Abstract
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 language | English (US) |
|---|---|
| Article number | 8801932 |
| Pages (from-to) | 1303-1313 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 67 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 2020 |
Funding
Manuscript received July 5, 2019; revised August 6, 2019; accepted August 7, 2019. Date of publication August 15, 2019; date of current version April 21, 2020. This work was supported by the National Heart, Lung and Blood Institute under Award R01HL130619. (Corresponding author: Varol Burak Aydemir.) V. B. Aydemir is with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA (e-mail:, [email protected]).
Keywords
- Ballistocardiography
- heart failure
- machine learning
ASJC Scopus subject areas
- Biomedical Engineering