Key result
Modified fuzzy-filtering algorithm reduces random variations in heart rate signals during head-up tilt testing.
A modified fuzzy-filtering algorithm can reduce uncertainty and random variations in physiological signal analysis, such as heart rate during head-up tilting.
May aid HR signal processing in research; leaves open clinical validation before practice impact.
This study suggests the use of fuzzy-filtering algorithms to deal with the uncertainties associated to the analysis of physiological signals. The signal characteristics, for a given situation or physiological state, vary for an individual over time and also vary among the individuals with the same state. These random variations are due to the several factors related to the physiological behavior of individuals, which cannot be taken into account in the interpretation of signal characteristics. Our approach is to reduce the effect of random variations on the analysis of signal characteristics via filtering out randomness or uncertainty from the signal using a nonlinear fuzzy filter. A fuzzy-filtering algorithm, which is based on a modification of filtering algorithm of Kumaret al.[M. Kumar, N. Stoll, and R. Stoll,IEEE Trans. Fuzzy Syst., vol. 17, no. 1, pp. 150-166, Feb. 2009], is proposed for an improved performance. The method is illustrated by studying the effect of head-up tilting on the heart-rate signal of 40 healthy subjects.
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Kumar et al. (2009) studied Healthy subjects (n=40). Fuzzy-filtering algorithm was evaluated on Effect of head-up tilting on the heart-rate signal. A modified fuzzy-filtering algorithm was proposed to reduce the effect of random variations in physiological signals and illustrated on heart-rate signals of 40 healthy subjects during head-up tilting.
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