Vijay S. Chourasia

Bio: Vijay S. Chourasia is an academic researcher from LNM Institute of Information Technology. The author has contributed to research in topics: Hearing aid & Filter bank. The author has an hindex of 8, co-authored 19 publications receiving 189 citations.

Design methodology of a new wavelet basis function for fetal phonocardiographic signals.

Abstract: 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 review and comparative analysis of recent advancements in fetal monitoring techniques.

Abstract: Over the past few years, various devices and techniques have been developed for electronic fetal monitoring (EFM), which is performed during pregnancy or continuously during labor to ensure normal delivery of a healthy baby. We reviewed and analyzed the performance of currently used EFM techniques with the goal of determining a noninvasive, cost-effective alternative for use in the home environment. This review includes research papers, publications, web sources, product manuals, interviews, formal discussions, and other available literature with the goal of providing a comprehensive comparative analysis of all available EFM techniques. We relate some of the insights gained from reviewing a large number of resources.

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.

Wavelet-based denoising of fetal phonocardiographic signals

Abstract: 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.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Recent Advances in Seismocardiography

Abstract: Cardiovascular disease is a major cause of death worldwide. New diagnostic tools are needed to provide early detection and intervention to reduce mortality and increase both the duration and quality of life for patients with heart disease. Seismocardiography (SCG) is a technique for noninvasive evaluation of cardiac activity. However, the complexity of SCG signals introduced challenges in SCG studies. Renewed interest in investigating the utility of SCG accelerated in recent years and benefited from new advances in low-cost lightweight sensors, and signal processing and machine learning methods. Recent studies demonstrated the potential clinical utility of SCG signals for the detection and monitoring of certain cardiovascular conditions. While some studies focused on investigating the genesis of SCG signals and their clinical applications, others focused on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification. This paper reviews the recent advances in the field of SCG.

Medical data classification using interval type-2 fuzzy logic system and wavelets

Abstract: Automated medical data classification using wavelets and interval type-2 fuzzy logic.Wavelet features reduce computational burden and enhance performance of IT2FLS.IT2FLS employs hybrid learning process by fuzzy c-means and genetic algorithm.Wavelet-IT2FLS demonstrates significant dominance against competitive methods.The approach is useful as a DSS for clinicians and practitioners in medical practice. This paper introduces an automated medical data classification method using wavelet transformation (WT) and interval type-2 fuzzy logic system (IT2FLS). Wavelet coefficients, which serve as inputs to the IT2FLS, are a compact form of original data but they exhibits highly discriminative features. The integration between WT and IT2FLS aims to cope with both high-dimensional data challenge and uncertainty. IT2FLS utilizes a hybrid learning process comprising unsupervised structure learning by the fuzzy c-means (FCM) clustering and supervised parameter tuning by genetic algorithm. This learning process is computationally expensive, especially when employed with high-dimensional data. The application of WT therefore reduces computational burden and enhances performance of IT2FLS. Experiments are implemented with two frequently used medical datasets from the UCI Repository for machine learning: the Wisconsin breast cancer and Cleveland heart disease. A number of important metrics are computed to measure the performance of the classification. They consist of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. Results demonstrate a significant dominance of the wavelet-IT2FLS approach compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus useful as a decision support system for clinicians and practitioners in the medical practice.

Depression diagnosis support system based on eeg signal entropies

Abstract: Electroencephalography (EEG) is a measure which represents the functional activity of the brain. We show that a detailed analysis of EEG measurements provides highly discriminant features which indicate the mental state of patients with clinical depression. Our feature extraction method revolves around a novel processing structure that combines wavelet packet decomposition (WPD) and non-linear algorithms. WPD was used to select appropriate EEG frequency bands. The resulting signals were processed with the non-linear measures of approximate entropy (ApEn), sample entropy (SampEn), renyi entropy (REN) and bispectral phase entropy (Ph). The features were selected using t-test and only discriminative features were fed to various classifiers, namely probabilistic neural network (PNN), support vector machine (SVM), decision tree (DT), k-nearest neighbor algorithm (k-NN), naive bayes classification (NBC), Gaussian mixture model (GMM) and Fuzzy Sugeno Classifier (FSC). Our classification results show that, with a classification accuracy of 99.5%, the PNN classifier performed better than the rest of classifiers in discriminating between normal and depression EEG signals. Hence, the proposed decision support system can be used to diagnose, and monitor the treatment of patients suffering from depression.

A Phonocardiographic-Based Fiber-Optic Sensor and Adaptive Filtering System for Noninvasive Continuous Fetal Heart Rate Monitoring

Abstract: This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio—SNR, Root Mean Square Error—RMSE, Sensitivity—S+, and Positive Predictive Value—PPV.