Characteristics of normal lung sounds after adaptive filtering.

Lung sounds were recorded from five normal male subjects during tidal breathing. Simultaneous electrocardiograms were recorded and used as index signals to generate simulated heart sounds for digital subtraction from recorded lung sounds to obtain purer lung sounds. Five random breaths from each subject were analyzed. Sound signals were band-pass filtered 25 to 1,000 Hz (antialiasing), digitized at 3,000 Hz, and then subjected to (1) direct fast Fourier transform (FFT) without filtering (NF); (2) digital high-pass filtering at 75 Hz and subsequent FFT (75 HzF); (3) adaptive filtering and subsequent FFT (AF). The FFT algorithms of all lung sounds were characterized by mean, median, and mode frequencies. The mean, median, and mode of NF were lower than those of 75 HzF (64.98 +/- 4.04 versus 150.42 +/- 17.49, mean +/- SE, p less than 0.003; 44.57 +/- 2.06 versus 111.81.5.78, p less than 0.0003; 36.81 +/- 1.77 versus 86.16 +/- 3.13, p less than 0.0001) and those of AF (64.98 +/- 4.04 versus 96.87 +/- 11.58, p less than 0.01; 44.57 +/- 2.06 versus 68.23 +/- 10.44, p less than 0.05; 36.81 +/- 1.78 versus 52.24 +/- 8.97, p less than 0.06). The mean, median, and mode of AF were lower than those of 75 HzF (96.87 +/- 11.58 versus 150.42 +/- 17.49, p less than 0.02; 68.23 +/- 10.44 versus 111.81 +/- 5.77, p less than 0.007; 52.24 +/- 8.97 versus 86.16 +/- 3.73, p less than 0.01). The results indicated that by filtering out low frequency heart sounds, the frequency spectrum of lung sounds was moved upward.(ABSTRACT TRUNCATED AT 250 WORDS)