Fetal ECG Extraction from Maternal ECG Using the Singular Value Decomposition

Kanjilal, P. P.; Palit, Sarbani; Dey, Prasenjit

doi:10.1016/s1474-6670(17)48710-6

Cited by 12 publications

(17 citation statements)

References 1 publication

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“…We generate a signal as in the upper plot of Fig. 2 which superimposes a baseline generated with filtered white Gaussian noise, two different decaying sinusoids, spikes, an offset, and white Gaussian noise of variance 10 ) and outputs a good estimation for both the individual signal shape componentsŝ [1] andŝ [2] and the baselineŝ [0] . If more unknown shapes (i.e., L > 4) than actually present are specified, unnecessary signal (1) …”

Section: Resultsmentioning

confidence: 99%

“…Such model generates filtered white Gaussian noise, which includes, under a suitable choice of parameters, spline smoothing [21], [22]. Note that the baseline model is the sparsest signal component in the representation (2). In the BSS literature, such a baseline is normally filtered out in a preprocessing step, but we will treat it as just another signal component.…”

Section: Problem Setup and Signal Modelmentioning

confidence: 99%

“…An obvious approach to such problems is successive cancellation, where one signal component after the other is identified and subtracted (e.g., see [1], [2]). The baseline is often removed with a frequency-selective filter [1] while other signal shapes are identified based on some criterion such as periodicity [2].…”

Section: Introductionmentioning

confidence: 99%

“…The baseline is often removed with a frequency-selective filter [1] while other signal shapes are identified based on some criterion such as periodicity [2]. However, errors in the cancellation will accumulate, which limits the robustness of such methods.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised feature extraction, signal labeling, and blind signal separation in a state space world

Zalmai

Keusch

Malmberg

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The paper addresses the problem of joint signal separation and estimation in a single-channel discrete-time signal composed of a wandering baseline and overlapping repetitions of unknown (or known) signal shapes. All signals are represented by a linear state space model (LSSM). The baseline model is driven by white Gaussian noise, but the other signal models are triggered by sparse inputs. Sparsity is achieved by normal priors with unknown variance (NUV) from sparse Bayesian learning. All signals and system parameters are jointly estimated with an efficient expectation maximization (EM) algorithm based on Gaussian message passing, which works both for known and unknown signal shapes. The proposed method outputs a sparse multi-channel representation of the given signal, which can be interpreted as a signal labeling.

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Section: Resultsmentioning

confidence: 99%

Section: Problem Setup and Signal Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unsupervised feature extraction, signal labeling, and blind signal separation in a state space world

Zalmai

Keusch

Malmberg

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…In addition, the effect of ultrasound on the fetus and young infant is not completely clear hence ultrasound is not recommended for long term monitoring [4]. In contrast, methods utilizing the AECG signal have a better prospect for long-term monitoring using signal processing techniques [5]. The abdominal ECG signal can also be used for antepartum non-invasive FHR determination through the detection of small fetal cardiac potentials at the surface of the maternal abdomen [6].…”

Section: Fhr Monitoring Techniquesmentioning

confidence: 99%

Techniques of FECG signal analysis: detection and processing for fetal monitoring

Hasan¹,

Ibrahimy²,

Reaz³

2007

WIT Transactions on Biomedicine and Health, Vol 12

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Fetal heart rate monitoring is a technique for obtaining important information about the condition of a fetus during pregnancy and labor, by detecting the FECG signal generated by the heart of the fetus. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature. A comparative study has been carried out to show the performance of various methods. This paper opens up a passage to biomedical researchers, physicians and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system by providing valuable information to help them in developing more dominant, flexible and resourceful applications.

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Detection of Fetal ECG R Wave From Single-Lead Abdominal ECG Using a Combination of RR Time-Series Smoothing and Template-Matching Approach

2019

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Fetal electrocardiograph (fECG) R-wave detection from maternal abdominal ECGs (aECGs) is challenging as it is easily distorted and often overwhelmed by a variety of unavoidable interference signals. In this study, we combined RR time-series smoothing with a template-matching (TM) approach to detect fECG R waves from a single-lead aECG. According to the quasi-periodicity of the R wave, we analyzed the cases of misaligned and missed R wave detection in the RR time series and developed an amendment algorithm to distinguish and correct them. The fECG R wave detection consists of two stages. First, an fECG R wave, which did not overlap with the maternal ECG QRS complex (mQRS), was detected. Subsequently, the amendment algorithm corrected the misaligned and missed R waves and predicted the approximate region of the fECG R wave that overlapped with the mQRS. A TM algorithm was implemented to detect the fECG R wave that overlapped with the mQRS in the approximate region. The fECG QRS complex (fQRS) and mQRS templates were built from the detected fQRS and mQRS that did not overlap with the fQRS, respectively, using singular value decomposition. The TM algorithm then determined the accurate location when the most optimal correlation occurred between the actual mQRS and the superposition of the mQRS and fQRS templates in the approximate region. The performance of the proposed method was assessed using F 1 measure and Bland-Altman analysis. High accuracy values were obtained corresponding to 95% and −0.04±9.00 ms, respectively. The proposed method was therefore considered to enhance accurate extraction of the fECG R wave in both the clinical and commercial applications. INDEX TERMS Fetal ECG, k-means, singular value decomposition, template matching, RR time-series smoothing.

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Fetal ECG Extraction from Maternal ECG Using the Singular Value Decomposition

Cited by 12 publications

References 1 publication

Unsupervised feature extraction, signal labeling, and blind signal separation in a state space world

Unsupervised feature extraction, signal labeling, and blind signal separation in a state space world

Techniques of FECG signal analysis: detection and processing for fetal monitoring

Detection of Fetal ECG R Wave From Single-Lead Abdominal ECG Using a Combination of RR Time-Series Smoothing and Template-Matching Approach

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