A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms

Papaloukas, Costas; Fotiadis, D.I.; Liavas, Athanasios P.; Likas, Aristidis; Michalis, Lampros K.

doi:10.1007/bf02345273

Cited by 64 publications

(37 citation statements)

References 27 publications

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“…The procedure starts with the preprocessing of the recorded ECG signal in order to eliminate noise distortions like baseline wandering, A/C interference and electromyographic contamination. Noise elimination is achieved by filtering each recorded cardiac beat separately using a signal processing procedure (details are provided in [22,23]). Briefly, baseline wandering is removed by subtracting from the recorded signal the first-order polynomial that best fits the cardiac beat.…”

Section: Methodsmentioning

confidence: 99%

“…Then a sliding adaptive window technique is applied, in order to identify ischemic windows [23]. More specifically, for each ECG lead we detect intervals of approximately 30 s in duration (in accordance with the European Society of Cardiology (ESC) recommendations) in which more than 75% of the contained beats are ischemic.…”

Section: Methodsmentioning

confidence: 99%

“…The ANN method for beat classification was integrated into the four-stage technique for ischemic episode detection [23], replacing the rule-based classification stage. To assess the performance of the improved method Se and PPA measures were adopted.…”

Section: Performance Assessment Of the Overall Methods In Detecting Ismentioning

confidence: 99%

“…Therefore, the use of 10 hidden units was considered sufficient. For comparison, when the rule-based approach [23] was used for beat classification, the Se and the Sp were 70 and 63%, respectively, while for the ANN method the results were 90 and 90% for both measures. The ROC analysis that was calculated for the adopted ANN (Fig.…”

Section: Performance Assessment Of the Overall Methods In Detecting Ismentioning

confidence: 99%

“…In a previous work [23] a four-stage knowledge-based method was implemented for ischemic episode detection. This method was based on a set of medical rules for beat classification.…”

Section: Introductionmentioning

confidence: 99%

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An ischemia detection method based on artificial neural networks

Papaloukas

Fotiadis

Likas

et al. 2002

Artificial Intelligence in Medicine

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An automated technique was developed for the detection of ischemic episodes in long duration electrocardiographic (ECG) recordings that employs an artificial neural network. In order to train the network for beat classification, a cardiac beat dataset was constructed based on recordings from the European Society of Cardiology (ESC) ST-T database. The network was trained using a Bayesian regularisation method. The raw ECG signal containing the ST segment and the T wave of each beat were the inputs to the beat classification system and the output was the classification of the beat. The input to the network was produced through a principal component analysis (PCA) to achieve dimensionality reduction. The network performance in beat classification was tested on the cardiac beat database providing 90% sensitivity (Se) and 90% specificity (Sp). The neural beat classifier is integrated in a four-stage procedure for ischemic episode detection. The whole system was evaluated on the ESC ST-T database. When aggregate gross statistics was used the Se was 90% and the positive predictive accuracy (PPA) 89%. When aggregate average statistics was used the Se became 86% and the PPA 87%. These results are better than other reported. #

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Performance Assessment Of the Overall Methods In Detecting Ismentioning

confidence: 99%

Section: Performance Assessment Of the Overall Methods In Detecting Ismentioning

confidence: 99%

“…In a previous work [23] a four-stage knowledge-based method was implemented for ischemic episode detection. This method was based on a set of medical rules for beat classification.…”

Section: Introductionmentioning

confidence: 99%

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An ischemia detection method based on artificial neural networks

Papaloukas

Fotiadis

Likas

et al. 2002

Artificial Intelligence in Medicine

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Electrocardiogram (ECG): Automated Diagnosis

Fotiadis

Likas

Michalis

et al. 2006

Wiley Encyclopedia of Biomedical Engineering

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Electrocardiogram (ECG) is one of the most common signals used in medical practice because of its noninvasive nature and the information it contains. Its analysis can be used to assess the pathophysiological condition of the heart. Several systems for ECG recording and analysis have been developed for more than a century. Early ECG systems included recording and printing of the signal. Modern systems use computer technology to provide automated diagnosis. The latter is a significant research field and many methods and approaches have been proposed for the detection of ischemia, arrhythmia detection and classification, and diagnosis of chronic myocardial diseases. Those methods are based on the processing of the signal to remove noise and artifacts, extraction of certain features related to diseases, and analysis of the features to obtain the final decision. The analysis is usually based on signal processing, fuzzy logic, and artificial neural networks concepts, mixed with knowledge provided by medical experts. Today, those systems are evaluated using standard databases and must be introduced in clinical practice to be further validated.

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A Rough Set Based Approach for ECG Classification

Mitra

Chaudhuri

2008

Transactions on Rough Sets IX

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A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms

Cited by 64 publications

References 27 publications

An ischemia detection method based on artificial neural networks

An ischemia detection method based on artificial neural networks

Electrocardiogram (ECG): Automated Diagnosis

A Rough Set Based Approach for ECG Classification

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