A REVIEW ON ATRIAL FIBRILLATION AND ITS RELATED ANALYSIS

2016 ◽  
Vol 78 (7-4) ◽  
Author(s):  
Priscilla Sim Chee Mei ◽  
Anita Ahmad
Keyword(s):  
Atrial Fibrillation ◽  
Signal Processing ◽  
Frequency Domain ◽  
Time Domain ◽  
Heart Rhythm ◽  
Basic Information ◽  
Heart Problem ◽  
Underlying Theory ◽  
The Common ◽  
Processing Steps

Atrial fibrillation (AF) has been widely stated as the most common arrhythmias (irregularities of heart rhythm) which could lead to severe heart problem such as stroke. Many studies have been conducted to understand and explain its mechanism by analyzing its signal, in either time domain or frequency domain. This paper aims to provide basic information on the AF by reviewing relevant papers. Overall, this paper will provide review on the underlying theory of AF, AF mechanism as well as the common relevant signal processing steps and analysis.

scholarly journals Signal-Processing Framework for Ultrasound Compressed Sensing Data: Envelope Detection and Spectral Analysis

2020 ◽  
Vol 10 (19) ◽  
pp. 6956
Author(s):  
Yisak Kim ◽  
Juyoung Park ◽  
Hyungsuk Kim
Keyword(s):  
Signal Processing ◽  
Compressed Sensing ◽  
Frequency Domain ◽  
Time Domain ◽  
Sampling Rate ◽  
Processing Technique ◽  
Original Form ◽  
Basis Matrix ◽  
Full Reconstruction ◽  
Processing Framework

Acquisition times and storage requirements have become increasingly important in signal-processing applications, as the sizes of datasets have increased. Hence, compressed sensing (CS) has emerged as an alternative processing technique, as original signals can be reconstructed using fewer data samples collected at frequencies below the Nyquist sampling rate. However, further analysis of CS data in both time and frequency domains requires the reconstruction of the original form of the time-domain data, as traditional signal-processing techniques are designed for uncompressed data. In this paper, we propose a signal-processing framework that extracts spectral properties for frequency-domain analysis directly from under-sampled ultrasound CS data, using an appropriate basis matrix, and efficiently converts this into the envelope of a time-domain signal, avoiding full reconstruction. The technique generates more accurate results than the traditional framework in both time- and frequency-domain analyses, and is simpler and faster in execution than full reconstruction, without any loss of information. Hence, the proposed framework offers a new standard for signal processing using ultrasound CS data, especially for small and portable systems handling large datasets.

METHODS TO QUANTITATE ATRIAL ELECTRICAL ACTIVITY USING ELECTROGRAMS ACQUIRED DURING ATRIAL FIBRILLATION

2014 ◽  
Vol 26 (06) ◽  
pp. 1430001
Author(s):  
Edward J. Ciaccio ◽  
Angelo B. Biviano ◽  
Hasan Garan
Keyword(s):  
Atrial Fibrillation ◽  
Electrical Activity ◽  
Frequency Domain ◽  
Time Domain ◽  
Periodic Pattern ◽  
Spectral Parameters ◽  
Domain Method ◽  
Frequency Domain Methods ◽  
Atrial Electrograms ◽  
Fractionated Electrograms

