Electrocardiosignal artificial intelligence processing circuit based on cardiac beat differential coding

Abstract

The invention discloses an electrocardiosignal artificial intelligence processing circuit based on cardiac beat differential coding, and belongs to the technical field of electrocardiosignal processing. According to the scheme, a preprocessing module is connected with a differential coding module through a cardiac beat memory, the differential coding module is sequentially connected with an activation module and a neural network classifier, and a differential network memory and a non-differential network memory are connected with the neural network classifier and are connected with a cardiac beat template memory through a template updating module; and the heart beat template memory is also connected with the differential coding module. Dynamic wakeup of the positive and abnormal cardiac beats is realized by using the waveform features of the differential cardiac beats and the adaptive threshold, so that the starting times of the artificial intelligence classifier are reduced, and the power consumption of the whole system is finally reduced. According to the method, the patient heart beat template is generated by using the historical classification result of the artificial intelligence classifier, the generalization accuracy of model processing is improved, the patient heart beat does not need to be additionally marked in the whole process, and the patient self-specificity can be learned in real time.

Description

technical field [0001] The invention belongs to the technical field of electrocardiographic signal processing, and in particular relates to an artificial intelligence processing circuit for electrocardiographic signals based on heart beat differential coding. Background technique [0002] According to the 2019 Global Health Estimates Report released by the World Health Organization (WHO), cardiovascular disease (CVD) is the leading cause of death worldwide. In order to accurately detect CVD patients, electrocardiogram (ECG) and professional physicians' manual analysis of arrhythmia are generally used in medical treatment, but such solutions are difficult to use in the field of home medical equipment because of heavy equipment and high labor costs . In recent years, the field of home medical care has become more and more popular with the advent of the intelligent era. In terms of ECG heart health monitoring, daily ECG monitoring equipment with intelligent analysis capabiliti...

AI Technical Summary

Problems solved by technology

Generally speaking, these five requirements often restrict each other, and it is difficult to meet the above requirements at the same time with the design directly based on the existing hardware. ASIC chips for physiological signal recognition are pursuing lower power consumption levels to meet long battery life requirements

[0004] In addition, applying existing technical achievements to actual daily heart health monitoring equipment also needs to solve the bottleneck of generalization accuracy: in the field of ECG monitoring, due to the differences in physiological structure between patients, each person's cardiac cycle activity There are inherent differences, so that the ECG waveforms that record cardiac cycle activity are patient-specific. In addition, due to differences in the position of cardiac electrodes or age changes, the patient's own ECG records also have patient-specificity that changes over time. sex

However, for arrhythmia identification, the artificial intelligence classifier can only access the ECG records of patients in the database for a period of time during the learning phase, so the current artificial intelligence classifiers often show extremely high recognition accuracy on the database. However, for patients outside the database, the generalization accuracy may drop significantly.

Some existing solutions such as online learning also have some inherent problems: 1) They all need to collect a part of the ECG records of the actual user (patient), and then mark the classification label of each heartbeat on the ECG waveform by a professional physician; 2) It is impossible to learn the latest heartbeat characteristics (ie ECG characteristics) of the patient in real time, and the accuracy rate may decrease as the user wears it again in the daily use of the user

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