• DocumentCode
    140784
  • Title

    Tracking clinical status for heart failure patients using ballistocardiography and electrocardiography signal features

  • Author

    Etemadi, Mozziyar ; Hersek, Sinan ; Tseng, Jocelyn M. ; Rabbani, Naveed ; Heller, J. Alex ; Roy, Sandip ; Klein, Liviu ; Inan, Omer T.

  • Author_Institution
    Dept. of Bioeng. & Therapeutic Sci., Univ. of California, San Francisco, San Francisco, CA, USA
  • fYear
    2014
  • fDate
    26-30 Aug. 2014
  • Firstpage
    5188
  • Lastpage
    5191
  • Abstract
    Heart failure (HF) is an escalating public health problem, with few effective methods for home monitoring. In HF management, the important clinical factors to monitor include symptoms, fluid status, cardiac output, and blood pressure - based on these factors, inotrope and diuretic dosages are adjusted day-by-day to control the disorder and improve the patient´s status towards a successful discharge. Previously, the ballistocardiogram (BCG) measured on a weighing scale has been shown to be capable of detecting changes in cardiac output and contractility for healthy subjects. In this study, we investigated whether the BCG and electrocardiogram (ECG) signals measured on a wireless modified scale could accurately track the clinical status of HF patients during their hospital stay. Using logistic regression, we found that the root-mean-square (RMS) power of the BCG provided a good fit for clinical status, as determined based on clinical measurements and symptoms, for the 85 patient days studied from 10 patients (p <; 0.01). These results provide a promising foundation for future studies aimed at using the BCG / ECG scale at home to track HF patient status remotely.
  • Keywords
    bioelectric potentials; biomedical telemetry; diseases; electrocardiography; feature extraction; mean square error methods; medical disorders; medical signal detection; medical signal processing; patient monitoring; regression analysis; telemedicine; ballistocardiography signal features; blood pressure monitoring; cardiac output monitoring; electrocardiography signal features; fluid status monitoring; heart failure patient management; home monitoring; logistic regression; medical disorder; root-mean-square power; symptom monitoring; wireless modified scale; Biomedical monitoring; Educational institutions; Electrocardiography; Hafnium; Heart; Monitoring; Weight measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
  • Conference_Location
    Chicago, IL
  • ISSN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/EMBC.2014.6944794
  • Filename
    6944794