Wang Kai, Xu Jicheng, Zhang Yu
Department of Health Management, Bengbu Medical College, Bengbu, 233030.
School of Computer and Information, Anhui Agriculture University, Hefei, 230027.
Zhongguo Yi Liao Qi Xie Za Zhi. 2019 Sep 30;43(5):341-344. doi: 10.3969/j.issn.1671-7104.2019.05.008.
A method for dynamically collecting and processing ECG signals was designed to obtain classification information of abnormal ECG signals.
Firstly, the ECG eigenvectors were acquired by real-time acquisition of ECG signals combined with discrete wavelet transform, and then the ECG fuzzy information entropy was calculated. Finally, the Euclidean distance was used to obtain the semantic distance of ECG signals, and the classification information of abnormal signals was obtained.
The device could effectively identify abnormal ECG signals on an embedded platform based on the Internet of Things, and improved the diagnosis accuracy of heart diseases.
The fuzzy diagnosis device of ECG signal could accurately classify the abnormal signal and output an online signal classification matrix with a high confidence interval.
设计一种动态采集与处理心电信号的方法，以获取异常心电信号的分类信息。
首先，通过结合离散小波变换实时采集心电信号来获取心电特征向量，然后计算心电模糊信息熵。最后，利用欧几里得距离得到心电信号的语义距离，从而获得异常信号的分类信息。
该装置能够在基于物联网的嵌入式平台上有效识别异常心电信号，提高了心脏病的诊断准确率。
心电信号模糊诊断装置能够准确地对异常信号进行分类，并输出具有高置信区间的在线信号分类矩阵。
