scholarly journals Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-Wave Alternans

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3070 ◽  
Author(s):  
Raúl Caulier-Cisterna ◽  
Manuel Blanco-Velasco ◽  
Rebeca Goya-Esteban ◽  
Sergio Muñoz-Romero ◽  
Margarita Sanromán-Junquera ◽  
...  
Keyword(s):  
Signal Processing ◽  
Cardiac Electrophysiology ◽  
Current Knowledge ◽  
Maximum Amplitude ◽  
Digital Signal ◽  
Digital Processing ◽  
Temporal Analysis ◽  
Electrocardiographic Imaging ◽  
T Wave ◽  
T Wave Alternans

During the last years, attention and controversy have been present for the first commercially available equipment being used in Electrocardiographic Imaging (ECGI), a new cardiac diagnostic tool which opens up a new field of diagnostic possibilities. Previous knowledge and criteria of cardiologists using intracardiac Electrograms (EGM) should be revisited from the newly available spatial–temporal potentials, and digital signal processing should be readapted to this new data structure. Aiming to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology, we previously presented two results: First, spatial consistency can be observed even for very basic cardiac signal processing stages (such as baseline wander and low-pass filtering); second, useful bipolar EGMs can be obtained by a digital processing operator searching for the maximum amplitude and including a time delay. In addition, this work aims to demonstrate the functionality of ECGI for cardiac electrophysiology from a twofold view, namely, through the analysis of the EGM waveforms, and by studying the ventricular repolarization properties. The former is scrutinized in terms of the clustering properties of the unipolar an bipolar EGM waveforms, in control and myocardial infarction subjects, and the latter is analyzed using the properties of T-wave alternans (TWA) in control and in Long-QT syndrome (LQTS) example subjects. Clustered regions of the EGMs were spatially consistent and congruent with the presence of infarcted tissue in unipolar EGMs, and bipolar EGMs with adequate signal processing operators hold this consistency and yielded a larger, yet moderate, number of spatial–temporal regions. TWA was not present in control compared with an LQTS subject in terms of the estimated alternans amplitude from the unipolar EGMs, however, higher spatial–temporal variation was present in LQTS torso and epicardium measurements, which was consistent through three different methods of alternans estimation. We conclude that spatial–temporal analysis of EGMs in ECGI will pave the way towards enhanced usefulness in the clinical practice, so that atomic signal processing approach should be conveniently revisited to be able to deal with the great amount of information that ECGI conveys for the clinician.

scholarly journals Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (I): Preprocessing and Bipolar Potentials

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3131 ◽  
Author(s):  
Raúl Caulier-Cisterna ◽  
Margarita Sanromán-Junquera ◽  
Sergio Muñoz-Romero ◽  
Manuel Blanco-Velasco ◽  
Rebeca Goya-Esteban ◽  
...  
Keyword(s):  
Signal Processing ◽  
Clinical Practice ◽  
Cardiac Electrophysiology ◽  
Current Knowledge ◽  
Maximum Amplitude ◽  
Digital Signal ◽  
Clinical Tool ◽  
Electrocardiographic Imaging ◽  
Tissue Properties ◽  
Clinical Indices

During the last years, Electrocardiographic Imaging (ECGI) has emerged as a powerful and promising clinical tool to support cardiologists. Starting from a plurality of potential measurements on the torso, ECGI yields a noninvasive estimation of their causing potentials on the epicardium. This unprecedented amount of measured cardiac signals needs to be conditioned and adapted to current knowledge and methods in cardiac electrophysiology in order to maximize its support to the clinical practice. In this setting, many cardiac indices are defined in terms of the so-called bipolar electrograms, which correspond with differential potentials between two spatially close potential measurements. Our aim was to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology. For this purpose, we first analyzed the basic stages of conventional cardiac signal processing and scrutinized the implications of the spatial-temporal nature of signals in ECGI scenarios. Specifically, the stages of baseline wander removal, low-pass filtering, and beat segmentation and synchronization were considered. We also aimed to establish a mathematical operator to provide suitable bipolar electrograms from the ECGI-estimated epicardium potentials. Results were obtained on data from an infarction patient and from a healthy subject. First, the low-frequency and high-frequency noises are shown to be non-independently distributed in the ECGI-estimated recordings due to their spatial dimension. Second, bipolar electrograms are better estimated when using the criterion of the maximum-amplitude difference between spatial neighbors, but also a temporal delay in discrete time of about 40 samples has to be included to obtain the usual morphology in clinical bipolar electrograms from catheters. We conclude that spatial-temporal digital signal processing and bipolar electrograms can pave the way towards the usefulness of ECGI recordings in the cardiological clinical practice. The companion paper is devoted to analyzing clinical indices obtained from ECGI epicardial electrograms measuring waveform variability and repolarization tissue properties.

