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When developing automated techniques for analysis of auscultation signals, the choice of a proper representational space that characterizes all attributes of interest in the signal is of paramount importance. In this paper, we investigate different feature representation methods and their benefits in distinguishing auscultation sounds. The importance of choosing an appropriate feature space is explored...
Goal: Chest auscultation constitutes a portable low-cost tool widely used for respiratory disease detection. Though it offers a powerful means of pulmonary examination, it remains riddled with a number of issues that limit its diagnostic capability. Particularly, patient agitation (especially in children), background chatter, and other environmental noises often contaminate the auscultation, hence...
Lung sound auscultation in non-ideal or busy clinical settings is challenged by contaminations of environmental noise. Digital pulmonary measurements are inevitably degraded, impeding the physician's work or any further processing of the acquired signals. The task is even harder when the patient population includes young children. Agitation and/or crying are captured into the recordings, additionally...
Automated analysis and detection of abnormal lung sound patterns has great potential for improving access to standardized diagnosis of pulmonary diseases, especially in low-resource settings. In the current study, we develop signal processing tools for analysis of paediatric auscultations recorded under non-ideal noisy conditions. The proposed model is based on a biomimetic multi-resolution analysis...
This paper presents a novel framework for assessing tumor changes based on histogram analysis of temporal Magnetic Resonance Image (MRI) data. The proposed method detects the distribution of tumor and quantitatively models its growth or shrinkage offering the potential to assist clinicians in objectively assessing subtle changes during therapy. The presented work and the initial validation refer to...
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