Reference Equations for Impulse Oscillometric and Respiratory System Model Parameters in Anglo and Hispanic Children / Ecuaciones de referencia para parámetros de oscilometría de impulsos y de modelo del sistema respiratorio en niños anglos e hispanos

Abstract The Impulse Oscillometry System (IOS) has been recognized as a useful diagnostic tool. Previous research by our group has shown that R5 (Resistance at 5 Hz), R5-R20 (frequency dependence of resistance), AX (the "Goldman Triangle") and the extended Resistor-Inductor-Capacitor (eRIC) model-derived parameter Cp (peripheral Compliance) are reliable measures that track respiratory system function. Here we determined the predictive equations from 112 asthmatic and non-asthmatic Anglo and Hispanic children data, that were previously classified into four groups: Normal, PSAI (Probable Small Airway Impairment), SAI (Small Airway Impairment), and Asthma by using regression analysis of IOS and eRIC model parameters as dependent variables regressed against age, height, and weight. Height showed the greatest correlation with IOS and eRIC model parameters and provided the most significant reference values for these measures in Anglo and Hispanic children. These values could serve as a useful framework for diagnosis, early interventions, and treatment of respiratory diseases in this population.

Resumen El Sistema de Oscilometría Impulsos (por sus siglas en Inglés: IOS) ha sido reconocido como una herramienta de diagnóstico útil. Investigaciones previas realizadas por nuestro grupo han demostrado que R5 (Resistencia a 5 Hz), R5-R20 (dependencia a la frecuencia de la resistencia), AX (el "Triángulo de Goldman") y el párametro Cp (Capacitancia periférica) derivado del modelo extendido Resistencia-inductor-condensador (eRIC) son medidas fiables que monitorean la función del sistema respiratorio. El presente estudio determina las ecuaciones de predicción de 112 niños Anglo e Hispanos asmáticos y no asmáticos, que previamente clasificados en cuatro grupos: Normal, PSAI (por su nombre en inglés: Probable daño en las vías aéreas menores), SAI (por su nombre en inglés: Daño en las vías aéreas menores), y Asma. Las ecuaciones de predicción se determinaron mediante análisis de regresión de parámetros del IOS y del modelo eRIC (variables dependientes) contra edad, estatura y peso. La estatura mostró la mejor correlación con los parámetros del IOS y del modelo eRIC, proporcionando los valores de referencia más significativos para estas medidas en niños Anglos e Hispanos. Estos valores podrían servir como un marco de referencia útil para el diagnóstico, intervenciones tempranas, y tratamiento de las enfermedades respiratorias en esta población.

Design and prototyping of a wristband-type wireless photoplethysmographic device for heart rate variability signal analysis.

Heart Rate Variability (HRV) signal analysis provides a quantitative marker of the Autonomic Nervous System (ANS) function. A wristband-type wireless photoplethysmographic (PPG) device was custom-designed to collect and analyze the arterial pulse in the wrist. The proposed device is comprised of an optical sensor to monitor arterial pulse, a signal conditioning unit to filter and amplify the analog PPG signal, a microcontroller to digitize the analog PPG signal, and a Bluetooth module to transfer the data to a smart device. This paper proposes a novel model to represent the PPG signal as the summation of two Gaussian functions. The paper concludes with a verification procedure for HRV signal analysis during sedentary activities.

Rapid Prototyping of a Smart Device-based Wireless Reflectance Photoplethysmograph.

This paper presents the design, fabrication, and testing of a wireless heart rate (HR) monitoring device based on photoplethysmography (PPG) and smart devices. PPG sensors use infrared (IR) light to obtain vital information to assess cardiac health and other physiologic conditions. The PPG data that are transferred to a computer undergo further processing to derive the Heart Rate Variability (HRV) signal, which is analyzed to generate quantitative markers of the Autonomic Nervous System (ANS). The HRV signal has numerous monitoring and diagnostic applications. To this end, wireless connectivity plays an important role in such biomedical instruments. The photoplethysmograph consists of an optical sensor to detect the changes in the light intensity reflected from the illuminated tissue, a signal conditioning unit to prepare the reflected light for further signal conditioning through amplification and filtering, a low-power microcontroller to control and digitize the analog PPG signal, and a Bluetooth module to transmit the digital data to a Bluetooth-based smart device such as a tablet. An Android app is then used to enable the smart device to acquire and digitally display the received analog PPG signal in real-time on the smart device. This article is concluded with the prototyping of the wireless PPG followed by the verification procedures of the PPG and HRV signals acquired in a laboratory environment.

