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

TL;DR

This paper introduces a neural network with a trainable DCT layer for more accurate real-time respiration rate estimation from wireless ECG data, outperforming traditional Fourier analysis methods.

Contribution

The novel neural network architecture with a trainable DCT layer enhances ECG-based respiration rate estimation accuracy compared to Fourier analysis.

Findings

Improved MSE and MAE over Fourier analysis-based methods

Effective denoising and decorrelation of ECG data

Enhanced real-time respiration monitoring

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.