Heart disease recognition and assessment method

Abstract

The invention relates to signal processing. The invention provides a heart disease recognition and assessment method. The method includes that a system acquires at least one grade of heart sound signals, takes the heart sound signals as training samples, preprocesses the training samples, carries out autocorrelation segmentation to obtain period signals, and calculates Mel-frequency cepstral coefficients of the period signals and a Stockwell transform time-frequency complex matrix; the system acquires the maximum of every plurality of rows in the Stockwell transform time-frequency complex matrix as a characteristic vector, to form a list of corresponding relation about types of heart sound signals, Mel-frequency cepstral coefficients thereof and characteristic vectors; the system acquires heart sound signals to be recognized, preprocesses the heart sound signals to be recognized, carries out autocorrelation segmentation on preprocessed heart sound signals to be recognized, calculates Mel-frequency cepstral coefficients of the heart sound signals to be recognized after autocorrelation segmentation and characteristic vectors of the heart sound signals to be recognized, compares the Mel-frequency cepstral coefficients and the characteristic vectors with the list of corresponding relation to obtain corresponding grades of the heart sound signals to be recognized. The heart disease recognition and assessment method is suitable for assessment of heart states.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to an unsteady periodic signal identification method and heart state evaluation. Background technique [0002] As a vibration signal generated by the mechanical movement of the heart and great blood vessels, heart sound is one of the most important physiological signals of the human body. Before cardiovascular disease develops enough to produce clinical and pathological changes, some important pathological information will appear in heart sounds, which are characteristically reflected in many diseases, which is very important for the diagnosis and estimation of cardiovascular disease. is very meaningful. Therefore, heart sound analysis is an important means of non-invasive detection of cardiovascular diseases, and has become one of the effective methods for clinical auxiliary diagnosis of such diseases. [0003] In the existing technology, most of the classification and ...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, most of the classification and recognition functions of heart sounds only distinguish between normal and abnormal heart sound signals, and seldom carry out more detailed discrimination of disease types for abnormal heart sound signals, and do not estimate and quantify the disease or health degree, so the classification is not yet refined , and the recognition accuracy needs to be further improved, and the quantitative evaluation of disease or health degree is conducive to transplantation to various platforms such as mobile terminals, and the cost is low, suitable for practical application in the field of health and medical electronic equipment

Images