Ежков Александр Викторович (RU),Бекмачев Александр Егорович (RU),Садовский Сергей Павлович (RU),Сунцова Ольга Валерьевна (RU)
申请号：
RU2016146176
公开号：
RU2016146176A
申请日:
2016.11.24
申请国别(地区):
RU
年份:
2018
代理人:
摘要：
The present invention relates to the field of computer science in medicine, and more particularly to methods and systems for screening for different pathologies and determining a person's physiological parameters, and can be used in the field of predictive, diagnostic, preventive and rehabilitative medicine. A method for the screening of pathologies or physiological parameters involves: generating a training set and a test set of patient records pertaining to patients with a specific pathology or having physiological parameters that are dependent on the patients' cardiac function, including records pertaining to patients of different sexes and ages, wherein each record contains at least one cardiac lead ECG signal and patient information； obtaining records from the training set, wherein for each record the at least one cardiac lead ECG signal is processed, and heart rate variability and average cardiac cycle parameters are calculated； training an artificial neural network to identify the specific pathology or physical parameters using the records of the training and test sets by correlating the parameters of the processed ECG signal, the calculated heart rate variability and average cardiac cycle parameters, and the patient information； recording the connections and weights of the trained artificial neural network； obtaining at least one cardiac lead ECG signal and information about a patient； processing the obtained at least one cardiac lead ECG signal, and calculating the heart rate variability and average cardiac cycle parameters； and determining the physiological parameters or the presence of the specific pathology with the aid of the trained neural network, using the parameters of the processed ECG signal, the calculated heart rate variability and average cardiac cycle parameters, and the patient information. The technical result is an increase in the accuracy with which pathologies or physiological parameter values are identified in a patient on the basis of n
