1986
DOI: 10.1515/jpme.1986.14.6.445
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Variability analysis of fetal heart rate signals as obtained from abdominal electrocardiographic recordings

Abstract: The present section describes an algorithm for the digital signal processing aimed at the detection of maternal and fetal QRS complexes from the abdominal ECG lead.The research described in here is connected to an extensive clinical experience of fetal QRS morphology studies described in [16]. 20 healthy pregnant women after the 25th week of gestation were considered in clinostatic, resting condition at the Department of Obstetrics and Gynecology "L. then stored on a DEC-VAX 750 computer for which all processi… Show more

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Cited by 97 publications
(71 citation statements)
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“…On the other hand, fewer algorithms depend on the singlelead aECG signal; e.g., template subtraction (TS) [13,[23][24][25][26], and its variation based on singular value decomposition (SVD) or principal component analysis [27,28], the time-frequency analysis, like wavelet transform, pseudo-smooth Wigner-Ville distribution [29][30][31][32] (in practice, three aECG channels are averaged in [30]), and S-transform [33], sequential total variation [34], adaptive neuro-fuzzy inference system and extended Kalman filter [35], particle swarm optimization and extended Kalman smoother [36] state space reconstruction via lag map [37,38], etc. We refer the reader to, e.g., Sameni and Clifford [4] and Andreotti et al [39] for a more detailed review.…”
Section: Introductionmentioning
confidence: 99%
“…On the other hand, fewer algorithms depend on the singlelead aECG signal; e.g., template subtraction (TS) [13,[23][24][25][26], and its variation based on singular value decomposition (SVD) or principal component analysis [27,28], the time-frequency analysis, like wavelet transform, pseudo-smooth Wigner-Ville distribution [29][30][31][32] (in practice, three aECG channels are averaged in [30]), and S-transform [33], sequential total variation [34], adaptive neuro-fuzzy inference system and extended Kalman filter [35], particle swarm optimization and extended Kalman smoother [36] state space reconstruction via lag map [37,38], etc. We refer the reader to, e.g., Sameni and Clifford [4] and Andreotti et al [39] for a more detailed review.…”
Section: Introductionmentioning
confidence: 99%
“…Several FECG extraction approaches have produced interesting results: methods based on blind or semi-blind source separation techniques, independent component analysis ( ICA), [1], [2], principal component analysis (PCA) or singular value decomposition (SVD) [3], [4], [5]; average MECG subtraction [6], [7]; different variants of adaptive filters [8], [9], [10]; wavelet decomposition [11].…”
Section: Introductionmentioning
confidence: 99%
“…One of the most frequently used classes of techniques, is the class of techniques that are based on template subtraction [4], [5], [6]. In general, template subtraction techniques operate by generating a template of the mECG and subtracting this template from each individual mECG complex.…”
Section: Introductionmentioning
confidence: 99%
“…In general, template subtraction techniques operate by generating a template of the mECG and subtracting this template from each individual mECG complex. Prior to subtraction, the template is often dynamically scaled to improve the resemblance to the individual mECG complex [4]. This approach is based on the assumption of (quasi-)stationary behavior of the mECG.…”
Section: Introductionmentioning
confidence: 99%