A dynamical model for generating synthetic Phonocardiogram signals

A piecewise spline approach for modeling of ECG signals

This paper presents a new spline-based modeling method of electrocardiogram (ECG) signal that can reproduce normal as well as abnormal ECG beats. Large volume ECG data is required for automatic machine learning diagnostic systems, medical education, research and testing purposes but due to privacy issues, access to this medical data is very difficult. Given this, modeling an ECG signal is a very challenging task in the field of biomedical signal processing. Spline-based modeling is the latest and one of the most efficient methods with very low computational complexity in the domain of ECG signal generation. In this paper, healthy ECG and arrhythmia conditions have been considered for the synthetic generation, (namely Atrial fibrillation and Congestive heart failure ECG beats) because these are the leading causes of death globally. To validate the performance of the presented modeling method, it is tested on 100 signals, also the percentage root mean square difference (PRD) and the root mean square error (RMSE) have been determined. These calculated values are analyzed and the results are found to be very promising and show that the presented method is one of the best methods in the field of synthetic ECG signal generation. A comparison amongst relevant existing techniques and the proposed method is also presented. The performance merit values PRD and RMSE, for the proposed method obtained are 38.99 and 0.10092, respectively, which are lower than the values obtained in other compared methods. To ensure fidelity of the proposed modeling technique with respect to IEC60601 standard, few Conformance Testing Services (CTS)database signals have also been modelled with a very close resemblance with the standard signals.

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.