Alvaro Camilo Dias Faria

Forced oscillation technique in the detection of smoking-induced respiratory alterations: diagnostic accuracy and comparison with spirometry.

INTRODUCTION: Detection of smoking effects is of utmost importance in the prevention of cigarette‐induced chronic airway obstruction. The forced oscillation technique offers a simple and detailed approach to investigate the mechanical properties of the respiratory system. However, there have been no data concerning the use of the forced oscillation technique to evaluate respiratory mechanics in groups with different degrees of tobacco consumption. OBJECTIVES: (1) to evaluate the ability of the forced oscillation technique to detect smoking‐induced respiratory alterations, with special emphasis on early alterations; and (2) to compare the diagnostic accuracy of the forced oscillation technique and spirometric parameters. METHODS: One hundred and seventy subjects were divided into five groups according to the number of pack–years smoked: four groups of smokers classified as <20, 20–39, 40–59, and >60 pack–years and a control group. The four groups of smokers were compared with the control group using receiver operating characteristic (ROC) curves. RESULTS: The early adverse effects of smoking in the group with <20 pack–years were adequately detected by forced oscillation technique parameters. In this group, the comparisons of the ROC curves showed significantly better diagnostic accuracy (p<0.01) for forced oscillation technique parameters. On the other hand, in groups of 20–39, 40–59, and >60 pack–years, the diagnostic performance of the forced oscillation technique was similar to that observed with spirometry. CONCLUSIONS: This study revealed that forced oscillation technique parameters were able to detect early smoking‐induced respiratory involvement when pathologic changes are still potentially reversible. These findings support the use of the forced oscillation technique as a versatile clinical diagnostic tool in helping with chronic obstructive lung disease prevention, diagnosis, and treatment.

Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease

The purpose of this study is to develop a clinical decision support system based on machine learning (ML) algorithms to help the diagnostic of chronic obstructive pulmonary disease (COPD) using forced oscillation (FO) measurements. To this end, the performances of classification algorithms based on Linear Bayes Normal Classifier, K nearest neighbor (KNN), decision trees, artificial neural networks (ANN) and support vector machines (SVM) were compared in order to the search for the best classifier. Four feature selection methods were also used in order to identify a reduced set of the most relevant parameters. The available dataset consists of 7 possible input features (FO parameters) of 150 measurements made in 50 volunteers (COPD, n = 25; healthy, n = 25). The performance of the classifiers and reduced data sets were evaluated by the determination of sensitivity (Se), specificity (Sp) and area under the ROC curve (AUC). Among the studied classifiers, KNN, SVM and ANN classifiers were the most adequate, reaching values that allow a very accurate clinical diagnosis (Se > 87%, Sp > 94%, and AUC > 0.95). The use of the analysis of correlation as a ranking index of the FOT parameters, allowed us to simplify the analysis of the FOT parameters, while still maintaining a high degree of accuracy. In conclusion, the results of this study indicate that the proposed classifiers may contribute to easy the diagnostic of COPD by using forced oscillation measurements.

Assessment of Respiratory Mechanics in Patients with Sarcoidosis Using Forced Oscillation: Correlations with Spirometric and Volumetric Measurements and Diagnostic Accuracy

Background: The forced oscillation technique (FOT) is a promising method for providing a detailed analysis of respiratory mechanics during spontaneous breathing. There is limited data about the use of FOT in patients with sarcoidosis. Objectives: The aims of this study were to test the ability of FOT to describe the changes in respiratory mechanics in sarcoidosis and to evaluate the clinical potential of FOT. Methods: Twenty healthy subjects and 31 patients were studied. All subjects performed spirometric exams, and pulmonary volumes were measured in patients. Resistive data were interpreted using the zero-intercept resistance (R0), the slope of the resistive component of the impedance (S), and the mean resistance (Rm). The analysis of reactance was used to measure the mean reactance (Xm) and the respiratory system dynamic compliance (Cdyn). The total mechanical load was evaluated using the absolute value of the respiratory impedance (Z4Hz). Results: In close agreement with pathophysiological fundamentals, significant (p < 0.001) increases in R0, Rm, and Z4Hz, and reductions in Cdyn (p < 0.003) were observed. S and Xm presented smaller modifications (p < 0.02). All FOT parameters were significantly correlated with spirometric indices. Z4Hz was the most adequate parameter for clinical use (Se = 75%, Sp = 75%), followed by R0 and Rm. Conclusions:FOT parameters were consistent with the pathophysiology of sarcoidosis and were also able to detect the effects of this disease. Because the FOT is easy to perform, such a technique may represent an alternative and/or a complement to other conventional exams to help the clinical evaluations of sarcoidotic patients.

