Electrocardiogram-Derived Respiratory Signal in Sleep Apnea by Segmented Beat Modulation Method

Sbrollini, Agnese; Marcantoni, Ilaria; Nasim, Amnah; Morettini, Micaela; Burattini, Laura

doi:10.1109/isce.2019.8900997

Cited by 3 publications

(6 citation statements)

References 10 publications

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“…Acquired electrocardiograms were processed using five different algorithms: our Segmented-Beat Modulation Method-based algorithm [20] , [21] , based on respiration- related ECG amplitude modulation, and the open source [22] algorithms by Van Gent et al [12] , Charlton et al [13] , Soni et al [10] and Sarkar et al [11] , based on respiration-related heart-rate modulation. Indirect electrocardiogram-derived respiratory signals were then characterized in terms of breathing rate and amplitude.…”

Section: Discussionmentioning

confidence: 99%

“…EDR signal by SBMM-based algorithm: Preprocessed ECG windows and their R-peak positions were submitted to the Segmented-Beat Modulation Method (SBMM)-based algorithm [18], [20], [21] for respiratory signal extraction. Block diagram of the SBMM-based algorithm is depicted in Fig.…”

Section: Electrocardiogram-derived Respiration Signalsmentioning

confidence: 99%

“…[14], [15] by considering the Mason-Likar electrodes configuration [16], with a sampling frequency of 1000 Hz and an amplitude resolution of 1 µV. The chest-strap BioHarness 3.0 provided 1-lead ECGs [17], [18] by positioning the sensor under the left arm (as suggested in the user's manual), with a sampling frequency of 250 Hz and an amplitude resolution of 1 µV. All subjects gave their informed consent prior to data collection and acquisitions, which were undertaken in compliance with ethical principles of the Helsinki Declaration and approved by the institutional expert committee.…”

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confidence: 99%

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

Sbrollini,

Morettini,

Gambi

et al. 2023

IEEE Open J. Eng. Med. Biol.

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Electrocardiogram-derived Respiration Signalsmentioning

confidence: 99%

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confidence: 99%

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

Sbrollini,

Morettini,

Gambi

et al. 2023

IEEE Open J. Eng. Med. Biol.

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“…Data consists of cardiorespiratory signals, recorded by the chest strap BioHarness 3.0 by Zephyr (www.zephyranywhere.com), a reliable wearable sensor [13][14][15][16][17][18] that directly records BRS (cpm; sampled at 1Hz), RES (mV; sampled at 17Hz) and ECG (mV; sampled at 250Hz). Data were acquired from 3 volunteers (Table 1) during a multidisciplinary expedition at Monte Rosa (Alps, Italy), from August 29 th to September 2 nd , 2021.…”

Section: Datamentioning

confidence: 99%

“…This approach guarantees good performance in long-term multi-parameter monitoring. The Segmented-Beat Modulation Method (SBMM)-based procedure is able to extract the ECG-derived respiration (EDR) [12,13] from single-lead ECGs, thus also providing respiratory information. Already tested in controlled low-noise conditions such as rest and sleep [12,13], this innovative method was never assessed in presence of high-noise, such as into-the-field exercise.…”

Section: Introductionmentioning

confidence: 99%

Segmented-Beat Modulation Method-Based Procedure for Extraction of Electrocardiogram-Derived Respiration from Data Acquired by Wearable Sensors During High-Altitude Activity

Sbrollini¹,

Bondi²,

Romagnoli³

et al. 2022

Computing in Cardiology Conference (CinC)

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High-altitude sports are affected by hypoxic stressrelated alterations and, consequently, may trigger severe events such as sport-related sudden death; thus, into-thefield monitoring of respiration is essential. A Segmented-Beat Modulation Method (SBMM)-based procedure was previously proposed to extract electrocardiogram (ECG)derived respiration (EDR). The aim of this study is to validate SBMM-based procedure for EDR extraction in data acquired by wearable sensors during high-altitude physical activities. Respiration signal (RES) and ECG were recorded using BioHarness 3.0 by Zephyr from 3 expeditioners, while performing a trek up to 4,556m of altitude. EDR was extracted from ECG by SBMM-based procedure. RES and EDR were segmented into 60-second windows and characterized in terms of breathing rate (BR RES and BR EDR , respectively). BR RES and BR EDR were compared by absolute difference (|δ|), concordance correlation coefficient (CCC) and linear regression analysis. Results confirmed EDR goodness, proved by low values of |δ| (2[1;4]cpm), satisfactory CCC (0.62; P-value<0.05) and good fit of regression line (BR RES =0.91•BR EDR +4.47cpm). In conclusion, SBMMbased procedure is a good method to extract EDR from data acquired by wearable sensors during high-altitude physical activities.

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Dynamic Segmented Beat Modulation Method for Denoising ECG Data from Wearable Sensors

Nasim

Pinti

Gentili

et al. 2019

2019 International Conference on Sensing and Instrumentation in IoT Era (ISSI)

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Electrocardiogram-Derived Respiratory Signal in Sleep Apnea by Segmented Beat Modulation Method

Cited by 3 publications

References 10 publications

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

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

Segmented-Beat Modulation Method-Based Procedure for Extraction of Electrocardiogram-Derived Respiration from Data Acquired by Wearable Sensors During High-Altitude Activity

Dynamic Segmented Beat Modulation Method for Denoising ECG Data from Wearable Sensors

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