阵发性和持续性房颤的分类方法研究

Guang-Xiong LIU,Ni-Ni RAO,Meng-Yao QIAN,Ding-Yu LIU,Jie LI,Li-Xue YIN,Xu CHEN,Gang WANG

Chinese Journal of Biomedical Engineering（2012）

Cited 5|Views26

Abstract

目前,人们对房颤维持和终止的机制还没有完全了解,因此对阵发性房颤和持续性房颤的分类具有非常重要的研究意义.鉴于此,本研究提出一种新的分类方法.根据主成分分析从单导联心电信号中提取出房颤信号,其次计算提取到的房颤信号的特征,最后用分类器对阵发性和持续性房颤进行分类.提出将房颤波的复杂度作为房颤波波动复杂度的表征.对阵发性和持续性房颤分类的实验结果表明,预测的总正确率是90％.在1 000次随机性实验中,最高分类正确率可达到92％,平均正确率为77.12％.该方法可以很好的对两类房颤进行分类,对预测房颤的自发性终止有一定的指导意义.

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Key words

Atrial fibrillation (AF) classification,Complexity,Principal component analysis

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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