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arXiv · 2211.08491

Real-time Wireless ECG-derived Respiration Rate Estimation Using an Autoencoder with a DCT Layer

Hongyi Pan·Xin Zhu·Zhilu Ye·Pai-Yen Chen·Ahmet Enis Cetin
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Abstract

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.

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Hongyi Pan, Xin Zhu, Zhilu Ye, Pai-Yen Chen, Ahmet Enis Cetin. 2022-11-15. Real-time Wireless ECG-derived Respiration Rate Estimation Using an Autoencoder with a DCT Layer. https://arxiv.org/abs/2211.08491

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