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基于双侧累计面积的心电信号P波检测 被引量:3

P-wave Detection in Electrocardiograms Based on Bilateral Accumulative Area
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摘要 在体表心电图中P波时限是判断心房传导情况的重要参考依据,P波的准确检测是非常关键的。本文提出了一种基于双侧累计面积的P波检测算法,并结合12导联同步的多窗口检测。该算法根据在不同窗口长度检测的极值点进行多尺度筛选获取最优极值点,然后通过12导联同步检测进行P波位置匹配,计算出P波的起点和终点。采用CSE标准数据库的第三个数据库进行验证,本算法可以胜任于不同波形下P波的检测,检测正确率高于99%。 P-wave duration in electrocardiogram is important evidence of atrial disease analysis in clinic diagnosis.It is critical for accurate delineation of P wave.This study presented an efficient and robust detection method for P-wave boundary points on the basis of bilateral accumulative area with combination of the multiwindow and 12-lead synchronous detection.First of all,through multi-scale screening,the optimal extreme points are picked out from all extreme points of different window lengths.And the 12-lead synchronous detection is used for P-wave location matching.The results of the proposed method were evaluated on the dataset-3 of the standard CSE database.As a result,the value of sensitivity Se = 99%was obtained for the detection of P-wave.
出处 《中国医药导刊》 2015年第B05期36-39,共4页 Chinese Journal of Medicinal Guide
基金 深圳市技术开发项目(编号:cxzz20130321094640079) 项目名称:基于转子/局灶机理的房颤及复杂心律失常标测系统 深圳市基础研究项目(编号:JCY20130402145002404) 项目名称:早期癌组织偏振内窥检测与导航的关键技术研究 深圳市基础研究项目(编号:JCY20140408153331811) 项目名称:超声导航技术辅助经皮穿刺胆管置管引流术(PTCD)治疗的研究
关键词 心电图 P波检测 双侧累计面积 多窗口检测 ECG P-wave detection Bilateral accumulative area Multi-window detection
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  • 1KaergaardK, Jensen SH, Puthusserypady S. A comprehensiveperformance analysis of EEMD-BLMS and DWT-NN hybridalgorithms for ECG denoising[J]. Biomed Sign Proc Contr, 2016,25: 178-187.
  • 2YochumM, Renaud C, Jacquir S. Automatic detection of P, QRSand T patterns in 12 leads ECG signal based on CWT[J]. BiomedSign Proc Contr, 2016, 25: 46-52.
  • 3PadhyS, Sharma LN, Dandapat S. Multilead ECG datacompression using SVD in multiresolution domain[J]. Biomed SignProc Contr, 2015, 23: 10-18.
  • 4GrossiG, Lanzarotti R, Lin J. High-rate compression of ECGsignals by an accuracy-driven sparsity model relying on naturalbasis[J]. Digit Sign Proc, 2015, 45: 96-106.
  • 5EdwardJS, Ramu P, Swaminathan R. Imperceptibility - robustnesstradeoff studies for ECG steganography using continuous antcolony optimization[J]. Expert Syst Appl, 2015, 49: 123-135.
  • 6Kumar R, Kumar A, Singh GK. Hybrid method based on singularvalue decomposition and embedded zero tree wavelet technique forECG signal compression.[J]. Comput Meth Prog Biomed, 2016.
  • 7TawficI, Kayhan S. Compressed sensing of ECG signal forwireless system with new fast iterative method[J]. Comput MethProg Biomed, 2015, 122(3): 437-449.
  • 8SharmaL N. Coding ECG beats using multiscale compressedsensing based processing[J]. Comput Electr Eng, 2015, 45(C): 211-221.
  • 9Liu T, Si Y, Wen D, et al. Dictionary learning for VQ featureextraction in ECG beats classification[J]. Expert Syst Appl, 2016,53: 129-137.
  • 10Fatin A, Naomie S, Arief R, et all. Arrhythmia recognition andclassification using combined linear and nonlinear features of ECGsignals[J]. Comput Meth Prog Biomed, 2016, 127: 52-63.

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