Classification of Decompensated Heart Failure from Clinical and Home Ballistocardiography

Varol Burak Aydemir*, Supriya Nagesh, Md Mobashir Hasan Shandhi, Joanna Fan, Liviu Klein, Mozziyar Etemadi, James Alex Heller, Omer T. Inan, James M. Rehg

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Objective: To improve home monitoring of heart failure patients so as to reduce emergency room visits and hospital readmissions. We aim to do this by analyzing the ballistocardiogram (BCG) to evaluate the clinical state of the patient. Methods: 1) High quality BCG signals were collected at home from HF patients after discharge. 2) The BCG recordings were preprocessed to exclude outliers and artifacts. 3) Parameters of the BCG that contain information about the cardiovascular system were extracted. These features were used for the task of classification of the BCG recording based on the status of HF. Results: The best AUC score for the task of classification obtained was 0.78 using slight variant of the leave one subject out validation method. Conclusion: This work demonstrates that high quality BCG signals can be collected in a home environment and used to detect the clinical state of HF patients. Significance: In future work, a clinician/caregiver can be introduced into the system so that appropriate interventions can be performed based on the clinical state monitored at home.

Original languageEnglish (US)
Article number8801932
Pages (from-to)1303-1313
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Issue number5
StatePublished - May 2020


  • Ballistocardiography
  • heart failure
  • machine learning

ASJC Scopus subject areas

  • Biomedical Engineering


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