Continuous computer-assisted electrocardiographic monitoring in patients with acute myocardial infarction: early experience

Krucoff, Mitchell W.; Croll, Martha A.; Pendley, Laura P.; Burdette, Deana L.; Pope, James E.; Hutchinson, D.D.; Stone, Janice S.; Weber, Rita; Califf, Robert M.

doi:10.1109/cic.1989.130520

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…In our work, we are committed to developing a practical, medical-grade detection algorithm of myocardial infarction. With the increasing popularity of machine learning, many researchers [3][4][5][6][7][8][9][10][11][12][13][14][15] have applied machine learning techniques to the detection of myocardial infarction and achieved promising results.…”

Section: Introductionmentioning

confidence: 99%

Application of Heartbeat-Attention Mechanism for Detection of Myocardial Infarction Using 12-Lead ECG Records

Zhang

2019

Applied Sciences

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Early detection and effective treatment of myocardial infarction can prevent the deterioration of ischemic heart disease and greatly reduce the possibility of sudden death. On the basis of standard 12-lead electrocardiogram (ECG) records, this paper proposes a bidirectional, long short-term memory (Bi-LSTM) network with a heartbeat-attention mechanism to effectively and automatically detect myocardial infarction (MI). First, we divide the standard 12-lead ECG records into sliding windows with the same number of heartbeats. Subsequently, we do not use any labels of heartbeats to train the Bi-LSTM network and the heartbeat-attention mechanism is applied to automatically weight the difference between unlabeled heartbeats. Finally, our method is validated by patients’ complete ECG records and real labels in the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database. When compared with the same network without the heartbeat-attention mechanism or other existing methods, our method achieves comparable or better performance. The accuracy, sensitivity, and specificity reach 94.77%, 95.58%, and 90.48%, respectively.

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Section: Introductionmentioning

confidence: 99%

Application of Heartbeat-Attention Mechanism for Detection of Myocardial Infarction Using 12-Lead ECG Records

Zhang

2019

Applied Sciences

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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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An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection

Ng¹,

Liu²,

Liu³

et al. 2018

j med imaging hlth inform

437

185

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Continuous computer-assisted electrocardiographic monitoring in patients with acute myocardial infarction: early experience

Cited by 4 publications

References 8 publications

Application of Heartbeat-Attention Mechanism for Detection of Myocardial Infarction Using 12-Lead ECG Records

Application of Heartbeat-Attention Mechanism for Detection of Myocardial Infarction Using 12-Lead ECG Records

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

An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection

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