A comparison of linear respiratory system models based on parameter estimates from PRN forced oscillation data.

The forced oscillation technique offers some advantages over spirometry for assessing pulmonary function. It requires only passive patient cooperation; it also provides data in a form, frequency-dependent impedance, which is very amenable to engineering analysis. In particular, the data can be used to obtain parameter estimates for electric circuit-based models of the respiratory system, which can in turn aid the detection and diagnosis of various diseases/pathologies. In this study, we compare the least-squares error performance of the RIC, extended RIC, augmented RIC, augmented RIC+I(p), DuBois, Nagels and Mead models in fitting 3 sets of impedance data. These data were obtained by pseudorandom noise forced oscillation of healthy subjects, mild asthmatics and more severe asthmatics. We found that the aRIC+I(p) and DuBois models yielded the lowest fitting errors (for the healthy subjects group and the 2 asthmatic patient groups, respectively) without also producing unphysiologically large component estimates.

The augmented RIC model of the human respiratory system.

This paper describes the augmented RIC model of respiratory impedance and analyzes its parameter values estimated--by a modified Newton method with least squares criterion--from impulse oscillometry data. The data were from asthmatic children, tested pre- and post-bronchodilator, and from healthy adults and a second group of adults with COPD. Our analyses show that the augmented RIC model was 13.7-66.6% more accurate than the extended RIC model at fitting these data, while its parameter estimates were within previously reported ranges, unlike the Mead 1969, DuBois and Mead models, which typically yielded compliance estimates exceeding 200 l/kPa. Additionally, the augmented RIC model's C(p) parameter, representing peripheral airway compliance, is a statistically significant discriminator between unconstricted and constricted conditions (with p < 0.001) occurring in asthma and COPD. This corresponds well with current medical understanding, so the augmented RIC model is potentially useful for detection and treatment of airflow obstruction.

A study of IOS data using two mead-related models of respiratory impedance.

This paper introduces two new respiratory system models, the Mead-Cw model and the Mead-Cl model, which are 6-component models that are intermediate in complexity between the well-known 7-component Mead model and the recently proposed 5-component augmented RIC model (derived from the Mead model by eliminating both Cw and Cl). Their modeling errors were compared to the RIC, extended RIC, augmented RIC and Mead models, for component values estimated from IOS data. The two new models yielded lower errors than all the other models, except for the Mead model. However, the Mead-Cl model and the Mead-Cw model also yielded unreasonably large values for Cw and Cl, respectively, which are known disadvantages of the Mead model. Hence the augmented RIC model appears to be the most useful at present for IOS-based computer-aided detection and diagnosis of respiratory disorders.

Assessment of Itakura Distance as a valuable feature for computer-aided classification of sleep stages.

Staging and detection of various states of sleep derived from EEG and other biomedical signals have proven to be very helpful in diagnosis, prognosis and remedy of various sleep related disorders. The time consuming and costly process of visual scoring of sleep stages by a specialist has always motivated researchers to develop an automatic sleep scoring system and the first step toward achieving this task is finding discriminating characteristics (or features) for each stage. A vast variety of these features and methods have been investigated in the sleep literature with different degrees of success. In this study, we investigated the performance of a newly introduced measure: the Itakura Distance (ID), as a similarity measure between EEG and EOG signals. This work demonstrated and further confirmed the outcomes of our previous research that the Itakura Distance serves as a valuable similarity measure to differentiate between different sleep stages.

Modeling human respiratory impedance in Hispanic asthmatic children.

Central (large airway) and peripheral (small airway) dysfunction frequently occur in patients with asthma and chronic obstructive lung disease. Measurement of the respiratory impedance can assist with diagnosis of pathological conditions. The forced Oscillation technique (FOT) superimposes small pressure perturbations at the mouth during tidal breathing of a subject to measure lung mechanical parameters. The Impulse Oscillometry System (IOS) is a commercial instrument that measures forced oscillatory impedance. IOS can be conveniently used in children as it only requires their passive cooperation during pulmonary function testing. Forced oscillatory impedance can be analyzed with respiratory system equivalent electrical circuit models. Models of varying complexity and fidelity have been developed to provide better understanding of respiratory mechanics and enable greater specificity of the diagnosis. Parameter estimates for these models can be used as reference values for detection and diagnosis of different respiratory pathologies. Previous work by our group has evaluated several known respiratory models and a new RIC model (augmented RIC) has emerged which offers advantages over earlier models. It has been shown that one parameter of this new model (representing peripheral airway compliance) is capable of discriminating between normal and asthmatic children. In this paper, we analyzed IOS data from 40 Hispanic asthmatic children and obtained sensitive impulse oscillometric parameters of lung function as well as parameter estimates for the augmented RIC (aRIC) model to distinguish between constricted (asthmatic condition) and non-constricted (non-asthmatic condition) airways with very promising results.