Study on Auto-Search Algorithm Based on Subsection Correlation for Loran-C

2012 ◽  
Vol 241-244 ◽  
pp. 1751-1755
Author(s):  
Yin Bing Zhu ◽  
Ke Jing Cao ◽  
Bao Li
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Arrival Time ◽  
Search Algorithm ◽  
Digital Signal ◽  
Digital Processing ◽  
Digital Receiver ◽  
Maximum Correlation ◽  
Key Steps ◽  
Signal Search

Auto-search is one of the key steps in digital signal processing for Loran-C receivers, however, for digital sampling Loran-C signal, the principle search algorithm is unable to realize signal search veraciously because of the asynchronism between sampling clock and transmitting station clock. For this question, an auto-search algorithm based on subsection correlation for Loran-C is presented after analyzing the principle search algorithm. The experiment results show that for the received digital Loran-C signal, there are several correlation and accumulation values of master and secondary stations to exceed the search thresholds; the maximum correlation and accumulation value of the presented algorithm is far higher than that of the principle algorithm. That is to say, the presented algorithm can search the arrival time of master and secondary station successfully, solve the problem of clock asynchronism effectively, and enhance the search sensitivity of the receiver, which have great significance for digital processing of Loran-C signal and the engineering realization of Loran-C digital receiver.

scholarly journals T-Wave Alternans Analysis With Electrocardiographic Imaging

2018 ◽  
Author(s):  
Jose Luis Rojo-Alvarez ◽  
Rebeca Goya-Esteban ◽  
Sergio Muñoz-Romero ◽  
Arcadi García-Alberola ◽  
Francisco-Manuel Melgarejo-Meseguer ◽  
...  
Keyword(s):  
Electrocardiographic Imaging ◽  
T Wave ◽  
T Wave Alternans

Digital Signal Processing

2016 ◽  
pp. 222-235 ◽  
Author(s):  
Terrence D. Lagerlund
Keyword(s):  
Signal Processing ◽  
Time Domain ◽  
Visual Analysis ◽  
Clinical Neurophysiology ◽  
Digital Signal ◽  
Digital Processing ◽  
Frequency Domain Analysis ◽  
Scientific Investigations ◽  
Key Features ◽  
Digital Computers

Digital computers can perform types of signal processing not readily available with analog devices, such as ordinary electrical circuits. This includes making the process of obtaining, storing, retrieving, and viewing clinical neurophysiology data easier; aiding in extracting information from waveforms that is not readily obtainable with visual analysis alone; and improving quantification of key features of waveforms. These processes are useful in accurate clinical diagnosis of electroencephalographic (EEG), electromyographic (EMG), and evoked potential studies, and it also lend themselves to serial comparisons between studies performed on the same subject at different times or between two groups of subjects in scientific investigations. Digital computers may also partially automate the interpretation of clinical neurophysiology studies. 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.

The MIT Geophysical Analysis Group (GAG) from inception to 1954

Geophysics ◽  
2005 ◽  
Vol 70 (4) ◽  
pp. 7JA-30JA ◽  
Author(s):  
Enders A. Robinson
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Digital Computer ◽  
Random Access ◽  
Digital Signal ◽  
Digital Processing ◽  
Digital Revolution ◽  
Digital Correction ◽  
Alan Turing ◽  
Analysis Group

The beginning of digital signal processing took place in the years 1950 to 1954. Using an econometric model, E. A. Robinson in 1951 came up with the method of deconvolution, which he tested on 32 seismic traces. Norbert Wiener, George Wadsworth, Paul Samuelson, and Robert Solow were his advisors. On the basis of this work, the MIT president's office in 1952 set up and sponsored the Geophysical Analysis Group (GAG) in the Department of Geology and Geophysics. GAG was made up of graduate students doing research in digital signal processing. In 1953, a consortium of oil and geophysical companies took over the sponsorship. At first, GAG used the MIT Whirlwind digital computer. In order to do the larger amount of computing required by the consortium, the Computer Service Section of Raytheon Manufacturing Company was enlisted in 1953. The Raytheon people who played key roles were Richard Clippinger, Bernard Dimsdale, and Joseph H. Levin, all of whom had worked on ENIAC, the world's first electronic digital computer. As originally built, ENIAC did not use programs stored in memory as does a modern computer; instead, the programming was done by rewiring the physical components for each new problem. In 1948, Clippinger was responsible for converting ENIAC into the world's first operational stored-program computer. ENIAC had 20 accumulators but no other random access memory (RAM). The programs were stored in the function tables, which acted as programmable read-only memory(PROM). For GAG work in 1953, Raytheon used the British Ferranti Mark 1 computer (which was the commercial version of the Manchester Mark 1 computer, for which Alan Turing played a key role). This computer was installed at the University of Toronto to help in the design of the St. Lawrence Seaway. Raytheon was plagued by frequent breakdowns of the computer but still produced several hundred seismic deconvolutions for the summer GAG meeting in 1953. The consortium was pleased with the geophysical results but was disheartened by the unreliability of the current state of digital technology. As a result, GAG was directed to find analog ways to do deconvolution. Instead, GAG found that all of the analog methods, and in particular, electric frequency filtering, could be done by digital signal processing. In fact, the digital way provided greater accuracy than the analog way. At the spring meeting in 1954, GAG proposed that all analog processing be thrown out and replaced by digital signal processing. Raytheon was at the meeting and offered to obtain or build all the elements required for digital signal processing, from input to output. The conversion to digital was not done at the time. However, that step did happen in the early 1960s, and exploration geophysics has the distinction of being the first science to experience a total digital revolution. Digital processing today provides seismic images of the interior of the Earth so startling that they compare to images of the stars made by the Hubble telescope. (In fact, the digital method of deconvolution first developed in geophysics made possible the digital correction of the lens of the Hubble telescope.)

