Evaluation of Pulmonary Function Tests by Using Fuzzy Logic Theory

Pulmonary Function Tests (PFTs) are very important in the medical evaluation of patients suffering from “shortness of breath”, and they are effectively used for the diagnosis of pulmonary diseases, such as COPD (i.e. chronic obstructive pulmonary diseases). Measurement of Forced Vital Capacity (FVC) and Forced Expiratory Flow in the 1st second (FEV1) are very important for controlling the treatment of COPD. During PFTs, some difficulties are encountered which complicate the comparison of produced graphs with the standards. These mainly include the reluctance of the patients to co-operate and the physicians’ weaknesses to make healthy interpretations. Main tools of the diagnostic process are the symptoms, laboratory tests or measurements and the medical history of the patient. However, quite frequently, most of the medical information obtained from the patient is uncertain, exaggerated or ignored, incomplete or inconsistent. Fuzziness encountered during PFT is very important. In this study, the purpose is to use “fuzzy logic” approach to facilitate reliable and fast interpretation of PFT graphical outputs. A comparison is made between this approach and methodologies adopted in previous studies. Mathematical models and their coefficients for the spirometric plots are introduced as fuzzy numbers. Firstly, a set of rules for categorizing coefficients of mathematical models obtained. Then, a fuzzy rule-base for a medical inference engine is constructed and a diagnostic “expert system COPDes” designed. This program, COPDes helps for diagnosing the degree of COPD for the patient under test.

[1]  James F. Baldwin,et al.  Medical diagnosis from patient records—a method using fuzzy discrimination and connectivity analyses , 1987 .

[2]  M. Small,et al.  Pulmonary function testing in laboratories associated with residency training programs in pulmonary diseases. , 1969, The American review of respiratory disease.

[3]  Shyi-Ming Chen.  FUZZY SYSTEM RELIABILITY-ANALYSIS USING FUZZY NUMBER ARITHMETIC OPERATIONS (VOL 64, PG 31, 1994) , 1994 .

[4]  G Sybrecht,et al.  The mechanism of increase in total lung capacity during acute asthma. , 1976, The American journal of medicine.

[5]  ATS statement--Snowbird workshop on standardization of spirometry. , 1979, The American review of respiratory disease.

[6]  Klaus-Peter Adlassnig,et al.  Fuzzy Set Theory in Medical Diagnosis , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  P. Smets.  Medical diagnosis: Fuzzy sets and degrees of belief , 1981 .

[8]  R M Gardner,et al.  Reference spirometric values using techniques and equipment that meet ATS recommendations. , 2015, The American review of respiratory disease.

[9]  R. G. Fraser,et al.  A COMPARISON BETWEEN CLINICAL, ROENTGENOLOGIC, FUNCTIONAL AND MORPHOLOGIC CRITERIA IN CHRONIC BRONCHITIS, EMPHYSEMA, ASTHMA AND BRONCHIECTASIS , 1970 .

[10]  K. Finucane,et al.  Elastic behavior of the lung in patients with airway obstruction. , 1969, Journal of applied physiology.

[11]  A. Woolcock,et al.  Lung volumes in exacerbations of asthma. , 1966, The American journal of medicine.

[12]  J. Morris,et al.  Spirometric standards for healthy nonsmoking adults. , 2015, The American review of respiratory disease.

[13]  R. Cherniack,et al.  Normal standards for ventilatory function using an automated wedge spirometer. , 1972, The American review of respiratory disease.

[14]  Ümit Üncü,et al.  Fuzziness in Spirometric Measurements , 2000 .

[15]  R. Ingram,et al.  Limitation of expiratory flow in chronic obstructive pulmonary disease. Relation of clinical characteristics, pathophysiological type, and mechanisms. , 1970, Annals of internal medicine.

[16]  R B Douglas,et al.  The maximal expiratory flow-volume curve. Normal standards, variability, and effects of age. , 1980, The American review of respiratory disease.

[17]  A H Kendrick,et al.  Evaluation of a new electronic spirometer: the vitalograph "Escort" spirometer. , 1994, Thorax.

[18]  J. Morris,et al.  Normal values for the ratio of one-second forced expiratory volume to forced vital capacity. , 2015, The American review of respiratory disease.

[19]  E. Koklukaya,et al.  A fuzzy rule-base model for classification of spirometric FVC graphs in chronical obstructive pulmonary diseases , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.