H. Nazeran

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

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

A Comparison of Various Respiratory System Models Based on Parameter Estimates From Impulse Oscillometry Data

Impulse oscillometry offers an advantage over spirometry when conducting pulmonary function tests. Not only does it require minimal patient cooperation, it provides useful data in a form amenable to engineering methods. 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. Of the six models analyzed during this study, the DuBois model and a newly proposed extended RIC model seem to provide the most robust parameter estimates for our entire data set of 106 subjects with various respiratory ailments such as asthma and chronic obstructive pulmonary disease. Such a diagnostic approach, relying on estimated parameter values, may require additional measures to ensure proper identification of diseases/pathologies but the preliminary results are promising.

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%

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, Meads 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

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-CI 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 Ct). 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-CI model and the Mead-Cw model also yielded unreasonably large values for Cw and CI, 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. Further work is underway to apply this model to IOS data acquired from Hispanic/asthmatic and non-asthmatic as well as Anglo/asthmatic and non-asthmatic children in the age range of 5-17 years to quantitatively characterize and then compare lung function between these groups.

A user-friendly integrated software environment for heart rate variability analysis of sleep disordered breathing

We present an integrated software environment that enables sleep specialists to analyze, quantity and diagnose sleep disordered breathing based upon time-domain, frequency-domain, and nonlinear dynamics measurement measures of heart rate variability (HRV) signal. The integrated graphical user interface (GUT) and the signal processing algorithms were developed and implemented in MATLAB, This environment provides the facility to import or read sleep data (ECG, EEG, blood pressure, respiratory, oxygen saturation, etc.) under user control and displays them individually or collectively in a data window for visual inspection. It then enables the user to clip the length of data to be used in carrying out the analysis. Raw ECG data is preprocessed for reliable QRS detection and the HRV signal is derived following the guidelines of the Task Force of the European Society of Cardiology and the North American Society for Pacing and Electrophysiology, which is then displayed along with respiration signal in the analysis window. After this step time-domain, frequency- domain, and nonlinear dynamics analyses of the HRV signal are performed to extract sensitive measures used in detecting and diagnosing sleep disordered breathing. The computer analysis can then generate a complete report for the specialist and further statistical and/or automatic analyses. The system was developed and validated using data from the MIT-BIH Polysomnographic Database. After validation and reliability testing it was used to analyze sleep data for detection of sleep disordered breathing (SDB) in children. Data from normal and children diagnosed with SDB showed that the system could potentially distinguish between normal children and children suffering from sleep disordered breathing.

Respiratory System Model Parameters Track Changes in Lung Function after Bronchodilation

Impulse Oscillometry (IOS) provides respiratory resistance, Rrs, and reactance, Xrs, between 3 - 35 (Hz). We measured IOS parameters in 26 randomly selected Anglo children 6-19 years in El Paso TX. An expert pulmonologist classified Rrs and Xrs as: Normal (n = 5 ); Possible Small Airway Impairment, Possible SAI, (n = 4); Definite SAI (n = 6); or Asthma (n = 11). We used the extended RIC (eRIC) and augmented RIC (aRIC) models of the human respiratory system to derive Large (R) and Peripheral (Rp) airway resistance, and small airway compliance, Cp. IOS Rrs and Xrs at selected frequencies reflecting large and peripheral airway function. Model-derived parameters were compared between pre- and post-bronchodilator tests. IOS Xrs at low frequencies (AX) and model-derived Cp were the most sensitive parameters for detecting bronchodilator responses, which were significant in children with SAI and asthma. Model-derived Cp correlated closely with AX. We conclude that response to bronchodilator in children with SAI is sensitively measured by IOS. Electrical equivalent model parameters are as sensitive as primary IOS data in detecting bronchodilator response.