Intracavitary Electrocardiogram Guidance Aids Excavation of Rhythm Abnormalities in Patients with Occult Heart Disease

In this paper, the analysis of intracavitary electrocardiograms is used to guide the mining of abnormal cardiac rhythms in patients with hidden heart disease, and the algorithm is improved to address the data imbalance problem existing in the abnormal electrocardiogram signals, and a weight-based automatic classification algorithm for deep convolutional neural network electrocardiogram signals is proposed. By preprocessing the electrocardiogram data from the MIT-BIH arrhythmia database, the experimental dataset training algorithm model is obtained, and the algorithm model is migrated into the project. In terms of system design and implementation, by comparing the advantages and disadvantages of the electrocardiogram monitoring system platform, the overall design of the system was carried out in terms of functional and performance requirements according to the system realization goal, and a mobile platform system capable of classifying common abnormal electrocardiogram signals was developed. The system is capable of long-term monitoring and can invoke the automatic classification algorithm model of electrocardiogram signals for analysis. In this paper, the functional logic test and performance test were conducted on the main functional modules of the system. The test results show that the system can run stably and monitor electrocardiogram signals for a long time and can correctly call the deep convolutional neural network-based automatic electrocardiogram signal classification algorithm to analyze the electrocardiogram signals and achieve the requirements of displaying the electrocardiogram signal waveform, analyzing the heartbeat type, and calculating the average heart rate, which achieves the goal of real-time continuous monitoring and analysis of the electrocardiogram signals.

A Two Channels Wireless Electrocardiograph System Using Bluetooth Communication

Health problems with cardiovascular system disorders are still ranked high, according to data from the WHO reported that there are about 31% of causes of death globally are cardiovascular diseases. The purpose of this study was to develop a 12 lead electrocardiograph with 2 displays and the HC-05 as a real-time transmitter of heart signal data. The electrocardiogram signal is obtained from the wiretapping by attaching the electrode cable to the Lead I, Lead II, Lead III, aVR, aVL, and aVF leads, then processed on IC AD620, HPF and LPF filters and non-inverting amplifiers and then processed using Arduino UNO for further display. in the form of a signal on the Delphi 7 application. The research method is to measure the heart signal on the ECG Simulator, by testing several BPMs, namely 30, 60, 120 and 240 on each lead. After testing the signal equation at the 0.5mV setting by calculating the error rate, the highest error value is obtained in lead I, lead aVL and aVF of 7.14% and the smallest error is 3.57% error in lead III. Then at the 1mV setting by calculating the error rate, the highest error value in lead aVL is 7.14% and the smallest error is 2.36%. at the 2mV setting by calculating the error rate, the highest error value is obtained in leads aVL and aVF of 5.71% and the smallest error is obtained by an error of 2.1% in lead II. the results of this study are implemented so that in the future an ECG examination can be carried out and then monitored remotely like a doctor's room because the data communication uses bluetooth.