Abstract：
:Arrhythmia is slow, fast or irregular heartbeat. Manual ECG assessment and disease classification is an error-prone task because of vast differences in ECG morphology and difficulty in accurate identifying ECG components. Moreover, proposing a computer-aided diagnosis system for heartbeat classification can be useful when access to medical care centers is difficult or impossible. Therefore, the main aim of this study is classifying ECG beats for arrhythmia detection (four beat classes are considered). Previous studies have proposed different methods based on traditional machine learning and/or deep learning. In this paper, a novel feature engineering method is proposed based on deep learning and K-NNs. The features extracted by our proposed method are classified with different classifiers such as decision trees, SVMs with different kernels and random forests. Our proposed method has reasonably good performance for beat classification and achieves the average Accuracy of 99.77%, AUC of 99.99%, Precision of 99.75% and Recall of 99.30% using fivefold Cross Validation strategy. The main advantage of the proposed method is its low computational time compared to training deep learning models from scratch and its high accuracy compared to the traditional machine learning models. The strength and suitability of the proposed method for feature extraction is shown by the high balance between sensitivity and specificity.
journal_name
Australas Phys Eng Sci Medjournal_title
Australasian physical & engineering sciences in medicineauthors
Khatibi T,Rabinezhadsadatmahaleh Ndoi
10.1007/s13246-019-00814-wsubject
Has Abstractpub_date
2019-11-26 00:00:00eissn
0158-9938issn
1879-5447pii
10.1007/s13246-019-00814-wpub_type
杂志文章abstract：:Radiofrequency-electromagnetic field (RF-EMF) exposure of human populations is increasing due to the widespread use of mobile phones and other telecommunication and broadcasting technologies. There are ongoing concerns about potential short- and long-term public health consequences from RF-EMF exposures. To elucidate ...
journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章,评审
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章
doi：
更新日期：1998-09-01 00:00:00
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journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章
doi：
更新日期：1997-12-01 00:00:00
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章
doi：
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journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章
doi：
更新日期：1989-09-01 00:00:00
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journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章
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journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章
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journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章,评审
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journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章,评审
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pub_type： 杂志文章
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journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章
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