Performance Analysis of Fetal-Phonocardiogram Signal Denoising Using The Discrete Wavelet Transform

The obligation for comprehensive fetal heart rate investigation had driven to improve the passive and non-invasive diagnostic instruments despite the USG or CTG method. Fetal phonocardiography (f-PCG) utilizing the auscultation method met the above criteria, but its interpretation frequently disturbed by the presence of noise. For instance, maternal heart and body organ sounds, fetal movements noise, and ambient noise from the environment where it is recording are the noise that corrupted the f-PCG signal. In this work, the use of discrete wavelet transforms (DWT) to eliminate noise in the f-PCG signal with SNR as the performance parameters observed. It was observing the effect of changes in wavelet type and threshold type on the SNR value. The test was carried out on f-PCG data taken from physio.net. Initial SNR values ranged from -26.7 dB to -4.4 dB; after application of DWT procedure to f-PCG, SNR increased significantly. Based on the test results obtained, wavelet type coif1 with the soft threshold gave the best result with 11.69 dB in SNR value. The coif1 had a superior result than other mother wavelets that use in this work, so the fPCG signal analysis for fetal heart rate investigation suggested to use it.The obligation for comprehensive fetal heart rate investigation had driven to improve the passive and non-invasive diagnostic instruments despite the USG or CTG method. Fetal phonocardiography (f-PCG) utilizing the auscultation method met the above criteria, but its interpretation frequently disturbed by the presence of noise. For instance, maternal heart and body organ sounds, fetal movements noise, and ambient noise from the environment where it is recording are the noise that corrupted the f-PCG signal. In this work, the use of discrete wavelet transforms (DWT) to eliminate noise in the f-PCG signal with SNR as the performance parameters observed. It was observing the effect of changes in wavelet type and threshold type on the SNR value. The test was carried out on f-PCG data taken from physio.net. Initial SNR values ranged from -26.7 dB to -4.4 dB; after application of DWT procedure to f-PCG, SNR increased significantly. Based on the test results obtained, wavelet type coif1 with the soft threshold gave the best result with 11.69 dB in SNR value. The coif1 had a superior result than other mother wavelets that use in this work, so the fPCG signal analysis for fetal heart rate investigation suggested to use it

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

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

A comparative study of single-channel signal processing methods in fetal phonocardiography

Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms.Web of Science178art. no. e026988

Non Invasive Foetal Monitoring with a Combined ECG - PCG System

Although modern ultrasound provides remarkable images and biophysical measures, the technology is expensive and the observations are only available over a short time. Longer term monitoring is achieved in a clinical setting using ultrasonic Doppler cardiotocography (CTG) but this has a number of limitations. Some pathologies and some anomalies of cardiac functioning are not detectable with CTG. Moreover, although frequent and/or long-term foetal heart rate (FHR) monitoring is recommended, mainly in high risk pregnancies, there is a lack of established evidence for safe ultrasound irradiation exposure to the foetus for extended periods (Ang et al., 2006). Finally, high quality ultrasound devices are too expensive and not approved for home care use. In fact, there is a remarkable mismatch between ability to examine a foetus in a clinical setting, and the almost complete absence of technology that permits longer term monitoring of a foetus at home. Therefore, in the last years, many efforts (Hany et al., 1989; Jimenez et al., 1999; Kovacs et al., 2000; Mittra et al., 2008; Moghavvemi et al., 2003; Nagal, 1986; Ruffo et al., 2010; Talbert et al., 1986; Varady et al., 2003) have been attempted by the scientific community to find a suitable alternative

Noise reduction method for the heart sound records from digital stethoscope

In recent years, digital instruments have been widely used in the medical area with the rapid development of digital technology. The digital stethoscope, which converts the acoustic sound waves in to electrical signals and then amplifies them, is gradually replacing the conventional acoustic stethoscope with the advantage of additional usage such as restoring, replaying and processing the signals for optimal listening. As the sounds are transmitted in to electrical form, they can be recorded for further signal processing. One of the major problems with recording heart sounds is noise corruption. Although there are many solutions available to noise reduction problems, it was found that most of them are based on the assumption that the noise is an additive white noise [1]. More research is required to find different de-noising techniques based on the specific noise present. Therefore, this study is motivated to answer the research question: ‘How might the noise be reduced from the heart sound records collected from digital stethoscope with suitable noise reduction method’. This research question is divided into three sub-questions, including the identification of the noise spectrum, the design of noise reduction method and the assessment of the method. In the identification stage, five main kinds of noise were chosen and their characteristics and spectrums were discussed. Compared with different kinds of adaptive filters, the suitable noise reduction filter for this study was confirmed. To assess the effect of the method, 68 pieces of sound resources were collected for the experiment. These sounds were selected based on the noise they contain. A special noise reduction method was developed for the noise. This method was tested and assessed with those sound samples by two factors: the noise level and the noise kind. The results of the experiment showed the effect of the noise reduction method for each kind of noise. The outcomes indicated that this method was suitable for heart sound noise reduction. The findings of this study, including the analysis of noise level and noise kind, indicated and concluded that the chosen method for heart sound noise reduction performed well. This is perhaps the first attempt to understand and assess the noise reduction method with classified heart sound signals which are collected from the real healthcare environment. This noise reduction method may provide a de-noising solution for the specific noise present in heart sound

