Abstract
Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on the hidden and sudden nature of the MIT-BIH ECG database signal and the small-signal amplitude, this paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data, to learn the deep-seated essential features of the target data, combine the characteristics of the information processing mechanism of the arrhythmia online automatic diagnosis system, and automatically extract the spatial features and temporal characteristics of the diagnostic data. First, a combination of median filter and bandstop filter is used to preprocess the data in the ECG database with individual differences in ECG waveforms, and there are problems of feature inaccuracy and useful feature omission which cannot effectively extract the features implied behind the massive ECG signals. Its diagnostic algorithm integrates feature extraction and classification into one, which avoids some bias in the feature extraction process and provides a new idea for the automatic diagnosis of cardiovascular diseases. To address the problem of feature importance variability in the temporal data of the MIT-BIH ECG database, a hybrid model is constructed by introducing algorithms in deep neural networks, which can enhance its diagnostic efficiency.
Highlights
Cardiac diseases are the deadliest chronic diseases and are characterized by high morbidity, disability, and mortality
Conducting research on data-driven and deep learning-based intelligent classification and identification of cardiac arrhythmias and proposing convenient and feasible methods for self-diagnosis of cardiovascular diseases are of great practical significance for economic and social development and people’s health [4]
ECG automatic diagnosis technology can reduce the labor intensity of ECG specialists, eliminate misdiagnosis and misdiagnosis caused by subjective factors of medical personnel, and improve monitoring and diagnosis. e ECG automatic diagnosis system can be embedded into wearable devices to provide long-term real-time monitoring of cardiac conditions, enhance prevention and timely detect heartbeat abnormalities, buy valuable time for further treatment of patients, provide favorable conditions, reduce medical costs, and ease the burden on patients, increasing the cure rate of cardiovascular diseases. erefore, the research of ECG automatic diagnosis technology has an important role in promoting the progress of medicine and is of great significance in solving the current problems in ECG diagnosis
Summary
Cardiac diseases are the deadliest chronic diseases and are characterized by high morbidity, disability, and mortality. E basis of automatic analysis technology of ECG data is to effectively extract features and use a priori knowledge or machine learning methods to classify and diagnose. Conducting research on data-driven and deep learning-based intelligent classification and identification of cardiac arrhythmias and proposing convenient and feasible methods for self-diagnosis of cardiovascular diseases are of great practical significance for economic and social development and people’s health [4]. E ECG automatic diagnosis system can be embedded into wearable devices (bracelets, medical vests, etc.) to provide long-term real-time monitoring of cardiac conditions, enhance prevention and timely detect heartbeat abnormalities, buy valuable time for further treatment of patients, provide favorable conditions, reduce medical costs, and ease the burden on patients, increasing the cure rate of cardiovascular diseases. ECG automatic diagnosis technology can reduce the labor intensity of ECG specialists, eliminate misdiagnosis and misdiagnosis caused by subjective factors of medical personnel, and improve monitoring and diagnosis. e ECG automatic diagnosis system can be embedded into wearable devices (bracelets, medical vests, etc.) to provide long-term real-time monitoring of cardiac conditions, enhance prevention and timely detect heartbeat abnormalities, buy valuable time for further treatment of patients, provide favorable conditions, reduce medical costs, and ease the burden on patients, increasing the cure rate of cardiovascular diseases. erefore, the research of ECG automatic diagnosis technology has an important role in promoting the progress of medicine and is of great significance in solving the current problems in ECG diagnosis
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