Real-Time Wireless ECG-Derived Respiration Rate Estimation using an Autoencoder with a DCT Layer

• Published in: Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1 - 5
• Main Authors: Pan, Hongyi, Zhu, Xin, Ye, Zhilu, Chen, Pai-Yen, Cetin, Ahmet Enis
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04.06.2023
• Subjects: autoencoder neural network; Electrocardiography; Estimation; Neural networks; Real-time systems; Respiration rate estimation; Training data; Transforms; Wireless communication; wireless ECG monitoring system
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP49357.2023.10094831

In this paper, we present a wireless ECG-derived Respiration Rate (RR) estimation using an autoencoder with a DCT Layer. The wireless wearable system records the ECG data of the subject and the respiration rate is determined from the variations in the baseline level of the ECG data. A straightforward Fourier analysis of the ECG data obtained using the wireless wearable system may lead to incorrect results due to uneven breathing. To improve the estimation precision, we propose a neural network that uses a novel Discrete Cosine Transform (DCT) layer to denoise and decorrelates the data. The DCT layer has trainable weights and soft-thresholds in the transform domain. In our dataset, we improve the Mean Squared Error (MSE) and Mean Absolute Error (MAE) of the Fourier analysis-based approach using our novel neural network with the DCT layer.