Anomaly Detection in Multi-Wavelength Photoplethysmography Using Lightweight Machine Learning Algorithms

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Over the past few years, there has been increased interest in photoplethysmography (PPG) technology, which has revealed that, in addition to heart rate and oxygen saturation, the pulse shape of the PPG signal contains much more valuable information. Lately, the wearable market has shifted towards a multi-wavelength and multichannel approach to increase signal robustness and facilitate the extraction of other intrinsic information from the signal. This transition presents several challenges related to complexity, accuracy, and reliability of algorithms. To address these challenges, anomaly detection stages can be employed to increase the accuracy and reliability of estimated parameters. Powerful algorithms, such as lightweight machine learning (ML) algorithms, can be used for anomaly detection in multi-wavelength PPG (MW-PPG). The main contributions of this paper are (a) proposing a set of features with high information gain for anomaly detection in MW-PPG signals in the classification context, (b) assessing the impact of window size and evaluating various lightweight ML models to achieve highly accurate anomaly detection, and (c) examining the effectiveness of MW-PPG signals in detecting artifacts.
Original languageEnglish
Article number6947
Pages (from-to)1-23
Number of pages23
Issue number15
Publication statusPublished - 4 Aug 2023

Bibliographical note

Funding Information:
This work has been possible thanks to the Post-doc VUB Global Minds 2021 call approved as VLIR396. This work has been partially supported by the Vrije Universiteit Brussel (VUB) through the SRP-Projects M3D2 and LSDS, the ETRO-IOF Project under Grant IOF3016; partially by the FWO-Flanders FWOSB106 PhD grant. Finally, this work has been also partially supported by the Belgian Development Cooperation through VLIR-UOS (Flemish Interuniversity Council-University Cooperation for Development) in the context of the Institutional University Cooperation program (IUC 2019 Phase 2 UO) with the Universidad de Oriente (Cuba).

Publisher Copyright:
© 2023 by the authors.


  • photoplethysmography
  • PPG
  • artifact
  • anomaly detection
  • multi-wavelength PPG
  • time series
  • machine learning
  • neural networks
  • supervised learning
  • unsupervised learning


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