Real-time filtering adaptive algorithms for non-stationary noise in electrocardiograms

2022 ◽  
Vol 72 ◽  
pp. 103308
Author(s):  
Nataliya Tulyakova ◽  
Oleksandr Trofymchuk
Keyword(s):  
Real Time ◽  
Adaptive Algorithms ◽  
Stationary Noise

scholarly journals ЛОКАЛЬНО-АДАПТИВНАЯ ФИЛЬТРАЦИЯ НЕСТАЦИОНАРНОГО ШУМА В ДЛИТЕЛЬНЫХ ЭЛЕКТРОКАРДИОГРАФИЧЕСКИХ СИГНАЛАХ

2020 ◽  
pp. 16-33
Author(s):  
Наталия Олеговна Тулякова ◽  
Александр Николаевич Трофимчук
Keyword(s):  
Real Time ◽  
High Efficiency ◽  
A Priori ◽  
Adaptive Algorithms ◽  
Adaptive Method ◽  
Noise Variance ◽  
Stationary Noise ◽  
Ecg Signals ◽  
Statistical Estimates ◽  
Locally Adaptive

The research subject of the article is the methods of locally adaptive filtering of non-stationary (from the point of view of its variance) noise in long-term electrocardiogram (ECG) signals. The goal is to develop locally adaptive algorithms for filtering noise with different a priori unknown levels of variance in real-time for ECG signals recorded with a standard sampling rate of 500 Hz. The tasks to be solved are: to investigate the effectiveness of the developed adaptive ECG filtering algorithms using numerical statistical estimates of processing quality in a wide range of additive Gaussian noise variance variation, to investigate the suppression of real non-stationary electromyographic (EMG) noise, and to analyze the application for normal and pathological ECG signals. The methods are integral and local indicators of the filter quality according to the criteria of the mean square error and the signal-to-noise ratio was obtained using numerical simulation (via Monte Carlo analysis). The following results were obtained: an adaptive method for real-time suppression of non-stationary noise in the ECG is proposed, the one-pass and the two-pass algorithms, and the algorithm with selective depending on the preliminary estimates of noise levels re-filtering have been developed on the method basis. Statistical estimates of the filters' efficiency and analysis of their outputs show a high degree of suppression of the noise with different levels of variance in the ECGs. The distortions absence while processing QRS-complex and high efficiency of suppression of Gaussian and real EMG noise with varying variance are demonstrated. The analysis of the output signals and plots of the local adaptation parameters and the adaptable parameters of the proposed algorithms confirms the high efficiency of filtering. The developed algorithms have been successfully tested for normal and pathological ECG signals. Conclusions. The scientific novelty of the results is the development of a locally adaptive method with noise and signal-dependent filter parameters switching and of the adaptive algorithms based on this method for non-stationary noise reduction in the ECG in real-time. This method does not require time for filter parameters adaptation and a priori information about the noise variance, and it has a high-speed performance in real-time mode.

scholarly journals A Real-Time Speech Separation Method Based on Camera and Microphone Array Sensors Fusion Approach

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3527
Author(s):  
Ching-Feng Liu ◽  
Wei-Siang Ciou ◽  
Peng-Ting Chen ◽  
Yi-Chun Du
Keyword(s):  
Real Time ◽  
Speech Processing ◽  
Microphone Array ◽  
Speech Sound ◽  
Separation Method ◽  
Supervised Machine Learning ◽  
Machine Language ◽  
Speech Separation ◽  
Real Time System ◽  
Stationary Noise

In the context of assisted human, identifying and enhancing non-stationary speech targets speech in various noise environments, such as a cocktail party, is an important issue for real-time speech separation. Previous studies mostly used microphone signal processing to perform target speech separation and analysis, such as feature recognition through a large amount of training data and supervised machine learning. The method was suitable for stationary noise suppression, but relatively limited for non-stationary noise and difficult to meet the real-time processing requirement. In this study, we propose a real-time speech separation method based on an approach that combines an optical camera and a microphone array. The method was divided into two stages. Stage 1 used computer vision technology with the camera to detect and identify interest targets and evaluate source angles and distance. Stage 2 used beamforming technology with microphone array to enhance and separate the target speech sound. The asynchronous update function was utilized to integrate the beamforming control and speech processing to reduce the effect of the processing delay. The experimental results show that the noise reduction in various stationary and non-stationary noise environments were 6.1 dB and 5.2 dB respectively. The response time of speech processing was less than 10ms, which meets the requirements of a real-time system. The proposed method has high potential to be applied in auxiliary listening systems or machine language processing like intelligent personal assistant.

