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PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals

Overview
Journal Sensors (Basel)
Publisher MDPI
Specialty Biotechnology
Date 2021 Jul 2
PMID 34200635
Citations 3
Authors
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Abstract

An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched together to generate a long, annotated PPG. Furthermore, this toolbox can simulate real-life PPG signals by introducing different noise levels and PPG waveforms. The toolbox can implement two stitching methods: one based on the systolic peak and the other on the onset. Additionally, cubic spline interpolation is used to smooth the waveform around the stitching point, and a skewness index is used as a signal quality index to select the final signal output based on the stitching method used. The developed toolbox is free and open-source software, and a graphical user interface is provided. The method of synthesizing by stitching introduced in this paper is a data augmentation strategy that can help researchers significantly increase the size and diversity of annotated PPG signals available for training and testing different feature extraction algorithms.

Citing Articles

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PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points.

Abdullah S, Hafid A, Folke M, Linden M, Kristoffersson A Front Bioeng Biotechnol. 2023; 11:1199604.

PMID: 37378045 PMC: 10292016. DOI: 10.3389/fbioe.2023.1199604.


Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss.

Ding C, Xiao R, Do D, Lee D, Lee R, Kalantarian S IEEE J Biomed Health Inform. 2023; PP.

PMID: 37018611 PMC: 11279526. DOI: 10.1109/JBHI.2023.3234557.

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