Patricia Nava

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

Impulse oscillometric features of lung function: Towards computer-aided classification of respiratory diseases in children

Asthma is the most prevalent chronic respiratory disease in children. Reliable and patient-friendly instruments and methods are required to help pulmonologists accurately detect asthma with acceptable clinical accuracy, specificity and sensitivity. Impulse Oscillometry System (IOS) based on the Forced Oscillation Technique (FOT) has been successfully used to measure lung function in children with a high degree of sensitivity and specificity to small airway dysfunction (SAD). IOS measures the mechanical impedance of the respiratory system. Equivalent electrical circuit models of lung function have been developed that can be used to quantify severity of SAD. It has been shown that impulse oscillometric parameters as well as parameter estimates of these electrical models provide useful indicators of lung function and therefore have the potential to be used as sensitive features for computer-aided classification of pulmonary function in health and disease.

Classification of Pulmonary Diseases Based on Impulse Oscillometric Measurements of Lung Function Using Neural Networks

Central and peripheral airflow obstructions frequently occur in patients with chronic obstructive lung disease or asthma and may have different pathophysiological mechanisms of obstruction and require different therapeutic inte rventions. Impulse oscillometry (IOS) is a patient-friendly method for studying respiratory function in health and disease. The enormous variety of patterns and the high degree of variability in the measured lung function parameters has made the automated diagnosis of pulmonary diseases very desirable by pulmonary physiologists and clinicians. Computer aided diagnosis can serve as a second but quantitative opinion to diagnosis and screening. Presently, there are no robust algorithms to classify the IOS patterns into particular disease groups. In this work, an artificial neural network (ANN) was used to recognize and classify the diseases of the central and peripheral airways. Using IOS measurements in 131 patients, a training set was created and used in a feedforward ANN that was trained by backpropagation algorithm to classify pulmonary diseases as either central or peripheral. After supervised training, the ANN was presented with the same data and produced a 98.47% correct classification rate. When a new set of unseen data was used, the classification accuracy was reduced to 61.53%. Having produced a promising classification rate in the first case, the accuracy of this classifier could be further improved. Inclusion of more training samples combined with fuzzy logic decision rules could facilitate the development of a software tool that assists pulmonary specialists with their diagnosis of lung function using the patient-friendly IOS system.

Classification of Impulse Oscillometric Patterns of Lung Function in Asthmatic Children using Artificial Neural Networks

Impulse oscillometry (IOS) is an innovative patient-friendly pulmonary testing technique which measures the respiratory system impedance (Z) by using the spectral components of pressure to flow ratio which yields resistance and reactance values at different frequencies. The high dimensionality of IOS measurement data makes the analysis of this information difficult. Artificial neural networks (ANNs) are mathematical models composed of a large number of highly interconnected neurons that are able to learn and generalize from data. An ANN-based approach to the analysis of IOS data can potentially provide an efficient and automatic method to recognize and classify pulmonary diseases. This would help characterize major respiratory illnesses such as asthma based on IOS measurements. Asthma can be difficult to diagnose, because the symptoms are sometimes similar to other lung conditions. A data set composed of 361 impulse oscillometric patterns from asthmatic children was used in this study. The ANN was capable of distinguishing between relatively constricted and nonconstricted airway conditions in these patients. Using all of the 361 patterns during training as well as in the feed-forward stage, a classification accuracy of 95.01% was obtained for validation. When the ANN was presented with only 60% of the original 361 patterns in the data set during training and with the remaining 40% as unseen patterns, the generalization stage, a classification accuracy of 98.61% was achieved. These results show that ANNs can successfully be trained with the IOS data, enabling them to generalize the IOS parameter relationships to classify previously unseen pulmonary patterns, such as in asthma. The next step is to obtain expert rules by extracting them from the knowledge acquired by the neural network and develop a fully automated classification system to aid physicians in classifying and characterizing pulmonary diseases based on the patient-friendly IOS measurements

Identification of Human Gait using Fuzzy Inferential Reasoning

The restoration of healthy locomotion (gait) after stroke, traumatic brain injury, and spinal cord injury, is a major task in neurological rehabilitation. The rehabilitation process is labor intensive. Patient evaluation is often subjective, foiling determination of precise rehabilitation goals and assessment of treatment effects. To date it is the experienced clinician who continues to perform functional gait assessment and training in the absence of virtually any technological assistance. This paper introduces an algorithm capable of identifying human gait patterns. The fuzzy inferential reasoning uses typical joint angle trajectories to identify varying gait patterns. The algorithm will, thus, offer doctors, therapists, and patients a significant tool to assess the efficacy and outcomes of medical rehabilitation therapies and practices.

Senior project design success and quality: A systems engineering approach

The capstone project, in most undergraduate engineering programs is the final phase of an academic career. It allows students to take knowledge acquired through the program and apply it to an innovative industry or research project. A well designed senior project affords the student the opportunity to demonstrate the skills and to model the behaviour, which are inherent in the education goals of the program. Most capstone projects focus on technical skills but soft skills oftentimes are not built into the experience. During the past three years we have observed that a significant amount of Sr. Design teams in our Electrical Engineering (EE) department do not complete the projects in a timely manner. We find that lack of technical knowledge is rarely the cause; more often the causes include lack of communication skills, lack of experience in organizing work in a team environment, and a bias towards focusing on the device level at the expense of having a clear, high level end-to-end view of the project. This paper describes our efforts to address these gaps through the incorporation of Systems Engineering disciplines into our undergraduate capstone course in EE and our results so far; thus, leading to more qualitative, competitive and successful projects.

An Integrated Software Package to Classify Human Respiratory Diseases

The Impulse Oscillometry System (IOS) is a commercially available instrument for performing forced oscillation, to diagnose and monitor human respiratory diseases. IOS is a patient-friendly and objective pulmonary function testing device that measures pressure/flow at the mouth to calculate the spectral components of respiratory system impedance (Z). Our research group has demonstrated that the augmented RIC (aRIC) model, a modification of our extended RIC (eRIC) model, provides physiologically meaningful estimates of small and large airway resistive, inertive, and elastic components. Due to the high dimensionality of the IOS data and the wide range of model parameters that may distinguish between health and disease states, neuro-fuzzy approaches have been proven to be very helpful in classification of pulmonary diseases. This paper introduces an integrated software package that classifies asthma, small airways impairment (SAI), mild SAI, and normal airway function based on aRIC model parameters and a high performance neuro-fuzzy clustering algorithm; and once fully implemented will present classification results in a clinician-friendly 3-D graphical user interface. IOS data from 112 children asthmatic and non-asthmatic children 7-15 years of age were used for this study.