A telemedicine instrument for remote evaluation of tremor: design and initial applications in fatigue and patients with Parkinson's Disease

IntroductionA novel system that combines a compact mobile instrument and Internet communications is presented in this paper for remote evaluation of tremors. The system presents a high potential application in Parkinsons disease and connects to the Internet through a TCP/IP protocol. Tremor transduction is carried out by accelerometers, and the data processing, presentation and storage were obtained by a virtual instrument. The system supplies the peak frequency (fp), the amplitude (Afp) and power in this frequency (Pfp), the total power (Ptot), and the power in low (1-4 Hz) and high (4-7 Hz) frequencies (Plf and Phf, respectively).MethodsThe ability of the proposed system to detect abnormal tremors was initially demonstrated by a fatigue study in normal subjects. In close agreement with physiological fundamentals, the presence of fatigue increased fp, Afp, Pfp and Pt (p < 0.05), while the removal of fatigue reduced all the mentioned parameters (p < 0.05). The system was also evaluated in a preliminary in vivo test in parkinsonian patients. Afp, Pfp, Ptot, Plf and Phf were the most accurate parameters in the detection of the adverse effects of this disease (Se = 100%, Sp = 100%), followed by fp (Se = 100%, Sp = 80%). Tests for Internet transmission that realistically simulated clinical conditions revealed adequate acquisition and analysis of tremor signals and also revealed that the user could adequately receive medical recommendations.ConclusionsThe proposed system can be used in a wide spectrum of telemedicine scenarios, enabling the home evaluation of tremor occurrence under specific medical treatments and contributing to reduce the costs of the assistance offered to these patients.

On the respiratory mechanics measured by forced oscillation technique in patients with systemic sclerosis.

Background Pulmonary complications are the most common cause of death and morbidity in systemic sclerosis (SSc). The forced oscillation technique (FOT) offers a simple and detailed approach to investigate the mechanical properties of the respiratory system. We hypothesized that SSc may introduce changes in the resistive and reactive properties of the respiratory system, and that FOT may help the diagnosis of these abnormalities. Methodology/Principal Findings We tested these hypotheses in controls (n = 30) and patients with abnormalities classified using spirometry (n = 52) and pulmonary volumes (n = 29). Resistive data were interpreted with the zero-intercept resistance (Ri) and the slope of the resistance (S) as a function of frequency. Reactance changes were evaluated by the mean reactance between 4 and 32 Hz (Xm) and the dynamic compliance (Crs,dyn). The mechanical load was evaluated using the absolute value of the impedance in 4 Hz (Z4Hz). A compartmental model was used to obtain central (R) and peripheral (Rp) resistances, and alveolar compliance (C). The clinical usefulness was evaluated by investigating the area under the receiver operating characteristic curve (AUC). The presence of expiratory flow limitation (EFL) was also evaluated. For the groups classified using spirometry, SSc resulted in increased values in Ri, R, Rp and Z4Hz (p<0.003) and reductions in Crs,dyn, C and Xm (p<0.004). Z4Hz, C and Crs,dyn exhibited a high diagnostic accuracy (AUC>0.90). In groups classified by pulmonary volume, SSc resulted in reductions in S, Xm, C and Crs,dyn (p<0.01). Xm, C and Crs,dyn exhibited adequate diagnostic accuracy (AUC>0.80). It was also observed that EFL is not common in patients with SSc. Conclusions/Significance This study provides evidence that the respiratory resistance and reactance are changed in SSc. This analysis provides a useful description that is of particular significance for understanding respiratory pathophysiology and to ease the diagnosis of respiratory abnormalities in these patients.

Machine learning algorithms and forced oscillation measurements to categorise the airway obstruction severity in chronic obstructive pulmonary disease

The purpose of this study was to develop automatic classifiers to simplify the clinical use and increase the accuracy of the forced oscillation technique (FOT) in the categorisation of airway obstruction level in patients with chronic obstructive pulmonary disease (COPD). The data consisted of FOT parameters obtained from 168 volunteers (42 healthy and 126 COPD subjects with four different levels of obstruction). The first part of this study showed that FOT parameters do not provide adequate accuracy in identifying COPD subjects in the first levels of obstruction, as well as in discriminating between close levels of obstruction. In the second part of this study, different supervised machine learning (ML) techniques were investigated, including k-nearest neighbour (KNN), random forest (RF) and support vector machines with linear (SVML) and radial basis function kernels (SVMR). These algorithms were applied only in situations where high categorisation accuracy [area under the Receiver Operating Characteristic curve (AUC)≥0.9] was not achieved with the FOT parameter alone. It was observed that KNN and RF classifiers improved categorisation accuracy. Notably, in four of the six cases studied, an AUC≥0.9 was achieved. Even in situations where an AUC≥0.9 was not achieved, there was a significant improvement in categorisation performance (AUC≥0.83). In conclusion, machine learning classifiers can help in the categorisation of COPD airway obstruction. They can assist clinicians in tracking disease progression, evaluating the risk of future disease exacerbations and guiding therapy.

