Nonlinear dynamic analysis of the cardiorespiratory system in patients undergoing the weaning process

Trapero, J.I.; Arizmendi, Carlos; González, H.; Forero, C. A.; Giraldo, Beatriz F.

doi:10.1109/embc.2017.8037609

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Learners in this context are the individuals who comprise an ensemble. Base learners are often created by a decision model, artificial neural model, or alternative learning algorithm as the foundational learning algorithm using suggested training data [35].…”

Section: Confusion Matrixmentioning

confidence: 99%

Machine learning algorithm for ventilator mode selection, pressure and volume control

T.,

G.,

et al. 2024

PLoS ONE

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Mechanical ventilation techniques are vital for preserving individuals with a serious condition lives in the prolonged hospitalization unit. Nevertheless, an imbalance amid the hospitalized people demands and the respiratory structure could cause to inconsistencies in the patient’s inhalation. To tackle this problem, this study presents an Iterative Learning PID Controller (ILC-PID), a unique current cycle feedback type controller that helps in gaining the correct pressure and volume. The paper also offers a clear and complete examination of the primarily efficient neural approach for generating optimal inhalation strategies. Moreover, machine learning-based classifiers are used to evaluate the precision and performance of the ILC-PID controller. These classifiers able to forecast and choose the perfect type for various inhalation modes, eliminating the likelihood that patients will require mechanical ventilation. In pressure control, the suggested accurate neural categorization exhibited an average accuracy rate of 88.2% in continuous positive airway pressure (CPAP) mode and 91.7% in proportional assist ventilation (PAV) mode while comparing with the other classifiers like ensemble classifier has reduced accuracy rate of 69.5% in CPAP mode and also 71.7% in PAV mode. An average accuracy of 78.9% rate in other classifiers compared to neutral network in CPAP. The neural model had an typical range of 81.6% in CPAP mode and 84.59% in PAV mode for 20 cm H2O of volume created by the neural network classifier in the volume investigation. Compared to the other classifiers, an average of 72.17% was in CPAP mode, and 77.83% was in PAV mode in volume control. Different approaches, such as decision trees, optimizable Bayes trees, naive Bayes trees, nearest neighbour trees, and an ensemble of trees, were also evaluated regarding the accuracy by confusion matrix concept, training duration, specificity, sensitivity, and F1 score.

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Section: Confusion Matrixmentioning

confidence: 99%

Machine learning algorithm for ventilator mode selection, pressure and volume control

T.,

G.,

et al. 2024

PLoS ONE

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“…To characterize the respiratory pattern, the following time series were obtained from the respiratory flow signal: inspiratory time (T I ), expiratory time (T E ), duration of the respiratory cycle (T Tot ), tidal volume (V T ), inspiratory fraction (T I /T Tot ), mean inspired flow (V T /T I ), frequency-tidal volume ratio ( f /V T ), where f is the respiratory rate. In addition, from the ECG signal, the cardiac beat to beat interval (RR) was obtained [15][16][17]. Figures 2 and 3 illustrate an example of T TOT and RR time series of a patient from each group: SG, FG and RG, respectively.…”

Section: Preprocessing Of Signalsmentioning

confidence: 99%

“…The final response of the MV depends on the patient's baseline cardiovascular conditions [15]. Several studies have been performed related to cardiorespiratory interaction and their influence on the weaning process spectral analysis of heart rate [16][17][18], respiration and blood pressure signals to study the cardiorespiratory control mechanism [19] or mutual spectral analysis of cardiac interval and respiratory flow signal [20] and the coherence between heart rate variability and respiratory flow signal [21].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence

Pinto

González

Arizmendi

et al. 2023

IJERPH

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The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several time series obtained from the respiratory flow and electrocardiogram signals, applying techniques based on artificial intelligence. 154 patients undergoing the extubating process were classified in three groups: successful group, patients who failed during weaning process, and patients who after extubating failed before 48 hours and need to reintubated. Power Spectral Density and time-frequency domain analysis were applied, computing Discrete Wavelet Transform. A new Q index was proposed to determine the most relevant parameters and the best decomposition level to discriminate between groups. Forward selection and bidirectional techniques were implemented to reduce dimensionality. Linear Discriminant Analysis and Neural Networks methods were implemented to classify these patients. The best results in terms of accuracy were, 84.61 ± 3.1% for successful versus failure groups, 86.90 ± 1.0% for successful versus reintubated groups, and 91.62 ± 4.9% comparing the failure and reintubated groups. Parameters related to Q index and Neural Networks classification presented the best performance in the classification of these patients.

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Mechanical Ventilator Pressure and Volume Control Using Classifier Machine Learning Algorithm for Medical Care

Anitha,

Gopu,

Arun Mozhi Devan

2023

J. Electr. Eng. Technol.

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Nonlinear dynamic analysis of the cardiorespiratory system in patients undergoing the weaning process

Cited by 5 publications

References 12 publications

Machine learning algorithm for ventilator mode selection, pressure and volume control

Machine learning algorithm for ventilator mode selection, pressure and volume control

Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence

Mechanical Ventilator Pressure and Volume Control Using Classifier Machine Learning Algorithm for Medical Care

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