This paper proposes a novel approach for exercise heart rate (HR) estimation by integrating PPG quality assessment with deep neural network (DNN) modeling. A frequency-domain kurtosis (kurtF) metric is introduced to identify high-quality PPG samples, optimizing DNN training data and mitigating motion artifacts. An E-K scatter plot is used to visualize sample quality distribution, aiding dataset variability analysis. The proposed DNN model, designed for single-channel PPG input, demonstrates strong HR estimation performance on public datasets, achieving a mean absolute error (MAE) values of 3.76 bpm (PPG_DaLiA) and 3.18 bpm (IEEE-Training). Theoretical analysis and experimental validation confirm that prioritizing high-quality samples enhances model stability, accuracy, and generalizability. Additionally, a dataset quality analysis method is introduced to facilitate comparative assessments. The kurtF metric and quality-driven sample selection strategy provide a robust framework for improving HR estimation, even in data-limited scenarios. This study underscores the importance of integrating sample quality assessment into HR estimation workflows, paving the way for more accurate and reliable PPG-based HR monitoring during exercise.