scholarly journals Artificial Immune Systems: An Overview for Faulting Actuators

Actuators ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 53
Author(s):  
Kidd
Keyword(s):  
Real Time ◽  
Artificial Immune Systems ◽  
Adaptive Algorithms ◽  
Actuator Faults ◽  
Artificial Immune ◽  
Immune Systems ◽  
Limited Success ◽  
Fault Compensation

This paper reviews Artificial Immune Systems (AIS) that can be implemented to compensate for actuators that are in a faulted state or operating abnormally. Eventually, all actuators will fail or wear out, and these actuator faults must be managed if a system is to operate safely. The AIS are adaptive algorithms which are inherently well-suited to these situations by treating these faults as infections that must be combated. However, the computational intensity of these algorithms has caused them to have limited success in real-time situations. With the advent of distributed and cloud-based computing these algorithms have begun to be feasible for diagnosing faulted actuators and then generating compensating controllers in near-real-time. To encourage the application of AIS to these situations, this work presents research for the fundamental operating principles of AIS, their applications, and a brief case-study on their applicability to fault compensation by considering an overactuated rover with four independent drive wheels and independent front and rear steering.

Adaptive Algorithms for Intelligent Geometric Computing

2012 ◽  
pp. 97-104
Author(s):  
M. L. Gavrilova
Keyword(s):  
Real Time ◽  
Adaptive Algorithms ◽  
Adaptive Mesh ◽  
Complex Data ◽  
Geometric Computing ◽  
Adaptive Computation ◽  
Image Capturing ◽  
Complex Data Sets ◽  
Very Large Datasets ◽  
Primary Focus

This chapter spans topics from such important areas as Artificial Intelligence, Computational Geometry and Biometric Technologies. The primary focus is on the proposed Adaptive Computation Paradigm and its applications to surface modeling and biometric processing. Availability of much more affordable storage and high resolution image capturing devices have contributed significantly over the past few years to accumulating very large datasets of collected data (such as GIS maps, biometric samples, videos etc.). On the other hand, it also created significant challenges driven by the higher than ever volumes and the complexity of the data, that can no longer be resolved through acquisition of more memory, faster processors or optimization of existing algorithms. These developments justified the need for radically new concepts for massive data storage, processing and visualization. To address this need, the current chapter presents the original methodology based on the paradigm of the Adaptive Geometric Computing. The methodology enables storing complex data in a compact form, providing efficient access to it, preserving high level of details and visualizing dynamic changes in a smooth and continuous manner. The first part of the chapter discusses adaptive algorithms in real-time visualization, specifically in GIS (Geographic Information Systems) applications. Data structures such as Real-time Optimally Adaptive Mesh (ROAM) and Progressive Mesh (PM) are briefly surveyed. The adaptive method Adaptive Spatial Memory (ASM), developed by R. Apu and M. Gavrilova, is then introduced. This method allows fast and efficient visualization of complex data sets representing terrains, landscapes and Digital Elevation Models (DEM). Its advantages are briefly discussed. The second part of the chapter presents application of adaptive computation paradigm and evolutionary computing to missile simulation. As a result, patterns of complex behavior can be developed and analyzed. The final part of the chapter marries a concept of adaptive computation and topology-based techniques and discusses their application to challenging area of biometric computing.

scholarly journals Adaptive algorithms for real-time filtering of electrocardiogram with multilevel noise estimation

Radiotekhnika ◽  
2020 ◽  
Vol 2 (201) ◽  
pp. 201-214
Author(s):  
Н.О. Тулякова ◽  
О.М. Трофимчук
Keyword(s):  
Real Time ◽  
Adaptive Algorithms ◽  
Noise Estimation
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