Heart Sound Localization in Respiratory Sounds Based on Singular Spectrum Analysis and Frequency Features

Abstract

Heart sounds are the main obstacle in lung sound analysis. To tackle this obstacle, we propose a diagnosis algorithm that uses singular spectrum analysis (SSA) and frequency features of heart and lung sounds. In particular, we introduce a frequency coefficient that shows the frequency difference between heart and lung sounds. The proposed algorithm is applied to a synthetic mixture of heart and lung sounds. The results show that heart sounds can be extracted successfully and localizations for the first and second heart sounds are remarkably performed. An error analysis of the localization results shows that the proposed algorithm has fewer errors compared to the SSA method, which is one of the most powerful methods in the localization of heart sounds. The presented algorithm is also applied in the cases of recorded respiratory sounds from the chest walls of five healthy subjects. The efficiency of the algorithm in extracting heart sounds from the recorded breathing sounds is verified with power spectral density evaluations and listening. Most studies have used only normal respiratory sounds, whereas we additionally use abnormal breathing sounds to validate the strength of our achievements.

Using Mobiles to Monitor Respiratory Diseases

“…After measuring SpO 2 , the patient's exhalations are recorded three times, as recommended by the GOLD pocket guide [7], and stored as wave files on the smartphone SD card. The possible range of breathing frequencies lies approximately between 100 Hz and 1200 Hz [32,33]. Signal processing is performed to extract this range of frequencies, analyze the signal, and calculate FVC and FEV1 lung measurements.…”