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Abstract: Guyton’s original integrative physiology model was a milestone in integrative physiology, combining significant physiological knowledge with an engineering perspective to develop a computational diagrammatic model. It is still used in research and teaching, with a small number of variants on the model also in circulation. However, though new research has added significantly to the knowledge represented by Guyton’s model, and significant advances have been made in computing and simulation software, an accepted common platform to integrate this new knowledge has not emerged. This paper discusses the issues in the selection of a suitable platform, together with a number of current possibilities, and suggests a graphical computing environment for modelling and simulation. By way of example, a validated version of Guyton’s 1992 model, implemented in the ubiquitous Simulink environment, is presented which provides a hierarchical representation amenable to extension and suitable for teaching and research uses. It is designed to appeal to the biomedical engineer and physiologist alike. [Copyright &y& Elsevier]

• We propose a novel method for recognition of pulse wave feature points based on angle mapping, which can amplify the changes in pulse waveform and provide better noise immunity. • The accuracy of this method is discussed by using approximate pulse wave and mathematical model. • This method is an efficient choice for the identification of unobvious feature points. • This method can provide threshold standard for existence of unobvious feature points. Pulse wave is one of the biomedical signals that has been studied over the past years. Accurate recognition of feature points is the basis of verifying the connections between pulse waves and certain diseases. Therefore, the aim of the study is to discuss the use of angle mapping on feature points recognition. The mathematical method is based on the application of angle curve with parameter " k " on pulse wave. The data used is collected by PVDF sensor. Approximate curve and mathematical model are used for the discussion of the influence of parameter k and pulse wave amplitude by numerical calculation. The conclusion drawn from the numerical solution is that when k changes to maximize the angle extremum value, the corresponding position of angle extremum point is the feature point position. For the sampling rate f = 455Hz in this paper, k can be taken from 5 to 15. We present the recognition results of unobvious feature points based on the "angle extremum maximum method" and corresponding angle values. The results are compared with traditional methods and the determination of angle threshold value is discussed. This method can be used for accurate and efficient feature points identification, and it can be better applied to pulse waves with noise or unobvious feature points. [ABSTRACT FROM AUTHOR]