This paper presents a novel low-cost method to predict: i) the vascular age of a healthy young person, ii) whether or not a person is a smoker, using only the lead-I of the electrocardiogram (ECG). We begin by collecting (lead-I) ECG data from 42 healthy subjects (male, female, smoker, non-smoker) aged 18 to 30 years, using our custom-built low-cost single-lead ECG module, and anthropometric data, e.g., body mass index, smoking status, blood pressure etc. Under our proposed method, we first pre-process our dataset by denoising the ECG traces, followed by baseline drift removal, followed by z-score normalization. Next, we divide ECG traces into overlapping segments of five-second duration, which leads to a 145-fold increase in the size of the dataset. We then feed our dataset to a number of machine learning models, a 1D convolutional neural network, a multi-layer perceptron (MLP), and ResNet18 transfer learning model. For vascular ageing prediction problem, Random Forest method outperforms all other methods with an R2 score of 0.99, and mean squared error of 0.07. For the binary classification problem that aims to differentiate between a smoker and a non-smoker, XGBoost method stands out with an accuracy of 96.5%. Finally, for the 4-class classification problem that aims to differentiate between male smoker, female smoker, male non-smoker, and female non-smoker, MLP method achieves the best accuracy of 97.5%. This work is aligned with the sustainable development goals of the United Nations which aim to provide low-cost but quality healthcare solutions to the unprivileged population.

翻译：本文提出一种新颖的低成本方法，仅通过心电图（ECG）I导联数据实现：i）健康年轻人血管年龄的预测，ii）个体吸烟者身份的判别。我们首先采用自研低成本单导联ECG模块，采集42名18至30岁健康受试者（含男/女性、吸烟/非吸烟者）的I导联ECG数据及人体测量数据（如体重指数、吸烟状态、血压等）。在提出的方法中，先对数据集进行预处理：ECG信号去噪、基线漂移校正及Z-score标准化；随后将ECG信号分割为5秒重叠片段，使数据集规模扩大145倍；最后将处理后的数据输入至多种机器学习模型——一维卷积神经网络、多层感知器（MLP）及ResNet18迁移学习模型。针对血管老化预测问题，随机森林方法以0.99的R²分数和0.07的均方误差表现最优；在吸烟者与非吸烟者的二分类任务中，XGBoost方法以96.5%的准确率脱颖而出；而在区分男性吸烟者、女性吸烟者、男性非吸烟者、女性非吸烟者的四分类问题中，MLP方法以97.5%的最佳准确率夺冠。本研究契合联合国可持续发展目标，致力于为弱势群体提供低成本高质量医疗解决方案。