by Keyword: Instantaneous frequency
Acevedo, Hernando Gonzalez, Rodriguez-Sotelo, Jose Luis, Arizmendi, Carlos, Giraldo, Beatriz F, (2025). Prediction of weaning failure using time-frequency analysis of electrocardiographic and respiration flow signals Biomedical Signal Processing And Control 108, 107872 
Acute respiratory distress syndrome often necessitates prolonged periods of mechanical ventilation for patient management. Therefore, it is crucial to make appropriate decisions regarding extubation to prevent potential harm to patients and avoid the associated risks of reintubation and extubation cycles. One atypical form of acute respiratory distress syndrome is associated with COVID-19, impacting patients admitted to the intensive care unit. This study presents the design of two classifiers: the first employs machine learning techniques, while the second utilizes a convolutional neural network. Their purpose is to assess whether a patient can safely be disconnected from a mechanical ventilator following a spontaneous breathing test. The machine learning algorithm uses descriptors derived from the variability of time-frequency representations computed with the non-uniform fast Fourier transform. These representations are applied to time series data, which consist of markers extracted from the electrocardiographic and respiratory flow signals sourced from the Weandb database. The input image for the convolutional neural network is formed by combining the spectrum of the RR signal and the spectrum of two parameters recorded from the respiratory flow signal, calculated using non-uniform fast Fourier transform. Three pre-trained network architectures are analyzed: Googlenet, Alexnet and Resnet-18. The best model is obtained with a CNN with the Resnet-18 architecture, presenting an accuracy of 90.1 +/- 4.3%.
JTD Keywords: Convolutional neural networ, Extubation, Instantaneous frequency, Mechanical ventilation, Mechanically ventilated patients, Non-uniform fast fourier transform, Variabilit, Weaning
Gonzalez, Hernando, Arizmendi, Carlos Julio, Giraldo, Beatriz F, (2024). Medical Support System for Spontaneous Breathing Trial Prediction Using Nonuniform Discrete Fourier Transform International Journal Of Online And Biomedical Engineering 20, 103-116
Spontaneous breathing trials (SBTs) represent a pivotal phase in the weaning process of mechanically ventilated patients. The objective of these trials is to assess patients' readiness to resume independent breathing, thereby facilitating timely weaning and reducing the duration of mechanical ventilation (MV). Nevertheless, accurately predicting the success or failure of SBT remains a significant challenge in clinical practice. This study proposes a healthcare system that employs machine learning techniques to predict the outcome of SBT. The model is trained on respiratory flow and electrocardiogram (ECG) signals, employing the non-uniform discrete Fourier transform (NUDFT) for frequency domain analysis. The SBT prediction model has the potential to significantly enhance clinical decision-making by enabling the early identification of patients at risk for SBT failure, achieving an accuracy of 84.4 +/- 3.2%.
JTD Keywords: Algorithms, Instantaneous frequency, Mechanical ventilation, Non-uniform discrete fourier transform (nudft, Signa, Spontaneous breathing trial (sbt), Weaning
Lozano-Garcia, M., Fiz, J. A., Jané, R., (2016). Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization 
Signal Processing , 120, 99-116
Abstract The use of the Hilbert–Huang transform in the analysis of biomedical signals has increased during the past few years, but its use for respiratory sound (RS) analysis is still limited. The technique includes two steps: empirical mode decomposition (EMD) and instantaneous frequency (IF) estimation. Although the mode mixing (MM) problem of EMD has been widely discussed, this technique continues to be used in many RS analysis algorithms. In this study, we analyzed the MM effect in RS signals recorded from 30 asthmatic patients, and studied the performance of ensemble EMD (EEMD) and noise-assisted multivariate EMD (NA-MEMD) as means for preventing this effect. We propose quantitative parameters for measuring the size, reduction of MM, and residual noise level of each method. These parameters showed that EEMD is a good solution for MM, thus outperforming NA-MEMD. After testing different IF estimators, we propose Kay׳s method to calculate an EEMD-Kay-based Hilbert spectrum that offers high energy concentrations and high time and high frequency resolutions. We also propose an algorithm for the automatic characterization of continuous adventitious sounds (CAS). The tests performed showed that the proposed EEMD-Kay-based Hilbert spectrum makes it possible to determine CAS more precisely than other conventional time-frequency techniques.
JTD Keywords: Hilbert–Huang transform, Ensemble empirical mode decomposition, Instantaneous frequency, Respiratory sounds, Continuous adventitious sounds
