短时经验模态分解实时识别生猪心电QRS波群  

Short-time empirical mode decomposition method of real-time QRS complex identifying for electrocardiography of pig

作　　者：贾桂锋[1,2] 王自唱 向兴发 武墩 高云 黎煊[1,2] 冯耀泽 Jia Guifeng;Wang Zichang;Xiang Xingfa;Wu Dun;Gao Yun;Li Xuan;Feng Yaoze(College of Engineering, Huazhong Agricultural University, Wuhan 430070, China;Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture, Wuhan 430070, China)

机构地区：[1]华中农业大学工学院,武汉430070 [2]农业部长江中下游农业装备重点实验室,武汉430070

出　　处：《农业工程学报》2018年第10期172-177,共6页Transactions of the Chinese Society of Agricultural Engineering

基　　金：中央高校基本科研业务专项基金(2662016QD002);湖北省自然科学青年基金(2018CFB099)

摘　　要：心率是猪的重要生命体征,而在健康监测中由于猪的依从性较差而造成心电信号呈现非平稳特性,给实时心率计算带来困难。该文针对此问题结合经验模态分解方法(empirical mode decomposition,EMD)提出一种对心电信号具有实时处理能力的短时经验模态分解算法(short-time empirical mode decomposition,ST-EMD)。该算法通过对数据分段并根据信号特征决定分段起点及长度等参数,然后对每段数据进行EMD分解,再基于能量窗变换法从分解结果中提取QRS波的特征参数并识别R波。通过动物试验表明,ST-EMD算法能够对猪的心电信号实时处理和识别QRS波群,识别正确率为99.6%,且表现出一定的自适应性。说明本文提出的ST-EMD算法思路是正确的,适用于生猪的心电实时健康监护。Pigs＇ physiological parameters monitoring instantaneously in commercial piggeries is essential for early disease detection and welfare assessment. Heart rate is vital sign of pigs because of its correlation with disease and environment. Usually, the heart rate is calculated by the intervals of QRS complex identified from ECG （electrocardiogram） signal. Due to the low compliance of livestock during monitoring, the pig＇s ECG signal presents non-stationary characteristics and varieties of noise, which makes it difficult to process instantaneously and correctly. To solve the problem, this study proposed a short-term empirical mode decomposition （ST-EMD） algorithm with the real-time and anti-noise property for ECG data processing based on empirical mode decomposition （EMD）. The ST-EMD algorithm comprises 3 procedures including data segmenting, EMD processing and QRS complex feature extracting. In the data segmenting step, the algorithm determines the starting point and terminal point for the next data block according to the latest QRS complex and RRI （RR interval） in current data segment, and then captures the ECG data to meet the predetermined length in real time. After the signal collecting, the new fragment data are decomposed into a series of intrinsic mode functions （IMFs） and residuals by EMD algorithm. Through experiment analysis and comparison, the first IMF contains the most of QRS complex information. In the third procedure, the QRS complex features are extracted based on the first IMF employing energy window transformation and the correctness of the QRS complex identification is checked. After the ST-EMD algorithm was developed in MATLAB, animal experiments were carried out to verify the efficiency. Three piglets born in the same nest were recruited in the experiments. The age of the piglets was 50 d, and the average weight was 18.2 kg. Pig＇s ECG signal was picked up by 2 electrodes attached on either side of chest and converted by BMD101 analog sensor with 512 Hz sampling frequenc

关 键 词：数据处理 图像分割 识别 猪 经验模态分解 QRS波群 

分 类 号：S818.2[农业科学—畜牧学]