A new method for the extraction of fetal ECG from the composite abdominal signal

Khamene, Ali; Negahdaripour, S.

doi:10.1109/10.828150

Cited by 161 publications

(70 citation statements)

References 20 publications

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“…However, the recovery of the signals using independent component analysis (ICA) employs a more adaptive strategy to separate the macro body movements of both the mother and child. In addition to powerline noise, Khamene and Negahdaripour (2000) and De Lathauwer et al (2000) proposed a deconvolution between the maternal and foetal ECG trace with varying success. Husoy et al (2002) offered a method to remove CPR effects from the ECG trace using signal processing.…”

Section: Signal Processing 221 Noise Removalmentioning

confidence: 99%

Heartbeat biometrics: a sensing system perspective

Israel¹,

Irvine²

2012

IJCB

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This paper reviews the emerging research into exploitation of heartbeat data as a biometric for human identification. A variety of methods have been proposed for acquiring heartbeat signatures and a range of processing methods has been examined. We approach the biometric identification and verification problem by characterising the three major factors affecting performance: individual variants, environmental variants, and sensor variants. The ability to collect and process the signal, exploit the data for individual identification or verification, and disseminate the information depends on all three of these factors. Within each component, we have identified the relevant research. Where possible, we have tied these research papers to practical examples using high resolution ECG data. The research indicates that the heartbeat contains rich information about the individual, their level of anxiety, and the cardiac state.Keywords: biometrics; heartbeat; ECG; sensing system; cardiovascular function; human identification. areas of interest include novel biometrics, signal and image processing, quantification and modelling of data quality, and cognitive modeling. He was a Principal Investigator for DARPA's Human Identification at a Distance Program (HumanID). He serves on planning committees for IEEE and SPIE, and has served on several advisory panels for the Department of Defence and the Department of Energy. He has authored over a hundred journal and conference papers and holds a PhD in Mathematical Statistics from Yale University.

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Section: Signal Processing 221 Noise Removalmentioning

confidence: 99%

Heartbeat biometrics: a sensing system perspective

Israel¹,

Irvine²

2012

IJCB

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“…Thus, the acquisition of the FECG from the AECG cannot be guaranteed. To overcome the above problems, some multiple-lead algorithms use the thoracic MECG to cancel the abdominal MECG [7], though this is inconvenient for the patient during long-term monitoring. Hence, to make the AECG suitable for the detection of the FECG, the signal to noise ratio (SNR) must be enhanced and then much signal processing is needed.…”

Section: Fhr Monitoring Techniquesmentioning

confidence: 99%

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

Hasan¹,

Ibrahimy²,

Reaz³

2007

Modelling in Medicine and Biology VII

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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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“…Linear decomposition methods use either fixed basis functions (e.g., wavelets [4]), or data-driven basis functions (e.g., singular vectors [5]). This limits performance of decomposition in nonlinear or degenerate mixtures of signal and noise [3].…”

Section: Introductionmentioning

confidence: 99%

Fetal ECG Extraction by Extended State Kalman Filtering Based on Single-Channel Recordings

Niknazar

Rivet

Jutten

2013

IEEE Trans. Biomed. Eng.

120

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Abstract-In this paper, we present an extended nonlinear Bayesian filtering framework for extracting ECGs from a singlechannel as encountered in the fetal ECG extraction from abdominal sensor. The recorded signals are modeled as the summation of several ECGs. Each of them is described by a nonlinear dynamic model, previously presented for the generation of a highly realistic synthetic ECG. Consequently, each ECG has a corresponding term in this model and can thus be efficiently discriminated even if the waves overlap in time. The parameter sensitivity analysis for different values of noise level, amplitude and heart rate ratios between fetal and maternal ECGs shows its effectiveness for a large set of values of these parameters. This framework is also validated on the extractions of fetal ECG from actual abdominal recordings, as well as of actual twin magnetocardiograms.

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A new method for the extraction of fetal ECG from the composite abdominal signal

Cited by 161 publications

References 20 publications

Heartbeat biometrics: a sensing system perspective

Heartbeat biometrics: a sensing system perspective

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

Fetal ECG Extraction by Extended State Kalman Filtering Based on Single-Channel Recordings

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