Analysis of changes in body surface potentials during PTCA-induced ischemia using the temporal wavelet transform

Brooks, Dana H.; On, H.; MacLeod, Robert S.; Krim, Hamid

doi:10.1109/cic.1994.470178

Cited by 3 publications

(1 citation statement)

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“…Some early approaches also include beat-to-beat quantification and backpropagation algorithm (Badilini et al, 1992;Stamkopoulos et al, 1992). Wavelet transform has been used to analyze ECG in ischemia (Thakor et al, 1993;Brooks et al, 1994;Lemire et al, 2000;Li et al, 2003). An approach based on continuous wavelet transform (CWT) for detection of ischemia using 12-lead ECG can be found (Gramatikov et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Myocardial Ischemia Detection from Slope of ECG ST Segment

Farhan¹,

Nahiyan²

2018

Bangla J Med Phys

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Myocardial ischemia occurs when blood flow to heart is reduced preventing it from receiving enough oxygen. It is a possible indication of partial or complete blockage of coronary arteries. Though ischemia is accompanied by symptoms (fatigue, chest pain, shortness of breath etc.) sometimes it can be silent. If not treated, it can lead to various heart diseases. Most importantly it can progress to myocardial infarction (heart attack), which can be fatal. Thus detecting ischemia at an early stage is important to prevent serious implications. Nowadays personal healthcare monitoring systems are used which provide vital physiological information. In future ECG measurement devices would also be common in homes. So, the proposed work intends to develop an algorithm in detecting myocardial ischemia from ECG, which would be computationally less complex and easy to implement in homecare ECG devices. One way to do it is through continuous or long term monitoring of ECG. The ST segment elevation (or depression) indicates presence of ischemia. The proposed method measures slope of ST segment which must vary in case of ST changes. The algorithm is tested on selected records of the European ST-T database and returns an accuracy of 83.33%.Bangladesh Journal of Medical Physics Vol.10 No.1 2017 12-24

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Section: Introductionmentioning

confidence: 99%

Myocardial Ischemia Detection from Slope of ECG ST Segment

Farhan¹,

Nahiyan²

2018

Bangla J Med Phys

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Electrical imaging of the heart

Brooks

MacLeod

1997

IEEE Signal Process. Mag.

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A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records

Ansari

Farzaneh

Duda

et al. 2017

IEEE Rev. Biomed. Eng.

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There is a growing body of research focusing on automatic detection of ischemia and myocardial infarction (MI) using computer algorithms. In clinical settings, ischemia and MI are diagnosed using electrocardiogram (ECG) recordings as well as medical context including patient symptoms, medical history, and risk factors-information that is often stored in the electronic health records. The ECG signal is inspected to identify changes in the morphology such as ST-segment deviation and T-wave changes. Some of the proposed methods compute similar features automatically while others use nonconventional features such as wavelet coefficients. This review provides an overview of the methods that have been proposed in this area, focusing on their historical evolution, the publicly available datasets that they have used to evaluate their performance, and the details of their algorithms for ECG and EHR analysis. The validation strategies that have been used to evaluate the performance of the proposed methods are also presented. Finally, the paper provides recommendations for future research to address the shortcomings of the currently existing methods and practical considerations to make the proposed technical solutions applicable in clinical practice.

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Analysis of changes in body surface potentials during PTCA-induced ischemia using the temporal wavelet transform

Cited by 3 publications

References 10 publications

Myocardial Ischemia Detection from Slope of ECG ST Segment

Myocardial Ischemia Detection from Slope of ECG ST Segment

Electrical imaging of the heart

A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records

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