Vijay S. Chourasia

Wavelet-based denoising of fetal phonocardiographic signals

Auscultation is still one of the first basic analytical tools used to evaluate functional state of the fetal heart, as well as the first indicator of fetal well-being. Its modern form is called fetal phonocardiography (fPCG). The fPCG technique is passive and can be used for long-term monitoring. In order to improve the diagnostic capabilities of fPCG, robust signal processing techniques are needed for denoising of the signals. Traditional denoising techniques apply a linear filter to remove the noise and interference from the fPCG signals. These methods have certain limitations for the non-stationary random fPCG signals. In this paper, an improved technique for denoising of fPCG signals is presented. A highly sensitive data recording module is used to acquire the fPCG signals from the maternal abdominal surface. The acquired fPCG signals are decomposed, denoised and reconstructed by utilising Matlab wavelet transform toolbox. The proposed approach improves the signal to noise ratio (SNR) of these signals. The presented technique can be used in preprocessing stage of all fPCG-based fetal monitoring applications.

A novel approach for phonocardiographic signals processing to make possible fetal heart rate evaluations

Abstract In this paper, a non-invasive, portable and inexpensive antenatal care system is developed using fetal phonocardiography. The fPCG technique has the potential to provide low-cost and long-term diagnostics to the under-served population. The fPCG signal contains valuable diagnostic information regarding fetal health during antenatal period. The fPCG signals are acquired from the maternal abdominal surface using a wireless data acquisition and recording system. The diagnostic parameters e.g., baseline, variability, acceleration and deceleration of the fetal heart rate are derived from the fPCG signal. A model based on adaptive neuro-fuzzy inference system is developed for the evaluation of fetal health status. To study the performance of the developed system, experiments were carried out with real fPCG signals under the supervision of medical experts. Its performance is found to be in close proximity with the widely accepted Doppler ultrasound based fetal monitor results. The overall performance shows that the developed system has a long-term monitoring capability with very high performance to cost ratio. The system can be used as first screening tool by the medical practitioners.

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.

Interval type-2 fuzzy logic based antenatal care system using phonocardiography

The assessment of fetal wellbeing depends heavily on variations in fetal heart rate (FHR) patterns. The variations in FHR patterns are very complex in nature thus its reliable interpretation is very difficult and often leads to erroneous diagnosis. We propose a new method for evaluation of fetal health status based on interval type-2 fuzzy logic through fetal phonocardiography (fPCG). Type-2 fuzzy logic is a powerful tool in handling uncertainties due to extraneous variations in FHR patterns through its increased fuzziness of relations. Four FHR parameters are extracted from each fPCG signal for diagnostic decision making. The membership functions of these four inputs and one output are chosen as a range of values so as to represent the level of uncertainty. The fuzzy rules are constructed based on standard clinical guidelines on FHR parameters. Experimental clinical tests have shown very good performance of the developed system in comparison with the FHR trace simultaneously recorded through standard fetal monitor. Statistical evaluation of the developed system shows 92% accuracy. With the proposed method we hope that, long-term and continuous antenatal care will become easy, cost effective, reliable and efficient.

Wireless data acquisition system for fetal phonocardiographic signals using Bluetooth

Diagnosing fetal health using stethoscope is a clinically established practice. Its modern form is called fetal phonocardiography. This paper presents a Bluetooth

Development of a Signal Simulation Module for Testing of Phonocardiography Based Prenatal Monitoring Systems

Prenatal monitoring enables early diagnosis of congenital anomalies and genetic disorders in the uterus. Several systems and techniques have been either developed or under development for this purpose. Every system under its developmental stage needs testing and validation of its performance. In this paper, a Signal Simulation Module (SSM) for simulation of maternal abdominal conditions has been developed. The developed module replicates the pregnant womens abdomen by generating various signals similar to actual signals using Matlab TM . A comparison between signals from developed module and actual abdominal signals shows that presented system closely simulates acoustical conditions of the mothers abdomen. The developed module can be used for testing and calibration of prenatal monitoring systems during

TIME-FREQUENCY CHARACTERIZATION OF FETAL PHONOCARDIOGRAPHIC SIGNALS USING WAVELET SCALOGRAM

Fetal phonocardiography is a simple and noninvasive diagnostic technique for surveillance of fetal well-being. The fetal phonocardiographic (fPCG) signals carry valuable information about the anatomical and physiological states of the fetal heart. This article is concerned with a study of continuous wavelet transform (CWT)-based scalogram in analyzing the fPCG signals. The scalogram has both spatial and frequency resolution powers, whereas traditional spectral estimation methods only have the frequency resolution ability. The fPCG signals are acquired by a specially developed data recording system. Segmentation of these signals into fundamental components of fetal heart sound (S1 & S2) is carried out through envelope detection and thresholding techniques. CWT-based scalogram is used for time-frequency characterization of the segmented fPCG signals. It has been shown that the wavelet scalogram provides enough features of the fPCG signals that will help to obtain qualitative and quantitative measurements of the time-frequency characteristics of the fPCG signals and consequently, assist in diagnosis. The proposed method for time-frequency analysis (TFA) and the associated pre-processing have been shown to be suitable for the characterization of fPCG signals, yielding relatively good and robust results in the experimental evaluation.

Performance analysis of seismocardiography for heart sound signal recording in noisy scenarios

Abstract This paper presents a system based on Seismocardiography (SCG) to monitor the heart sound signal for the long-term. It uses an accelerometer, which is of small size and low weight and, thus, convenient to wear. Such a system should also be robust to various noises which occur in real life scenarios. Therefore, a detailed analysis is provided of the proposed system and its performance is compared to the performance of the Phoncardiography (PCG) system. For this purpose, both signals of five subjects were simultaneously recorded in clinical and different real life noisy scenarios. For the quantitative analysis, the detection rate of fundamental heart sound components, S1 and S2, is obtained. Furthermore, a quality index based on the energy of fundamental components is also proposed and obtained for the same. Results show that both the techniques are able to acquire the S1 and S2, in clinical set-up. However, in real life scenarios, we observed many favourable features in the proposed system as compared to PCG, for its use for long-term monitoring.

Foetal phonocardiographic signal denoising based on non-negative matrix factorization

Foetal phonocardiography (fPCG) is a non-invasive, cost-effective and simple technique for antenatal care. The fPCG signals contain vital information of diagnostic importance regarding the foetal health. However, the fPCG signal is usually contaminated by various noises and thus requires robust signal processing to denoise the signal. The main aim of this paper is to develop a methodology for removal of unwanted noise from the fPCG signal. The proposed methodology utilizes the non-negative matrix factorization (NMF) algorithm. The developed methodology is tested on both simulated and real-time fPCG signals. The performance of the developed methodology has been evaluated in terms of the gain in signal-to-noise ratio (SNR) achieved through the process of denoising. In particular, using the NMF algorithm, a substantial improvement in SNR of the fPCG signals in the range of 12–30 dB has been achieved, providing a high quality assessment of foetal well-being.

FETAL HEART RATE VARIABILITY ANALYSIS FROM PHONOCARDIOGRAPHIC RECORDINGS

This paper presents an algorithm for classification of fetal health status using fetal heart rate variability (fHRV) analysis through phonocardiography. First, the fetal heart sound signals are acquired from the maternal abdominal surface using a specially developed Bluetooth-based wireless data recording system. Then, fetal heart rate (FHR) traces are derived from these signals. Ten numbers of linear and nonlinear features are extracted from each FHR trace. Finally, the multilayer perceptron (MLP) neural network is used to classify the health status of the fetus. Results show very promising performance toward the prediction of fetal wellbeing on the set of collected fetal heart sound signals. Finally, this work is likely to lead to an automatic screening device with additional potential of predicting fetal wellbeing.