An Algorithm for Automated Detection of Ischemic ECG Beats Using Support Vector Machines

Mohebbi, Maryam; Moghadam, H Abrishami

doi:10.1109/siu.2007.4298793

Cited by 5 publications

(7 citation statements)

References 9 publications

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“…This type of slowly varying noise can be eliminated by curve fitting. A cubic spline [ 23 , 42 , 43 ] is implemented using each isoelectric point of an ECG beat as a knot for optimal fitting of the wandering curve. An averaging filter is then introduced to smooth the ECG signal.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection

Tseng

Lin

Jaw

2016

Computational and Mathematical Methods in Medicine

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An automatic method is presented for detecting myocardial ischemia, which can be considered as the early symptom of acute coronary events. Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signals. The methods in this study are proposed to detect abnormal ECG beats using knowledge-based features and classification methods. A novel classification method, sparse representation-based classification (SRC), is involved to improve the performance of the existing algorithms. A comparison was made between two classification methods, SRC and support-vector machine (SVM), using rule-based vectors as input feature space. The two methods are proposed with quantitative evaluation to validate their performances. The results of SRC method encompassed with rule-based features demonstrate higher sensitivity than that of SVM. However, the specificity and precision are a trade-off. Moreover, SRC method is less dependent on the selection of rule-based features and can achieve high performance using fewer features. The overall performances of the two methods proposed in this study are better than the previous methods.

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Section: Methodsmentioning

confidence: 99%

Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection

Tseng

Lin

Jaw

2016

Computational and Mathematical Methods in Medicine

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“…1) Support Vector Machines: Support vector machines (SVM) have been around for quite some time and have grabbed much attention for ECG beat classification [9], [10], [20]. In this technique, training datasets with known classes are given to the SVM program.…”

Section: B Beat Classificationmentioning

confidence: 99%

Section: B Beat Classificationmentioning

confidence: 99%

“…Mohebbi and Moghadam [20] used the information of the ST segment in each beat of the ECG wave as elements of the feature vectors. Elevation of ST segment is proven to have information about myocardial ischemia episodes that may lead to certain heart attacks [20]. In another approach, transforms such as discrete Fourier transform, discrete cosine transform, discrete wavelet transform, and adaptive autoregressive coefficients are used in constructing the feature vector [21].…”

Section: B Beat Classificationmentioning

confidence: 99%

“…The time and amplitude of 11 points lying 16 ms apart on the ST segment were extracted as the feature vector. The ST segment starts at the J point (which is the junction between the QRS complex and ST segment), and ends after a predefined interval of 160 ms [20]. According to [36], J is identified as a point in a sliding window of 20 ms, which has a signal slope equal or less than 2.5 mV · s −1 and is located within 20-120 ms after the R wave.…”

Section: Performance Evaluation-beat Classificationmentioning

confidence: 99%

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A Patient-Adaptive Profiling Scheme for ECG Beat Classification

Faezipour

Saeed

Bulusu

et al. 2010

IEEE Trans. Inform. Technol. Biomed.

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Abstract-Recent trends in clinical and telemedicine applications highly demand automation in electrocardiogram (ECG) signal processing and heart beat classification. A patient-adaptive cardiac profiling scheme using repetition-detection concept is proposed in this paper. We first employ an efficient wavelet-based beat-detection mechanism to extract precise fiducial ECG points. Then, we implement a novel local ECG beat classifier to profile each patient's normal cardiac behavior. ECG morphologies vary from person to person and even for each person, it can vary over time depending on the person's physical condition and/or environment. Having such profile is essential for various diagnosis (e.g., arrhythmia) purposes. One application of such profiling scheme is to automatically raise an early warning flag for the abnormal cardiac behavior of any individual. Our extensive experimental results on the MIT-BIH arrhythmia database show that our technique can detect the beats with 99.59% accuracy and can identify abnormalities with a high classification accuracy of 97.42%.

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