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Abstract
In recent years, with the rapid and healthy economic development, people's
material living standards have greatly improved, unhealthy lifestyles have become
increasingly prominent, coupled with the impact of population aging and other issues,
the prevalence of cardiovascular diseases has continued to rise. Among them, heart
failure is the severe terminal stage of various chronic heart diseases. Compared with
other cardiovascular diseases, it has a higher risk. The hospitalization rate and
mortality rate remain high. It is one of the diseases that needs the most attention and
attention. One. At present, the diagnosis of heart failure mainly relies on
comprehensive judgments such as physical examination, heart-related imaging
examinations and other auxiliary examinations. However, due to the high price, the
complicated detection process, the inconvenience of operation, the influence of the
observer's experience, and the invasiveness, it is not conducive to promotion and daily
use. The technology of non-invasive automatic diagnosis of diseases based on
physiological signal detection and analysis is one of the main research topics in the
field of biomedical engineering. For the early diagnosis of heart failure, it is possible
to study the electrophysiological characteristics of the heart in different states,
especially through body surface detection, non-invasively obtain physiological signals
and body surface characteristics that can reflect changes in heart disease, with the help
of fast-developing computer-aided diagnosis technology , And combined with
existing artificial intelligence methods to establish an automatic diagnosis model of
heart failure to assist doctors in clinical diagnosis. In this study, three physiological
parameters of ECG signal, heart sound signal and bioimpedance signal were selected
for analysis. Because traditional detection methods and measuring instruments are
mostly used to complete specific signal detection tasks and can only achieve a single
physiological signal acquisition function, this study also designed and produced a
corresponding multi-physiological signal acquisition system that can collect three
signals at the same time. It is convenient for subsequent measurement and analysis.
First of all, the relevant algorithms of ECG signals in the diagnosis of heart
failure are discussed. Comprehensive selection of multiple characteristic parameters
based on HRV analysis and calculation of heart rate variability, and machine learning
classification method for heart failure detection. Specifically, HRV analysis is
performed on the pre-processed ECG signal, and multiple relevant indicators are