SUTIL: Intelligent ischemia monitoring system

Vila, José L.; Presedo, J.; Delgado, María Gabriela; Barro, Senén; Ruíz, Ramón Eduardo; Palácios, Francisco

doi:10.1016/s1386-5056(97)00095-6

Cited by 44 publications

(25 citation statements)

References 19 publications

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“…It must be noted that the majority of the results reported in Table 4 refer to subsets of ECG recordings of the ESC ST-T database [27,[29][30][31] and only some have used all the ESC ST-T database recordings [13,23,26]. Also, the currently evaluated beat classification network performs better than similar approaches [28], as indicated in Table 1.…”

Section: Discussionmentioning

confidence: 89%

“…Rule-based approaches [2,5,15,20,[25][26][27] exhibit the highly desirable feature of interpreting their decisions. Fuzzy expert systems [31,32] manage to keep this feature without applying strict threshold values. ANNs [4,21,[26][27][28][29] due to their non-linear characteristics and learning capabilities have provided good performance results.…”

Section: Discussionmentioning

confidence: 99%

“…Also, in case of disagreement between the episodes detected from the proposed method and those annotated in the database, three medical experts examined the recordings and their diagnosis was taken into account. It is noted that other researchers have also addressed the need of refining the annotations in the ESC-ST database [30,31]. Table 1 displays the experimental results obtained from the use of various training algorithms and different numbers of units in the hidden layer of the neural network.…”

Section: Performance Assessment Of the Overall Methods In Detecting Ismentioning

confidence: 99%

“…Se a (%) PPA b (%) Jager et al [13] 85 86 Papaloukas et al [23] 94 79 Silipo and Marchesi [26] 77 82 Silipo and Marchesi [26] 77 83 Silipo and Marchesi [26] 77 85 Silipo and Marchesi [26] 71 66 Silipo and Marchesi [26] 77 86 Silipo et al [27] 85 88 Silipo et al [27] 78 90 Stamkopoulos et al [29] 84 79 Taddei et al [30] 84 85 Vila et al [31] 83 A disadvantage of the proposed method is that it cannot provide any interpretation, for the decisions made due to the employment of the neural network model. Proper combination [18] of the knowledge-based approach [23] with the neural network model can eliminate this drawback.…”

Section: Methodsmentioning

confidence: 99%

“…The accuracy of beat classification influences ischemic episode definition where sequences of ischemic beats need to be identified. Various methods have been proposed for ischemia detection based on set of rules [2,5,15,20,[25][26][27], artificial neural networks (ANNs) [4,21,[26][27][28][29], fuzzy logic [31,32] or other signal analysis techniques [3,11,13,16,30].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Performance Assessment Of the Overall Methods In Detecting Ismentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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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