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

arXiv: https://arxiv.org/abs/2211.08491 · HTML: https://mlsanity.com/paper/2211.08491/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2022.

## Built on by (most cited first, 4 of 4)

- [A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer](https://mlsanity.com/paper/2305.17510/) (2023)
- [Domain Generalization with Fourier Transform and Soft Thresholding](https://mlsanity.com/paper/2309.09866/) (2023)
- [Electroencephalogram Sensor Data Compression Using An Asymmetrical Sparse Autoencoder With A Discrete Cosine Transform Layer](https://mlsanity.com/paper/2309.12201/) (2023)
- [The Blind Normalized Stein Variational Gradient Descent-Based Detection for Intelligent Random Access in Cellular IoT](https://mlsanity.com/paper/2403.18846/) (2024)

## Abstract (arXiv)

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.