Diagnosis and Analysis of Respiratory Diseases Using Different Approach For Crackles and Rip
A neuro-fuzzy system is a fuzzy system that uses a learning algorithm derived from or inspired by neural network
theory to determine its parameters (fuzzy sets and fuzzy rules) by processing data samples. Artificial intelligence and
neuro-fuzzy is not only used in respiratory infection or lung cancer analysis but also tried to be used to diagnose thyroid
disorder, diabetes, heart diseases, neuro diseases, asthma disease. In order to proper classification of respiratory diseases
waveforms similar to the ones generated within the lungs must be recovered from the attenuated sounds. The equalization of
crackle sounds recorded on the chest can be done for accurate classification of respiratory sounds. The paper discussed on
different improvement of extraction of features from the crackle sound with modified equation for classifying disease and
presents one innovative approach, which may assist researchers in the analysis of respiratory inductive plethysmography (RIP)
data by calculating derived parameters from previously acquired physiological waveforms. The presented system is able to
import RIP data stored in a dataset and plot the raw data traces for visualization of signal quality and phasing between the
thoracic and abdominal signals.
Index Terms- Respiratory Sound, Auscultation, Plethysmography, Sound Analysis, Lung Sound, Thoracic and Abdominal.