摘要
针对心冲击描记(BCG)信号中心跳波形缺乏确定模型且受多种因素影响的特点,提出了基于自学习的心跳识别算法。使用聚类分析的方法,有效提取了BCG信号中具有高度相关性的一组曲线,并将其作为心跳模型。将心跳模型与BCG信号进行匹配,捕捉到心跳信号进而得到心跳周期。经实验验证:算法得出的心跳周期误差在±2%以内,并在Android平台上进一步验证了其准确性和实用性。
Aiming at problem that in ballistocardiogram（BCG） signal heartbeat waveform has characteristics of no certain model and is influenced by various factors,propose a heartbeat recognition algorithm based on self-learning.During self-learning process,a group of curves with high correlation which are extracted from the BCG signals by means of clustering analysis,and make it as heartbeat model.The heartbeat model is matched with BCG signals,so catch heartbeat signal,so as to obtain heartbeat cycle.By experimental verification,error of heartbeat cycle which is calculated based on this algorithm is within ±2 %,besides,the algorithm is proved to be accurate and practical on Android platform.
作者
王炬
王田苗
栾胜
倪自强
WANG Ju;WANG Tian-miao;LUAN Sheng;NI Zi-qiang(School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China)
出处
《传感器与微系统》
CSCD
2018年第4期41-43,47,共4页
Transducer and Microsystem Technologies
基金
北京市科技计划重大项目(D141100003614003)
关键词
心冲击描记
心率监测
自学习
聚类分析
ballistocardiogram（BCG）
heart rate monitor
self-learning
clustering analysis
