A new method for QRS complex detection in multichannel ECG: Application to self-monitoring of fetal health.
This paper proposes a new approach for QRS complex detection in multichannel ECG and presents its application to fetal QRS (fQRS) detection in signals acquired from maternal abdominal leads. The method exploits the characteristics of pseudo-periodicity and time shape of QRS, it consists of devising a quality index (QI) which synthesizes these characteristics and of finding the linear combination of the acquired ECGs, which maximizes this QI. In the application for fQRS detection two QIs are devised, one QI (mQI) for maternal ECG (mECG) and one QI (fQI) for fetal ECG (fECG). The method is completely unsupervised and based on the following steps: signal pre-processing; maternal QRS-enhanced signal extraction by finding the linear combination that maximize the mQI; detection of maternal QRSs; mECG component approximation and canceling by weighted Singular Value Decomposition (SVD); fQRS-enhanced signal extraction by finding the linear combination that maximize the fQI and fQRS detection. The proposed method was compared with our previously developed Independent Component Analysis (ICA) based method as well as with simple mECG canceling and simple ICA methods. The comparison was carried out by evaluating the performances of the procedures in fQRS detection. The new method outperformed the results of the other approaches on the annotated open set of the Computing in Cardiology Challenge 2013 database. The proposed method seems to be promising for its implementation on portable device and for use in self-monitoring of fetal health in pregnant women.
Institute of Clinical Physiology, National Research Council, Pisa, Italy. Electronic address: maurizio.varanini@ifc.cnr.it.
Maurizio
M
Varanini
Institute of Applied Sciences and Intelligent Systems, National Research Council, Messina, Italy.
Gennaro
G
Tartarisco
Institute of Clinical Physiology, National Research Council, Pisa, Italy.
Rita
R
Balocchi
University of Pisa, Pisa, Italy.
Alberto
A
Macerata
Institute of Applied Sciences and Intelligent Systems, National Research Council, Messina, Italy.
Giovanni
G
Pioggia
Institute of Clinical Physiology, National Research Council, Pisa, Italy; Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
Lucia
L
Billeci
D000465:Algorithms; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D005323:Fetal Monitoring / Q000379:methods*; D006801:Humans; D011247:Pregnancy; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted
Fetal QRS detection; Fetal electrocardiography; Independent component analysis (ICA); Multichannel ECG processing; Nelder–Mead simplex; Optimization; Self-monitoring