Pattern Detection in Vital Signals Daily Collected by Means of a Tele-monitoring Application
This work deals with methods for similarity measuring and indexing in physiological time series. A similarity measure is introduced based on the natural set of features generated by the Haar wavelet transform, which reflect the fundamental dynamics of a time series. Basically, a time series transformation procedure is carried out by means of a set of orthogonal wavelet basis functions, which is then reduced to an optimal subset through the application of the Karhunen Loève transform. As result, a physiological signal is efficiently described by a linear combination of a reduced set of wavelet basis with the corresponding coefficients reflecting its main dynamic behavior. These coefficients are then used to efficiently evaluate the similarity between biosignals, allowing a significant reduction of the computational complexity of the method.
The validation of the proposed similarity is assessed by the comparison with other common measures, when several variations in a baseline are introduced. For this purpose, blood pressure and heart rate daily collected by means of a tele-monitoring application (TEN-HMS) are employed. The obtained results suggest that the proposed similarity is particularly appropriate to deal with noise, trends and signals that are not perfectly aligned in time.
KeywordsTime series similarity biosignal analysis tele-monitoring applications
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