Source separation of Foetal Heart Sounds and maternal activity from single-channel phonograms: A temporal independent component analysis approach

Jiménez-González, Aída; James, Christopher J.

doi:10.1109/cic.2008.4749200

Cited by 10 publications

(9 citation statements)

References 14 publications

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“…In addition, the signals obtain various interference of different types and intensities, and also use different evaluation techniques such as mean square error (MSE) [33] or software quality index (SQI) [81]. Some authors do not use any objective evaluation parameters and only evaluate the visual quality of the signals in time domain or [12], [46], [82] or their spectrograms [14], [43]. For these reasons, we made both objective and subjective comparison of the results summarized in Table 8.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, they were able to effectively separate the maternal HSs (mHSs) and fHSs. • The authors in [12] tested temporal decorrelation source separation (TDSEP) on real records with relatively low interference intensity. The resulting extracted signals were not evaluated using statistical parameters.…”

Section: Discussionmentioning

confidence: 99%

“…• The authors of study [12] successfully extracted source signals from noisy single-channel abdominal recordings. Firstly, an appropriate matrix of delays was constructed, then multiple independent components were calculated, and, finally, the components were projected back onto the measurement space and grouped using Kmeans method.…”

Section: A Methods For Fetal Pcg Extractionmentioning

confidence: 99%

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Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography

et al. 2020

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Fetal phonocardiography (fPCG) is a non-invasive technique for detection of fetal heart sounds (fHSs), murmurs and vibrations. This acoustic recording is passive and provides an alternative lowcost method to ultrasonographic cardiotocography (CTG). Unfortunately, the fPCG signal is often disturbed by the wide range of artifacts that make it difficult to obtain significant diagnostic information from this signal. The study focuses on the filtering of an fPCG signal containing three types of noise (ambient noise, Gaussian noise, and movement artifacts of the mother and the fetus) having different amplitudes. Three advanced signal processing methods: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and adaptive wavelet transform (AWT) were tested and compared. The evaluation of the extraction was performed by determining the accuracy of S1 sounds detection and by determining the fetal heart rate (fHR). The evaluation of the effectiveness of the method was performed using signal-to-noise ratio (SNR), mean error of heart interval measurement (|∆T i |), and the statistical parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). Using the EMD method, ACC > 95% was achieved in 7 out of 12 types and levels of interference with average values of ACC = 88.73%, SE = 91.57%, PPV = 94.80% and F1 = 93.12%. Using the EEMD method, ACC > 95% was achieved in 9 out of 12 types and levels of interference with average values of ACC = 97.49%, SE = 97.89%, PPV = 99.53% and F1 = 98.69%. In this study, the best results were achieved using the AWT method, which provided ACC > 95% in all 12 types and levels of interference with average values of ACC = 99.34%, SE = 99.49%, PPV = 99.85% a F1 = 99.67%. INDEX TERMSFetal phonocardiography (fPCG), fetal heart rate (fHR), non-invasive fetal monitoring, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), adaptive wavelet transform (AWT).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: A Methods For Fetal Pcg Extractionmentioning

confidence: 99%

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Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography

et al. 2020

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“…This may cause improper component estimation. In order to reduce the problem we applied technique described in [26] , [27] . Briefly the main idea is to add time delayed data in to the dataset, forming new extended data set.…”

Section: Methodsmentioning

confidence: 99%

Independent Component Analysis and Decision Trees for ECG Holter Recording De-Noising

et al. 2014

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We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE) between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG), but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact) were compared.

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“…As heart sound are easily corrupted by the noise, therefore many studies have been lately based on denoising of PCG signals. A. Jimenez-Gonzalez et al extracted fetal heart sounds from noisy single channel abdominal phonograms [21] using temporal independent component analysis (ICA) approach [22] and blind source separation (BSS) approach [23]. The underlying heart disorders and its nonlinearities can be observed by wavelet packet transform [24].…”

Section: Related Workmentioning

confidence: 99%

Doppler Ultrasound Based Non-Invasive Heart Rate Telemonitoring System for Wellbeing Assessment

Queyam¹,

Pahuja²,

Singh³

2018

IJISA

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Telemonitoring in the field of healthcare has vastly improved the quality of clinical diagnosis and disease prevention by providing timely medical consultation to people living in rural and remote areas. To monitor the health state of a patient certain vital physiological parameter like electrocardiogram (ECG), respiration rate, blood pressure, oxygen saturation, etc. are acquired and analyzed. Listening to the heart sounds (auscultation) is also a quick method to monitor the health state of the patient's heart. In this paper, we propose the use of a portable Doppler ultrasound sensor for measuring the heart sounds reliably and to transmit the data for further clinical telemonitoring. We have developed an ultrasound-based hardware prototype which is non-invasive in nature and easy to operate. Its portability, high accuracy, low cost, and wireless nature make this device suitable for home-based self-diagnostic applications. The developed prototype was successfully able to capture both fundamental heart sounds S 1 and S 2 reliably and transfer the signal wirelessly to the LabVIEW-based monitoring and data logging unit. This unit extracts clinically useful health information like heart rate (HR), R-R interval and heart rate variability (HRV) using signal processing algorithms. Health information is then transmitted via the Internet to a distant hospital for further improved clinical diagnosis and consultancy. The prototype was validated on 40 healthy males in the age group of 25-35 years, and the results show an overall accuracy of 96.74% in HR detection when compared with an ECG sensor, a photoplethysmograph (PPG) sensor, a pulse oximeter device and manual auscultation.

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Source separation of Foetal Heart Sounds and maternal activity from single-channel phonograms: A temporal independent component analysis approach

Cited by 10 publications

References 14 publications

Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography

Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography

Independent Component Analysis and Decision Trees for ECG Holter Recording De-Noising

Doppler Ultrasound Based Non-Invasive Heart Rate Telemonitoring System for Wellbeing Assessment

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