Trends in voice characteristics in patients with heart failure
Congestive Heart Failure (CHF) is characterised by the heart’s incapacity to pump sufficient blood to meet the body’s metabolic needs. It afflicts over 64 million people worldwide and is increasing in prevalence. The high hospital readmission rates do not only pose a tremendous burden on patient’s health status, morbidity, and mortality but also significantly increase national healthcare costs. In fact, two-thirds of HF hospitalisations can be prevented according to the leading experts. In order to reduce hospitalisations, traditional self-management require patients to log their weight and blood pressure daily in written plans of action that cover how to recognise and respond to a decompensation. However, the self-management require high adherence and active engagement from the patients and they are lack of predictive power.
Due to the decreased cardiac output or increased blood pressure, fluid can accumulate in the entire body of CHF patients. Thus, we hypothesise that the vocal folds might be particularly sensitive to fluid accumulation in contrast to weight changes that may not be detected in time to prevent hospitalisation due to decompensation. For the current project, we aim to collect voice samples with mobile devices and using machine learning methods to investigate how voice characteristics fluctuations are associated with the health status changes of HF patients. Integrating passive voice analysis into patient self-care has the potential to reduce the burden on the patient while providing objective information with clinical valuable accuracy. We contribute towards the development of a novel digital prognostic vocal biomarker for CHF patients.
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