Abstract-The monitoring of breathing dynamics is an essential diagnostic tool in various clinical environments, such as sleep diagnostics, intensive care and neonatal monitoring. This paper introduces an innovative signal classification method that is capable of on-line detection of the presence or absence of normal breathing. Four different artificial neural networks are presented for the recognition of three different patterns in the respiration signals (normal breathing, hypopnea, and apnea). Two networks process the normalized respiration signals directly, while another two use sophisticatedly preprocessed signals. The development of the networks was based on training sets from the polysomnographic records of nine different patients. The detection performance of the networks was tested and compared by using up to 8000 untrained breathing patterns from 16 different patients. The networks which classified the preprocessed respiration signals produced an average detection performance of over 90%. In the light of the moderate computational power used, the presented method is not only viable in clinical polysomnographs and respiration monitors, but also in portable devices.

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Abstract-The various noise components make the diagnostic evaluation of phonocardiographic records difficult or in some cases even impossible. This paper presents a novel wavelet-based denoising method using two-channel signal recording and an adaptive cross-channel coefficient thresholding technique. The qualitative evaluation of the denoising performance has shown that the proposed method cancels noises more effectively than the other examined techniques. The introduced method can be used as preprocessor stage in all fields of phonocardiography, including the recording of fetal heart sounds on the maternal abdominal surface.

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