Signal Analysis for ECG Interpretation

1990 ◽  
Vol 29 (04) ◽  
pp. 317-329 ◽  
Author(s):  
J. H. van Bemmel ◽  
Chr. Zywietz ◽  
J. A. Kors
Keyword(s):  
Feature Selection ◽  
Data Reduction ◽  
Signal Analysis ◽  
Digital Filtering ◽  
Data Transformation ◽  
Diagnostic Classification ◽  
Ecg Signal ◽  
Boundary Recognition ◽  
Transformation Stage ◽  
Ecg Interpretation

AbstractIn ECG interpretation usually two main areas are discerned: the signal analysis and the diagnostic classification. This article reviews the major developments in the first area. ECG signal analysis itself is subdivided into the stages data acquisition, data transformation, feature selection, and data reduction. These stages are consecutively reviewed, while in the data transformation stage digital filtering, detection, wave typing, beat selection, and boundary recognition are discussed.

Methodology of ECG Interpretation in the Hannover Program

1990 ◽  
Vol 29 (04) ◽  
pp. 375-385 ◽  
Author(s):  
D. Borovsky ◽  
G. Gotsch ◽  
G. Joseph ◽  
Chr. Zywietz
Keyword(s):  
Decision Trees ◽  
Diagnostic Criteria ◽  
Sensitivity And Specificity ◽  
Hybrid Model ◽  
Signal Analysis ◽  
Diagnostic Classification ◽  
Moderate Exercise ◽  
Classification Technique ◽  
Scoring Algorithms ◽  
Ecg Interpretation

AbstractThe Hannover ECG program HES has been designed for measurement and interpretation of resting and (moderate) exercise electrocardiograms. In the signal analysis part the program follows an averaging strategy. For diagnostic classification a hybrid model with decision trees and scoring algorithms, and with multivariate probabilistic tests for derivation of category A statements is applied. The multivariate classification technique allows to adjust sensitivity and specificity for specific application areas without changing the diagnostic criteria.

scholarly journals bSSA: Binary Salp Swarm Algorithm with Hybrid Data Transformation for Feature Selection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sayar Singh Shekhawat ◽  
Harish Sharma ◽  
Sandeep Kumar ◽  
Anand Nayyar ◽  
Basit Qureshi
Keyword(s):  
Feature Selection ◽  
Data Transformation ◽  
Salp Swarm Algorithm ◽  
Hybrid Data ◽  
Swarm Algorithm

A novel feature extraction-based ECG signal analysis

2021 ◽  
Author(s):  
Varun Gupta ◽  
Monika Mittal ◽  
Vikas Mittal ◽  
Arvind Kumar Sharma ◽  
Nitin Kumar Saxena
Keyword(s):  
Feature Extraction ◽  
Signal Analysis ◽  
Ecg Signal

ECG Signal Analysis for Classification of Congenital Heart Defects

2020 ◽  
Author(s):  
Muhammad Umar Khan ◽  
Sumair Aziz ◽  
Mumtaz Ch. Javeria ◽  
Anber Shahjehan ◽  
Zohaib Mushtaq ◽  
...  
Keyword(s):  
Congenital Heart Defects ◽  
Signal Analysis ◽  
Congenital Heart ◽  
Heart Defects ◽  
Ecg Signal

scholarly journals Arrhythmia ECG Signal Analysis using Non Parametric Time-Frequency Techniques

2012 ◽  
Vol 41 (4) ◽  
pp. 25-30 ◽  
Author(s):  
A. Dliou ◽  
R. Latif ◽  
M. Laaboubi ◽  
F. M. R. Maoulainine
Keyword(s):  
Signal Analysis ◽  
Ecg Signal ◽  
Time Frequency ◽  
Non Parametric

scholarly journals A study on ECG Signal Analysis and ECG databases

2019 ◽  
Vol 7 (4) ◽  
pp. 188-195 ◽  
Author(s):  
J.S. Karnewar ◽  
Dr.V.K. Shandilya ◽  
M.D. Tambakhe
Keyword(s):  
Signal Analysis ◽  
Ecg Signal

scholarly journals Noisy ECG Signal Data Transformation to Augment Classification Accuracy

2022 ◽  
Vol 71 (2) ◽  
pp. 2191-2207
Author(s):  
Iqra Afzal ◽  
Fiaz Majeed ◽  
Muhammad Usman Ali ◽  
Shahzada Khurram ◽  
Akber Abid Gardezi ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Data Transformation ◽  
Ecg Signal

A corpus study of variation in written Chinese

2012 ◽  
Vol 8 (1) ◽  
pp. 209-240 ◽  
Author(s):  
Zheng-sheng Zhang,
Keyword(s):  
Feature Selection ◽  
Correspondence Analysis ◽  
Data Reduction ◽  
Reduction Method ◽  
Three Dimensions ◽  
Linguistic Variation ◽  
Corpus Study ◽  
Distributional Patterns ◽  
Preliminary Study ◽  
Data Reduction Method

AbstractThe present paper reports on the findings of a preliminary study of written Chinese, using the Lancaster Corpus of Mandarin Chinese (LCMC, McEnery & Xiao 2004). The first part of the paper introduces the stylistic features, and briefly describes the distributional patterns of these features across the selected written registers. Then, using a multi-feature, multi-dimensional framework (Biber 1988) and the data reduction method of correspondence analysis, three dimensions are identified and interpreted. The study reveals extensive linguistic variation across written Chinese registers, thus complementing previous observations about stylistic differences between spoken and written Chinese. Finally, issues concerning feature selection and dimension interpretation are discussed.

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