Key result
Wearable seismocardiography and machine learning assess HF compensation states by analyzing submaximal exercise responses.
Why the study?
Can wearable seismocardiography and machine learning algorithms assess compensated and decompensated states in patients with heart failure?
Observational
Can wearable seismocardiography and machine learning algorithms assess compensated and decompensated states in patients with heart failure?
Answers are grounded in this paper and cite their sources.
Wearable seismocardiography combined with machine learning can assess compensated and decompensated heart failure states during submaximal exercise.
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 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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Inan et al. (2018) conducted an observational in Heart failure. Wearable seismocardiography and machine learning algorithms was evaluated on Assessment of compensated and decompensated HF states (p=<0.05). Wearable seismocardiography and machine learning algorithms can assess compensated and decompensated heart failure states by analyzing cardiac response to submaximal exercise (P<0.05).
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