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August 1, 2000Transactions of the Institute of Measurement and Control

Time-frequency analysis of biomedical signals

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Key result

Time-frequency analysis methods, including the Cohen class, wavelet transform, and recursive autoregressive estimation, enhance different characteristics of non-stationary biological signals.

Population

Biomedical signals including heart rate variability, electroencephalogram, evoked potentials, and magnetic…

Design

Review

Authors

ABAnna Maria BianchiLMLuca MainardiSCS. Cerutti

Discussion

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Overview

May aid non-stationary signal analysis in HRV protocols; leaves open clinical adoption pending prospective validation.

Structured PICO

P
Population
Biomedical signals including heart rate variability, electroencephalogram, evoked potentials, and magnetic resonance imaging spectroscopic signals
E
Exposure
Time-frequency analysis methods (Cohen class of distributions, wavelet transform, recursive autoregressive estimation)

This review highlights the utility of time-frequency analysis methods for processing non-stationary biological signals.

Cite This Study

Bianchi et al. (2000) reported a review. Time-frequency analysis methods was evaluated. Time-frequency analysis methods, including the Cohen class, wavelet transform, and recursive autoregressive estimation, enhance different characteristics of non-stationary biological signals.

synapsesocial.com/papers/6a35da28a4e90ae4d3ef5006https://doi.org/10.1177/014233120002200302
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