Comput. Biol. Med. - Improved wavelet entropy calculation with window functions and its preliminary application to study intracranial pressure.

Tópicos

{ signal(2180) analysi(812) frequenc(800) }
{ imag(2830) propos(1344) filter(1198) }
{ general(901) number(790) one(736) }
{ studi(1410) differ(1259) use(1210) }
{ data(1737) use(1416) pattern(1282) }
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Resumo

The wavelet entropy is a novel way to measure the signal regularity, and its calculation is based on the energy distribution in wavelet sub-bands. However, wavelet entropy will be largely influenced by the noise usually existed in signals, especially in physiological signals. With aim to get more stable entropy calculation, a windowed wavelet entropy approach is proposed. In this paper, we systemically studied the difference between wavelet entropy and approximate entropy, which has yet not been studied in detail before. The conducted comparison with various signals reveals that wavelet entropy can measure the signal complexity like approximate entropy. Moreover, the relative wavelet entropy can be used to measure the dissimilarity between two signals. Compared to the original wavelet entropy approach, the comparison result also shows that the proposed window approach can get smoother and more stable calculation for both wavelet entropy and relative wavelet entropy, which is more meaningful to measure signal regularity and dissimilarity. The application to the time series recorded from a patient having the intracranial hypertension reveals that the new approach can clearly differentiate the normal and hypertension states, which may serve as a promising tool for prediction of intracranial pressure in future.

Resumo Limpo

wavelet entropi novel way measur signal regular calcul base energi distribut wavelet subband howev wavelet entropi will larg influenc nois usual exist signal especi physiolog signal aim get stabl entropi calcul window wavelet entropi approach propos paper system studi differ wavelet entropi approxim entropi yet studi detail conduct comparison various signal reveal wavelet entropi can measur signal complex like approxim entropi moreov relat wavelet entropi can use measur dissimilar two signal compar origin wavelet entropi approach comparison result also show propos window approach can get smoother stabl calcul wavelet entropi relat wavelet entropi meaning measur signal regular dissimilar applic time seri record patient intracrani hypertens reveal new approach can clear differenti normal hypertens state may serv promis tool predict intracrani pressur futur

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