Development of a Novel Dataset and Tools for Non-Invasive Fetal Electrocardiography Research

This PhD thesis presents the development of a novel open multi-modal dataset for advanced studies on fetal cardiological assessment, along with a set of signal processing tools for its exploitation. The Non-Invasive Fetal Electrocardiography (ECG) Analysis (NInFEA) dataset features multi-channel electrophysiological recordings characterized by high sampling frequency and digital resolution, maternal respiration signal, synchronized fetal trans-abdominal pulsed-wave Doppler (PWD) recordings and clinical annotations provided by expert clinicians at the time of the signal collection. To the best of our knowledge, there are no similar dataset available. The signal processing tools targeted both the PWD and the non-invasive fetal ECG, exploiting the recorded dataset. About the former, the study focuses on the processing aimed at the preparation of the signal for the automatic measurement of relevant morphological features, already adopted in the clinical practice for cardiac assessment. To this aim, a relevant step is the automatic identification of the complete and measurable cardiac cycles in the PWD videos: a rigorous methodology was deployed for the analysis of the different processing steps involved in the automatic delineation of the PWD envelope, then implementing different approaches for the supervised classification of the cardiac cycles, discriminating between complete and measurable vs. malformed or incomplete ones. Finally, preliminary measurement algorithms were also developed in order to extract clinically relevant parameters from the PWD. About the fetal ECG, this thesis concentrated on the systematic analysis of the adaptive filters performance for non-invasive fetal ECG extraction processing, identified as the reference tool throughout the thesis. Then, two studies are reported: one on the wavelet-based denoising of the extracted fetal ECG and another one on the fetal ECG quality assessment from the analysis of the raw abdominal recordings. Overall, the thesis represents an important milestone in the field, by promoting the open-data approach and introducing automated analysis tools that could be easily integrated in future medical devices

Fetal PCG Signal Processing

Tato diplomová práce se zabývá extrakcí plodového fonokardiogramu (fFKG) ze záznamů pořízených na abdominální oblasti matky neinvazivním senzorem pomocí navrženého softwarového řešení. Abdominální fonokardiogram (aFKG) se skládá ze směsi fFKG, mateřského (mFKG) a šumu ve formě pohybů matky a plodu, zvuků z orgánů a vnějších zvuků. V práci je nejprve popsána teorie o vývoji plodu a jeho srdci. Poté je v práci popsána rešerše zabývající se všemi metodami, pomocí kterých lze extrahovat fFKG a poté jsou popsány algoritmy vybraných metod. Navržený systém se zakládá na FIR filtraci a vlnkové transformaci (WT) a primárně je zaměřen na stanovení plodové tepové frekvence (fHR) a vykreslení průběhů fHR. Funkčnost tohoto softwarového systému je testována na syntetických záznamech a reálných záznamech z databází. Hodnocení kvality je provedeno stanovením fHR a odstupu signálu od šumu (SNR).This thesis deals with extraction of fetal phonocardiogram (fFKG) from the recorded signals acquired on the abdominal area of the mother with a non-invasive sensor using the proposed software solution. The abdominal phonocardiogram (aFKG) consists of a mixture of fFKG, maternal (mFKG) and noise in the form of mother and fetal movements, organ sounds and external sounds. The thesis describes first the theory of the development of the fetus and its heart. Then, the research describes all the methods by which fFKG can be extracted and algorithms of the selected methods are described. The proposed system is based on FIR filtering and wavelet transformation (WT) and primarily focuses on determining fetal heart rate (fHR) and plotting the fHR waveforms. Functionality of this software system is tested on synthetic records and real records from databases. Quality assessment is performed by determining fHR and SNR.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Hybrid Methods for Processing of Fetal Electrocardiogram

