Heart failure early detection method

Abstract

The invention discloses a heart failure early detection method, and aims to introduce big data and an artificial intelligence technology into the field of medical health, discover various diseases andsyndromes, mine valuable information and provide a systematic decision for heart failure diagnosis and treatment. The method is characterized in that daily physiological data of a patient is collected through a cloud platform, and electrocardio data association relation analysis which describes massive medical data characteristics in a unified mode and meets complex semantics and a physiologicalindex similarity evaluation strategy based on a time sequence are provided, so that early detection and early warning of heart failure are carried out. According to the method, physiological data of electrocardio monitoring such as blood pressure, respiratory rate, heart rate and weight collected from 132 patients (47 decompensated events and 85 normal events) are used to verify that the proposedscheme is particularly suitable for detecting early heart failure decompensation, so that efficient, intelligent and personalized services are provided for users. The method is suitable for early detection of heart failure, so that the development of heart failure can be effectively delayed by changing the life style, conducting drug intervention and the like.

Description

technical field [0001] The invention belongs to the field of new generation information technology, and relates to the copyright protection of big data and artificial intelligence applied to medical care and health. Background technique [0002] The prevalence of heart failure (HF) is increasing year by year, and it is one of the most costly diseases in health insurance. There are approximately 5.7 million heart failure patients in the United States, approximately 825,000 new cases per year, and an annual cost of approximately $33 billion. 45% of all deaths in Europe are from cardiovascular diseases, and more than 20% of European citizens suffer from chronic cardiovascular diseases such as myocardial infarction, cardiac arrhythmias and heart failure. There are more than 30 million heart failure patients in China, and the mortality rate within 5 years after diagnosis is as high as 50%. On average, about 1.5 million people die of heart failure every year. It can be seen that...

AI Technical Summary

Problems solved by technology

[0003] Existing methods such as discrete Fourier transform and singular value decomposition are limited to a single condition, up to two, and the complexity of the modeling system increases dramatically when a large number of conditions are included

In fact, heart failure is often accompanied and caused by multiple complications, which makes these modeling methods full of errors; in addition, the existing methods do not consider the high dimensionality of time series and lack real-time processing capabilities

Massive medical data has the characteristics of real-time high-frequency, multi-source heterogeneity, complex relationship, and random personality. All these factors make it very difficult to detect ECG abnormalities in time and accurately issue early warning signals.

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