Cognitive amalgam with a distributed fuzzy case-based reasoning system for an accurate cardiac arrhythmias diagnosis

Khelassi, Abdeldjalil; Chikh, Mohammed Amine

doi:10.1504/ijict.2015.070297

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…A detailed explanation of the electrocardiogram was presented in ( 2 , 3 , 5 ). Also in Figure 1 , we present an example of normal ECG from 6 leads.…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, the automatic detection of the cardiac arrhythmias established a domain among which several works and studies were developed for the discrimination between normal rhythm and its irregularity ( 1 – 3 ). The Electrocardiogram ECG represents the mechanical activity of the heart (the contraction and the relaxation, Slackening).…”

Section: Introductionmentioning

confidence: 99%

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Multi-arrhythmias detection with an XML rule-based system from 12-Lead Electrocardiogram

Khelassi¹,

Yelles-Chaouche²,

Benais³

2017

Electron physician

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BackgroundThe computer-aided detection of cardiac arrhythmias stills a crucial application in medical technologies. The rule based systems RBS ensure a high level of transparency and interpretability of the obtained results.AimTo facilitate the diagnosis of the cardiologists and to reduce the uncertainty made in this diagnosis.MethodsIn this research article, we have realized a classification and automatic recognition of cardiac arrhythmias, by using XML rules that represent the cardiologist knowledge. Thirteen experiments with different knowledge bases were realized for improving the performance of the used method in the detection of 13 cardiac arrhythmias. In the first 12 experiments, we have designed a specialized knowledge base for each cardiac arrhythmia, which contains just one arrhythmia detection rule. In the last experiment, we applied the knowledge base which contains rules of 12 arrhythmias. We used, for the experiments, an international data set with 279 features and 452 records characterizing 12 leads of ECG signal and social information of patients. The data sets were constructed and published at Bilkent University of Ankara, Turkey. In addition, the second version of the self-developed software “XMLRULE” was used; the software can infer more than one class and facilitate the interpretability of the obtained results.ResultsThe 12 first experiments give 82.80% of correct detection as the mean of all experiments, the results were between 19% and 100% with a low rate in just one experiment. The last experiment in which all arrhythmias are considered, the results of correct detection was 38.33% with 90.55% of sensibility and 46.24% of specificity. It was clearly show that in these results the good choice of the classification model is very beneficial in terms of performance. The obtained results were better than the published results with other computational methods for the mono class detection, but it was less in multi-class detection.ConclusionThe RBS is the most transparent method for cardiac arrhythmias detection and multi arrhythmias detection. It improves an exceptional recognition of arrhythmias, but due to conflicts between rules, multi-arrhythmias and uncertainty of measures, the rate of correct classification was less than the other methods.

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“…A detailed explanation of the electrocardiogram was presented in ( 2 , 3 , 5 ). Also in Figure 1 , we present an example of normal ECG from 6 leads.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-arrhythmias detection with an XML rule-based system from 12-Lead Electrocardiogram

Khelassi¹,

Yelles-Chaouche²,

Benais³

2017

Electron physician

Self Cite

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“…The accuracy of this system was also measured via a benchmark realized by two international medical data sets: MIT-BIH for the cardiac arrhythmia in [30] and a breast cancer dataset collected and published by Dr Wollberg in [24]. Other results and deep comparisons with the related works exist in [23][24][25][26][27][28][29][30][31][32].…”

Section: Empirical Experimentsmentioning

confidence: 99%

Explainer: An interactive Agent for Explaining the Diagnosis of Cardiac Arrhythmia Generated by IK-DCBRC

Khelassi

Estrela

Hemanth

2019

Mèd Technologies J.

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Interactions between medical applications and users involve a high level of trust, since many complex, automated applications are integrated and involve critical domains in which public health is paramount. Although uncertainty decreases the accuracy and trust of such medical applications under these circumstances, explanation-aware computing becomes crucial in improving the efficiency of these applications. This paper describes an intelligent agent that interacts with users to provide meaningful explanations of previous diagnoses supported by IK-DCBRC. The agent ensures intelligent interactions with users via a rule-based system that generates appropriate explanations according to the selected level of abstraction and the detected cardiac arrhythmia. The paper also describes a particular medical application, that is, cardiac arrhythmia with automatic diagnoses supported by the case-based reasoning classifier, IK-DCBRC.

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Cognitive amalgam with a distributed fuzzy case-based reasoning system for an accurate cardiac arrhythmias diagnosis

Cited by 2 publications

References 23 publications

Multi-arrhythmias detection with an XML rule-based system from 12-Lead Electrocardiogram

Multi-arrhythmias detection with an XML rule-based system from 12-Lead Electrocardiogram

Explainer: An interactive Agent for Explaining the Diagnosis of Cardiac Arrhythmia Generated by IK-DCBRC

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