An arrhythmia classification system based on the RR-interval signal

Tsipouras, Markos G.; Fotiadis, Dimitris; Sideris, Dimitris A.

doi:10.1016/j.artmed.2004.03.007

Cited by 200 publications

(102 citation statements)

References 23 publications

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“…The hybrid model constructed uses intelligent medical information system with decision making support system of Atrial Fibrillation, different knowledge based systems of Atrial Fibrillation prepared in an expert system with ar-tificial intelligence techniques such as neural networks and other technologies used by Knowledge Engineers. The future extension of this paper includes developing some new trends, dependencies of the medical information data for Atrial Fibrillation [22,23]. This will deal with assumptions on performance of various other data mining techniques integrated with knowledge driven approach applied in mathematical formulation.…”

Section: Resultsmentioning

confidence: 99%

Role of Knowledge Engineering in the Development of a Hybrid Knowledge Based Medical Information System for Atrial Fibrillation

Kaur¹,

Alam²

2013

AJIBM

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In this paper, we are describing role of knowledge engineering in the development of a hybrid knowledge based medical information system. Knowledge engineering plays important role in development of various technologies such as: expert systems, neural network, artificial intelligence, hybrid intelligent systems, data mining, decision support systems, and knowledge based systems etc. The hybrid medical information system mainly consists of medical information system and medical knowledge base systems. These poly techniques of knowledge engineering when integrated with hybrid techniques of intelligent systems for designing, implementing knowledge bases to deal with medical informational data of Atrial Fibrillation. Atrial Fibrillation is the most common heart rhythm disorder which increases the risk of mortality and morbidity.

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Section: Resultsmentioning

confidence: 99%

Role of Knowledge Engineering in the Development of a Hybrid Knowledge Based Medical Information System for Atrial Fibrillation

Kaur¹,

Alam²

2013

AJIBM

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“…As applied in a medical context, a conditional probability is the likelihood of some conclusion, C, given some evidence/observation, E, where a dependence relationship exists between C and E. This probability is denoted as P(C| E) where (1) Bayes' theorem is the method of finding the converse probability of the conditional, (2) This conditional relationship allows an investigator to gain probability information about either C or E with the known outcome of the other. Now consider a complex problem with n binary variables, where the relationships among them are not known for the purpose of predicting a single class output variable (e.g., node 1 in Figure 6).…”

Section: Bayesian Network Structure Discoverymentioning

confidence: 99%

“…Trained physicians are able to recognize patterns in a patient's ECG signal and use them as the basis for diagnosis [1], for instance to diagnose heart ailments such as arrhythmia [2], ischemia [3,4], or prediction of an impending heart attack [5]. Researchers have tried since the inception of computers to develop techniques and algorithms for automated processing of ECG signals for various medical applications [6,7], whether as standalone applications or as a decision aid to physicians.…”

Section: Introductionmentioning

confidence: 99%

Evolving a Bayesian classifier for ECG-based age classification in medical applications

Wiggins

Saad

Litt

et al. 2008

Applied Soft Computing

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Objective-To classify patients by age based upon information extracted from their electrocardiograms (ECGs). To develop and compare the performance of Bayesian classifiers.Methods and Material-We present a methodology for classifying patients according to statistical features extracted from their ECG signals using a genetically evolved Bayesian network classifier. Continuous signal feature variables are converted to a discrete symbolic form by thresholding, to lower the dimensionality of the signal. This simplifies calculation of conditional probability tables for the classifier, and makes the tables smaller. Two methods of network discovery from data were developed and compared: the first using a greedy hill-climb search and the second employed evolutionary computing using a genetic algorithm (GA).Results and Conclusions-The evolved Bayesian network performed better (86.25% AUC) than both the one developed using the greedy algorithm (65% AUC) and the naïve Bayesian classifier (84.75% AUC). The methodology for evolving the Bayesian classifier can be used to evolve Bayesian networks in general thereby identifying the dependencies among the variables of interest. Those dependencies are assumed to be non-existent by naïve Bayesian classifiers. Such a classifier can then be used for medical applications for diagnosis and prediction purposes.

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“…The minutes, marked on the ECG by the number of changes concerning the complex rhythm or frequency QRS are cardiac arrhythmias (fatal diseases). A number of works [1], [2], [3], [4], [5], [6], and [7] have been successfully performed for clinical and technological problem highlighting arrhythmias, but they defy by (means and approaches). They are varied and the choice of types and number of parameters to approach them are also varied, significant that characterize the arrhythmia in question.…”

Section: Figure 2 Qrs Complex Determination Of Qrs Complex Through mentioning

confidence: 99%

Analysis of Automatic Classification of Electrocardiogram (Ecg) Beats Using Wavelet Transform and SVM and Pca-SVM

2017

IJAERD

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-A method for the automatic classification of cardio beats from an electrocardiogram (ECG) is presented in the paper. This beats classification is based on an analysis of QRS and DWT based feature extraction. The principal component analysis (PCA) is used for parameter analysis and recognition of cardiac beats. These parameters are calculated for beats with 4 types of classes (L, A, P and R) from ECG records retrieved from the MIT-BIH arrhythmia database.Further SVM is applied as classifier for automatic detection of heart beats. Analysis of the different groups shows the overall recognition performance was

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An arrhythmia classification system based on the RR-interval signal

Cited by 200 publications

References 23 publications

Role of Knowledge Engineering in the Development of a Hybrid Knowledge Based Medical Information System for Atrial Fibrillation

Role of Knowledge Engineering in the Development of a Hybrid Knowledge Based Medical Information System for Atrial Fibrillation

Evolving a Bayesian classifier for ECG-based age classification in medical applications

Analysis of Automatic Classification of Electrocardiogram (Ecg) Beats Using Wavelet Transform and SVM and Pca-SVM

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