Discrimination between Demand and Supply Ischemia Episodes in Holter Recordings

Mincholé, Ana; Jager, F.; Laguna, Pablo

doi:10.1109/iembs.2007.4352856

Cited by 4 publications

(6 citation statements)

References 10 publications

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“…Furthermore, many researchers who conducted ECG research also such as [7] whose research related to heart abnormalities arrhythmia using fuzzy logic method. Various method approaches are used to obtain a good end result, such as fuzzy, wavelet, machine learning or statistical methods [8]. There are even those who use tools (CSA) on Linux [9].…”

Section: Methodsmentioning

confidence: 99%

Use of Wavelets in Electrocardiogram Research: a Literature Review

Darwan¹,

Mustafidah

2021

Jurnal Informatika

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Currently the introduction and detection of heart abnormalities using electrocardiogram (ECG) is very much. ECG conducted many research approaches in various methods, one of which is wavelet. This article aims to explain the trends of ECG research using wavelet approach in the last ten years. We reviewed journals with the keyword title "ecg wavelet" and published from 2011 to 2020. Articles classified by the most frequently discussed topics include: datasets, case studies, pre-processing, feature extraction and classification/identification methods. The increase in the number of ECG-related articles in recent years is still growing in new ways and methods. This study is very interesting because only a few researchers focus on researching about it. Several approaches from many researchers are used to obtain the best results, both by using machine learning and deep learning. This article will provide further explanation of the most widely used algorithms against ECG research with wavelet approaches. At the end of this article it is also shown that the critical aspect of ECG research can be done in the future is the use of datasets, as well as the extraction of characteristics and classifications by looking at the level of accuracy.

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Section: Methodsmentioning

confidence: 99%

Use of Wavelets in Electrocardiogram Research: a Literature Review

Darwan¹,

Mustafidah

2021

Jurnal Informatika

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“…Feature specification HR -Mean HR (Xing et al, 2007) -∆ HR (Faganeli and Jager, 2008;Minchole et al, 2007) -Maximum HR (Faganeli and Jager, 2008;Minchole et al, 2007) HRV -Low frequency high frequency ratio (Xing et al, 2007) -Center frequency (Xing et al, 2007)…”

Section: Groupmentioning

confidence: 99%

“…-ST-deviation (Langley et al, 2003) -∆ ST-deviation (Faganeli and Jager, 2008;Langley et al, 2003;Minchole et al, 2007) -ST-segment morphology (ST-segment sample values, ∆ ST-Slope, ∆ Legendre coefficients, ST-segment root mean square) (Faganeli and Jager, 2008;Zimmerman et al, 2003) Depolarisation -∆ Maximum QRS-slopes (Minchole et al, 2007) -∆ QRS morphology (KLT based Mahalanobis distance) (Faganeli and Jager, 2008) Others -Correlation between ST-deviation and heart rate (Minchole et al, 2007) -Group Delay (caluclated from Smoothed Pseudo-Wigner-Ville Distribution) (Xing et al, 2007) changes in the ventricular conduction (Dranca et al, 2006;Smrdel and Jager, 2004). Typically, those approaches are based on the assessment of modifications of ventricular depolarization and are referred to as reference tracking (Smrdel and Jager, 2004).…”

Section: Repolarisationmentioning

confidence: 99%

“…Examined features include the ST-deviation, HR related features, features based on the heart rate variability (HRV) as well as different morphological features (for a more comprehensive list see table 1). The feature selection process, if carried out, and the classification process is usually accomplished by linear methods (Faganeli and Jager, 2008;Minchole et al, 2007;Xing et al, 2007). Table 2 gives the results yielded in different studies distinguishing between ischemic episodes and HR related ones.…”

Section: Former Studiesmentioning

confidence: 99%

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DISCRIMINATION BETWEEN ISCHEMIC AND HEART-RATE RELATED ST-EPISODES - Non-linear Classification for an Online Capable Approach

Zaunseder¹,

Aipperspach²,

Poll

2010

Proceedings of the Third International Conference on Bio-Inspired Systems and Signal Processing

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Transient ST-epsiodes recognized in the ECG are regarded as marker of myocardial ischemia. As disturbed ST-sections may appear as ST-episodes a differentiated analysis is necessary to avoid misinterpretations. The presented study aims for the discrimination of ischemic and heart-rate related ST-episodes. Our approach includes the morphologic description of the ventricular repolarization by means of the Karhunen-Loève-Transformation and the non-linear classification using an artificial neural network. The proposed selection of used ECG segments guarantees that the classification procedure indicating ischemic attacks can be done before the complete episode is acquired. This online-capable approach gains accuracies up to 94,2 % for the discrimination of ischemic and heart-rate related ST-episodes.