Herein, commonly used quantitative bioengineering methods that have been developed to analyze fractionated electrograms recorded from the surface of the atria during atrial fibrillation (AF) are described. Techniques were categorized as time-domain and frequency-domain methods. The main time-domain method is peak counting. Its variations based on preprocessing and thresholding are discussed. The main frequency-domain method is spectral analysis. Two spectral estimators, the discrete Fourier transform (DFT) and the new spectral estimator (NSE) are described. The ability of each estimator to detect the main periodic component of fractionated atrial electrograms is compared. Several spectral parameters that are used for analysis of atrial electrograms including the dominant frequency (DF), dominant amplitude (DA) and mean spectral profile (MP) are defined. Mean values of these parameters are compared in paroxysmal versus persistent AF fractionated electrograms based upon the results of several studies. Time-domain methods are shown to work best for analysis with deterministic, not fractionated atrial electrograms. For fractionated atrial electrograms, frequency-domain methods are often used. The DF, DA and MP spectral parameters are significantly different in paroxysmal versus longstanding persistent AF recordings. The DF and the DA are significantly higher, and the MP is significantly lower, in persistent AF electrogram recordings. The higher DF and DA parameter values reflect substrate remodeling in persistent AF, which increases the stability of the electrical activation pattern. The lower MP value in persistent AF reflects the lower spectral noise floor, indicative of a less complex and more periodic pattern of electrical activity.

scholarly journals STUDY ON AUDIO AND VIDEO WATERMARKING

2013 ◽  
pp. 34-38
Author(s):  
HARLEEN KAUR
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Copyright Protection ◽  
Video Data ◽  
Video Watermarking ◽  
Video Signals ◽  
Common Group ◽  
The Common ◽  
Cropping Rotation ◽  
Pitch Shift

This paper gives the overview of audio and video watermarking. This paper introduces the basic requirements that affect the algorithms for audio and video watermarking which are perceptibility, robustness and security. The attacks which cause manipulations of the audio and video signals are also discussed. The common group of attacks on audio and video data is dynamics, filtering, conversion, compression, noise, modulation, time stretch and pitch shift, multiple watermark, cropping, rotation etc. The applications of audio and video watermarking are Fingerprinting, copyright protection, authentication, copy control etc. The audio watermarking techniques can be classified into Time-domain and Frequencydomain methods and video watermarking techniques are classified into spatial domain, frequency domain and formatspecific domain.

scholarly journals Cardio-Diagnostic Assisting Computer System

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 322 ◽  
Author(s):  
Galya Georgieva-Tsaneva ◽  
Evgeniya Gospodinova ◽  
Mitko Gospodinov ◽  
Krasimir Cheshmedzhiev
Keyword(s):  
Frequency Domain ◽  
Computer System ◽  
Time Domain ◽  
Heart Rhythm ◽  
Recurrence Plot ◽  
Diagnostic Process ◽  
Control Group ◽  
Fluctuation Analysis ◽  
Poincaré Plot ◽  
Time Frequency

The mathematical analysis and the assessment of heart rate variability (HRV) based on computer systems can assist the diagnostic process with determining the cardiac status of patients. The new cardio-diagnostic assisting computer system created uses the classic Time-Domain, Frequency-Domain, and Time-Frequency analysis indices, as well as the nonlinear methods (Poincaré plot, Recurrence plot, Hurst R/S method, Detrended Fluctuation Analysis (DFA), Multi-Fractal DFA, Approximate Entropy and Sample Entropy). To test the feasibility of the software developed, 24-hour Holter recordings of four groups of people were analysed: healthy subjects and patients with arrhythmia, heart failure and syncope. Time-Domain (SDNN < 50 ms, SDANN < 100 ms, RMSSD < 17 ms) and Frequency-Domain (the spectrum of HRV in the LF < 550 ms2, and HF < 540 ms2) parameter values decreased in the cardiovascular disease groups compared to the control group as a result of lower HRV due to decreased parasympathetic and increased sympathetic activity. The results of the nonlinear analysis showed low values of (SD1 < 56 ms, SD2 < 110 ms) at Poincaré plot (Alpha < 90 ms) at DFA in patients with diseases. Significantly reducing these parameters are markers of cardiac dysfunction. The examined groups of patients showed an increase in the parameters (DET% > 95, REC% > 38, ENTR > 3.2) at the Recurrence plot. This is evidence of a pathological change in the regulation of heart rhythm. The system created can be useful in making the diagnosis by the cardiologist and in bringing greater accuracy and objectivity to the treatment.

