ECG analysis using nonlinear PCA neural networks for ischemia detection

Stamkopoulos, T.; Diamantaras, Konstantinos I.; Maglaveras, Nicos; Strintzis, M.G.

doi:10.1109/78.726818

Cited by 146 publications

(81 citation statements)

References 35 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: 88%

“…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. The above methods when tested with the ESC ST-T database demonstrated a Se that ranged from 71 to 94% and a PPA from 66 to 90%.…”

Section: Discussionmentioning

confidence: 99%

“…Some works [3][4][5]15,20,21,25,32] report better results, which are not comparable since they refer to their own datasets. In [28] a non-linear PCA neural network is proposed for ischemic beat classification instead of episode detection. Using 34 out of the 90 recordings of the ESC ST-T database, a Se of 79% and a Sp of 75% are reported.…”

Section: Discussionmentioning

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%

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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: Discussionmentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 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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“…Regarding the electrocardiogram (ECG), it is well known the extraction of the fetal ECG from maternal recordings [2], the separation of breathing artifacts and other disturbances [3], analysis of ST segments for ischemia detection [4], identification of humans using the ECG [5], ventricular arrhythmia detection and classification [6] and the study of atrial fibrillation (AF).…”

Section: Introductionmentioning

confidence: 99%

Mixing Matrix Pseudostationarity and ECG Preprocessing Impact on ICA-Based Atrial Fibrillation Analysis

Rieta¹,

Sánchez²,

Sanchis³

et al. 2004

Independent Component Analysis and Blind Signal Separation

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Abstract. In this work two relevant considerations in the ICA-based estimation of atrial activity (AA) in atrial fibrillation (AF) episodes from real electrocardiogram (ECG) recordings are presented. Firstly, the impact of low-pass filtering preprocessing on the extraction quality of AA is analyzed, showing an average improvement over 17% in spectral concentration (SC) when low-pass filtering is applied after ICA with respect to the application of the same filtering before ICA. Secondly, it is demonstrated that the ICA mixing matrix obtained from one AF segment can also be used to estimate the AA present in different segments of the same recording, thus proving the pseudostationarity of the mixing matrix. Results over 32 AF segments show a mean cross-correlation of R dp = 81.5% between the directly estimated AA and the estimated using presudostationarity. Changes in spectral concentration from one case to the other (∆SC dp = 1.4%) are negligible.

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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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ECG analysis using nonlinear PCA neural networks for ischemia detection

Cited by 146 publications

References 35 publications

An ischemia detection method based on artificial neural networks

An ischemia detection method based on artificial neural networks

Mixing Matrix Pseudostationarity and ECG Preprocessing Impact on ICA-Based Atrial Fibrillation Analysis

Electrocardiogram (ECG): Automated Diagnosis

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