A Robust PPG Heart Rate Estimation Network Based on Frequency-Domain NLMS and Dual-Stream Hierarchical Self-Attention (International Conference on Energy, Intelligence Systems, and Cloud Computing (Ingenio 2026), 2026)
The heart rate estimation based on the wearables' photoplethysmography (PPG) sensor always faces serious motion artefacts (MA) under high-intensity exercise. Although adaptive filtering and deep learning methods have demonstrated effectiveness in practice so far; currently, there are problems such as system divergence under the influence of strong power signals, poor generalization due to insufficiently trained high-heart-rate sample sets. The main deficiencies in the existing systems are as follows: Filter instability due to significant noise; The coupling of physiological and motion variables; Imbalanced datasets. To solve the above-mentioned problems, a full-ended design of frequency-domain signal processing and deep neural networks is proposed. Through an energy-normalized NLMS adaptive filter to establish the system's absolute stability constraint condition, in conjunction with a dual-stream multiple-head self-attention (MHSA) mechanism for controllably managing features weights; Moreover, by incorporating a specific data augmentation strategy. On the Public PPG-DaLiA dataset, this built network achieves an average absolute error of 1.81 beats per minute (BPM). Based on the results, a feasible approach can be used in clinical practice to improve the credibility of wearable device applications by providing reasonable uncertainly ranges.