Estimation of the Respiratory Rate from Localised ECG at Different Auscultation Sites

Bao, Xinqi; Abdala, Aimé K.; Kamavuako, Ernest Nlandu

doi:10.3390/s21010078

Cited by 17 publications

(11 citation statements)

References 30 publications

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“…The dominant frequency in all the observed signals is around 1 Hz, illustrating the possibility of extracting heart rate information, typically between 1 and 1.67 Hz in healthy adults, from all signals [45]. The carotid and SSN signals also reveal frequencies below 0.5 Hz, corresponding to the breathing rate (BR), which in resting healthy adults can lie between 0.2 and 0.3 Hz [46]. Comparing the ECG signal time-frequency scalograms in Fig.…”

Section: A Signal Morphology and Compositionmentioning

confidence: 81%

Monitoring Cardiovascular Physiology Using Bio-Compatible AlN Piezoelectric Skin Sensors

Shumba,

Demir,

Mastronardi

et al. 2024

IEEE Access

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This research is funded by Progetto "RAISE (Robotics and AI for Socio-economic Empowerment)" ECS00000035 supported by European Union -NextGenerationEU PNRR MUR -M4C2 -I1.5 -Avviso "Ecosistemi dell'Innovazione" and by Progetto "SOMNIIA MONITOR" (Contratto ASI n. 2023-7-U.0 "SOMNIIA MONITOR: poliSOnnigrafo Multi-sensore Non-Invasivo Indossabile per Astronauti con MONITOraggio Remoto dei parametri vitali e della qualità del sonno") However, the views and opinions expressed are those of the authors alone and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.

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Section: A Signal Morphology and Compositionmentioning

confidence: 81%

Monitoring Cardiovascular Physiology Using Bio-Compatible AlN Piezoelectric Skin Sensors

Shumba,

Demir,

Mastronardi

et al. 2024

IEEE Access

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“…A FFT of the quasi-DC components were acquired, and the signal's fundamental frequency was isolated which corresponded to the RR. SS-ECG based RR was obtained from applying a 0.05-0.5 Hz bandpass filter to remove artifacts that were not related to respiration [6].…”

Section: Signal Processing For Heart Rate and Respiration Rate Calcul...mentioning

confidence: 99%

Physiological sensing on the upper arm with a wireless multi-modal wearable

Branan,

McMurray,

Jennings

et al. 2024

Optical Diagnostics and Sensing XXIV: Toward Point-of-Care Diagnostics

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In this research, we examine the potential of measuring physiological variables, including heart rate (HR) and respiration rate (RR) on the upper arm using a wireless multimodal sensing system consisting of an accelerometer, a gyroscope, a three-wavelength photoplethysmography (PPG), single-sided electrocardiography (SS-ECG), and bioimpedance (BioZ). The study included collecting HR data when the subject was at rest and typing, and RR data when the subject was at rest. The data from three wavelengths of PPG and BioZ were collected and compared to the SS-ECG as the standard. The accelerometer and gyro signals were used to exclude data with excessive noise due to motion. The results showed that when the subject remained sedentary, the mean absolute error (MAE) for the HR calculation for all three wavelengths of the PPG modality was less than two bpm, while the BioZ was 3.5 bpm compared with SS-ECG HR. The MAE for typing increased for both modalities and was less than three bpm for all three wavelengths of the PPG but increased to 7.5 bpm for the BioZ. Regarding RR, both modalities resulted in RR within one breath per minute of the SS-ECG modality for the one breathing rate. Overall, all modalities on this upper arm wearable worked well when the subject was sedentary. Still, the SS-ECG and PPG showed less variability for the HR signal in the presence of motion during micro-motions such as typing.

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“…The data collection was performed on 10 healthy subjects in London. The database is acquired at King’s College London, London WC2R 2LS, UK [ 41 , 42 ]. The King’s research Ethics Committee approved the experimentation (Approval No.…”

Section: Ecg Biosensor Designmentioning

confidence: 99%

Smart ECG Biosensor Design with an Improved ANN Performance Based on the Taguchi Optimizer

et al. 2022

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This paper aims to design a smart biosensor to predict electrocardiogram (ECG) signals in a specific auscultation site from other ECG signals measured from other measurement sites. The proposed design is based on a hybrid architecture using the Artificial Neural Networks (ANNs) model and Taguchi optimizer to avoid the ANN issues related to hyperparameters and to improve its accuracy. The proposed approach aims to optimize the number and type of inputs to be considered for the ANN model. Indeed, different combinations are considered in order to find the optimal input combination for the best prediction quality. By identifying the factors that influence a model’s prediction and their degree of importance via the modified Taguchi optimizer, the developed biosensor improves the prediction accuracy of ECG signals collected from different auscultation sites compared to the ANN-based biosensor. Based on an actual database, the simulation results show that this improvement is significant; it can reach more than 94% accuracy.

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Estimation of the Respiratory Rate from Localised ECG at Different Auscultation Sites

Cited by 17 publications

References 30 publications

Monitoring Cardiovascular Physiology Using Bio-Compatible AlN Piezoelectric Skin Sensors

Monitoring Cardiovascular Physiology Using Bio-Compatible AlN Piezoelectric Skin Sensors

Physiological sensing on the upper arm with a wireless multi-modal wearable

Smart ECG Biosensor Design with an Improved ANN Performance Based on the Taguchi Optimizer

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