Suspicious polyphase patterns of normal looking ECGs provide fast early diagnoses of a coronary artery disease

Sabry-Rizk, M.; El-Khafif, S.; Carson, Ewart R.; Zgallai, Walid A.; Grattan, K.T.V.; Morgan, Charity; Hardiman, Paul

doi:10.1109/iembs.1999.804133

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Several methods are based on heart sounds associated with coronary occlusions (Akay & Welkowitz, 1993). Also, methods which employ the resting or exercise electrocardiogram (ECG) of the patient, extracting features from it, like the R wave (Szildgyi, Szildgyi, & David, 1997), the QT interval (Ng, Wong, Mora, Passariello, & Almeida, 1998), the T wave amplitude (Sabry-Rizk et al, 1999), the heart rate variability (Tkacz & Kostka, 2000), and the ST segment (Lewenstein, 2001) have been proposed. Furthermore, there are methods using medical images, such as SPECT (Haddad, Adlassnig, & Porenta, 1997), and methods based on arterio-scillography (Pouladian, Golpayegani, Tehrani-Fard, & Bubvay-Nejad, 2005).…”

Section: Introductionmentioning

confidence: 99%

Decision Support Systems for Cardiovascular Diseases Based on Data Mining and Fuzzy Modelling

Tsipouras

Exarchos

Fotiadis

et al. 2008

Encyclopedia of Healthcare Information Systems

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This article addresses the decision support regarding cardiovascular diseases, using computer-based methods, focusing on the coronary artery disease (CAD) diagnosis and on the prediction of clinical restenosis in patients undergoing angioplasty. Methods reported in the literature are reviewed with respect to (i) the medical information that are employing in order to reach the diagnosis and (ii) the data analysis techniques used for the creation of the CDSSs. In what concerns medical information, easily and noninvasively-obtained data present several advantages compared to other types of data, while data analysis techniques that are characterized by transparency regarding their decisions are more suitable for medical decision making. A recently developed approach that complies with the above requirements is presented. The approach is based on data mining and fuzzy modelling. Using this approach, one CDSS has been developed for each of the two cardiovascular problems mentioned above. These CDSSs are extensively evaluated and comments about the discovered knowledge are provided by medical experts. The later is of great importance in designing and evaluating CDSSs, since it allows them to be integrated into real clinical environments.

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Section: Introductionmentioning

confidence: 99%

Decision Support Systems for Cardiovascular Diseases Based on Data Mining and Fuzzy Modelling

Tsipouras

Exarchos

Fotiadis

et al. 2008

Encyclopedia of Healthcare Information Systems

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show abstract

“…Sampling frequency is 0.5 KHz and a resolution of 12 bits. Recurrent neural networks have been used to classify the patterns in Fig 4.1 c and d, respectively (Rizk et al, 1999b). The employed neural network has an input layer, a middle layer, and an output layer.…”

Section: Ecg Abnormality Classification Using Polyphasementioning

confidence: 99%

Detection and Classification of Adult and Fetal ECG Using Recurrent Neural Networks, Embedded Volterra and Higher-Order Statistics

Zgallai¹

2012

Recurrent Neural Networks and Soft Computing

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A Decision Support System for the Diagnosis of Coronary Artery Disease

Tsipouras

Exarchos

Fotiadis

et al. 2006

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)

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Suspicious polyphase patterns of normal looking ECGs provide fast early diagnoses of a coronary artery disease

Cited by 5 publications

References 2 publications

Decision Support Systems for Cardiovascular Diseases Based on Data Mining and Fuzzy Modelling

Decision Support Systems for Cardiovascular Diseases Based on Data Mining and Fuzzy Modelling

Detection and Classification of Adult and Fetal ECG Using Recurrent Neural Networks, Embedded Volterra and Higher-Order Statistics

A Decision Support System for the Diagnosis of Coronary Artery Disease

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