Abstract:Clinical percussion is a method of eliciting sounds from the body by tapping with either a percussion hammer or fingers to determine the area under the perused is air filled, fluid filled, or solid, and is used in clinical examinations to assess the condition of the thorax or abdomen. Successful diagnosis today is still highly subjective and dependent's on physician skill, experience and require quite surrounding areas. An automated system capable of delivering standardized percussion analysis would remove these limitations on the technique and allow for its usage by those without such specialized training and years of necessary experience. For this to be possible, efficient and informative signal processing algorithms must be employed. In this investigation, clinical percussions from healthy volunteers taken by trained medical professionals were analysed via the matching pursuit (MP) algorithm. Various types of possible dictionaries are discussed comparing their efficiency and convergence behaviour. Noise filtering methods are discussed and a noise reduction method based on MP analysis results is presented.MP is also compared to other methods for representing clinical percussions with regards to informativeness and efficiency. MP is ( log ) which is more efficient than current methods which have a complexity of at least ( 3 ).