Corpus ID: 36420255

Analysis of ECG signal by chaos principle to help automatic diagnosis of myocardial infarction

@article{Lahiri2009AnalysisOE,
  title={Analysis of ECG signal by chaos principle to help automatic diagnosis of myocardial infarction},
  author={Tapobrata Lahiri and Upendra Kumar and Hrishikesh Mishra and Subrata Sarkar and Arunava Roy},
  journal={Journal of Scientific \& Industrial Research},
  year={2009},
  volume={68},
  pages={866-870}
}
Chaotic behavior of electrocardiogram (ECG) signal of myocardial and non-myocardial infarctions is differentiated using neuro-GA approach, incorporating heuristically chosen phase space fractal dimension (PSFD) of ECG data. A remarkable improvement of diagnostic efficiency, sensitivity and specificity was observed in case study. 

Figures and Tables from this paper

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework
TLDR
The proposed automated method for automatic diagnosis of MI using ECG beat with flexible analytic wavelet transform (FAWT) method can be installed in the intensive care units of hospitals to aid the clinicians in confirming their diagnosis. Expand
Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals
TLDR
A novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs is used to overcome the limitation of manual ECG signal analysis and can be used by clinical staff to make faster and accurate diagnosis ofCVDs. Expand
Nonlinear analysis of coronary artery disease, myocardial infarction, and normal ECG signals
TLDR
It is found that nonlinear features can effectively represent the physiological realities of the human heart. Expand
CHAOTIC FEATURE EXTRACTION AND NEURO-FUZZY CLASSIFIER FOR ECG SIGNAL CHARACTERIZATION
In this paper, a neuro-fuzzy network is employed to classify the ECG beats based on the extracted chaotic features. Six groups of ECG beats (MIT-BIH Normal Sinus rhythm, BIDMC congestive heartExpand
Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features
TLDR
The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. Expand
Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads
TLDR
This paper proposes a novel method of automated detection and localization of MI by using ECG signal analysis that can aid the physicians and clinicians in accurate and faster location of MIs, and thereby providing adequate time available for the requisite treatment decision. Expand
R-Peak Detection Using Chaos Analysis in Standard and Real Time ECG Databases
TLDR
There are strong merits in using chaos analysis as a feature extraction method to reduce the incidence of false diagnosis of electrocardiogram (ECG) signal in critical conditions. Expand
ECG based Myocardial Infarction detection using Hybrid Firefly Algorithm
  • Padmavathi Kora
  • Medicine, Computer Science
  • Comput. Methods Programs Biomed.
  • 2017
TLDR
The proposed approach has shown that methods that are based on the feature optimization of the ECG signals are the perfect to diagnosis the condition of the heart patients. Expand
Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study
TLDR
In this study, ECG signals are subjected to DCT, DWT and EMD to obtain respective coefficients, which are reduced using Locality Preserving Projection (LPP) data reduction method, and ranked using F-value to achieve the best classification performance. Expand
Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach
TLDR
Stationary wavelet transform has been used to decompose the segmented multilead electrocardiogram (ECG) signal into different sub-bands and the proposed technique has been scrutinized under both “class-oriented,” and more practical, “subject-oriented” approach. Expand
...
1
2
3
4
...

References

SHOWING 1-10 OF 28 REFERENCES
Feature Extraction for ECG Time-Series Mining Based on Chaos Theory
  • A. Jovic, N. Bogunovic
  • Mathematics
  • 2007 29th International Conference on Information Technology Interfaces
  • 2007
Chaos theory applied to ECG feature extraction is presented in this article. Several chaos methods, including phase space and attractors, correlation dimension, spatial filling index, centralExpand
Human ECG: nonlinear deterministic versus stochastic aspects
TLDR
It is argued that deterministic chaos is not a likely explanation for the short-time variablity of the inter-beat interval times, except for certain pathologies, and methods of deterministic nonlinear time-series analysis can yield new insights. Expand
The analysis of ECG signals using time-frequency techniques
  • M. Tagluk, M. English
  • Mathematics
  • Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136)
  • 1997
To overcome the limitations which occur in the Wigner-Ville Distribution (WVD), a filtering technique based on processing of the image of the Ambiguity function (AF) is proposed. This work includes aExpand
Detection of the R wave peak of QRS complex using neural network
  • M. Reaz, L.S. Wei
  • Computer Science
  • Proceedings. 2004 International Conference on Information and Communication Technologies: From Theory to Applications, 2004.
  • 2004
TLDR
A robust algorithm for QRS detection using neural network is proposed, which allows R peak to be differentiated from large peaked T and P waves with a high degree of accuracy and minimizes the problem associated with the noises in the ECG signal. Expand
ECG beat recognition using fuzzy hybrid neural network
TLDR
The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution and show that the method may find practical application in the recognition and classification of different type heart beats. Expand
Lack of sensitivity of the electrocardiogram for detection of old myocardial infarction: a cardiac magnetic resonance imaging study.
TLDR
The lack of sensitivity and the resulting low negative predictive value of Q/QS criteria seriously limit its accuracy as a marker of prior myocardial infarction. Expand
Combining algorithms in automatic detection of R-peaks in ECG signals
TLDR
This paper suggests an approach to automatically combine different algorithms, here the Pan Tompkins and wavelet algorithms, for detection of R-peaks in ECG signals, in order to benefit from the strengths of both algorithms. Expand
How accurate is the use of ECGs in the diagnosis of myocardial infarct
The electrocardiogram (ECG) is a fairly accurate test in the diagnosis of myocardial infarction (MI). However, given more sensitive technologies, such as cardiac biomarker testing, its primary roleExpand
The 〈〈Chaos Theory〉〉 and Nonlinear Dynamics in Heart Rate Variability Analysis: Does it Work in Short‐Time Series in Patients with Coronary Heart Disease?
TLDR
Clinical and prognostic significance of dynamic changes in short‐time series applied on patients with coronary heart disease (CHD) during the exercise electrocardiograph (ECG) test is emphasized. Expand
Is fibrillation chaos?
TLDR
The results suggest that fibrillation is similar to a nonchaotic random signal, however, it is noted that such random-looking butNonchaotic behavior can also be generated by a nonlinear deterministic system. Expand
...
1
2
3
...