Cited by 207Non-Invasive Vital Sign MonitoringHeart Failure Treatment and ManagementECG Monitoring and Analysis

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

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Inan, Omer T., Baran Pouyan, Maziyar, Javaid, Abdul Q., Dowling, Sean, Etemadi, Mozziyar, Dorier, Alexis, Heller, J. Alex, Bicen, A. Ozan, Roy, Shuvo, De Marco, Teresa, Klein, Liviu·2018·Circulation: Heart Failure

DOI 10.1161/circheartfailure.117.004313PMC PMC5769154

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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.

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Authors: 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.
Affiliations: Northwestern University (US)
From the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).
ORCID: Inan, Omer T.
Javaid, Abdul Q.
Dowling, Sean
Etemadi, Mozziyar
Dorier, Alexis
Heller, J. Alex
Bicen, A. Ozan
Roy, Shuvo
Klein, Liviu
Venue: Circulation: Heart Failure
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2018-01
Volume: 11
Issue: 1
Pages: e004313-e004313
ISSN: 1941-3289 (ISSN-L)
1941-3297
DOI: 10.1161/circheartfailure.117.004313
PMC: PMC5769154
OpenAlex: W2783795641
Cited by: 207
References: 33
Citations by year: 2026: 8 · 2025: 23 · 2024: 26 · 2023: 21 · 2022: 36 · 2021: 29 · 2020: 39 · 2019: 18 · 2018: 7
FWCI: 9.30
Type: journal article
Language: en
Open access: green
europepmc.org
Locations: Circulation Heart Failure (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: National Institute on Aging (R56AG048458)
National Heart, Lung, and Blood Institute (R01HL130619)
National Institute of General Medical Sciences (T32GM007618)
Topics: Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
Keywords: heart failure, seismocardiography, wearable sensors, remote patient monitoring, acute decompensated heart failure, submaximal exercise, 6-minute walk test, cardiac function, machine learning
MeSH terms: Machine Learning, Wearable Electronic Devices, Adult, Aged, Algorithms, Electrocardiography, Equipment Design, Female, Heart Failure, Humans, Kinetocardiography, Male, Middle Aged, Exercise

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

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