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Heart Failure Readmission Prediction Using Seismocardiogram Signal 
  • +4
  • Rajkumar Dhar,
  • Rakib Hossen,
  • Peshala T Gamage,
  • Richard H Sandler,
  • Nirav Y Raval,
  • Robert J Mentz,
  • Hansen A Mansy
Rajkumar Dhar

Corresponding Author:[email protected]

Author Profile
Rakib Hossen
Peshala T Gamage
Richard H Sandler
Nirav Y Raval
Robert J Mentz
Hansen A Mansy


Heart failure (HF) is considered a global pandemic because of increasing prevalence, high mortality rate, frequent hospitalization and associated economic burden. This study explores a noninvasive method that may help in managing HF patients by predicting HF readmission. Methods: Seismocardiogram (SCG) signal is the low frequency chest vibration produced by the mechanical activity of heart. SCG signal was acquired from 101 patients with HF including in those readmitted to the hospital during the study period. Features were extracted from SCG signals. Several conventional machine learning (ML) models were developed using selected SCG and heart rate variability features. Furthermore, SCG signals were transformed into images using a time-frequency distribution method. Images were used to train a deep learning model. The models were able to predict the readmission status of the HF patients. Results: ML algorithms achieved higher accuracy than the deep learning model in classifying the readmitted and non-readmitted HF patients. K-nearest neighbor (KNN) achieved the highest classification accuracy (89.4% accuracy, 87.8% sensitivity, and 90.1% specificity). The study results suggest that SCG signal may be useful for readmission prediction of HF patients. Significance: Use of SCG signal may help the management of HF patients and improve their quality of life.
04 Mar 2024Submitted to TechRxiv
06 Mar 2024Published in TechRxiv