With the growing application of deep learning in wearable devices, lightweight and efficient models are critical to address the computational constraints in resource-limited platforms. The performance of these approaches can be potentially improved by using various preprocessing methods. This study proposes a lightweight ResNet-based deep learning framework with Squeeze-and-Excitation (SE) modules for photoplethysmography (PPG) signal quality assessment (SQA) and compares different input configurations, including the PPG signal alone, its first derivative (FDP), its second derivative (SDP), the autocorrelation of PPG (ATC), and various combinations of these channels. Experimental evaluations on the Moore4Medical (M4M) and MIMIC-IV datasets demonstrate the model’s performance, achieving up to 96.52% AUC on the M4M test dataset and up to 84.43% AUC on the MIMIC-IV dataset. The novel M4M dataset was collected to explore PPG-based monitoring for detecting atrial fibrillation (AF) and AF burden in high-risk patients. Compared to the five reproduced existing studies, our models achieves over 99% reduction in parameters and more than 60% reduction in floating-point operations (FLOPs).Clinical Relevance—Accurate PPG signal quality assessment is crucial for continuous cardiovascular monitoring. By reducing false alarms and enhancing detection reliability, the proposed lightweight framework supports clinical decisions and practical deployment in resource-limited wearable devices, aiding broader adoption in telemedicine and remote care.