ABSTRACT
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.
ABSTRACT
It's necessary to interrupt cardiopulmonary resuscitation (CPR) for a reliable automatic external defibrillator (AED) rhythm analysis, because the mechanical activity from the chest compressions introduces artifacts in the electrocardiogram (ECG) that substantially lower the capacity of an AED to judge cardio-electric rhythm. However, repeated interruptions of compression will reduce the quality of CPR, which in turn affect the prognosis of patients with cardiac arrest (CA). In order to improve the quality of CPR, reduce the interruptions of chest compression and implement accurate defibrillation, people have made many efforts on identifying ECG rhythm in CPR. The studies can be grouped into two broad categories: those based on the artificial mixture of ECG data and CPR artifacts and those based on CA data recorded during CPR. This article introduced researches for rhythm recognition in CPR, including sources and characteristics of CPR artifacts, methods of rhythm analysis, and provided a basis for the study of how to improve the accuracy of cardio-electric rhythm recognition.
ABSTRACT
It's necessary to interrupt cardiopulmonary resuscitation (CPR) for a reliable automatic external defibrillator (AED) rhythm analysis, because the mechanical activity from the chest compressions introduces artifacts in the electrocardiogram (ECG) that substantially lower the capacity of an AED to judge cardio-electric rhythm. However, repeated interruptions of compression will reduce the quality of CPR, which in turn affect the prognosis of patients with cardiac arrest (CA). In order to improve the quality of CPR, reduce the interruptions of chest compression and implement accurate defibrillation, people have made many efforts on identifying ECG rhythm in CPR. The studies can be grouped into two broad categories: those based on the artificial mixture of ECG data and CPR artifacts and those based on CA data recorded during CPR. This article introduced researches for rhythm recognition in CPR, including sources and characteristics of CPR artifacts, methods of rhythm analysis, and provided a basis for the study of how to improve the accuracy of cardio-electric rhythm recognition.