Recursive least squares adaptive noise cancellation filtering for heart sound reduction in lung sounds recordings

Abstract

It is rarely possible to obtain recordings of lung sounds that are 100% free of contaminating sounds from non-respiratory sources, such as the heart. Depending on pulmonary airflow, sensor location, and individual physiology, heart sounds may obscure lung sounds in both time and frequency domains, and thus pose a challenge for development of semi-automated diagnostic techniques. In this study, recursive least squares (RLS) adaptive noise cancellation (ANC) filtering has been applied for heart sounds reduction, using lung sounds data recorded from anterior-right chest locations of six healthy male and female subjects, aged 10-26 years, under three standardized flow conditions: 7.5 (low), 15 (medium) and 22.5 mL/s/kg (high). The reference input for the RLS-ANC filter was derived from a modified band pass filtered version of the original signal. The comparison between the power spectral density (PSD) of original lung sound segments, including, and void of, heart sounds, and the PSD of RLS-ANC filtered sounds, has been used to gauge the effectiveness of the filtering. This comparison was done in four frequency bands within 20 to 300 Hz for each subject. The results show that RLS-ANC filtering is a promising technique for heart sound reduction in lung sounds signals.