Abstract
- Background
The detection of peaks is a well known challange across various domains. Peaks indicate significant events such as sudden increase in price/volume, sharp rise in demand, bursts in data traffic. Most routinely used detection methods do not employ any assumption for peak shapes or baseline noise. All the information these peak detection methods use is that peak is a signal that goes up and comes down. A peak is detected when a threshold is exceeded[1].
- Aim
This library aims to detect peaks in both a 1-dimensional vector and 2-dimensional arrays (images) without making any assumption on the peak shape or baseline noise. To make sure that peaks can be detected across global and local heights, and in noisy data, multiple pre-processing and denoising methods are implemented or utilized.
- Results
Three peak-detection methods are incorporated into this package, namely Topology, Mask and Peakdetect. The peaks can be ranked with among others persistence scores. The pre-processing approaches are among others, denoising methods, interpolation and smoothing, resizing and normalizing methods. In addition we implemented various plots to easily intepretate the results, such as the conversion of 2d-images to 3d-mesh plots, persistence plot, and peak detection plots.
[1] Peak detection. Data Analysis and Signal Processing in Chromatography, https://doi.org/10.1016/S0922-3487(98)80027-0, Volume 21, 1998, Pages 183-190