Ultrasound myocardial integrated backscatter signal processing: Frequency domain versus time domain

1993 ◽  
Vol 19 (3) ◽  
pp. 211-219 ◽  
Author(s):  
H. Rijsterborgh ◽  
F. Mastik ◽  
C.T. Lancée ◽  
P. Verdouw ◽  
J. Roelandt ◽  
...  
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Time Domain ◽  
Domain Versus ◽  
Backscatter Signal ◽  
Integrated Backscatter

Application of Mathematical Morphological Filter in Vibration Signal Processing

2011 ◽  
Vol 71-78 ◽  
pp. 4564-4567
Author(s):  
Ai Jun Hu ◽  
Jing Jing Sun ◽  
Wan Li Ma
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Time Domain ◽  
Nonlinear Filtering ◽  
Vibration Signal ◽  
Morphological Operations ◽  
Morphological Filter ◽  
Original Signal ◽  
Low Pass ◽  
The Time Domain

The morphological filter as a nonlinear filtering method has been widely used for image (or signal) processing. Unlike the traditional digital filters, mathematical morphological operations are shape-based computing. Feature extraction of signals is entirely in the time domain without the transforming of the signal from the time domain to frequency domain. The vibration signal contaminated with noise is processed using morphological filter and Butterworth filter respectively. To compare the outputs of the two filters, we find that morphological filter shows better performance. It is effective in suppressing noise while maintaining the original signal both in the time and frequency domain. In addition, an outstanding advantage of morphological filter is its ability to keep the phase of the original signal. Its computing speed is faster. In the end, its low-pass characteristic is verified by processing vibration signal.

Digital Signal Processing

2009 ◽  
pp. 53-68
Author(s):  
Terrence D. Lagerlund
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Frequency Domain ◽  
Time Domain ◽  
Clinical Neurophysiology ◽  
Digital Signal ◽  
Digital Filtering ◽  
Digital Processing ◽  
Domain Analysis ◽  
Frequency Domain Analysis

This chapter reviews the principles of digitization, the design of digitally based instruments for clinical neurophysiology, and several common uses of digital processing, including averaging, digital filtering, and some types of time-domain and frequency-domain analysis. An understanding of these principles is necessary to select and use digitally based instruments appropriately and to understand their unique features.

Development of neural network model for signs determination of atrial fibrillation for subsystem of cardiorhythmogram signals processing

2021 ◽  
Author(s):  
Yu.A. Chelebaeva
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Signal Processing ◽  
Network Model ◽  
Neural Network Model ◽  
High Reliability ◽  
Heart Rhythm ◽  
Programmable Logic ◽  
Processing Subsystem

Task of the analysis of a cardio rhythm in real time is detection of early arrhythmias for the purpose of their treatment and prevention of life-endangering arrhythmias. In order to solve the problem of classification of heart rhythm features based on cardiorhythmogram processing, an apparatus of artificial neural networks can be used. One of the most dangerous arrhythmias is atrial fibrillation. Therefore, the development of a neural network model for determining atrial fibrillation features, suitable for implementation on the programmable logic basis, for a subsystem for processing cardiorhythmogram signals is an urgent task. Purpose – development of a neural network model for determining atrial fibrillation features for a signal processing subsystem characterized by high reliability and the implementation possibility on the basis of programmable logic. A neural network model for features determining of atrial fibrillation has been developed, characterized by high reliability and insignificant hardware costs when implemented on field programmable gate arrays (FPGA). Program modeling of neural network model for signs determination of atrial fibrillation is performed. A neural network model for characteristics determining of atrial fibrillation on hardware description language VHDL for use in the signal processing subsystem of a cardiorhythmogram based on FPGA was implemented. The findings suggest that the proposed model can be used in the construction of real-time heart rhythm control systems both for monitoring already diagnosed cardiovascular diseases, especially in intensive care wards, and for the prevention and early diagnosis of arrhythmias in individuals at high myocardial risk.

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