title: Acoustic analysis of infantile stridor: a review.
abstract: The review compares five methods that utilise electronic/computer acoustic processing techniques for the analysis of infantile stridor sounds. The first method uses traditional spectrographic techniques to produce time/frequency/intensity three-dimensional representation of the waveform. The second method is computer-based and uses the fast Fourier transformation (FFT) to show the frequency composition of the waveform. The third uses linear prediction coefficients (LPCs) to produce a power spectrum and inverse filtering to estimate the cross-sectional area of the human upper airway. The fourth technique employs a proprietary digital filterbank to analyse normal infant vocalisations, which may be used as a control by subsequent researchers. In the fifth method, a physiologically based digital filterbank, designed to closely model the human ear response, is proposed. It is envisaged that this approach will offer the flexibility of all the previous techniques and also closely model the analysis procedure carried out using subjective auscultation. It is concluded that none of the above techniques are sufficiently robust to provide unambiguous diagnosis of stridor type and that a reappraisal is required in terms of feature extraction so that relevant features can be identified. To this end, the authors propose that a physiologically based model of the human airway, including the vocal cords, be developed as an aid to the assessment of acoustic features.
authors.affiliation: Department of Electrical and Electronic Engineering, University of Ulster, Jordanstown, Newtonabbey, Northern Ireland, UK.
authors.forename: M
authors.initials: M
authors.lastname: Malone
authors.forename: N D
authors.initials: ND
authors.lastname: Black
authors.forename: M
authors.initials: M
authors.lastname: Lydon
authors.forename: M
authors.initials: M
authors.lastname: Cinnamond
mesh_terms: D000162:Acoustics; D005374:Filtration / Q000379:methods; D005583:Fourier Analysis; D006801:Humans; D007223:Infant; D012135:Respiratory Sounds / Q000175:diagnosis*; D012815:Signal Processing, Computer-Assisted