A software platform for AI-ECG algorithm research is designed and implemented to better serve the research of ECG artificial intelligence classification algorithm and to solve the problem of subjects data information management. Matlab R2019b and MySQL Sever 8.0 are used to design the software platform. The software platform is divided into three modules including data management module, data receiving module and data processing module. The software platform can be used to query and set the subjects information. It has realized the functions of data receiving, signal processing and the display, analysis and storage of ECG data. The software platform is easy to operate and meets the basic needs of scientific research. It is of great significance to the research of AI-ECG algorithm.
Algorithms ; Artificial Intelligence ; Electrocardiography ; Humans ; Signal Processing, Computer-Assisted ; Software

Atrial fibrillation (AF) is the most common arrhythmia in clinic, which can cause hemodynamic changes, heart failure and stroke, and seriously affect human life and health. As a self-promoting disease, the treatment of AF can become more and more difficult with the deterioration of the disease, and the early prediction and intervention of AF is the key to curbing the deterioration of the disease. Based on this, in this study, by controlling the dose of acetylcholine, we changed the AF vulnerability of five mongrel dogs and tried to assess it by analyzing the electrophysiology of atrial epicardium under different states of sinus rhythm. Here, indices from four aspects were proposed to study the atrial activation rule. They are the variability of atrial activation rhythm, the change of the earliest atrial activation, the change of atrial activation delay and the left-right atrial dyssynchrony. By using binary logistic regression analysis, multiple indices above were transformed into the AF inducibility, which were used to classify the signals during sinus rhythm. The sensitivity, specificity and accuracy of classification reached 85.7%, 95.8% and 91.7%, respectively. As the experimental results show, the proposed method has the ability to assess the AF vulnerability of atrium, which is of great clinical significance for the early prediction and intervention of AF.

Atrial fibrillation (AF) is one of the most common arrhythmias, which does great harm to patients. Effective methods were urgently required to prevent the recurrence of AF. Four methods were used to analyze RR sequence in this paper, and differences between Pre-AF (preceding an episode of AF) and Normal period (far away from episodes of AF) were analyzed to find discriminative criterion. These methods are: power spectral analysis, approximate entropy (ApEn) and sample entropy (SpEn) analysis, recurrence analysis and time series symbolization. The RR sequence data used in this research were downloaded from the Paroxysmal Atrial Fibrillation Prediction Database. Supporting vector machine (SVM) classification was used to evaluate the methods by calculating sensitivity, specificity and accuracy rate. The results showed that the comprehensive utilization of recurrence analysis parameters reached the highest accuracy rate (95%); power spectrum analysis took second place (90%); while the results of entropy analyses and time sequence symbolization were not satisfactory, whose accuracy were both only 70%. In conclusion, the recurrence analysis and power spectrum could be adopted to evaluate the atrial chaotic state effectively, thus having certain reference value for prediction of AF recurrence.
Atrial Fibrillation ; diagnosis ; Entropy ; Heart Atria ; physiopathology ; Humans ; Recurrence ; Sensitivity and Specificity ; Support Vector Machine

The study of atrial fibrillation (AF) has been known as a hot topic of clinical concern. Body surface potential mapping (BSPM), a noninvasive electrical mapping technology, has been widely used in the study of AF. This study adopted 10 AF patients' preoperative and postoperative BSPM data (each patient's data contained 128 channels), and applied the autocorrelation function method to obtain the activation interval of the BSPM signals. The activation interval results were compared with that of manual counting method and the applicability of the autocorrelation function method was verified. Furthermore, we compared the autocorrelation function method with the commonly used fast Fourier transform (FFT) method. It was found that the autocorrelation function method was more accurate. Finally, to find a simple rule to predict the recurrence of atrial fibrillation, the autocorrelation function method was used to analyze the preoperative BSPM signals of 10 patients with persistent AF. Consequently, we found that if the patient's proportion of channels with dominant frequency larger than 2.5 Hz in the anterior left region is greater than the other three regions (the anterior right region, the posterior left region, and the posterior right region), he or she might have a higher possibility of AF recurrence. This study verified the rationality of the autocorrelation function method for rhythm analysis and concluded a simple rule of AF recurrence prediction based on this method.

If heart function is normal, the atrial cells are excited in a stable rhythm. But this would change during atrial fibrillation. In this paper, after comparing with the method of characteristic point, we use the dominant frequency method to analyze the activation pattern under sinus and atrial fibrillation rhythm in different parts of atria based on epicardial mapping system. It is found that the activation rhythm changes a lot in different parts of atria, and the automaticity of atrial cells change obviously in somewhere. The result shows that dominant frequency method is very suitable for the analysis of atrial fibrillation signal. What's more, we also roughly discuss the role of this method in exploring the driving sources during atrial fibrillation.
Atrial Fibrillation ; Epicardial Mapping ; Heart Atria ; Humans

If heart function is normal, the atrial cel s are excited in a stable rhythm. But this would change during atrial fibril ation. In this paper, after comparing with the method of characteristic point, we use the dominant frequency method to analyze the activation pattern under sinus and atrial fibril ation rhythm in different parts of atria based on epicardial mapping system. It is found that the activation rhythm changes a lot in different parts of atria, and the automaticity of atrial cel s change obviously in somewhere. The result shows that dominant frequency method is very suitable for the analysis of atrial fibril ation signal. What's more, we also roughly discuss the role of this method in exploring the driving sources during atrial fibril ation.

Image segmentation is a key step for image processing. This study developed an improved region growing algorithm to extract the outline of the heart for 3D-modeling which based on the acquisition of canine cardiac CT images from animal experiment. In this paper the method was also compared with the classic algorithm of threshold segmentation. The result showed that the method can be used for the 3D display technology of cardiac electrical activity in clinical electrophysiology mapping.
Algorithms ; Animals ; Dogs ; Electrophysiology ; Image Processing, Computer-Assisted ; Imaging, Three-Dimensional ; Models, Anatomic ; Models, Cardiovascular ; Tomography, X-Ray Computed

This paper applied the Shannon Entropy based on the cross correlation to analyze the epicardial signals from anterior wall of the canine atria. The result demonstrated that during sinus rhythm, the stability level of the correlation among signals from anterior right atria is much higher than the signals from anterior left atria. All the signals from the anterior wall descended when the rhythm changed from sinus rhythm to atrial fibrillation(AF). However, there were some regions still having a stable correlation during AF. The results will be helpful to enhance understanding of the correlation characteristic of AF.
Animals ; Atrial Fibrillation ; physiopathology ; Dogs ; Entropy ; Heart Atria ; physiopathology ; Pericardium ; physiopathology

This paper applied the Shannon Entropy based on the cross correlation to analyze the epicardial signals from anterior wal of the canine atria. The result demonstrated that during sinus rhythm, the stability level of the correlation among signals from anterior right atria is much higher than the signals from anterior left atria. Al the signals from the anterior wal descended when the rhythm changed from sinus rhythm to atrial fibril ation(AF). However, there were some regions stil having a stable correlation during AF. The results wil be helpful to enhance understanding of the correlation characteristic of AF.