Wearable devices are increasingly being used to monitor electrocardiogram (ECG) signals in real time, enabling earlier diagnosis and more effective monitoring of heart health. However, single-lead ECG signals captured by these devices are often contaminated with noise and artefacts, which can degrade signal quality and compromise the accuracy of subsequent diagnostic steps. To prevent this, it’s essential to assess the quality of ECG signals before further processing. Manual evaluation of signal quality can be laborious and prone to human error, especially in continuous monitoring contexts with large volumes of data. As a result, developing a streamlined approach for classifying ECG signal quality is critical to improving clinical workflows, reducing human error, and ensuring that diagnostic algorithms receive high-quality input for accurate and timely health assessments.