Identification of Respiration Types Through Respiratory Signal Derived From Clinical and Wearable Electrocardiograms

Agnese Sbrollini; Micaela Morettini; Ennio Gambi; Laura Burattini

doi:10.1109/OJEMB.2023.3343557

Identification of Respiration Types Through Respiratory Signal Derived From Clinical and Wearable Electrocardiograms

IEEE Open J Eng Med Biol. 2023 Dec 15:4:268-274. doi: 10.1109/OJEMB.2023.3343557. eCollection 2023.

Authors

Agnese Sbrollini¹, Micaela Morettini¹, Ennio Gambi¹, Laura Burattini¹

Affiliation

¹ Department of Information EngineeringUniversità Politecnica delle Marche 60121 Ancona Italy.

Abstract

Goal: To evaluate suitability of respiratory signals derived from clinical 12-lead electrocardiograms (ECGs) and wearable 1-lead ECG to identify different respiration types.

Methods: ECGs were simultaneously acquired through the M12R ECG Holter by Global Instrumentation and the chest strap BioHarness 3.0 by Zephyr from 42 healthy subjects alternating normal breathing, breath holding, and deep breathing. Respiration signals were derived from the ECGs through the Segmented-Beat Modulation Method (SBMM)-based algorithm and the algorithms by Van Gent, Charlton, Soni and Sarkar, and characterized in terms of breathing rate and amplitude. Respiration classification was performed through a linear support vector machine and evaluated by F1 score.

Results: Best F1 scores were 86.59%(lead V2) and 80.57%, when considering 12-lead and 1-lead ECGs, respectively, and using SBMM-based algorithm.

Conclusion: ECG-derived respiratory signals allow reliable identification of different respiration types even when acquired through wearable sensors, if associated to appropriate processing algorithms, such as the SBMM-based algorithm.

Keywords: Apnea; deep breathing; electrocardiogram-derived respiration; normal breathing; segmented-beat modulation method.

Grants and funding

This work was supported by the Project Chaalenge under Grant CUP B39J2200305000.