Radana Kahankova

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 μ 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.

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

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.

Adaptive signal processing techniques for extracting abdominal fetal electrocardiogram

The extraction of the Fetal Electrocardiogram (fECG) from the composite Electrocardiogram (ECG) signal obtained from the abdominal lead is discussed. The main point of this paper is to introduce some of the most used Least Mean Squares (LMS) based Finite Impulse Response (FIR) Adaptive Filters and to determine which of them are the most effective under varying circumstances. Experimental results suggest the ideal combination of the chosen settings for these functions. Results of fECG extraction are assessed by Percentage Root-Mean-Square Difference (PRD), input and output Signal to Noise Ratios (SNRs), and Root Mean Square Error (RMSE). Based on simulations conclusions, optimal convergence constant value and filter order were empirically determined. Setting the optimal value of the convergence constant and filter order of adaptive algorithm can be considered a contribution to original results. These results can be used on real records fECG, where it is difficult to determine because of the missing reference.

Design of a synthetic ECG signal based on the Fourier series

The main objective of this work is creation of a synthetic ECG signal in software MATLAB based on the analysis of Fourier series. The individual elements of ECG signal are approximated by mathematical model, which is thoroughly described, explained and then applied. The output is a synthetic model of an ECG. Our approach to modeling biological signals allows change input parameters (amplitude and period of significant elements ECG). Synthetic models of biological signals can be used for demonstration purposes, but mainly serves as a material for functionality detectors for measuring and predicting lengths of waves and intervals.

Non-invasive Fetal ECG Extraction from Maternal Abdominal ECG Using LMS and RLS Adaptive Algorithms

This paper focuses on the fetal electrocardiogram (fECG) recorded transabdominally. This method could become very efficient and essential tool in monitoring and diagnosing endangered fetuses during the pregnancy and the delivery. The greatest challenge connected with this kind of monitoring is the amount of noise that is recorded within the desired signal. Thus, the extraction of the fECG from the composite abdominal signal is discussed. The authors’ aim is to introduce the most suitable representatives from the Least Mean Squares (LMS) and Recursive Least Square (RLS) based Finite Impulse Response (FIR) Adaptive Filters. Experimental results suggest the ideal combination of the chosen filters’ settings (Step size, filter length, forgetting factor etc.). Results of fECG extraction are evaluated by the objective parameters, namely Percentage Root-Mean-Square Difference (PRD), input and output Signal to Noise Ratios (SNRs), and Root Mean Square Error (RMSE).

Optimization of the Training Symbols for Minimum Mean Square Error Equalizer

The theory of Minimum Mean Square Error (MMSE) and Symbol Error Rate (SER) will be introduced and used as a parameter of analysis, we will find the optimized number of training symbols for different amounts of data. The training symbols are used in adaptive channel equalization where the communication channel is totally unknown, the training symbols are the data sent via the channel, the receiver already know which symbols it will receive, this way the equalizer can analyse the unknown channel and configure it’s coefficients to improve the communication. Simulations of a communication channel made in Matlab together with the parameter SER will show the optimized settings for different amounts of numbers of symbols for different values of Eb/E0 (the energy per bit to noise power spectral density ratio). After the simulations results, the settings will be implemented in a real hardware device (NI RF VSG PXI-5670 Vector Signal Generator and NI RF PXI VSA 5661 Vector Signal Analyzer) and the concepts of Modulation Error Ratio (MER) and Additive White Gaussian Noise (AWGN) will be used to evaluate the communication. The main purpose of this paper is verifying the theoretical assumptions concerning the impact of the number of training symbols on the quality of channel equalization in case of a real hardware in the form of software-defined radio (SDR). The real experiments brought the unique results, which can be used for the implementation of the feed-forward software defined equalization.

Speech Quality Assessment Based on Virtual Instrumentation

This paper introduces a program for objective and subjective evaluation of speech quality. Using this environment, a lot of speech recordings and various indoor and outdoor noises were processed. As a subjective speech evaluation method, the Dynamic time warping (DTW) method was selected, with PARCOR coefficients being chosen as symptom vectors. For the filtration of the noise in the recording, adaptive filtering based on LMS and RLS algorithms was used and the performance of the adaptive filtering was assessed. Similarity ranged from 70% to 95% for both algorithms. In terms of signal to noise ratio, the RLS algorithm ranged from 36 dB to 42 dB, while the LMS algorithm only varied from 20 dB to 29 dB.

Speech Signal Processing using Microphones NI 9234 and LabVIEW

The paper deals with the speech processing and adaptive filtration. Introduced application is implemented in both online and offline mode in LabVIEW. The online mode program is used to create a database of speech recordings and various interferences from the outdoor environment as well as from the home. The offline application then serves to test adaptive algorithms for the needs of speech processing. The criterion for comparing the efficiency of individual algorithms is primarily to increase the signal to noise ratio. To test the filtration rate, a global SNR method was chosen.

Comparison of the LMS, NLMS, RLS, and QR-RLS algorithms for vehicle noise suppression

The paper deals with the speech processing and adaptive filtration. For the analysis we used application implemented in both online and offline mode in LabVIEW. The experiments included comparison of the noise caused by electric car and diesel car which was measured and analyzed by means of Microphones NI 9234 and our application. We tested four different adaptive filters to cancel the noise and compared their efficiency. The criterion for comparing the efficiency of individual algorithms is primarily to increase the global signal to noise ratio (GSNR).

Fetal ECG Preprocessing Using Wavelet Transform

Fetal electrocardiography is one of the most promising methods of Electronic fetal monitoring, which helps physicians to assess the fetal well-being diagnose the hypoxic states. This paper focuses on introducing Wavelet Transform as an effective tool to suppress the most frequent types of fetal electrocardiogram interferences, such as powerline or myopotential interference. We also suggest optimal type of the wavelet and threshold for this purpose.