Tato doktorská disertační práce se zaměřuje na návrh, realizaci a následnou verifikaci nového typu hybridního extrakčního systému pro zpracování neinvazivního plodového elektrokardiogramu (NI-fEKG). Navržený systém sdružuje výhody jednotlivých adaptivních a neadaptivních metod. Tato práce ověřuje dva inovativní hybridní systémy s názvem ICA-ANFIS-WT a ICA-RLS-WT. Jedná se o kombinaci analýzy nezávislých komponent (ICA), adaptivního neuro-fuzzy inferenčního systému (ANFIS) nebo algoritmu založeném na rekurzivní optimální adaptaci (RLS) a vlnkové transformace (WT). Studie byla realizována na datech z klinické praxe (rozšířená databáze abdominálního a přímého fetálního elektrokardiogramu (ADFECGDB) a databáze EKG physionet challenge 2013) z pohledu neinvazivního monitorování fetální tepové frekvence (fHR) na základě stanovení celkové pravděpodobnosti správné detekce (ACC), senzitivity (SE), pozitivní prediktivní hodnoty (PPV) a harmonického průměru mezi SE a PPV (F1). Funkcionalita systému byla verifikována vůči relevantní referenci dané anotacemi. Tato práce ukázala, že hybridní systém ICA-RLS-WT dosáhl lepších výsledků než ICA-ANFIS-WT. Při experimentu na záznamech z databáze ADFECGDB dosáhla hybridní metoda ICA-RLS-WT hodnoty ACC > 80 % u 10 z 12 záznamů a hybridní metoda ICA-ANFIS-WT hodnoty ACC > 80 % pouze u 6 z 12 záznamů. Při experimentu na záznamech z databáze EKG physionet challenge 2013 dosáhla hybridní metoda ICA-RLS-WT hodnoty ACC > 80 % u 13 z 25 záznamů a hybridní metoda ICA-ANFIS-WT hodnoty ACC > 80 % pouze u 7 z 25 záznamů. Oba navržené hybridní systémy dosáhly prokazatelně lepších výsledků než jednotlivé metody v předchozích studiích.This dissertation focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The designed system combines the advantages of individual adaptive and non-adaptive methods. This thesis reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) or recursive least squares (RLS) algorithm and wavelet transform (WT). The study was conducted on clinical practice data (extended abdominal and direct fetal electrocardiogram database (ADFECGDB) and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate (fHR) monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by annotations. The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 10 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual methods tested in previous studies.450 - Katedra kybernetiky a biomedicínského inženýrstvívyhově

Unsupervised Classification of Uterine Contractions Recorded Using Electrohysterography

Pregnancy still poses health risks that are not attended to by current clinical practice motorization procedures. Electrohysterography (EHG) record signals are analyzed in the course of this thesis as a contribution and effort to evaluate their suitability for pregnancy monitoring. The presented work is a contributes with an unsupervised classification solution for uterine contractile segments to FCT’s Uterine Explorer (UEX) project, which explores analysis procedures for EHG records. In a first part, applied processing procedures are presented and a brief exploration of the best practices for these. The procedures include those to elevate the representation of uterine events relevant characteristics, ease further computation requirements, extraction of contractile segments and spectral estimation. More detail is put into the study of which characteristics should be chosen to represent uterine events in the classification process and feature selection methods. To such end, it is presented the application of a principal component analysis (PCA) to three sets: interpolated contractile events, contractions power spectral densities, and to a number of computed features that attempt evidencing time, spectral and non-linear characteristics usually used in EHG related studies. Subsequently, a wrapper model approach is presented as a mean to optimize the feature set through cyclically attempting the removal and re-addition of features based on clustering results. This approach takes advantage of the fact that one class is known beforehand to use its classification accuracy as the criteria that defines whether the modification made to the feature set was ominous. Furthermore, this work also includes the implementation of a visualization tool that allows inspecting the effect of each processing procedure, the uterine events detected by different methods and clusters they were associated to by the final iteration of the wrapper model

A Comparative Study of the Efficacy of Non-Invasive Fetal ECG Extraction Methods using Data from Clinical Practice

Hlavní náplní této práce je srovnávací studie hybridních extrakčních metod neivazivního fetáního elektrokardiogramu. Pro přesnější stanovení efektivity metod je zde představen nový evaluační systém fungující na podobném principu, jaký je využíván u přístrojů v klinické praxi. Úspěšnost extrakce je v této práci stanovena určením délek jednotlivých segmentů elektrokardiografické křivky a vzájemným porovnáváním jejich morfologie. Protože posouzení účinnosti extrakce na syntetických datech je často zavádějící, byly v této práci metody porovnávány výhradně dle dosažených výsledků na reálných signálech z klinické praxe. Metody jsou srovnávány s referencí na základě Bland-Altmanovy analýzy a stanovení statistických parametrů ACC, Se, PPV a F1.This thesis introduces a comparative study of hybrid methods for extraction of non-invasive fetal electrocardiograms (fECGs). In order to assess the effectiveness of each method more precisely, a new evaluation system is proposed. The system is inspired by the devices used in clinical practice; the efficacy is determined by evaluating the lengths of the individual segments of the fECG waveform and comparing their morphology. Since the assessment of the extraction quality using synthetic data is often misleading, the dataset used in this thesis was composed solely from the real signals from the clinical practice. The direct fECG was used as the reference; the statistics includes Bland-Altman analysis and determination of quality parameters such as ACC, Se, PPV, and F1.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn