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用于检测心肌梗死的改进蝙蝠算法

Improved Bat algorithm for the detection of myocardial infarction.

作者信息

Kora Padmavathi, Kalva Sri Ramakrishna

机构信息

Department of ECE, GRIET, Bachupally, 500090 Hyderabad, India.

Department of ECE, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, India.

出版信息

Springerplus. 2015 Nov 3;4:666. doi: 10.1186/s40064-015-1379-7. eCollection 2015.

DOI:10.1186/s40064-015-1379-7
PMID:26558169
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4631839/
Abstract

The medical practitioners study the electrical activity of the human heart in order to detect heart diseases from the electrocardiogram (ECG) of the heart patients. A myocardial infarction (MI) or heart attack is a heart disease, that occurs when there is a block (blood clot) in the pathway of one or more coronary blood vessels (arteries) that supply blood to the heart muscle. The abnormalities in the heart can be identified by the changes in the ECG signal. The first step in the detection of MI is Preprocessing of ECGs which removes noise by using filters. Feature extraction is the next key process in detecting the changes in the ECG signals. This paper presents a method for extracting key features from each cardiac beat using Improved Bat algorithm. Using this algorithm best features are extracted, then these best (reduced) features are applied to the input of the neural network classifier. It has been observed that the performance of the classifier is improved with the help of the optimized features.

摘要

医学从业者研究人类心脏的电活动，以便从心脏病患者的心电图（ECG）中检测出心脏病。心肌梗死（MI）或心脏病发作是一种心脏病，当一条或多条向心肌供血的冠状动脉（动脉）通路中出现阻塞（血凝块）时就会发生。心脏中的异常情况可以通过心电图信号的变化来识别。检测心肌梗死的第一步是对心电图进行预处理，通过使用滤波器去除噪声。特征提取是检测心电图信号变化的下一个关键过程。本文提出了一种使用改进的蝙蝠算法从每个心跳中提取关键特征的方法。使用该算法提取出最佳特征，然后将这些最佳（简化）特征应用于神经网络分类器的输入。据观察，在优化特征的帮助下，分类器的性能得到了提高。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3732/4631839/71e818ab0a3d/40064_2015_1379_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3732/4631839/eab7f4fbc645/40064_2015_1379_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3732/4631839/a9fe4b6f3619/40064_2015_1379_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3732/4631839/1c7be2b1cfeb/40064_2015_1379_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3732/4631839/b684e1ebefa5/40064_2015_1379_Fig11_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3732/4631839/95754c407ce0/40064_2015_1379_Fig14_HTML.jpg

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