Wavedna liquid rhythm intelligent beat generation software
However, the lack of labelled ECG data has slowed down the building of excellent auxiliary diagnosis models. Computer hardware systems are sufficient to meet the auxiliary diagnosis model's calculation and storage requirements with computer science development. The ECG-assisted diagnosis system requires accurate and real-time feedback, which has high performance and operating efficiency requirements. Increased numbers of cardiovascular patients face the problem of a lack of specialist physicians to diagnose ECG in most hospitals, especially in China. With the development of information technology, computer-assisted medical treatment has attracted increasing attention from academia and industry to free doctors from tedious work. The proposed annotation system can also be extended to other data annotation applications. The proposed ECG intelligent annotation system's self-learning mechanism could improve pre-annotation performance and annotation efficiency by generating more labelled data. The experimental results show that the simulation beat has high similarity to real beat and the accuracy of the pre-annotation model on the test set of 14 classes of beats is 99.28%. Since beat annotation is the most basic and important part, a GAN-based generation model that can generate 14 types of simulation beats and a CNN-based beat pre-annotation model are proposed.
#WAVEDNA LIQUID RHYTHM INTELLIGENT BEAT GENERATION SOFTWARE MANUAL#
This study proposes an intelligent ECG-assisted annotation system, that not only supplements labelled data, but also significantly reduces the workload compared with manual annotation. The shortage of labelled ECG data is one of the essential factors that affect ECG intelligent analysis's long-term development.
Manual ECG annotation is challenging and time-consuming, even for specialist physicians. An electrocardiogram (ECG) consists of complex segments, such as P-QRS-T waves.