Title :
Wheeze detection in the respiratory sounds using Hilbert-Huang transform
Author_Institution :
Biyomed. Muhendisligi Bolumu, Kocaeli Univ., Kocaeli, Turkey
Abstract :
Wheezes in the respiratory sounds are indicators for a number of diseases. Automatic detection of these signals - which have musical character and one or more dominant frequencies-is very important. In this study investigation of wheeze detection in the respiratory sounds using Hilbert-Huang transform is studied. This method allows analysis of signals which are neither stationary nor linear. Initially Intrinsic Mode Functions are found using Empirical Mode Decomposition. Instantaneous frequencies calculated using Hilbert transform of these signals were found to have distinct values with low variations, and constancy and variation of these instantaneous frequencies were affected by the power of the dominant frequencies in the wheeze.
Keywords :
Hilbert transforms; medical signal detection; Hilbert-Huang transform; dominant frequencies; empirical mode decomposition; instantaneous frequencies; intrinsic mode functions; musical character; respiratory sounds; signal detection; wheeze detection; Conferences; Diseases; Empirical mode decomposition; Lungs; Signal processing; System-on-chip; Hilbert-Huang transform; empirical mode decomposition; respiratory sounds; wheeze detection;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2014 22nd
Conference_Location :
Trabzon
DOI :
10.1109/SIU.2014.6830699