Comparative analysis of ECG signal processing methods in the time-frequency domain

2018 ◽  
Author(s):  
Yeldos A. Altay ◽  
Artem S. Kremlev
Keyword(s):  
Signal Processing ◽  
Comparative Analysis ◽  
Frequency Domain ◽  
Ecg Signal ◽  
Processing Methods ◽  
Time Frequency ◽  
Ecg Signal Processing ◽  
Signal Processing Methods

scholarly journals Verification and comparison of MIT-BIH arrhythmia database based on number of beats

2021 ◽  
Vol 11 (6) ◽  
pp. 4950
Author(s):  
Akram Jaddoa Khalaf ◽  
Samir Jasim Mohammed
Keyword(s):  
Signal Processing ◽  
Ecg Signal ◽  
Annotation File ◽  
Qrs Detection ◽  
Detection Algorithms ◽  
Matlab Program ◽  
Ecg Signal Processing ◽  
The Difference ◽  
Signal Processing Methods ◽  
Ecg Database

<span lang="EN-US">The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number that should be used. We propose a simple function to standardize the beats number for any ECG PhysioNet database to improve the waveform database toolbox (WFDB) for the MATLAB program. This function is based on the annotation's description from the databases and can be added to the Toolbox. The function is removed the non-beats annotation without any errors. The results show a high percentage of 71% from the reviewed methods used an incorrect number of beats for this database.</span>

Application of Advanced Signal Processing Methods to Automotive Systems Testing

2004 ◽  
Author(s):  
Steve M. Rohde ◽  
William J. Williams ◽  
Mitchell M. Rohde
Keyword(s):  
Signal Processing ◽  
Consumer Preferences ◽  
Processing Methods ◽  
Time Frequency ◽  
Vehicle Systems ◽  
Pedal Feel ◽  
Mathematical Analyses ◽  
Noise Vibration ◽  
Virtual Product Development ◽  
Signal Processing Methods

During the past twenty years there have been rapid developments in the creation and application of mathematical computer-based capabilities and tools (e.g., FEA) to simulate and synthesize vehicle systems. This has led to the concept of virtual product development. In parallel with the development of these tools, an equally sophisticated set of tools have been developed in the area of advanced signal processing. These tools, based upon mathematical and statistical modeling techniques, enable the extraction of useful information from data and have application throughout the entire vehicle creation process. Moreover, signal processing bridges the gap between the “virtual” and the “real” worlds — an extremely important concept that is changing the entire nature of what is thought of as “testing.” This paper discusses the use of advanced signal processing methods in vehicle creation with particular emphasis on its use in vehicle systems testing. Modern Time Frequency Analysis (TFA), a technique that was specifically designed to study transient signals and was in part pioneered by one of the authors (WJW), is highlighted. TFA expresses a signal jointly in time and frequency at very high resolution and as such can often provide profound insights. Applications of TFA to vehicle systems testing are presented related to Noise, Vibration, and Harshness (NVH) that enable sound quality analyses. For example, using TFA predictive models of consumer preferences for transient sounds that are useful to the automotive engineer in testing and modifying new vehicle subsystem designs are discussed. Other applications that are discussed deal with brake pedal feel, and characterizing vehicle crash signals. In the latter case TFA has resulted in some unique insights that were not provided by conventional statistical and mathematical analyses.

Comparative characteristics of anti-collision processing of radio signal from identification tags on surface acoustic waves

2019 ◽  
pp. 48-56 ◽  
Author(s):  
A. V. Sorokin ◽  
A. P. Shepeta ◽  
V. A. Nenashev ◽  
G. M. Wattimena
Keyword(s):  
Signal Processing ◽  
Surface Acoustic Waves ◽  
Group Identification ◽  
Sensor Data ◽  
Correct Identification ◽  
Processing Methods ◽  
Time Frequency ◽  
Advantages And Disadvantages ◽  
Identification Tags ◽  
Signal Processing Methods

Introduction:Collision of information signals is a common problem in the measurement of physical magnitudes, such as temperature, pressure, stress, etc., with acoustic-electronic sensors. This problem is caused by overlapping response signals in the time domain, which makes it difficult to interpret correctly the device identification codes or the sensor data received.Purpose:Analysis of anticollision algorithms for radio-frequency tag code detection and identification by response information signals from acoustic-electronic devices which use the methods of time, frequency and frequency-time division of the response radio signals.Methods:Probabilistic methods for calculating the parameters of digital detectors of radio pulse bursts with given false alarm values and gaussian white noise background; individual code group identification methods when studying the attenuation of acoustic-electric signal during their propagation in the tag substrate, taking into account the dependence of the attenuation on the tag topology.Results:We have derived analytical expressions to calculate the probability of the correct identification of each tag, taking into account the dependence on tag topology, attenuation characteristics, the anti-collision signal processing methods and the signal-to-noise ratios. Curves which allow you to compare the advantages and disadvantages of the considered anti-collision signal processing methods are calculated and shown in the article. The analysis of the graphic charts demonstrating the correct identification probability has shown that identification tags with frequency-time coding have better ratios as compared to frequency or time methods of collision prevention.Practical relevance:The obtained result allows you to effectively evaluate the condition of technical objects, improving the predictability and prevention of possible environmental and man-made disasters.

Vibration Measurement and Fault Diagnosis System for Experimental Teaching

2013 ◽  
Vol 401-403 ◽  
pp. 1226-1229
Author(s):  
Xiao Yan Yang ◽  
You Gang Xiao ◽  
Jian Feng Huang
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Virtual Instrument ◽  
Experimental System ◽  
Vibration Measurement ◽  
Diagnosis System ◽  
Time Frequency ◽  
Experimental Teaching ◽  
Equipment State ◽  
Signal Processing Methods

Based on LabVIEW, vibration measurement and diagnosis system of equipment was developed for experimental teaching. On the platform, vibration signals from running equipment can be sampled, displayed, stored, and analyzed in time domain, frequency domain and time-frequency domain. The advanced signal processing technology such as power spectrum analysis, cepstrum analysis, demodulation analysis can also be executed. Using pattern recognition technology, the processed signals can be integrated for intelligent diagnosis of equipment state. The experimental system is helpful for students to learn signal processing methods, and to design virtual instrument.

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