Wavelet-based segmentation and feature extraction of heart sounds for intelligent PDA-based phonocardiography.

OBJECTIVES: Many pathological conditions of the cardiovascular system cause murmurs and aberrations in heart sounds. Phonocardiography provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intracardiac phonocardiography combined with modern digital signal processing techniques has strongly renewed researchers' interest in studying heart sounds and murmurs. The aim of this work is to investigate the applicability of different spectral analysis methods to heart sound signals and explore their suitability for PDA-based implementation. METHODS: Fourier transform (FT), short-time Fourier transform (STFT) and wavelet transform (WT) are used to perform spectral analysis on heart sounds. A segmentation algorithm based on Shannon energy is used to differentiate between first and second heart sounds. Then wavelet transform is deployed again to extract 64 features of heart sounds. RESULTS: The FT provides valuable frequency information but the timing information is lost during the transformation process. The STFT or spectrogram provides valuable time-frequency information but there is a trade-off between time and frequency resolution. Wavelet analysis, however, does not suffer from limitations of the STFT and provides adequate time and frequency resolution to accurately characterize the normal and pathological heart sounds. CONCLUSIONS: The results show that the wavelet-based segmentation algorithm is quite effective in localizing the important components of both normal and abnormal heart sounds. They also demonstrate that wavelet-based feature extraction provides suitable feature vectors which are clearly differentiable and useful for automatic classification of heart sounds.

EOG and EMG: two important switches in automatic sleep stage classification.

Sleep is a natural periodic state of rest for the body, in which the eyes are usually closed and consciousness is completely or partially lost. In this investigation we used the EOG and EMG signals acquired from 10 patients undergoing overnight polysomnography with their sleep stages determined by expert sleep specialists based on RK rules. Differentiation between Stage 1, Awake and REM stages challenged a well trained neural network classifier to distinguish between classes when only EEG-derived signal features were used. To meet this challenge and improve the classification rate, extra features extracted from EOG and EMG signals were fed to the classifier. In this study, two simple feature extraction algorithms were applied to EOG and EMG signals. The statistics of the results were calculated and displayed in an easy to visualize fashion to observe tendencies for each sleep stage. Inclusion of these features show a great promise to improve the classification rate towards the target rate of 100%

Nonlinear dynamics analysis of heart rate variability signals to detect sleep disordered breathing in children.

This paper reports a preliminary investigation to evaluate the significance of various nonlinear dynamics approaches to analyze the heart rate variability (HRV) signal in children with sleep disordered breathing (SDB). Data collected from children in the age group of 1-17 years diagnosed with sleep apnea were used in this study. Both short term (5 minutes) and long term data from a full night polysomnography (7-9 hours) were analyzed. For short term data, the presence of nonstationarity in the derived HRV signal was determined by calculating the local Hurst exponent. Poincare plots and approximate entropy (ApEn) were then used to show the presence of correlation in the data. For long term data, the derived HRV signal was first separated into corresponding sleep stages with the aid of the recorded sleep hypnogram values at 30 seconds epochs. The scaling exponents using detrended fluctuation analysis (DFA) and the ApEn were then calculated for each sleep stage. Data from two sample subjects recorded for different sleep stages and breathing patterns were considered for short term analysis. Data from 7 sample subjects (after sleep staging) were considered for long term analysis. The accuracy rate of ApEn was about 72% for both long term and short term data sets. The accuracy rate of Alpha (alpha) derived from DFA for long term correlations was 57%. Further work is necessary to improve on the accuracies of these useful nonlinear dynamic measures and determine their sensitivity and specificity to detect SDB in children.

Comparison of heart rate variability signal features derived from electrocardiography and photoplethysmography in healthy individuals.

