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P.-F. Migeotte is with the Department of Cardiology, Universite Libre de Bruxelles 1050, Brussels, Belgium (e-mail: Pierre-Francois.Migeotte@ulb.ac.be).K.-S. Park is with the Department of Biomedical Engineering, Seoul National University, Seoul 110-799, Korea (e-mail: kspark@bmsil.snu.ac.kr).M. Etemadi is with the Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA 94143 USA (e-mail: mozziyar.etemadi@ucsf.edu).K. Tavakolian is with the Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58202 USA (e-mail: kouhyart@gmail.com).R. Casanella is with the Instrumentation, Sensors, and Interfaces Group, Universitat Politecnica de Catalunya, 08034 Barcelona, Spain (e-mail: ramon. casanella@upc.edu).J. Zanetti is with Acceleron Medical Systems, Arkansaw, WI 54721 USA (e-mail: jmzsenior@gmail.com).J. Tank is with the Klinsche Pharmakologie, Medizinische Hochschule Hannover, 30625 Hannover, Germany (e-mail: Tank.Jens@mh-hannover.de).I. Funtova is with the

The ballistocardiogram (BCG) measures the reaction of the body to cardiac ejection forces, and is an effective, non-invasive means of evaluating cardiovascular function. A simple, robust method is presented for acquiring high-quality, repeatable BCG signals from a modified, commercially available scale. The measured BCG waveforms for all subjects qualitatively matched values in the existing literature and physiologic expectations in terms of timing and IJ amplitude. Additionally, the BCG IJ amplitude was shown to be correlated with diastolic filling time for a subject with premature atrial contractions, demonstrating the sensitivity of the apparatus to beat-by-beat hemodynamic changes. The signal-to-noise ratio (SNR) of the BCG was estimated using two methods, and the average SNR over all subjects was greater than 12 for both estimates. The BCG measurement was shown to be repeatable over 50 recordings taken from the same subject over a three week period. This approach could allow patients at home to monitor trends in cardiovascular health.

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.