Key result
Adaptive Hermite basis functions achieved a balanced performance in compact modelling and discriminant analysis of cardiovascular physiological signals compared to wavelet basis functions.
Adaptive Hermite basis functions provide a balanced performance for compact modelling and discriminant analysis of cardiovascular physiological signals compared to wavelet basis functions.
May aid compact CV signal analysis; leaves open prospective clinical validation before practice change.
This study presented a unified perspective of adaptive basis functions to compare Hermite decomposition and wavelet transform for the analysis of cardiovascular physiological signals. Three different algorithms were presented to carry out physiological signal modelling with adaptive Hermite basis functions (HBFs), orthonormal wavelet basis functions (OWBFs) and adaptive wavelet basis functions (AWBFs). The modelling with OWBFs is computationally efficient. However, the concomitant restrictions in mathematics make OWBFs not optimal for compact modelling. In contrast, the optimised AWBFs can model cardiovascular physiological signals compactly with the cost of losing orthonormality. It not only sacrifices the fast implementation but also degrades AWBFs in discriminant analysis. In summary, merely HBFs achieve a balanced performance in compact modelling and discriminant analysis.
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Li et al. (2010) studied Cardiovascular physiological signals. Adaptive Hermite basis functions (HBFs) vs. Orthonormal wavelet basis functions (OWBFs) and adaptive wavelet basis functions (AWBFs) was evaluated on Compact modelling and discriminant analysis performance. Adaptive Hermite basis functions achieved a balanced performance in compact modelling and discriminant analysis of cardiovascular physiological signals compared to wavelet basis functions.
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