Extended Noninvasive Fetal Monitoring by Detailed Analysis of Data Measured With Phonocardiography

Kovács, Ferenc; Horváth, Csaba; Balogh, A; Hosszú, Gábor

doi:10.1109/tbme.2010.2071871

Cited by 41 publications

(29 citation statements)

References 21 publications

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“…However, these fPCG signals are heavily contaminated by noise from various sources [6–11]. Hence to extract important diagnostic information such as FHR, frequency/pitch, intensity, timing, and energy of fetal heart sound, this technique requires matured signal processing strategies.…”

Section: Introductionmentioning

confidence: 99%

Design Methodology of a New Wavelet Basis Function for Fetal Phonocardiographic Signals

Chourasia¹,

Tiwari²

2013

The Scientific World Journal

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Fetal phonocardiography (fPCG) based antenatal care system is economical and has a potential to use for long-term monitoring due to noninvasive nature of the system. The main limitation of this technique is that noise gets superimposed on the useful signal during its acquisition and transmission. Conventional filtering may result into loss of valuable diagnostic information from these signals. This calls for a robust, versatile, and adaptable denoising method applicable in different operative circumstances. In this work, a novel algorithm based on wavelet transform has been developed for denoising of fPCG signals. Successful implementation of wavelet theory in denoising is heavily dependent on selection of suitable wavelet basis function. This work introduces a new mother wavelet basis function for denoising of fPCG signals. The performance of newly developed wavelet is found to be better when compared with the existing wavelets. For this purpose, a two-channel filter bank, based on characteristics of fPCG signal, is designed. The resultant denoised fPCG signals retain the important diagnostic information contained in the original fPCG signal.

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Section: Introductionmentioning

confidence: 99%

Design Methodology of a New Wavelet Basis Function for Fetal Phonocardiographic Signals

Chourasia¹,

Tiwari²

2013

The Scientific World Journal

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“…A publication by Kovcs, Ferenc et al [9] presents a complex heuristic method for the evaluation of fetal heart sounds, applying simultaneously several algorithms, where the autocorrelation technique has been completed with the wavelet transform and the matching pursuit methods. In this way, a more reliable heart rate variability can be achieved and further parameters of the cardiac operation can be assessed in addition to the conventional cardiotocographic examination.…”

Section: Related Workmentioning

confidence: 99%

Mobile Phonocardiogram Diagnosis in Newborns Using Support Vector Machine

et al. 2017

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Phonocardiogram (PCG) monitoring on newborns is one of the most important and challenging tasks in the heart assessment in the early ages of life. In this paper, we present a novel approach for cardiac monitoring applied in PCG data. This basic system coupled with denoising, segmentation, cardiac cycle selection and classification of heart sound can be used widely for a large number of the data. This paper describes the problems and additional advantages of the PCG method including the possibility of recording heart sound at home, removing unwanted noises and data reduction on a mobile device, and an intelligent system to diagnose heart diseases on the cloud server. A wide range of physiological features from various analysis domains, including modeling, time/frequency domain analysis, an algorithm, etc., is proposed in order to extract features which will be considered as inputs for the classifier. In order to record the PCG data set from multiple subjects over one year, an electronic stethoscope was used for collecting data that was connected to a mobile device. In this study, we used different types of classifiers in order to distinguish between healthy and pathological heart sounds, and a comparison on the performances revealed that support vector machine (SVM) provides 92.2% accuracy and AUC = 0.98 in a time of 1.14 seconds for training, on a dataset of 116 samples.

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“…Kalman filtering (Adithya et al, 2017) needs accurate reference signal, as it has high computational complexity. Wavelet Transform (Kosa et al, 2008; Kovacs et al, 2011; Fodor et al, 2012; Adithya et al, 2017; Koutsiana et al, 2017), showed good performance in de-nosing noisy fPCG and analyzing the fetal heart sounds (fHS), but it lacks the ability to finely de-noise the overlapped frequency components. Independent Component Analysis (ICA) (Nigam and Priemer, 2004; Jimenez-Gonzalez and James, 2008; Jiménez-Gonzàlez and James, 2010; Jimenez and James, 2013) assumes source components as independent (which is hardly met in case of abdominal sources of sounds) and needs post processing with high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Fetal Heart Rates Estimated from Fetal Phonography and Cardiotocography

Ibrahim¹,

Awar

Balayah

et al. 2017

Front. Physiol.

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The aim of this study is to investigate that fetal heart rates (fHR) extracted from fetal phonocardiography (fPCG) could convey similar information of fHR from cardiotocography (CTG). Four-channel fPCG sensors made of low cost (<$1) ceramic piezo vibration sensor within 3D-printed casings were used to collect abdominal phonogram signals from 20 pregnant mothers (>34 weeks of gestation). A novel multi-lag covariance matrix-based eigenvalue decomposition technique was used to separate maternal breathing, fetal heart sounds (fHS) and maternal heart sounds (mHS) from abdominal phonogram signals. Prior to the fHR estimation, the fPCG signals were denoised using a multi-resolution wavelet-based filter. The proposed source separation technique was first tested in separating sources from synthetically mixed signals and then on raw abdominal phonogram signals. fHR signals extracted from fPCG signals were validated using simultaneous recorded CTG-based fHR recordings.The experimental results have shown that the fHR derived from the acquired fPCG can be used to detect periods of acceleration and deceleration, which are critical indication of the fetus' well-being. Moreover, a comparative analysis demonstrated that fHRs from CTG and fPCG signals were in good agreement (Bland Altman plot has mean = −0.21 BPM and ±2 SD = ±3) with statistical significance (p < 0.001 and Spearman correlation coefficient ρ = 0.95). The study findings show that fHR estimated from fPCG could be a reliable substitute for fHR from the CTG, opening up the possibility of a low cost monitoring tool for fetal well-being.

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Extended Noninvasive Fetal Monitoring by Detailed Analysis of Data Measured With Phonocardiography

Cited by 41 publications

References 21 publications

Design Methodology of a New Wavelet Basis Function for Fetal Phonocardiographic Signals

Design Methodology of a New Wavelet Basis Function for Fetal Phonocardiographic Signals

Mobile Phonocardiogram Diagnosis in Newborns Using Support Vector Machine

A Comparative Study on Fetal Heart Rates Estimated from Fetal Phonography and Cardiotocography

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