Multi-Scale Peak and Trough Detection Optimised for Periodic and Quasi-Periodic Neuroscience Data

  • Steven M. BishopEmail author
  • Ari Ercole
Conference paper
Part of the Acta Neurochirurgica Supplement book series (NEUROCHIRURGICA, volume 126)


Objectives: The reliable detection of peaks and troughs in physiological signals is essential to many investigative techniques in medicine and computational biology. Analysis of the intracranial pressure (ICP) waveform is a particular challenge due to multi-scale features, a changing morphology over time and signal-to-noise limitations. Here we present an efficient peak and trough detection algorithm that extends the scalogram approach of Scholkmann et al., and results in greatly improved algorithm runtime performance.

Materials and methods: Our improved algorithm (modified Scholkmann) was developed and analysed in MATLAB R2015b. Synthesised waveforms (periodic, quasi-periodic and chirp sinusoids) were degraded with white Gaussian noise to achieve signal-to-noise ratios down to 5 dB and were used to compare the performance of the original Scholkmann and modified Scholkmann algorithms.

Results: The modified Scholkmann algorithm has false-positive (0%) and false-negative (0%) detection rates identical to the original Scholkmann when applied to our test suite. Actual compute time for a 200-run Monte Carlo simulation over a multicomponent noisy test signal was 40.96 ± 0.020 s (mean ± 95%CI) for the original Scholkmann and 1.81 ± 0.003 s (mean ± 95%CI) for the modified Scholkmann, demonstrating the expected improvement in runtime complexity from \( \mathbb{O}\left({n}^2\right) \) to \( \mathbb{O}(n) \).

Conclusions: The accurate interpretation of waveform data to identify peaks and troughs is crucial in signal parameterisation, feature extraction and waveform identification tasks. Modification of a standard scalogram technique has produced a robust algorithm with linear computational complexity that is particularly suited to the challenges presented by large, noisy physiological datasets. The algorithm is optimised through a single parameter and can identify sub-waveform features with minimal additional overhead, and is easily adapted to run in real time on commodity hardware.


Peak detection Trough detection Algorithm design Optimisation Neuroinformatics Intracranial pressure waveform analysis 


Conflicts of interest statement

We declare that we have no conflicts of interest.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Division of AnaesthesiaUniversity of Cambridge, Cambridge University Hospitals NHS Foundation TrustCambridgeUK

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