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- W2743235446 abstract "Background: Remote monitoring of heart failure (HF) patients (pts) using wearable devices may allow for personalized titration of care, and thereby potentially reduce hospitalizations. Objective: We propose a robust graph-theory-based technique to assess cardio-mechanical changes in response to activity as a physiological biomarker of pts' clinical state. Method and Results: We measured electrocardiogram (ECG) and seismocardiogram (SCG) signals using a custom wearable patch placed on the mid-sternum before and after pts performed a six-minute walk test (6MWT). Forty-one pts (29 compensated [outpatient] and 12 decompensated [hospitalized]) were recruited and participated; six of the decompensated pts participated twice each, once when admitted (decompensated), and once when discharged (compensated). We quantified the effects of this controlled “dosage” of exercise on the mechanical aspects of ventricular function (captured by the spectral domain structure of the SCG signal) by applying a graph-mining technique called the Graph Similarity Score (GSS). GSS quantifies, in high-dimensional space, the similarity between two graphs—derived from the signals themselves. Compared to conventional approaches, GSS is robust to motion, respiration, and other artifacts that typically present challenges in analyzing non-invasive physiological measurements. A significant difference was found cross-sectionally between the groups in GSS (44.2 ± 5.2 [Decompensated] vs. 35.2 ± 10.6 [Compensated], P < .005) (Fig. 1c) and longitudinally among the five decompensated pts (44 ± 4.1 [Admitted] vs. 35 ± 3.9 [Discharged], P < .05) (Fig. 1d). Conclusions: The calculated GSS metric between rest and exercise recovery segments of the recorded SCG can accurately classify decompensated and compensated HF pts. The proposed method is robust to noise and day to day variability and can be used as an analytic approach to assess risk of HF related exacerbations for pts at home." @default.
- W2743235446 created "2017-08-17" @default.
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- W2743235446 date "2017-08-01" @default.
- W2743235446 modified "2023-10-18" @default.
- W2743235446 title "Quantifying the Accuracy of Heart Failure Decompensation Classification Using Wearable Seismocardiography and Graph Mining Algorithms" @default.
- W2743235446 doi "https://doi.org/10.1016/j.cardfail.2017.07.369" @default.
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