Ectopic Ventricular Activity

Studies on the Mechanism of Ventricular Activity

Circulation Research ◽

10.1161/01.res.2.3.221 ◽

1954 ◽

Vol 2 (3) ◽

pp. 221-230 ◽

Cited By ~ 25

Author(s):

L. ALLEN SMITH ◽

REXFORD KENNAMER ◽

MYRON PRINZMETAL

Keyword(s):

Ventricular Activity

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ECG Monitoring After Cardiac Surgery

AACN Advanced Critical Care ◽

10.4037/15597768-2007-3008 ◽

2007 ◽

Vol 18 (3) ◽

pp. 294-304

Author(s):

Leslie S. Kern ◽

Marion E. McRae ◽

Marjorie Funk

Keyword(s):

Atrial Fibrillation ◽

Cardiac Surgery ◽

Preventive Therapy ◽

Cost Of Care ◽

Adverse Outcomes ◽

Postoperative Atrial Fibrillation ◽

History Of ◽

Bedside Monitor ◽

Atrial Electrogram ◽

Ventricular Activity

Atrial fibrillation is one of the most common complications after cardiac surgery and is associated with adverse outcomes such as increased mortality, neurological problems, longer hospitalizations, and increased cost of care. Major risk factors for the development of postoperative atrial fibrillation include older age and a history of atrial fibrillation. β-Blockers are the most effective preventive therapy, although sotalol and amiodarone can also be used for prophylaxis. In the postoperative period, the nurse plays an important role in the early detection of atrial fibrillation by the recording of an atrial electrogram, which is easily obtained from the bedside monitor. Because an atrial electrogram records larger atrial activity than ventricular activity, it can be invaluable in establishing the diagnosis of postoperative atrial fibrillation. Once atrial fibrillation begins, treatment can be started with either rhythm conversion or rate-controlling medications.

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Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features

Journal of Healthcare Engineering ◽

10.1155/2021/4123471 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Wenzhi Zhang ◽

Runchuan Li ◽

Shengya Shen ◽

Jinliang Yao ◽

Yan Peng ◽

...

Keyword(s):

Myocardial Infarction ◽

Clinical Decision Making ◽

Human Life ◽

Principal Component ◽

Fusion Rule ◽

Clinical Decision ◽

Gradient Boosting ◽

Discrete Wavelet ◽

Extreme Gradient Boosting ◽

Ventricular Activity

Myocardial infarction (MI) is one of the most common cardiovascular diseases threatening human life. In order to accurately distinguish myocardial infarction and have a good interpretability, the classification method that combines rule features and ventricular activity features is proposed in this paper. Specifically, according to the clinical diagnosis rule and the pathological changes of myocardial infarction on the electrocardiogram, the local information extracted from the Q wave, ST segment, and T wave is computed as the rule feature. All samples of the QT segment are extracted as ventricular activity features. Then, in order to reduce the computational complexity of the ventricular activity features, the effects of Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), and Locality Preserving Projections (LPP) on the extracted ventricular activity features are compared. Combining rule features and ventricular activity features, all the 12 leads features are fused as the ultimate feature vector. Finally, eXtreme Gradient Boosting (XGBoost) is used to identify myocardial infarction, and the overall accuracy rate of 99.86% is obtained on the Physikalisch-Technische Bundesanstalt (PTB) database. This method has a good medical diagnosis basis while improving the accuracy, which is very important for clinical decision-making.

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