The heart rate variability (HRV) signal is indicative of autonomic regulation of the heart rate (HR). It could be used as a noninvasive marker in monitoring the physiological state of an individual. Currently, the primary method of deriving the HRV signal is to acquire the electrocardiogram (ECG) signal, apply appropriate QRS detection algorithms to locate the R wave and its peak, find the RR intervals, and perform suitable interpolation and resampling to produce a uniformly sampled tachogram. This process could sometimes result in errors in the HRV signal due to drift, electromagnetic and biologic interference, and the complex morphology of the ECG signal. The photoplethysmographic (PPG) signal has the potential to eliminate the problems with the ECG signal to derive the HRV signal. To investigate this point, a PDA-based system was developed to simultaneously record ECG and PPG signals to facilitate accurately controlled sampling and recording durations. Two healthy young volunteers participated in this pilot study to evaluate the applicability of our approach. To improve data quality, ECG and PPG recordings were acquired three times/subject. A comparison between different features of the HRV signals derived from both methods was performed to test the validity of using PPG signals in HRV analysis. We used autoregressive (AR) modeling, Poincare' plots, cross correlation, standard deviation, arithmetic mean, skewness, kurtosis, and approximate entropy (ApEn) to derive and compare different measures from both ECG and PPG signals. This study demonstrated that our PDA-based system was a convenient and reliable means for acquisition of PPG-derived and ECG-derived HRV signals. The excellent agreement between different measures of HRV signals acquired from both methods provides potential support for the idea of using PPGs instead of ECGs in HRV signal derivation and analysis in ambulatory cardiac monitoring of healthy individuals.

Can asthma in children be detected by the estimated parameter values of the augmented RIC model?

This paper describes the estimation of the parameter values for the recently introduced augmented RIC respiratory system model from impulse oscillometry data obtained from both asthmatic and normal children. An analysis of these values has indicated that one of the capacitance parameters of the model provides good discrimination between these two groups of children; moreover, this finding corresponds well with current medical understanding of the pathology of asthma.

Wavelet Transform-Based ECG Baseline Drift Removal for Body Surface Potential Mapping.

This paper gives a new approach for the removal of slow baseline drift components of electrocardiographic (ECG) signals based on the discrete wavelet transform. The baseline drift is efficiently removed by zeroing the scaling coefficients of the discrete wavelet transform. Such approach can easily be combined with other wavelet based approaches for random noise reduction or power line interference reduction. The new pre-processing approach can remove the low-frequency components without introducing distortions in the ECG waveform.

Detrended Fluctuation Analysis: A Suitable Long-term Measure of HRV Signals in Children with Sleep Disordered Breathing.

On the body surface the electric field generated by the cardiac muscles consists of electric potential maxima and minima that increase and decrease during each cardiac cycle. The recording of these electric potentials as a function of time is called electrocardiography, and the resulting signal is called the electrocardiogram (ECG). The ECG signal is used extensively as a low cost diagnostic tool to provide information concerning the heart's state of health. Reliable and accurate detection of the QRS complex and R wave peak in ECG signals is essential in computer-based ECG analysis. In this paper we evaluate the significance of Detrended Fluctuation Analysis (DFA) for studying heart rate variability in children with sleep disordered breathing. An Enhanced Hilbert Transform (EHT) algorithm was used to derive the Heart Rate Variability (HRV) signal. We compare the DFA values with Approximate Entropy and Poincaré Plots of HRV signals as these are very useful in characterization and visualization of HRV data. Our data demonstrated differences in DFA parameters between periods of normal and abnormal breathing and also between sleep stages. These results suggest that DFA is suitable for the long-term analysis of non-stationary time series such as HRV signals and may also be applied in the detection of sleep disordered breathing.

Itakura Distance: A Useful Similarity Measure between EEG and EOG Signals in Computer-aided Classification of Sleep Stages.

Sleep is a natural periodic state of rest for the body, in which the eyes usually close and consciousness is completely or partially lost. Consequently, there is a decrease in bodily movements and responsiveness to external stimuli. Slow wave sleep is of immense interest as it is the most restorative sleep stage during which the body recovers from weariness. During this sleep stage, electroencephalographic (EEG) and electro-oculographic (EOG) signals interfere with each other and they share a temporal similarity. In this investigation we used the EEG and EOG signals acquired from 10 patients undergoing overnight polysomnography with their sleep stages determined by certified sleep specialists based on RK rules. In this pilot study, we performed spectral estimation of EEG signals by Autoregressive (AR) modeling, and then used Itakura Distance to measure the degree of similarity between EEG and EOG signals. We finally calculated the statistics of the results and displayed them in an easy to visualize fashion to observe tendencies for each sleep stage. We found that Itakura Distance is the smallest for sleep stages 3 and 4. We intend to deploy this feature as an important element in automatic classification of sleep stages.

