Resource-Efficient Derivative PPG-Based Signal Quality Assessment Using One-Dimensional CNN With Optimal Hyperparameters for Quality-Aware PPG Analysis

Photoplethysmogram (PPG) is a bio-optical technology used heavily in wearable health devices for monitoring vital sign parameters. Therefore, ensuring the quality of PPG signals is crucial for accurate measurements, as these signals are susceptible to various artifacts and noises. This article proposes a derivative-based PPG (dPPG) signal quality assessment (SQA) method to distinguish high-quality data from artifact-laden signals. The proposed method includes a first-order derivative, followed by a 3-point moving average filter to smooth the high-frequency components present in the dPPG signal. Further, the smoothed dPPG signal is fed into 2, 4, and 6-layer 1D-convolutional neural networks (1D-CNN) to classify it as a clean or noisy dPPG signal. The proposed derivative-based PPG-SQA method is tested using various PPG signals collected from standard databases. The noisy PPG signals are collected from the wrist-cup (WC-PPG) database, whereas acceleration (SYN-ACCE-PPG) and random noise (SYN-RN-PPG) affected PPG signals are synthetically generated using noise-free PPG (NF-PPG) signals. We evaluate the proposed method using performance metrics like sensitivity (SE), specificity (SP), accuracy (ACC), model size (in MB), and processing time (PT). We also implemented the proposed method on the Raspberry Pi 4 (R-Pi-4) to study its real-time feasibility. The 6-layer 1D-CNN with 32 kernels using the ReLU activation function is observed to outperform the other models and existing PPG-SQA methods. The proposed method, when compared to NF signals, achieves: 99.84% of SE, 97.94% of SP, and 99.70% of ACC for WC-PPG; 99.79% of SE, 100.00% of SP, and 99.96% of ACC for SYN-RN-PPG; and 84.48% of SE, 74.63% of SP, and 78.17% of ACC for SYN-ACCE-PPG, respectively. Results demonstrate that selecting the appropriate 1D-CNN model can achieve higher SE, SP, and ACC with a lower computational load on PC-CPU and R-Pi computing platforms. Furthermore, we test the reliability and robustness of the dPPG-1D-CNN-SQA model using the unseen databases. Our model may reduce the false alarms and energy consumption of wearable healthcare devices, which have limited battery capacity and computational resources.