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Abstract:
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Respiratory disease is common in cetaceans both in the wild and under human care. Diagnosing lung disease is complicated, and recent development of spirometry in dolphins may provide an alternative minimally invasive, cheap and logistically feasible method to assess lung disease. Data from dolphins under managed care are used to measure baseline respiratory lung function under stress-free conditions. Because of new features in the data, new statistical methods are required for the breath data analysis. In this paper, we investigate one potential method for analyzing breath data. We consider an entire breath cycle to be one unit of observation. Starting and ending points of breath cycles can be difficult to determine, and cause a large amount of variation in size and shape of breath curves. To reduce cycle to cycle variability, we apply curve registration to synchronize a set of breath cycles. Breath cycles are described using magnitude information and geometric shape information. We propose three shape models, namely, simple oval model, quadratic spline model, and piecewise linear model. Furthermore, principal component analysis is applied to the magnitude/ shape descriptors to obtain main features of breath cycles. Criteria for disease diagnosis are developed by identifying key differences among these main features between healthy and unhealthy animals. The proposed methods were applied to check whether two testing animals are diseased or not. The results were consistent with the status of both animals.
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