Towards Automatic and Fast Annotation of Seismocardiogram Signals Using Machine Learning

Hiren Kumar Thakkar, Prasan Kumar Sahoo*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

25 Scopus citations

Abstract

The automatic annotation of Seismocardiogram (SCG) potentially aid to estimate various cardiac health parameters continuously. However, the inter-subject variability of SCG poses great difficulties to automate its accurate annotation. The objective of the research is to design SCG peak retrieval methods on the top of the ensemble features extracted from the SCG morphology for the automatic annotation of SCG signals. The annotation scheme is formulated as a binary classification problem. Three binary classifiers such as Naïve Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) are employed for the annotation and the results are compared with the recent state-of-the-art schemes. The performance evaluation is carried out using 9000 SCG signals of 20 presumably healthy volunteers with no known serious cardiac abnormalities. The SCG signals are acquired from the Physionet public repository 'cebsdb'. The models are rigorously validated using metrics 'Precision', 'Recall', and 'F-measure' followed by 5-fold cross-validation. The experimental validation with recent state-of-the-art solutions establishes the robustness of the proposed NB, SVM and LR with average annotation accuracy of 0.86, 0.925 and 0.935, respectively. The mean response time of proposed models is in the fraction of 1 10 sec, which establishes its application for the real-time annotation.

Original languageEnglish
Article number8889690
Pages (from-to)2578-2589
Number of pages12
JournalIEEE Sensors Journal
Volume20
Issue number5
DOIs
StatePublished - 01 03 2020

Bibliographical note

Publisher Copyright:
© 2001-2012 IEEE.

Keywords

  • Seismocardiogram (SCG)
  • automatic annotation
  • cardiac health parameters (CHPs)
  • machine learning
  • non-invasive

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