2012
DOI: 10.3390/a5040588
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An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals

Abstract: We present a new method for automatic detection of peaks in noisy periodic and quasi-periodic signals. The new method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima. The usefulness of the proposed method is shown by applying the AMPD algorithm to simulated and real-world signals

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Cited by 344 publications
(177 citation statements)
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“…Near-Infrared Spectroscopy PRV NIRS was extracted using the automatic multiscale-based peak detection (AMPD) algorithm developed by Scholkmann et al 30 AMPD is based on the calculation and analysis of the local HR maxima in the raw fNIRS time series. AMPD detects the HR peaks, which are then used to calculate the interpeak intervals frequency via interpolating the time difference signal.…”
Section: Pulse Rate Variability Extraction From Functionalmentioning
confidence: 99%
“…Near-Infrared Spectroscopy PRV NIRS was extracted using the automatic multiscale-based peak detection (AMPD) algorithm developed by Scholkmann et al 30 AMPD is based on the calculation and analysis of the local HR maxima in the raw fNIRS time series. AMPD detects the HR peaks, which are then used to calculate the interpeak intervals frequency via interpolating the time difference signal.…”
Section: Pulse Rate Variability Extraction From Functionalmentioning
confidence: 99%
“…For comparison, K-means [14] and Fuzzy C-means methods [15] are also employed to perform the experiments. As the peak detection methods [25,26] are commonly used to detect the traffic peak periods. A peak detection method, Billauer's method [26], is adopted to perform the experiments at the same time.…”
Section: Methodsmentioning
confidence: 99%
“…Distance of the targets is derived using the algorithms of the local maximum detection. One example of the algorithm for local maximum detection is described in [10]. Figures 10 and 11 show false alerts on ranges smaller than 1 km, which are caused by the length of the reflected signal in the recorded sequence.…”
Section: Detection Of the Rangementioning
confidence: 99%