The European ST-T database: development, distribution and use

Taddei, Alessandro; Biagini, Andrea; Distante, G.; Emdin, Michele; Mazzei, M. G.; Pisani, P; Roggero, N.; Varanini, M.; Mark, Roger G.; Moody, G.B.; Braaksma, L.; Zeelenberg, C.; Marchesi, Carlo

doi:10.1109/cic.1990.144191

Cited by 48 publications

(28 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Myocardial ischemia (MI) is considered a major complication of cardiac function, and a prime cause for the occurrence of cardiac infarction and dangerous cardiac arrhythmias [6].The main characteristic of MI at the cellular level is manifested in the electrocardiogram (ECG) by an ST depression or elevation [7]. Several researchers have addressed the problem of automatic detection and classification of MI [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of HRV Time and Frequency Domain Features for Myocardial Ischemia Detection

Tian¹,

Zhang²,

Lee³

et al. 2011

The Journal of the Korea Contents Association

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

“…Several researchers have addressed the problem of automatic detection and classification of MI [5][6][7][8][9]. Some techniques are based on the detection of MI episodes and beat-by-beat MI detection [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Comparison of HRV Time and Frequency Domain Features for Myocardial Ischemia Detection

Tian¹,

Zhang²,

Lee³

et al. 2011

The Journal of the Korea Contents Association

View full text Add to dashboard Cite

“…MIT-BIH 부정맥 데이터베이스 [14] 와 유럽 심장학회 ST-T 데이터베이스 [15] , 그리고 보스톤 Berth Israel [10] 과 김 등의 알고리즘 [11] 을 사용하였다. …”

unclassified

R-wave Detection Algorithm in ECG Signal Using Adaptive Refractory Period

김정준¹,

김진섭²,

박길흠³

2013

Journal of the Institute of Electronics Engineers of Korea

View full text Add to dashboard Cite

In this paper, R-wave detection algorithm using refractory period to reflect the depolarization and repolarization of the myocardial cells of the heart is proposed. The proposed algorithm detects R-peaks using the features of R-wave and variable refractory period. First, the proposed algorithm extracts candidate R-peaks that have a relatively high potential and calculates the refractory period based on the kurtosis and potential for candidate R-peaks. Next, R-peak is determined by morphological features of the R-wave within the refractory period. In addition, due to less computation in the proposed algorithm, real-time processing is possible. The algorithm is applied to all records of the MIT-BIH arrhythmia database and the obtained results show a competitive detection rate of over 99.7%.

show abstract

“…The ECG signals from the European ST-T Database are a set of long-term Holder recordings provided by eight countries [5], [6]. The training set is extracted from 110 fifteen minutes ECG records, consisting of representative normal and abnormal ST-T waveforms.…”

Section: Extraction Of St-t Complexesmentioning

confidence: 99%

“…This extension continues until the total number of training patterns that are mapped to neurons with high entropy (and therefore to ambiguous classification) reduces to a size that can be managed to a proper supervised model effectively (this determines the upper bound) and is sufficient for valid generalization (this determines the lower bound). We used for our evaluation data from "The European ST-T Database", [5], [6], which includes two channels from Holter recordings corresponding to 79 patients with ischemic episodes of all types. Cardiology specialists have annotated the ischemic episodes in the original database.…”

Section: Introductionmentioning

confidence: 99%

Ischemia detection using supervised learning for hierarchical neural networks based on kohonen-maps

Vladutu

Papadimithou

Mavroudi

et al.

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

Abstract-The detection of ischemic episodes is a difficult pattern classification problem. The motivation for developing the Supervising Network -Self Organizing Map (sNet-SOM) model is to design computationally effective solutions for the particular problem of ischemia detection and other similar applications. The sNet-SOM uses unsupervised learning for the regions where the classification is not ambiguous and supervised for the "difficult" onesin a two-stage learning process. The unsupervised learning approach extends and adapts the Self-Organizing Map (SOM) algorithm of Kohonen. The basic SOM is modified with a dynamic expansion process controlled with an entropy based criterion that allows the adaptive formation of the proper SOM structure. This extension proceeds until the total number of training patterns that are mapped to neurons with high entropy (therefore with ambiguous classification) reduces to a size manageable numerically with a proper supervised model. The second learning phase (supervised training) has the objective of constructing better decision boundaries of the ambiguous regions. In this phase, a special supervised network is trained for the task of reduced computationally complexity-to perform the classification only of the ambiguous regions. After we tried with different classes of supervised networks , we obtained the best results with the Support Vector Machines (SVM) as local experts.

show abstract

The European ST-T database: development, distribution and use

Cited by 48 publications

References 7 publications

Comparison of HRV Time and Frequency Domain Features for Myocardial Ischemia Detection

Comparison of HRV Time and Frequency Domain Features for Myocardial Ischemia Detection

R-wave Detection Algorithm in ECG Signal Using Adaptive Refractory Period

Ischemia detection using supervised learning for hierarchical neural networks based on kohonen-maps

Contact Info

Product

Resources

About