Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding--Part II: application results.
The application of the wavelet transform-fractal dimension-based (WT-FD) filter of Part I of this paper to real bioacoustic data, which include explosive lung sounds (ELS) and explosive bowel sounds (EBS) recorded from patients with pulmonary or gastrointestinal dysfunction, respectively, is presented in this paper. The objective of the latter is the evaluation of the performance of the WT-FD filter on different types of bioacoustic signals, varying not only in their structural morphology but also in the degree of their noise contamination. As it is thoroughly described in Part I of this paper, the WT-FD filter uses the fractal dimension to form an efficient way of thresholding the WT coefficients at different resolution scales, keeping, thus, only those that can contribute to the accurate reconstruction of the ELS and EBS signals. Quantitative and qualitative analysis of the experimental results show an efficient performance of the WT-FD filter to circumvent the noise presence (100% detectability rate, 100% sensitivity, 100% specificity) by faithfully extracting the authentic structure of ELS and EBS from the background noise. The WT-FD filter does not require any noise reference signal or noise reference templates. The results from a noise stress test (mean cross-correlation index of the original and the estimated signal converging to 100%; mean normalized maximum amplitude error converging to 0.7%) prove its robustness to various noise levels (0-20 dB), enabling its potential use in similar noise cases met in everyday clinical medicine. Furthermore, the efficient performance of the WT-FD filter facilitates the physician to better interpret the auscultation findings. Due to its simplicity and low computational cost, the WT-FD filter can possibly be implemented in a real-time context to serve as a tool for the continuous ELS and EBS screening.
Hadjileontiadis
Leontios J
LJ
Faculty of Engineering, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, University Campus, GR 541 24 Thessaloniki, Greece. leontios@auth.gr
D000328:Adult; D000368:Aged; D000465:Algorithms; D001314:Auscultation / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004056:Differential Threshold; D005260:Female; D017709:Fractals; D006801:Humans; D007410:Intestinal Diseases / Q000175:diagnosis* / Q000503:physiopathology; D007422:Intestines / Q000503:physiopathology; D008168:Lung / Q000503:physiopathology; D008171:Lung Diseases / Q000175:diagnosis* / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography / Q000379:methods*