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

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

doi:10.1109/access.2020.3045603

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…Our system is built upon a wearable temperature-based sensor previously employed in the prediction of respiratory rate (RR) and minute ventilation in subjects at rest. 22,23 Here we validate the wearable RR predictions in 17 healthy adults undergoing a ramp test on a cycle ergometer, see Figure 1. Further, using segmented regression analysis on the sensor signals, we estimate VTs from respiratory breakpoints and contrast these estimations against expert predictions using gold-standard ergospirometry.…”

Section: Introductionmentioning

confidence: 65%

“…To validate the predictions of RR and VTs made by the respiratory wearable system and algorithms, we considered the gold standard and predicted values of RR, VT1, and VT2 and computed the bias, precision, accuracy, and Pearson correlation coefficient for these parameters as detailed in previous contributions. 23 For intergroup comparison, normality was assessed by the Shapiro-Wilk test and mean differences were tested using the unpaired ttest. Numerical results are expressed in terms of mean ± standard deviation.…”

Section: Validation Metrics and Statistical Analysismentioning

confidence: 99%

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Estimation of ventilatory thresholds during exercise using respiratory wearable sensors

Hurtado,

Contreras–Briceño,

Cancino

et al. 2023

Preprint

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Ventilatory thresholds (VTs) are key physiological parameters to evaluate physical performance and determine aerobic and anaerobic transitions during exercise. Current assessment of these parameters requires ergospirometry, limiting the evaluation to laboratory or clinical settings. In this work, we introduce a wearable respiratory system that continuously tracks breathing during exercise and estimates VTs during ramp–tests. We validate the respiratory rate and VTs predictions in 17 healthy adults using ergospirometry analysis. In addition, we use the wearable system to evaluate the VTs in 107 recreational athletes through ramp tests outside the laboratory and show that the mean population values agree with previous studies in large groups. We envision that respiratory wearables can be useful in determining aerobic and anaerobic parameters outside clinical setting with promising applications in health telemonitoring and human performance.

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Section: Introductionmentioning

confidence: 65%

Section: Validation Metrics and Statistical Analysismentioning

confidence: 99%

Estimation of ventilatory thresholds during exercise using respiratory wearable sensors

Hurtado,

Contreras–Briceño,

Cancino

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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“…In addition to the conventional methods of measuring respiratory parameters, Respiratory Inductive Plethysmography (RIP) is a non-invasive method of measuring lung ventilation. Tidal volume measured with RIP shows a strong correlation with spirometry in healthy subjects [21,22].…”

Section: Respiratory Inductive Plethysmography (Rip)mentioning

confidence: 91%

Artificial Intelligence-Based Algorithms and Healthcare Applications of Respiratory Inductance Plethysmography: A Systematic Review

Rahman,

Chowdhury,

Rasheduzzaman

et al. 2024

Algorithms

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Respiratory Inductance Plethysmography (RIP) is a non-invasive method for the measurement of respiratory rates and lung volumes. Accurate detection of respiratory rates and volumes is crucial for the diagnosis and monitoring of prognosis of lung diseases, for which spirometry is classically used in clinical applications. RIP has been studied as an alternative to spirometry and shown promising results. Moreover, RIP data can be analyzed through machine learning (ML)-based approaches for some other purposes, i.e., detection of apneas, work of breathing (WoB) measurement, and recognition of human activity based on breathing patterns. The goal of this study is to provide an in-depth systematic review of the scope of usage of RIP and current RIP device developments, as well as to evaluate the performance, usability, and reliability of ML-based data analysis techniques within its designated scope while adhering to the PRISMA guidelines. This work also identifies research gaps in the field and highlights the potential scope for future work. The IEEE Explore, Springer, PLoS One, Science Direct, and Google Scholar databases were examined, and 40 publications were included in this work through a structured screening and quality assessment procedure. Studies with conclusive experimentation on RIP published between 2012 and 2023 were included, while unvalidated studies were excluded. The findings indicate that RIP is an effective method to a certain extent for testing and monitoring respiratory functions, though its accuracy is lacking in some settings. However, RIP possesses some advantages over spirometry due to its non-invasive nature and functionality for both stationary and ambulatory uses. RIP also demonstrates its capabilities in ML-based applications, such as detection of breathing asynchrony, classification of apnea, identification of sleep stage, and human activity recognition (HAR). It is our conclusion that, though RIP is not yet ready to replace spirometry and other established methods, it can provide crucial insights into subjects’ condition associated to respiratory illnesses. The implementation of artificial intelligence (AI) could play a potential role in improving the overall effectiveness of RIP, as suggested in some of the selected studies.

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“…Recent equipment shortages due to infectious diseases have evidenced the possibility of developing a multiplexed NIV paradigm [27]. Furthermore, the continuous monitoring of ventilatory parameters has led to the implementation of novel signal processing and machine learning techniques to enable the predictive capabilities of such equipment [28,29]. The proposed NIV's core is a turbine for airflow generation and a microcontroller for operational monitoring and control.…”

Section: Introductionmentioning

confidence: 99%

Development of Non-Invasive Ventilator for Homecare and Patient Monitoring System

Menniti,

Laganà,

Oliva

et al. 2024

Electronics

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Recently, the incidence of, and interest in, respiratory diseases has been amplified by severe acute respiratory syndrome coronavirus (SARS-CoV-2) and other respiratory diseases with a high prevalence. Most of these diseases require mechanical ventilation for homecare and clinical therapy. Herein, we propose a portable and non-invasive mechanical fan (NIV) for home and clinical applications. The NIV’s core is a turbine for airflow generation, which can provide and monitor a positive two-level pressure of up to approximately 500 lpm at 50 cmH2O according to the inspiration/expiration phase. After calibration, the proposed NIV can precisely set the airflow with a pressure between 4 cmH2O and 20 cmH2O, providing a versatile device that can be used for continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BiPAP). The airflow is generated by a turbine monitored using a mass flow sensor. The whole NIV is monitored with a 16 MHz clock microcontroller. An analog-to-digital converter is used as the input for analog signals, while a digital-to-analog converter is used to drive the turbine. I2C protocol signals are used to manage the display. Moreover, a Wi-Fi system is interfaced for the transmission/reception of clinical and technical information via a smartphone, achieving a remote-controlled NIV.

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

Cited by 9 publications

References 25 publications

Estimation of ventilatory thresholds during exercise using respiratory wearable sensors

Estimation of ventilatory thresholds during exercise using respiratory wearable sensors

Artificial Intelligence-Based Algorithms and Healthcare Applications of Respiratory Inductance Plethysmography: A Systematic Review

Development of Non-Invasive Ventilator for Homecare and Patient Monitoring System

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