Digitization and synchronization method for electrocardiogram printouts.

A method of digitization and synchronization of ECG signals for use in vectorcardiography is hereby presented. It is intended to allow the computerized use of the ECG printout used by most cardiologists. The problem is solved by digitizing the printout, which can then be used for vectorcardiography and other modern techniques, once it is synchronized using cross-correlation. Cross-correlation was found to be a superior technique for synchronization over other methods, like R-peak synchronization that can be erroneously used, due to time correspondence. The highest and lowest normalized rms error of the digitized and original signals was found to be 13.49% and 9.85% for recording V1 and Lead III, respectively. The error found was expected because of the comparison of an image with many points in a single instant of time, in comparison to only one point as it is in a digital signal. The result obtained from the work presented here is that the ECG printout needs no longer to remain only as a graphical report, but could also be used for computerized analyses.

Evaluation of respiratory system models based on parameter estimates from impulse oscillometry data.

Impulse oscillometry offers advantages over spirometry because it requires minimal patient cooperation, it yields pulmonary function data in a form that is readily amenable to engineering analysis. In particular, the data can be used to obtain parameter estimates for electric circuit-based models of the respiratory system, which in turn may assist the detection and diagnosis of various diseases/pathologies. Of the six models analyzed during this study, Mead's model seems to provide the most robust and accurate parameter estimates for our data set of 5 subjects with airflow obstruction including asthma and chronic obstructive pulmonary disease and another 5 normal subjects with no identifiable respiratory disease. Such a diagnostic approach, relying on estimated parameter values from a respiratory system model estimate and the degree of their deviation from the normal range, may require additional measures to ensure proper identification of diseases/pathologies but the preliminary results are promising.

Compensation of limb weight on interfaced raw torque signals from a KIN-COM dynamometer to an AMLAB workstation.

The effect of gravity should be considered when using isokinetic devices to measure human movement performance. In most isokinetic dynamometers gravity compensation is controlled by software through a gravity correction option. However in some complex research protocols the dynamometer signal acquisition and processing capability is not adequate to effectively synchronize or process a wide range of captured signals. Therefore when the force/torque signals from a commonly used dynamometer such as KIN-COM are interfaced into a signal processing workstation such as AMLAB, it is necessary to further process the received raw signals for gravity correction. The aim of this study was to evaluate the effectiveness of an AMLAB-based instrument designed for gravity compensation of raw torque signals acquired from a KIN-COM dynamometer. To check the accuracy of weight compensation within the AMLAB, environment, torque signals produced by a known weight during a 180-degree range of KIN-COM lever arm movement were compared with and without weight compensation. The results indicated that this technique is an accurate means for weight compensation when raw torque signals from a KIN-COM dynamometer are interfaced to an AMLAB workstation.

Reducing power line interference in digitised electromyogram recordings by spectrum interpolation.

Interference from power lines (50 or 60 Hz) is the largest source of extraneous noise in many bio-electric signals and is within the bandwidth of many such signals. In this study, two different methods were compared for their efficacy in removing 50 Hz noise added to surface electromyogram (EMG) signals free of power line interference. The first was a simple second-order recursive digital notch filter. The second was an approach called spectrum interpolation, in which it is assumed that the magnitude of the original 50 Hz component of the EMG signal can be approximated by interpolation of the amplitude spectrum of the signal. When the spectrum was based on records containing an integer number of cycles of 50 Hz interference, and the frequency resolution was finer than 1 Hz, spectrum interpolation performed similarly to, or significantly better than, the notch filter (p < 0.01). It was also possible to make spectrum interpolation more robust than the notch filter. The Pearson squared correlation coefficient r2 between clean signals and signals processed using the notch filter was reduced from 0.98 to 0.65 when the interference frequency was increased by 0.5 Hz, but r2 for spectrum interpolation at 0.2 Hz resolution was only reduced from 0.99 to 0.85 if spectral values between approximately 49.5 and 50.5 Hz were modified by interpolation.