Evaluation of respiratory properties by means of fractional order models

Assadi, I.; Charef, Abdelfatah; Copot, Dana; Keyser, Robain De; Bensouici, Tahar; Ionescu, Clara-Mihaela

doi:10.1016/j.bspc.2017.02.006

Cited by 32 publications

(16 citation statements)

References 14 publications

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“…As a result, drug molecules may be trapped for longer residence times than initially though [53], with recirculatory effects from liver latency processing at macromolecular and metabolic rate dynamic variability [69]. By their analogy of kinetic compartmental models to transfer function models as proposed in [26], [70], the fractal kinetics are approximated by fractional order transfer function models as those proposed in [50], [51], [71]. Their advantage is that memory effects of drug latency can be more accurately modelled, providing better models for optimal regulation of drug dosing profiles to better avoid over-and under-dosing risks.…”

Section: Discussionmentioning

confidence: 99%

An Open Source Patient Simulator for Design and Evaluation of Computer Based Multiple Drug Dosing Control for Anesthetic and Hemodynamic Variables

et al. 2021

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Section: Discussionmentioning

confidence: 99%

An Open Source Patient Simulator for Design and Evaluation of Computer Based Multiple Drug Dosing Control for Anesthetic and Hemodynamic Variables

et al. 2021

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“…Furthermore, the model from ( 2 ) is a combination of power law and exponential, which has been already proven mathematically to be fitted by the Mittag-Leffler function, which is a solution of the fractional derivative [ 8 ]. Such models have been shown to fit well the respiratory tissue properties in COPD [ 60 , 61 ] and other patients [ 62 , 63 ].…”

Section: Discussionmentioning

confidence: 99%

A Minimal PKPD Interaction Model for Evaluating Synergy Effects of Combined NSCLC Therapies

Ionescu

Ghita

Copot

et al. 2020

JCM

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This paper introduces a mathematical compartmental formulation of dose-effect synergy modelling for multiple therapies in non small cell lung cancer (NSCLC): antiangiogenic, immuno- and radiotherapy. The model formulates the dose-effect relationship in a unified context, with tumor proliferating rates and necrotic tissue volume progression as a function of therapy management profiles. The model accounts for inter- and intra-response variability by using surface model response terms. Slow acting peripheral compartments such as fat and muscle for drug distribution are not modelled. This minimal pharmacokinetic-pharmacodynamic (PKPD) model is evaluated with reported data in mice from literature. A systematic analysis is performed by varying only radiotherapy profiles, while antiangiogenesis and immunotherapy are fixed to their initial profiles. Three radiotherapy protocols are selected from literature: (1) a single dose 5 Gy once weekly; (2) a dose of 5 Gy × 3 days followed by a 2 Gy × 3 days after two weeks and (3) a dose of 5 Gy + 2 × 0.075 Gy followed after two weeks by a 2 Gy + 2 × 0.075 Gy dose. A reduction of 28% in tumor end-volume after 30 days was observed in Protocol 2 when compared to Protocol 1. No changes in end-volume were observed between Protocol 2 and Protocol 3, this in agreement with other literature studies. Additional analysis on drug interaction suggested that higher synergy among drugs affects up to three-fold the tumor volume (increased synergy leads to significantly lower growth ratio and lower total tumor volume). Similarly, changes in patient response indicated that increased drug resistance leads to lower reduction rates of tumor volumes, with end-volume increased up to 25–30%. In conclusion, the proposed minimal PKPD model has physiological value and can be used to study therapy management protocols and is an aiding tool in the clinical decision making process. Although developed with data from mice studies, the model is scalable to NSCLC patients.

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“…FOT measures the air pressure and flow and helps in estimating the respiratory impedance. Several studies in literature [ [2] , [3] , [4] ] proved better results using the respiratory impedance measurement for asthma and COPD patients. However, the auscultation method is simple, patient friendly and easier to obtain data than spirometry or radiation imaging [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Conditional GAN based augmentation for predictive modeling of respiratory signals

Jayalakshmy

Sudha

2021

Computers in Biology and Medicine

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Respiratory illness is the primary cause of mortality and impairment in the life span of an individual in the current COVID–19 pandemic scenario. The inability to inhale and exhale is one of the difficult conditions for a person suffering from respiratory disorders. Unfortunately, the diagnosis of respiratory disorders with the presently available imaging and auditory screening modalities are sub-optimal and the accuracy of diagnosis varies with different medical experts. At present, deep neural nets demand a massive amount of data suitable for precise models. In reality, the respiratory data set is quite limited, and therefore, data augmentation (DA) is employed to enlarge the data set. In this study, conditional generative adversarial networks (cGAN) based DA is utilized for synthetic generation of signals. The publicly available repository such as ICBHI 2017 challenge, RALE and Think Labs Lung Sounds Library are considered for classifying the respiratory signals. To assess the efficacy of the artificially created signals by the DA approach, similarity measures are calculated between original and augmented signals. After that, to quantify the performance of augmentation in classification, scalogram representation of generated signals are fed as input to different pre-trained deep learning architectures viz Alexnet, GoogLeNet and ResNet-50. The experimental results are computed and performance results are compared with existing classical approaches of augmentation. The research findings conclude that the proposed cGAN method of augmentation provides better accuracy of 92.50% and 92.68%, respectively for both the two data sets using ResNet 50 model.

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Evaluation of respiratory properties by means of fractional order models

Cited by 32 publications

References 14 publications

An Open Source Patient Simulator for Design and Evaluation of Computer Based Multiple Drug Dosing Control for Anesthetic and Hemodynamic Variables

An Open Source Patient Simulator for Design and Evaluation of Computer Based Multiple Drug Dosing Control for Anesthetic and Hemodynamic Variables

A Minimal PKPD Interaction Model for Evaluating Synergy Effects of Combined NSCLC Therapies

Conditional GAN based augmentation for predictive modeling of respiratory signals

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