal effects of smoking phases. Results show that smoking markedly alters PPG features in such it significantly affects systolic time, heart rate, peak pulse interval variability, and augmentation index. BP variations were evident across all smoking habits and phases. The ML model demonstrated strong accuracy in estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) during and post-smoking, with a mean error of 0.01 ± 0.29 mmHg and a root mean square error (RMSE) of 0.2924 mmHg for DBP and RMSE of 0.0082 mmHg for SBP. Such a study underscores the pronounced effect of smoking on BP and its potential role in cardiovascular system alterations, offering insights into the development of related diseases.https://doi.org/10.1186/s12938-025-01373-wSmoking habitsBlood pressuree-SmokingPhotoplethysmogramStatistical analysisCardiovascular disease
spellingShingle Q. Qananwah
H. Quran
A. Dagamseh
V. Blazek
S. Leonhardt
Investigating the correlation between smoking and blood pressure via photoplethysmography
BioMedical Engineering OnLine
Smoking habits
Blood pressure
e-Smoking
Photoplethysmogram
Statistical analysis
Cardiovascular disease
title Investigating the correlation between smoking and blood pressure via photoplethysmography
title_full Investigating the correlation between smoking and blood pressure via photoplethysmography
title_fullStr Investigating the correlation between smoking and blood pressure via photoplethysmography
title_full_unstemmed Investigating the correlation between smoking and blood pressure via photoplethysmography
title_short Investigating the correlation between smoking and blood pressure via photoplethysmography
title_sort investigating the correlation between smoking and blood pressure via photoplethysmography
topic Smoking habits
Blood pressure
e-Smoking
Photoplethysmogram
Statistical analysis
Cardiovascular disease
url https://doi.org/10.1186/s12938-025-01373-w
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