Fetal cardiac time intervals in healthy pregnancies – an observational study by fetal ECG (Monica Healthcare System)

2018 ◽  
Vol 46 (6) ◽  
pp. 587-592 ◽  
Author(s):  
Annette Wacker-Gussmann ◽  
Cordula Plankl ◽  
Maria Sewald ◽  
Karl-Theo Maria Schneider ◽  
Renate Oberhoffer ◽  
...  

Abstract Background: Fetal electrocardiogram (fECG) can detect QRS signals in fetuses from as early as 17 weeks’ gestation; however, the technique is limited by the minute size of the fetal signal relative to noise ratio. The aim of this study was to evaluate precise fetal cardiac time intervals (fCTIs) with the help of a newly developed fetal ECG device (Monica Healthcare System). Methods: In a prospective manner we included 15–18 healthy fetuses per gestational week from 32 weeks onwards. The small and wearable Monica AN24 monitoring system uses standard ECG electrodes placed on the maternal abdomen to monitor fECG, maternal ECG and uterine electromyogram (EMG). Fetal CTIs were estimated on 1000 averaged fetal heart beats. Detection was deemed successful if there was a global signal loss of less than 30% and an analysis loss of the Monica AN24 signal separation analysis of less than 50%. Fetal CTIs were determined visually by three independent measurements. Results: A total of 149 fECGs were performed. After applying the requirements 117 fECGs remained for CTI analysis. While the onset and termination of P-wave and QRS-complex could be easily identified in most ECG patterns (97% for P-wave, PQ and PR interval and 100% for QRS-complex), the T-wave was detectable in only 41% of the datasets. The CTI results were comparable to other available methods such as fetal magnetocardiography (fMCG). Conclusions: Although limited and preclinical in its use, fECG (Monica Healthcare System) could be an additional useful tool to detect precise fCTIs from 32 weeks’ gestational age onwards.

2002 ◽  
Vol 22 (5) ◽  
pp. 408-414 ◽  
Author(s):  
Christiane Kähler ◽  
Ekkehard Schleußner ◽  
Barbara Grimm ◽  
Alina Schneider ◽  
Uwe Schneider ◽  
...  

2003 ◽  
Vol 74 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Barbara Grimm ◽  
Christiane Kaehler ◽  
Ekkehard Schleussner ◽  
Uwe Schneider ◽  
Jens Haueisen ◽  
...  

Lupus ◽  
2014 ◽  
Vol 23 (9) ◽  
pp. 919-925 ◽  
Author(s):  
I Kiefer-Schmidt ◽  
M Lim ◽  
H Preissl ◽  
R Draganova ◽  
M Weiss ◽  
...  

2017 ◽  
Vol 69 (4) ◽  
pp. 485-490 ◽  
Author(s):  
Satish Gurunathrao Patil ◽  
Shankargouda S. Patil ◽  
Manjunatha R. Aithala ◽  
Kusal Kanti Das

2018 ◽  
Vol 17 (3) ◽  
pp. 129-137
Author(s):  
S. J. Eschbach ◽  
M. Gijtenbeek ◽  
N. van Geloven ◽  
D. Oepkes ◽  
M. C. Haak

2012 ◽  
Vol 12 (04) ◽  
pp. 1240012 ◽  
Author(s):  
GOUTHAM SWAPNA ◽  
DHANJOO N. GHISTA ◽  
ROSHAN JOY MARTIS ◽  
ALVIN P. C. ANG ◽  
SUBBHURAAM VINITHA SREE

The sum total of millions of cardiac cell depolarization potentials can be represented by an electrocardiogram (ECG). Inspection of the P–QRS–T wave allows for the identification of the cardiac bioelectrical health and disorders of a subject. In order to extract the important features of the ECG signal, the detection of the P wave, QRS complex, and ST segment is essential. Therefore, abnormalities of these ECG parameters are associated with cardiac disorders. In this work, an introduction to the genesis of the ECG is given, followed by a depiction of some abnormal ECG patterns and rhythms (associated with P–QRS–T wave parameters), which have come to be empirically correlated with cardiac disorders (such as sinus bradycardia, premature ventricular contraction, bundle-branch block, atrial flutter, and atrial fibrillation). We employed algorithms for ECG pattern analysis, for the accurate detection of the P wave, QRS complex, and ST segment of the ECG signal. We then catagorited and tabulated these cardiac disorders in terms of heart rate, PR interval, QRS width, and P wave amplitude. Finally, we discussed the characteristics and different methods (and their measures) of analyting the heart rate variability (HRV) signal, derived from the ECG waveform. The HRV signals are characterised in terms of these measures, then fed into classifiers for grouping into categories (for normal subjects and for disorders such as cardiac disorders and diabetes) for carrying out diagnosis.


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