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The current paper focuses on the implementation of hybrid expert systems for audiovisual content description and management, by means of pattern analysis. The proposed methodology combines audio detection-segmentation, surveillance-video motion-detection and hierarchical audio pattern recognition, using neural networks, statistical clustering and syntactic pattern classification. The associated, video-assisted, bioacoustics application focuses on Abdominal Sounds (AS) pattern classification, promising to deliver new potentials in non-invasive Gastro-Intestinal Motility (GIM) monitoring. The current work introduces new techniques for content analysis automation of prolonged multi-channel recordings, facilitating automated GIM auscultation analysis. Thus, it seeks for the establishment of novel diagnostic tools over functional GIM disorders, with the advantage that subjects' behavior and related psycho-physiological issues can be monitored to further analyze their dependencies. Qualitative and quantitative analysis validated the soundness of the adopted pattern classification taxonomy, resulting remarkable pattern recognition accuracy. Based on preliminary results, the proposed methodology can be successfully applied to general audiovisual content classification, description and management tasks (besides bioacoustics).

This work focuses on the implementation of an autonomous system appropriate for long-term, unsupervised monitoring of bowel sounds, captured by means of abdominal surface vibrations. The autonomous intestinal motility analysis system (AIMAS) promises to deliver new potentials in gastrointestinal auscultation, towards the establishment of novel non-invasive methods for prolonged intestinal monitoring and diagnosis over functional disorders. The system was developed utilizing time-frequency features and wavelet-adapted parameters in combination with multi-layer perceptrons, that exhibit remarkable adaptation in pattern classification applications. Various network topologies and sizes were tested in combination with different features' sets. Quantitative analysis and validation results showed that the implemented system exhibits an overall recognition accuracy of 94.84%, while the error in separating bowel sounds from other sound patterns, representing interfering noises, was 2.19%.

The current paper describes a wavelet-based method for long-term processing and analysis of gastrointestinal sounds (GIS). Windowing techniques are used to select sequential blocks of the prolonged multi-channel recordings and proceed to various wavelet-domain processing stages. De-noising, significant-activity detection, automated segmentation and extraction of summary curves are applied in an integrated mode, allowing for enhanced content manipulation and analysis. The proposed analysis scheme combines flexible long-term graphical representation tools, while maintaining the ability of quick browsing via visualization and auralization of the detected short-term events. This work is part of a project aiming to implement non-invasive diagnosis over gastrointestinal-motility (GIM) physiology. However, the proposed techniques might be applied to any study of long-term bioacoustics time series.

This work focuses on the design and evaluation of efficient and accurate de-noising algorithms that combine robust signal enhancement and minimum signal distortion. The proposed method introduces novel, frequency depended, parametric, Wiener filtering techniques that involve Discrete Wavelet Transform and Wavelet Packets. Implementations of various decomposition schemes, different mother wavelets and various thresholding options were tested, while perceptual criteria were also taken into account. The introduced de-noising approach has been extensively tested on human bowel sounds, captured by means of abdominal surface vibration recordings, in order to be further utilized as a diagnostic tool. Qualitative and quantitative analysis of the method's performance, when applied to various types of recorded and synthetic sounds, revealed that the new approach works excellent with favourable results.

Hereditary hemochromatosis is a genetically heterogeneous disease. Common HFE mutations (C282Y and H63D) are related to the majority of hereditary hemochromatosis cases in populations of Northern European ancestry (HFE1). Juvenile hemochromatosis (JH) is a more severe iron overload disorder, usually presenting at the second decade of life. The gene responsible for JH lies on a genetic locus at chromosome 1q. We have performed a genetic linkage study in three families of Northern Greek origin with typical clinical features of JH. In two families results were in accordance with linkage to chromosome 1q. In one family linkage of the disease to the genetic loci at 1q21, 7q22, and 6p22 was excluded. We suggest that more than one gene may underlie the JH phenotype. This genetic type of hemochromatosis may be designated 1q unlinked juvenile hemochromatosis. Family studies are necessary to establish the genetic diagnosis of JH.

This paper describes the design, construction and performance of a test facility for transmission loss measurement. The receiving portion of the facility consists of a 118 m 3 reverberation room, while the source room is a 1·0 m 3 pit with high sound insulation. The partition being measured makes up the top of the pit. A series of measurements concerning the performance of the test facility are presented and discussed.