Journal ArticleDOI
Separation and Analysis of Fetal-ECG Signals From Compressed Sensed Abdominal ECG Recordings
TLDR
The experiments confirm that the proposed framework may be used for compression of abdominal f-ECG and to obtain real-time information of the fHR, providing a suitable solution for real time, very low-power f- ECG monitoring.Abstract:
Objective: Analysis of fetal electrocardiogram (f-ECG) waveforms as well as fetal heart-rate (fHR) evaluation provide important information about the condition of the fetus during pregnancy. A continuous monitoring of f-ECG, for example using the technologies already applied for adults ECG tele-monitor-ing (e.g., Wireless Body Sensor Networks (WBSNs)), may increase early detection of fetal arrhythmias. In this study, we propose a novel framework, based on compressive sensing (CS) theory, for the compression and joint detection/classification of mother and fetal heart beats. Methods : Our scheme is based on the sparse representation of the components derived from independent component analysis (ICA), which we propose to apply directly in the compressed domain. Detection and classification is based on the activated atoms in a specifically designed reconstruction dictionary. Results : Validation of the proposed compression and detection framework has been done on two publicly available datasets, showing promising results (sensitivity S = 92.5 $\%$ , P += 92 $\%$ , F 1 = 92.2 $\%$ for the Silesia dataset and S = 78 $\%$ , P += 77 $\%$ , F 1 = 77.5 $\%$ for the Challenge dataset A, with average reconstruction quality PRD = 8.5 $\%$ and PRD = 7.5 $\%$ , respectively). Conclusion : The experiments confirm that the proposed framework may be used for compression of abdominal f-ECG and to obtain real-time information of the fHR, providing a suitable solution for real time, very low-power f-ECG monitoring. Significance : To the authors’ knowledge, this is the first time that a framework for the low-power CS compression of fetal abdominal ECG is proposed combined with a beat detector for an fHR estimation.read more
Citations
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Iconographies supplémentaires de l'article : Clinically accurate fetal ECG parameters acquired from maternal abdominal sensors
TL;DR: In this article, the root mean square error between the FHR and ST data calculated by both methods over all processed segments was 0.36 beats per minute, and 3.2% deviation from the isoelectric point ranged from 0-14.
Journal ArticleDOI
Non-Adaptive Methods for Fetal ECG Signal Processing: A Review and Appraisal.
TL;DR: It is limiting that a different non-adaptive method works well for each type of signal, but independent component analysis, principal component analysis and wavelet transforms are the most commonly published methods of signal processing and have good accuracy and speed of algorithms.
Journal ArticleDOI
Matched Filtering for Heart Rate Estimation on Compressive Sensing ECG Measurements
TL;DR: The problem of heart rate estimation from CS ECG recordings, avoiding the reconstruction of the entire signal is addressed, and the proposed method proves to be very convenient for real-time low-power applications.
Journal ArticleDOI
Is Abdominal Fetal Electrocardiography an Alternative to Doppler Ultrasound for FHR Variability Evaluation
Janusz Jezewski,Janusz Wrobel,Adam Matonia,Krzysztof Horoba,Radek Martinek,Tomasz Kupka,Michal Jezewski +6 more
TL;DR: The obtained results prove that the abdominal FECG, considered as an alternative to the ultrasound approach, does not change the interpretation of the FHR signal, which was confirmed during both visual assessment and automated analysis, and that ability of clinical parameters to distinguish between normal and abnormal groups do not depend on the acquisition method.
Journal ArticleDOI
Compressed Sensing Reconstruction of Hyperspectral Images Based on Spectral Unmixing
TL;DR: Experimental results on synthetic and real hyperspectral data demonstrate that the proposed algorithm could obtain the endmember and abundance information effectively, and the accuracy of reconstructed HSIs as well as the computational efficiency are superior to the state-of-the-art reconstruction algorithms.
References
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Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Journal ArticleDOI
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
TL;DR: In this paper, the authors considered the model problem of reconstructing an object from incomplete frequency samples and showed that with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the lscr/sub 1/ minimization problem.
Journal ArticleDOI
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Journal ArticleDOI
Atomic Decomposition by Basis Pursuit
TL;DR: Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions.
Journal ArticleDOI
An Introduction To Compressive Sampling
TL;DR: The theory of compressive sampling, also known as compressed sensing or CS, is surveyed, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition.
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