부산대학교 도서관

Background: A new wearable pulse audiogram (PAG) of radial artery was developed with the main purpose to quickly screen atrial fibrillation (AF) and monitor pulse amplitude variation. Methods: Subjects with sinus rhythm (SR), AF, ectopic arrhythmia (EA), and a pacemaker rhythm (PM) were recruited to measure the PAG of radial artery. In total, 91 subjects were recruited: SR ( $n =45$ ), AF ( $n =21$ ), EA ( $n =11$ ), and PM ( $n =14$ ). For signal processing, the inter-pulse interval (IPI) and pulse height (PH) were extracted. Then, an automatic classification algorithm combining fuzzy c-means (FCM) or sample entropy (CEn) with an adaptive-network-based fuzzy inference system was constructed. The PAG data were divided into different segment lengths (10 to 30 beats) to investigate the robustness of the algorithm in short intervals. Furthermore, linear regression was performed to evaluate the relation between the normalized IPI and PH in the AF group. Results: The identification rate of AF increased with the number of beats and decreased with the number of classified types of arrhythmia. Results of combining CEn and FCM, or of FCM alone were better than those of CEn alone. When the combined method was used for the two types of arrhythmia and the number of beats was greater than 10, the rate of successful identification was greater than 90%, validating the technique. Furthermore, for the AF group, PH increased with IPI, while the amplitude of electrocardiogram (ECG) did not. Conclusions: Results indicated that our PAG can effectively identify AF, even in a time window as short as 10 beats. In addition, PAG can monitor the trend of pulse amplitude, possibility that cannot be offered by an ECG.