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Hypersampling of pseudo-periodic signals by analytic phase projection.

ABSTRACT: A method to upsample insufficiently sampled experimental time series of pseudo-periodic signals is proposed. The result is an estimate of the pseudo-periodic cycle underlying the signal. This "hypersampling" requires a sufficiently sampled reference signal that defines the pseudo-periodic dynamics. The time series and reference signal are combined by projecting the time series values to the analytic phase of the reference signal. The resulting estimate of the pseudo-periodic cycle has a considerably higher effective sampling rate than the time series. The procedure is applied to time series of MRI images of the human brain. As a result, the effective sampling rate could be increased by three orders of magnitude. This allows for capturing the waveforms of the very fast cerebral pulse waves traversing the brain. Hypersampling is numerically compared to the more commonly used retrospective gating. An outlook regarding EEG and optical recordings of brain activity as the reference signal is provided.

SUBMITTER: Voss HU

PROVIDER: S-EPMC5999588 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Hypersampling of pseudo-periodic signals by analytic phase projection.

Voss Henning U HU

Computers in biology and medicine 20180516

A method to upsample insufficiently sampled experimental time series of pseudo-periodic signals is proposed. The result is an estimate of the pseudo-periodic cycle underlying the signal. This "hypersampling" requires a sufficiently sampled reference signal that defines the pseudo-periodic dynamics. The time series and reference signal are combined by projecting the time series values to the analytic phase of the reference signal. The resulting estimate of the pseudo-periodic cycle has a consider ...[more]

PMID: 29800881

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Dataset Information

Hypersampling of pseudo-periodic signals by analytic phase projection.

Publications

Hypersampling of pseudo-periodic signals by analytic phase projection.

Similar Datasets

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