A smart spirometry device for asthma diagnosis

A Machine-Learning Model for Lung Age Forecasting by Analyzing Exhalations

Spirometers are important devices for following up patients with respiratory diseases. These are mainly located only at hospitals, with all the disadvantages that this can entail. This limits their use and consequently, the supervision of patients. Research efforts focus on providing digital alternatives to spirometers. Although less accurate, the authors claim they are cheaper and usable by many more people worldwide at any given time and place. In order to further popularize the use of spirometers even more, we are interested in also providing user-friendly lung-capacity metrics instead of the traditional-spirometry ones. The main objective, which is also the main contribution of this research, is to obtain a person’s lung age by analyzing the properties of their exhalation by means of a machine-learning method. To perform this study, 188 samples of blowing sounds were used. These were taken from 91 males (48.4%) and 97 females (51.6%) aged between 17 and 67. A total of 42 spirometer and frequency-like features, including gender, were used. Traditional machine-learning algorithms used in voice recognition applied to the most significant features were used. We found that the best classification algorithm was the Quadratic Linear Discriminant algorithm when no distinction was made between gender. By splitting the corpus into age groups of 5 consecutive years, accuracy, sensitivity and specificity of, respectively, 94.69%, 94.45% and 99.45% were found. Features in the audio of users’ expiration that allowed them to be classified by their corresponding lung age group of 5 years were successfully detected. Our methodology can become a reliable tool for use with mobile devices to detect lung abnormalities or diseases.

Complex System for Monitoring the Patient's Condition and Diagnosis of Bronchial Asthma

In the present work, three versions of implementation of non-invasive bronchial asthma diagnostic system based on the author’s technique of noninvasive diagnosis of bronchopulmonary diseases are considered. The offered variants of diagnostic system can be used both in medical institutions and at patient’s home for the control of the patient’s condition with the purpose of monitoring the dynamics of the disease during treatment, as well as for preventive purposes. Three variants of implementation of the diagnostic system with various complexity are considered. The results of a radiofrequency scanning of a human chest phantom with included heterogeneity simulating the presence of sputum in the human chest is a consequence of bronchial asthma.

Asthma Device Calibrator (ADC)

Spirometer testing is considered the most common method used to evaluate the pulmonary system. It diagnoses asthma, chronic obstructive pulmonary disease (COPD) and other conditions that affect breathing. Nowadays many pharmaceutical companies produce spirometer devices for patients with asthma, designed for home use. However, the machines are replaced annually due to the lack of control and results' reproducibility. Lacking a reliable mean of calibration leads to unnecessary replacement of expensive devices and/or inaccurate measurements. The objective of this work is designing and prototyping a controlled air flow machine, which will simulate the exhalation of a human based on correlations and predefined graphs found in medicine. This prototyped machine will be used to control and/or calibrate the spirometer device of the physician. The simulation of the human's exhalation was accomplished by varying the rotational speed of an AC single phase motor using a Variable Frequency Drive in addition of a hardware device. The motor is connected directly to a centrifugal fan with forward curved blades. Using the digital pitot tube anemometer/manometer, the flow rate was measured by taking an average value of 40 frequency samples in the range 0 Hz to 100 Hz. After obtaining a relationship between the flow rate and the frequency, the data were implemented in a hardware controller, to adequately control the motor. The control was made from an Android mobile application as follows: Manual and Automatic. The Manual feature enables the user to control the flow rate manually by choosing the required maximum peak flow values to run the test. By adding an air flow sensor, the automatic feature runs automatically and the results of the test shall be obtained as a feedback on the application's screen.