Non-invasive continuous respiratory monitoring using temperature-based sensors

Hurtado, Daniel E.; Abusleme, Angel; Chávez, Javier A. P.

doi:10.1007/s10877-019-00329-5

Cited by 25 publications

(24 citation statements)

References 20 publications

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“…However, the number and quality of studies was too low to allow final conclusions. Recent publications support the need for continuous ward monitoring [48,49], and several different systems are available, including new measurement techniques [50,51]. In the current study we demonstrated that with the used wireless monitoring system it is feasible to electronically measure RespR up to 4 days post-operatively on the ward.…”

Section: Discussionmentioning

confidence: 59%

Insights into postoperative respiration by using continuous wireless monitoring of respiratory rate on the postoperative ward: a cohort study

Posthuma

Visscher

Lirk

et al. 2019

J Clin Monit Comput

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Change of respiratory rate (RespR) is the most powerful predictor of clinical deterioration. Brady-(RespR ≤ 8) and tachypnea (RespR ≥ 31) are associated with serious adverse events. Simultaneously, RespR is the least accurately measured vital parameter. We investigated the feasibility of continuously measuring RespR on the ward using wireless monitoring equipment, without impeding mobilization. Continuous monitoring of vital parameters using a wireless SensiumVitals® patch was installed and RespR was measured every 2 mins. We defined feasibility of adequate RespR monitoring if the system reports valid RespR measurements in at least 50% of time-points in more than 80% of patients during day-and night-time, respectively. Data from 119 patients were analysed. The patch detected in 171,151 of 227,587 measurements valid data for RespR (75.2%). During postoperative day and night four, the system still registered 68% and 78% valid measurements, respectively. 88% of the patients had more than 67% of valid RespR measurements. The RespR's most frequently measured were 13-15; median RespR was 15 (mean 16, 25th-and 75th percentile 13 and 19). No serious complications or side effects were observed. We successfully measured electronically RespR on a surgical ward in postoperative patients continuously for up to 4 days post-operatively using a wireless monitoring system. While previous studies mentioned a digit preference of 18-22 for RespR, the most frequently measured RespR were 13-16. However, in the present study we did not validate the measurements against a reference method. Rather, we attempted to demonstrate the feasibility of achieving continuous wireless measurement in patients on surgical postoperative wards. As the technology used is based on impedance pneumography, obstructive apnoea might have been missed, namely in those patients receiving opioids post-operatively.

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Section: Discussionmentioning

confidence: 59%

Insights into postoperative respiration by using continuous wireless monitoring of respiratory rate on the postoperative ward: a cohort study

Posthuma

Visscher

Lirk

et al. 2019

J Clin Monit Comput

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“…Oral and nasal respiratory signals were acquired using the non-invasive temperature-based respiratory monitoring system (TRMS) described in [22]. Figure 2 shows an schematic of the instrumentation setup.…”

Section: A Respiratory Data Acquisitionmentioning

confidence: 99%

“…Respiratory cycles and RR were identified from the TRMS signal using a mean-cross algorithm [22]. In brief, a 5second-window moving average was applied to the respiratory temperature signal to obtain a filtered signal.…”

Section: B Feature Extractionmentioning

confidence: 99%

“…In this work, we present and validate an innovative RVM for non-intubated subjects. To this end, we use a novel non-invasive wearable temperature sensor that allows for the time-continuous acquisition of respiratory signals [22]. Further, we employ state-of-the-art machine-learning (ML) algorithms in the construction of a predictive model for ventilatory parameters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Respiratory Volume Monitoring: A Machine-Learning Approach to the Non-Invasive Prediction of Tidal Volume and Minute Ventilation

Hurtado

Chávez²,

Mansilla

et al. 2020

IEEE Access

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“…A recent attempt described in [ 20 ] presents a non-invasive method to measure the RR. The rate is computed using the thermal transfer between the breathing airflow under the nose and the mouth.…”

Section: Introductionmentioning

confidence: 99%

Smart Recognition COVID-19 System to Predict Suspicious Persons Based on Face Features

Ayed

Massaoudi

Alshaya

2021

J. Electr. Eng. Technol.

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The coronavirus (COVID-19) is identified at first in Wuhan in December 2019. The apparition of the COVID-19 virus is widely spread to concern all countries worldwide. The World Health Organization (WHO) on March 11 declare COVID-19 a pandemic. This Virus causes a serious infection of the respiratory system. Its high transmission constitutes great problems and challenges. The WHO proposes many actions to limit the spread of the virus such as quarantine and decrease or halt flights between states. The actions taken by states in airports are to detect suspicious persons with COVID-19. We aimed to provide a Computer-Aided Diagnosis (CAD) framework to predict suspicious COVID-19 person. This prediction identifies suspicious persons who suffer from shortness breath which is the main symptom of this disease. Extract shortness breath anomaly through the estimated heart rate from face based-video is the main contribution of the present paper. We developed a Smart Recognition COVID-19 (SRC) system to estimate the breath score. In conclusion, our study achieves an accurate breath score. The error is about 1 breath per minute. The proposed solution is of great importance because it helps managers in the airport to predict suspicious COVID-19 passengers.

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Non-invasive continuous respiratory monitoring using temperature-based sensors

Cited by 25 publications

References 20 publications

Insights into postoperative respiration by using continuous wireless monitoring of respiratory rate on the postoperative ward: a cohort study

Insights into postoperative respiration by using continuous wireless monitoring of respiratory rate on the postoperative ward: a cohort study

Respiratory Volume Monitoring: A Machine-Learning Approach to the Non-Invasive Prediction of Tidal Volume and Minute Ventilation

Smart Recognition COVID-19 System to Predict Suspicious Persons Based on Face Features

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