Design And Implementation Of Remote Myocardial Ischemia Diagnosis System Based On Hadoop And Deep Learning

At present,myocardial ischemic diseases,whose incidence is increasing year by year and showing a trend of youthfulness,have seriously affected national health.Therefore,it is of great significance to detect myocardial ischemia at the early stage of the disease.In clinical,electrocardiogram is often used to diagnose the condition of myocardial infarction.However,this method is inefficient and its accuracy depends on the clinical experience.With the advance of information science and technology,most of the existing researches are based on transforming electrocardiogram signal analysis into static pattern modeling and recognition problem.The electrocardiogram signal,generated by a complex nonlinear dynamic system,is essentially a non-stationary signal.So only extracting features from time-frequency domains,it is difficult to fully describe dynamic change characteristics.Deterministic learning theory breaks through the limitations of the existing methods to extract only the time-frequency characteristics or statistical characteristics of the signal.Under the condition of satisfying persistent excitation,the radial basis neural network is used to reconstruct locally a mathematical model for the small variability of the ST-T segments in the continuous periodic electrocardiogram.Then the dynamic features are extracted from the modeling results and visualized in three dimensions to obtain the cardiodynamicsgram.After lots of experimental analysis,there exists a clear relationship between cardiodynamicsgram morphology and myocardial ischemia.In order to explore this relationship,we propose a deep learning model combining convolution and long-term memory recurrent neural networks to determine whether myocardial ischemia was positive or negative by classifying the cardiodynamicsgram without extracting features manually.Having conducted clinical trials in cooperative hospitals,we have researched patients with suspected coronary heart disease who underwent coronary angiography,so we can use the coronary angiography results as a standard for testing the accuracy of our experiments.The experimental results show that the accuracy of the model for detecting myocardial ischemia is 89.0%,the sensitivity is 91.7%,and the specificity is 81.5%.Base on a standard 12-lead electrocardiogram,the proposed method’s advantage is non-invasive,convenient and low cost.So it is expected to provide a real-time software tool towards assisting the physician in cardiology departments for the early detection of ischemic heart diseases.Moreover,based on the preliminary work of the laboratory,the architecture of the remote myocardial ischemia diagnosis system was proposed.The functions of each level were analyzed and implemented according to the characteristics of the Hadoop framework.The system consists of three functions.The first one is the sharing and storage of electronic medical record,the second one is the distributed realization of deterministic learning algorithm and achievement of classifying of myocardial ischemia by deep learning,and the third one is creating a web system to assist doctor diagnosis.These lay the foundation for building a myocardial ischemia research data center.Doctors and users can use the various functions of the system throw the web browser so that they can diagnose early myocardial ischemia and manage medical records throw,which achieves a significant improvement of diagnostic efficiency.