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“…To estimate how the values of diagnostic and morphologic parameters change during transient ST segment episodes, we first calculated means of parameter values for each of the parameter, in their time series, on three 20-second intervals (see figure 1), similarly as in [3], located before the beginning (interval I 1 ), right after the beginning (I 2 ) and around the extrema (I 3 ) for each ST segment episode. We then calculated features, F , for each of the parameter, P , and for each ST episode, as the differences of the mean parameter values in the intervals I 1 , I 2 , and I 3 , following:…”

Section: The Selected Featuresmentioning

confidence: 99%

Automatic distinguishing between ischemic and heart-rate related transient ST segment episodes in ambulatory ECG records

Faganeli

Jager

2008

2008 Computers in Cardiology

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In ambulatory ECG records, ischemia is manifested by transient ST segment episodes which may or may not be accompanied by increase in heart rate. There are also transient heart-rate related non-ischemic ST segment episodes present which are caused by change in heart rate. The goal of this work was to classify between these two types of ST episodes. The selected features to classify the ST episodes were changes of heart rate, changes of time domain morphologic parameters of the ST segment and changes of the Legendre orthonormal polynomial coefficients of the ST segment, all obtained on 20-second intervals at the beginning and at the extrema of each ST episode. The obtained sensitivity in classifying ischemic versus heart-rate related ST episodes using the LTST DB was 77.9%, while specificity was 73.9%. IntroductionDuring ischemia, an imbalance occurs due to increased myocardial oxygen demand (demand ischemia, heart rate increase typically precedes ischemia), or due to decreased oxygen supply (supply ischemia, heart rate increase typically follows ischemia) what can lead to injury or death of heart tissue. Early markers of ischemia are transient ST segment deviation and transient ST segment morphology change, and can be detected in 24-hour ambulatory ECG records. There can also be transient non-ischemic ST segment morphology-change episodes which are not caused by an obstruction of the blood flow to the heart, but are caused by simultaneous change in heart rate. These transient non-ischemic heart-rate related ST segment episodes complicate automatic detection of true ischemia. The goal of this work was to automatically distinguish between transient ischemic and heart-rate related ST segment episodes. Methods The Long-Term ST DatabaseThe Long-Term ST Database (LTST DB) [1] contains 86 2-or 3-lead 24-hour ambulatory ECG records, sampled at 250 samples s −1 per channel, and is intended for development and testing of automatic ischemia detectors. The records were collected during routine clinical practice to model significant number of real-world clinical conditions. During development of the LTST DB, a considerable preprocessing phase took place in order to derive a number of time series of diagnostic and morphologic parameters. The preprocessing phase included: ARISTO-TLE's [2] analysis yielding stable QRS complex fiducial points (FP), removal of noise, derivation of the instantaneous heart rate, automatic search for the isoelectric level, measurement of the ST segment level, derivation of the Karhunen-Loève Transform (KLT) based ST segment and QRS complex morphology feature vectors, removal of abnormal beats and their neighbors, and removal of noisy beats. Then the positions of the isoelectric level and J point were set manually by human expert annotators using time averaged heart beats computed over 16-second intervals surrounding each normal and non-noisy heart beat which passed the preprocessing phase. These positions were then used to derive the ST segment level functions, stlev(i, j), where i denotes t...

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Discrimination between Demand and Supply Ischemia Episodes in Holter Recordings

Cited by 4 publications

References 10 publications

Use of Wavelets in Electrocardiogram Research: a Literature Review

Use of Wavelets in Electrocardiogram Research: a Literature Review

DISCRIMINATION BETWEEN ISCHEMIC AND HEART-RATE RELATED ST-EPISODES - Non-linear Classification for an Online Capable Approach

Automatic distinguishing between ischemic and heart-rate related transient ST segment episodes in ambulatory ECG records

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