Neuronal networks are computer-based techniques for the evaluation and control of complex information systems and processes. So far, they have been used in engineering, telecommunications, artificial speech and speech recognition. A new approach in neuronal network is the self-organizing map (Kohonen map). In the phase of 'learning', the map adapts to the patterns of the primary signals. If, in the phase of 'using the map', the input signal hits the field of the primary signals, it resembles them and is called a 'winner'. In our study, we recorded the cries of newborns and young infants using digital audio tape (DAT) and a high quality microphone. The cries were elicited by tactile stimuli wearing headphones. In 27 cases, delayed auditory feedback was presented to the children using a headphone and an additional three-head tape-recorder. Spectrographic characteristics of the cries were classified by 20-step bark spectra and then applied to the neuronal networks. It was possible to recognize similarities of different cries of the same children as well as interindividual differences, which are also audible to experienced listeners. Differences were obvious in profound hearing loss. We know much about the cries of both healthy and sick infants, but a reliable investigation regimen, which can be used for clinical routine purposes, has yet not been developed. If, in the future, it becomes possible to classify spectrographic characteristics automatically, even if they are not audible, neuronal networks may be helpful in the early diagnosis of infant diseases.
|International Journal of Pediatric Otorhinolaryngology
|Number of pages
|Published - 05.12.1996