Abstract

The joint study of respiratory and cardiac activity suggests indirect methods to derive the respiratory signal by electrocardiogram (ECG) processing. Potential advantages of such methods are low cost, high convenience, and continuous noninvasive respiratory monitoring. Recent works show that the respiratory signal can be accurately evaluated by single-channel ECG processing. The aim of this paper is to introduce a new method based on the Empirical Mode Decomposition (EMD) for the respiratory signal evaluation. A comparison versus popular algorithms for the respiratory signal extraction is also shown. Preliminary results confirm that EMD algorithm provides better performances, with respect to others, especially in the case of respiratory waveform reconstruction.

Keywords:
Hilbert–Huang transform SIGNAL (programming language) Computer science Signal processing Waveform Respiratory system Artificial intelligence Signal reconstruction Pattern recognition (psychology) Algorithm Speech recognition Medicine Internal medicine Computer vision Digital signal processing Telecommunications

Metrics

47
Cited By
2.36
FWCI (Field Weighted Citation Impact)
19
Refs
0.89
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

ECG Monitoring and Analysis
Health Sciences →  Medicine →  Cardiology and Cardiovascular Medicine
Heart Rate Variability and Autonomic Control
Health Sciences →  Medicine →  Cardiology and Cardiovascular Medicine
Non-Invasive Vital Sign Monitoring
Physical Sciences →  Engineering →  Biomedical Engineering

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