Influence of the ageing process on the resistive and reactive properties of the respiratory system

INTRODUCTION In an increasingly old society, the study of the respiratory system changes and new techniques dedicated to older patients are of interest in physiologic studies as well as in the diagnosis of respiratory diseases. OBJECTIVES (1) To investigate the impact of ageing on the resistive and reactive properties of the respiratory system, and (2) to compare the easiness of accomplishment of spirometry and forced oscillation for assessing lung function. METHODS We conducted a cross-sectional study in which forced oscillation was used to investigate respiratory system resistive and reactive properties, while spirometry was used as a reference test to evaluate 80 normal subjects aged between 20 and 86 years. A questionnaire was used to evaluate the easiness of accomplishment of spirometry and forced oscillation. RESULTS There was a significant increase in the respiratory system resonance frequency (p<0.003) and a reduction in the mean reactance (p<0.004) with increasing age. Respiratory system resistance and dynamic compliance were not related to the ageing process. The easiness of accomplishment of forced oscillation measurements was greater than that of spirometry. This result was particularly relevant in subjects over 70 years old (p<0.05). CONCLUSIONS Respiratory system resistance and dynamic compliance are not modified with ageing. On the other hand, respiratory system homogeneity decreases during the ageing process. Forced oscillation is easy to perform and provides information complementary to spirometry. This technique may be a promising alternative and/or complement to other conventional exams used to evaluate older people who are unable to adequately perform spirometric tests.

An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms

The purpose of this study was to develop an automatic classifier to increase the accuracy of the forced oscillation technique (FOT) for diagnosing early respiratory abnormalities in smoking patients. The data consisted of FOT parameters obtained from 56 volunteers, 28 healthy and 28 smokers with low tobacco consumption. Many supervised learning techniques were investigated, including logistic linear classifiers, k nearest neighbor (KNN), neural networks and support vector machines (SVM). To evaluate performance, the ROC curve of the most accurate parameter was established as baseline. To determine the best input features and classifier parameters, we used genetic algorithms and a 10-fold cross-validation using the average area under the ROC curve (AUC). In the first experiment, the original FOT parameters were used as input. We observed a significant improvement in accuracy (KNN=0.89 and SVM=0.87) compared with the baseline (0.77). The second experiment performed a feature selection on the original FOT parameters. This selection did not cause any significant improvement in accuracy, but it was useful in identifying more adequate FOT parameters. In the third experiment, we performed a feature selection on the cross products of the FOT parameters. This selection resulted in a further increase in AUC (KNN=SVM=0.91), which allows for high diagnostic accuracy. In conclusion, machine learning classifiers can help identify early smoking-induced respiratory alterations. The use of FOT cross products and the search for the best features and classifier parameters can markedly improve the performance of machine learning classifiers.

Automatic identification of Chronic Obstructive Pulmonary Disease Based on forced oscillation measurements and artificial neural networks

The purpose of this study is to develop an automatic classifier based on Artificial Neural Networks (ANNs) to help the diagnostic of Chronic Obstructive Pulmonary Disease (COPD) using forced oscillation measurements (FOT). The classifier inputs are the parameters provided by the FOT and the output is the indication if the parameters indicate COPD or not. The available dataset consists of 7 possible input features (FOT parameters) of 90 measurements made in 30 volunteers. Two feature selection methods (the analysis of the linear correlation and forward search) were used in order to identify a reduced set of the most relevant parameters. Two different training strategies for the ANNs were used and the performance of resulting networks were evaluated by the determination of accuracy, sensitivity (Se), specificity (Sp) and AUC. The ANN classifiers presented high accuracy (Se > 0.9, Se > 0.9 and AUC > 0.9) both in the complete and the reduce sets of FOT parameters. This indicates that ANNs classifiers may contribute to easy the diagnostic of COPD using forced oscillation measurements.

An internet-based system for home monitoring of respiratory muscle disorders

Home telemonitoring is of great interest in respiratory medicine where large numbers of people have long term conditions. We developed a telemedicine instrument for home monitoring of patients with disturbed respiratory muscles. The instrument measures the maximum inspiratory pressure (Pimax), the inspiratory time constant (τi) and connects to the Internet through TCP/IP protocol. The instrument was evaluated by means of a comparative analysis in 18 normal individuals and 15 COPD patients. In close agreement with the pathophysiology, a reduction in Pimax (p<0.0001) and an increase in τi (p<0.001) was observed in COPD patients. We concluded that the developed system could be a useful tool for the evaluation of inspiratory muscle and for the implementation of telemedicine services, contributing to reduce the costs of the assistance offered to patients with respiratory diseases.