Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients
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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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The fundamental frequency of crying measured is 410.7 ± 47.9, consistent with published results.
“The fundamental frequency of 410.7 ± 47.9 (Supplementary Fig.24e) is consistent with published results46.”
From Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units (2020) · cites this paper as [46]
The Methods (Pre-Processing and Feature Extraction; page unavailable) discuss frequency components of cardiac seismocardiography signals, not crying. Searches for crying, vocal pitch, and the reported frequency found no passage in our copy addressing whether 410.7 ± 47.9 is consistent with published results. (LLM, conf=0.77)
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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
- Venue
- Circulation: Heart Failure
- Published
- 2018-01
- Volume
- 11
- Issue
- 1
- Pages
- e004313-e004313
- ISSN
- 1941-3289 (ISSN-L)
- 1941-3297
- 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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