Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients

Omer T. Inan, Maziyar Baran Pouyan, Abdul Q. Javaid, Sean Dowling, Mozziyar Etemadi, Alexis Dorier, J. Alex Heller, A. Ozan Bicen, Shuvo Roy, Teresa De Marco, Liviu Klein

Research output: Contribution to journalArticle

44 Scopus citations

Abstract

BACKGROUND: Remote monitoring of patients with heart failure (HF) using wearable devices can allow patient-specific adjustments to treatments and thereby potentially reduce hospitalizations. We aimed to assess HF state using wearable measurements of electrical and mechanical aspects of cardiac function in the context of exercise. METHODS AND RESULTS: Patients with compensated (outpatient) and decompensated (hospitalized) HF were fitted with a wearable ECG and seismocardiogram sensing patch. Patients stood at rest for an initial recording, performed a 6-minute walk test, and then stood at rest for 5 minutes of recovery. The protocol was performed at the time of outpatient visit or at 2 time points (admission and discharge) during an HF hospitalization. To assess patient state, we devised a method based on comparing the similarity of the structure of seismocardiogram signals after exercise compared with rest using graph mining (graph similarity score). We found that graph similarity score can assess HF patient state and correlates to clinical improvement in 45 patients (13 decompensated, 32 compensated). A significant difference was found between the groups in the graph similarity score metric (44.4±4.9 [decompensated HF] versus 35.2±10.5 [compensated HF]; P<0.001). In the 6 decompensated patients with longitudinal data, we found a significant change in graph similarity score from admission (decompensated) to discharge (compensated; 44±4.1 [admitted] versus 35±3.9 [discharged]; P<0.05). CONCLUSIONS: Wearable technologies recording cardiac function and machine learning algorithms can assess compensated and decompensated HF states by analyzing cardiac response to submaximal exercise. These techniques can be tested in the future to track the clinical status of outpatients with HF and their response to pharmacological interventions.

Original languageEnglish (US)
Pages (from-to)e004313
JournalCirculation. Heart failure
Volume11
Issue number1
DOIs
StatePublished - Jan 1 2018

Keywords

  • heart failure
  • hospitalization
  • outpatient
  • walk test
  • wearable electronic devices

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Fingerprint Dive into the research topics of 'Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients'. Together they form a unique fingerprint.

  • Cite this

    Inan, O. T., Baran Pouyan, M., Javaid, A. Q., Dowling, S., Etemadi, M., Dorier, A., Heller, J. A., Bicen, A. O., Roy, S., De Marco, T., & Klein, L. (2018). Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients. Circulation. Heart failure, 11(1), e004313. https://doi.org/10.1161/CIRCHEARTFAILURE.117.004313