Evaluation of a Low-Cost Single-Lead ECG Module for Vascular Ageing Prediction and Studying Smoking-Induced Changes in ECG
摘要
Vascular age is traditionally measured using invasive methods or through 12-lead electrocardiogram (ECG). This paper utilizes a low-cost single-lead (lead-I) ECG internet of thing (IoT) module to predict the vascular age of an apparently healthy young person. In addition, we also study the impact of smoking on ECG traces of the light-but-habitual smokers. We begin by collecting (lead-I) ECG data from 42 apparently healthy subjects (smokers and non-smokers) aged 18 to 30 years, using our custom-built low-cost, portable, single-lead ECG IoT 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 create another dataset by dividing the ECG traces into overlapping segments of five-second duration. We then feed both segmented and unsegmented datasets to a number of machine learning models, a 1D convolutional neural network, and ResNet18 model, for vascular ageing prediction. We also do transfer learning whereby we pre-train our models on a public PPG dataset, and later, fine-tune and evaluate them on our unsegmented ECG dataset. The random forest model outperforms all other models by achieving a mean squared error (MSE) of 0.07 and coefficient of determination