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：本文提出了一种新颖的低成本方法，仅通过心电图（ECG）的I导联数据即可预测：i）健康青年人的血管年龄，ii）个体是否为吸烟者。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（男性/女性、吸烟者/非吸烟者）的I导联心电数据，并同步记录人体测量数据（如体重指数、吸烟状态、血压等）。在提出的方法中，我们首先对数据集进行预处理：对心电信号进行去噪、基线漂移校正及Z-score标准化。随后将心电信号分割为5秒重叠片段，使数据集规模扩大145倍。随后将数据集输入多种机器学习模型：一维卷积神经网络、多层感知器（MLP）及ResNet18迁移学习模型。在血管年龄预测任务中，随机森林方法以R²分数0.99、均方误差0.07的表现优于所有其他方法。在区分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任务中，MLP方法以97.5%的准确率取得最优效果。本研究符合联合国可持续发展目标中为弱势群体提供低成本高质量医疗解决方案的宗旨。