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

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

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 application

Discrete Wavelet Methods for Interference Mitigation: An Application To Radio Astronomy

The field of wavelets concerns the analysis and alteration of signals at various resolutions. This is achieved through the use of analysis functions which are referred to as wavelets. A wavelet is a signal defined for some brief period of time that contains oscillatory characteristics. Generally, wavelets are intentionally designed to posses particular qualities relevant to a particular signal processing application. This research project makes use of wavelets to mitigate interference, and documents how wavelets are effective in the suppression of Radio Frequency Interference (RFI) in the context of radio astronomy. This study begins with the design of a library of smooth orthogonal wavelets well suited to interference suppression. This is achieved through the use of a multi-parameter optimization applied to a trigonometric parameterization of wavelet filters used for the implementation of the Discrete Wavelet Transform (DWT). This is followed by the design of a simplified wavelet interference suppression system, from which measures of performance and suitability are considered. It is shown that optimal performance metrics for the suppression system are that of Shannon’s entropy, Root Mean Square Error (RMSE) and normality testing using the Lilliefors test. From the application of these heuristics, the optimal thresholding mechanism was found to be the universal adaptive threshold and entropy based measures were found to be optimal for matching wavelets to interference. This in turn resulted in the implementation of the wavelet suppression system, which consisted of a bank of matched filters used to determine which interference source is present in a sampled time domain vector. From this, the astronomy based application was documented and results were obtained. It is shown that the wavelet based interference suppression system outperforms existing flagging techniques. This is achieved by considering measures of the number of sources within a radio-image of the Messier 83 (M83) galaxy and the power of the main source in the image. It is shown that designed system results in an increase of 27% in the number of sources in the recovered radio image and a 1.9% loss of power of the main source

Development of a sub-miniature acoustic sensor for wireless monitoring of heart rate

This thesis presents the development of a non-invasive, wireless, low-power, phonocardiographic (PCG) or heart sound sensor platform suitable for long-term monitoring of heart function. The core of this development process involves a study of the feasibility of this conceptual system and the development of a prototype mixed-signals integrated circuit (IC) to form the integral component of the proposed sensor. The feasibility study of the proposed long-term monitoring sensor is divided into two main parts. The first part of the study investigates the technological aspect of the conceptual system, via a system level design. This is to prove the technological or operational feasibility of the system, where the system can be built completely using discrete, off-the-shelf electronics components to satisfy the size, power consumption, battery life and operational requirements of the sensor platform. The second part of the study concentrates on the post-processing of the heart sounds and murmurs or PCG data recorded. This is where a number of different de-noising algorithms are studied and their relative performance compared when applied to a variety of different noisy heart sound signals that would likely be acquired using the proposed sensor in everyday life. This was done to demonstrate the functional feasibility of the proposed system, where the ambient acoustic noise in the recorded PCG data can be effectively suppressed and therefore meaningful analysis of heart function i.e. heart rate, can be performed on the data. After the feasibility of the conceptual system has been demonstrated, the final part of this thesis discusses the synthesis and testing of a 0.35 μm CMOS technology prototype mixed analog-digital integrated circuit (IC) to miniaturise part of this sensor platform outlined in the system level design, conducted in the earlier part of this thesis, to achieve the objective specifications – in terms of the size and power consumption. A new implementation of the multi-tanh triplet transconductor is introduced to construct a pair of 100 nW analogue 4th order Gm-C signal conditioning filters. Furthermore, a 7 μW digital circuit was designed to drive the analog-to-digital conversion cycle of the Linear Technology LTC1288 ADC and synchronise the ADC’s output to generate the Manchester encoded data compatible with the Holt Integrated Circuit HI-15530 Manchester Encoder/Decoder

Synthesis of normal and abnormal heart sounds using Generative Adversarial Networks

En esta tesis doctoral se presentan diferentes métodos propuestos para el análisis y síntesis de sonidos cardíacos normales y anormales, logrando los siguientes aportes al estado del arte: i) Se implementó un algoritmo basado en la transformada wavelet empírica (EWT) y la energía promedio normalizada de Shannon (NASE) para mejorar la etapa de segmentación automática de los sonidos cardíacos; ii) Se implementaron diferentes técnicas de extracción de características para las señales cardíacas utilizando los coeficientes cepstrales de frecuencia Mel (MFCC), los coeficientes de predicción lineal (LPC) y los valores de potencia. Además, se probaron varios modelos de Machine Learning para la clasificación automática de sonidos cardíacos normales y anormales; iii) Se diseñó un modelo basado en Redes Adversarias Generativas (GAN) para generar sonidos cardíacos sintéticos normales. Además, se implementa un algoritmo de eliminación de ruido utilizando EWT, lo que permite una disminución en la cantidad de épocas y el costo computacional que requiere el modelo GAN; iv) Finalmente, se propone un modelo basado en la arquitectura GAN, que consiste en refinar señales cardíacas sintéticas obtenidas por un modelo matemático con características de señales cardíacas reales. Este modelo se ha denominado FeaturesGAN y no requiere una gran base de datos para generar diferentes tipos de sonidos cardíacos. Cada uno de estos aportes fueron validados con diferentes métodos objetivos y comparados con trabajos publicados en el estado del arte, obteniendo resultados favorables.DoctoradoDoctor en Ingeniería Eléctrica y Electrónic

A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

Heart sound auscultation has been demonstrated to be beneficial in clinical usage for early screening of cardiovascular diseases. Due to the high requirement of well-trained professionals for auscultation, automatic auscultation benefiting from signal processing and machine learning can help auxiliary diagnosis and reduce the burdens of training professional clinicians. Nevertheless, classic machine learning is limited to performance improvement in the era of big data. Deep learning has achieved better performance than classic machine learning in many research fields, as it employs more complex model architectures with stronger capability of extracting effective representations. Deep learning has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were given before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning in the past six years 2017--2022. We introduce both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis

Non-invasive fetal monitoring: a maternal surface ECG electrode placement-based novel approach for optimization of adaptive filter control parameters using the LMS and RLS algorithms

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size mu and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.Web of Science175art. no. 115

Characterization, Classification, and Genesis of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG