Qananwah Q, Quran H, Dagamseh A, Blazek V, Leonhardt S
Department of Biomedical Systems and Informatics Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan.
Department of Electronics Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan.
Biomed Eng Online. 2025 May 12;24(1):57. doi: 10.1186/s12938-025-01373-w.
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.
吸烟对人体健康的有害影响已广为人知，尤其是对心血管健康的影响。通过监测生命体征以及心率、血压（BP）、心电图（ECG）和光电容积脉搏波描记图（PPG）等其他生理信号或参数的动态变化，可以预测这些影响，这些信号或参数微妙地编码了与吸烟相关的影响。我们通过分析心电图和光电容积脉搏波描记图信号，研究了不同吸烟习惯——普通香烟（NC）、电子烟（EC）和水烟（SH）——对血压的影响。这些生理信号的测量是在三个不同的吸烟阶段进行的：“吸烟前”、“吸烟期间”和“吸烟后”。该研究评估了吸烟引起的心率变化以及心电图和光电容积脉搏波描记图信号的形态学和统计学特征。开发了一种机器学习（ML）模型来预测不同吸烟习惯和吸烟阶段的血压值，同时评估吸烟阶段的时间效应。结果表明，吸烟显著改变了光电容积脉搏波描记图特征，因为它显著影响收缩期时间、心率、峰值脉搏间隔变异性和增强指数。在所有吸烟习惯和阶段，血压变化都很明显。该机器学习模型在估计吸烟期间和吸烟后的收缩压（SBP）和舒张压（DBP）方面表现出很高的准确性，舒张压的平均误差为0.01±0.29 mmHg，均方根误差（RMSE）为0.2924 mmHg，收缩压的均方根误差为0.0082 mmHg。这样的研究强调了吸烟对血压的显著影响及其在心血管系统改变中的潜在作用，为相关疾病的发展提供了见解。