scholarly journals Design and validation of an e-textile-based wearable system for remote health monitoring

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 220
Author(s):  
Armando Coccia ◽  
Federica Amitrano ◽  
Leandro Donisi ◽  
Giuseppe Cesarelli ◽  
Gaetano Pagano ◽  
...  
Keyword(s):  
Signal Processing ◽  
Clinical Status ◽  
Digital Signal ◽  
Digital Processing ◽  
Time Signal ◽  
The Novel ◽  
Custom Made ◽  
Trunk Acceleration ◽  
Clinical Environments ◽  
Remote Health

<p class="Abstract">The paper presents a new e-textile-based system, named SWEET Shirt, for the remote monitoring of biomedical signals. The system includes a textile sensing shirt, an electronic unit for data transmission, a custom-made Android application for real-time signal visualisation and a software desktop for advanced digital signal processing. The device allows for the acquisition of electrocardiographic, bicep electromyographic and trunk acceleration signals. The sensors, electrodes, and bus structures are all integrated within the textile garment, without any discomfort for users. A wide-ranging set of algorithms for signal processing were also developed for use within the system, allowing clinicians to rapidly obtain a complete and schematic overview of a patient’s clinical status. The aim of this work was to present the design and development of the device and to provide a validation analysis of the electrocardiographic measurement and digital processing. The results demonstrate that the information contained in the signals recorded by the novel system is comparable to that obtained via a standard medical device commonly used in clinical environments. Similarly encouraging results were obtained in the comparison of the variables derived from the signal processing.</p>

Design and Implementation of Seismic Data Digital Filter

2013 ◽  
Vol 300-301 ◽  
pp. 1669-1672
Author(s):  
Yong Li
Keyword(s):  
Signal Processing ◽  
Seismic Data ◽  
Fourier Transformation ◽  
Digital Signal ◽  
Digital Processing ◽  
Discrete Fourier Transformation ◽  
Design And Implementation ◽  
Interference Wave ◽  
Computation Speed

In order to improve the quality and precision of seismic data, taking out or suppressing interference wave consisting in the earthquake wave will be one important link in the digital processing of seismic data .The fast Fournier transformation resolves big points N into certain dot’s DFT combinations .And then breaking a large number of multiply operations to add operations and a small quality of multiply operations, thus the computation speed of the Discrete Fourier Transformation (DFT) will be enhanced greatly. The widespread uses of FFT make it to be a powerful tool in digital signal processing. The present paper will introduce the quite comprehensive narration of the principle of filter, the characteristic of fast Fournier transformation algorithm principle as well as the realization.

scholarly journals HARDWARE-SOFTWARE COMPLEX FOR DIGITAL PROCESSING OF HYDROACOUSTIC SIGNALS

2019 ◽  
pp. 60-63
Author(s):  
S. A. Koltakov ◽  
A. A. Cherepnev
Keyword(s):  
Signal Processing ◽  
High Speed ◽  
Digital Signal ◽  
Digital Processing ◽  
General Purpose ◽  
Software Complex ◽  
General Purpose Processor ◽  
Input And Output ◽  
Programming Tools ◽  
Modern Methods

The  article  describes  the  hardware‑software  complex  (HSC)  based  on  the  debugging  stand,  its  composition,  modules  and  operations. A method for synthesizing the output signal is described, a formula and a table of parameters for its calculation are  given. Signals and spectra at the input and output of the developed HSC are shown. The obtained parameters of the performance  of various agribusiness, based on the signal processor with a General‑purpose processor and two variants with General‑purpose  processors.  The  proposed  version  of  the  HSC2–3  times  wins  in  performance  compared  to  the  HSC  based  on  the  general‑ purpose processor of Intel. This is achieved through the use of modern methods and programming tools, digital signal processing  modules, as well as the optimization of the executable code. Recommendations for possible further improvement of the proposed  complex are given, which is possible due to the use of modern FPGAs and high‑speed interface.

Nonparametric Signal Processing Validation in T-Wave Alternans Detection and Estimation

2014 ◽  
Vol 61 (4) ◽  
pp. 1328-1338 ◽  
Author(s):  
R. Goya-Esteban ◽  
O. Barquero-Perez ◽  
M. Blanco-Velasco ◽  
A. J. Caamano-Fernandez ◽  
A. Garcia-Alberola ◽  
...  
Keyword(s):  
Signal Processing ◽  
Detection And Estimation ◽  
T Wave ◽  
T Wave Alternans
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