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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with Multilayered Probabilistic Neural Network classifier
ترجمه فارسی عنوان مقاله:
استخراج ویژگی منحصر به فرد با استفاده از MRDWT برای طبقه بندی خودکارضربان قلب غیر طبیعی از داده های بزرگ ECG با چند لایه طبقه بندی احتمالی شبکه عصبی
منبع:
Sciencedirect - Elsevier - Applied Soft Computing Journal, Corrected proof: doi:10:1016/j:asoc:2018:04:005
نویسنده:
Hari Mohan Rai ∗, Kalyan Chatterjee
چکیده انگلیسی:
This paper employs a novel adaptive feature extraction techniques of electrocardiogram (ECG) signal for
detection of cardiac arrhythmias using multiresolution discrete wavelet transform from ECG big data.
In this paper, five types ECG arrhythmias including normal beats have been classified. The MIT-BIH
database of 48 patient records is utilized for detection and analysis of cardiac arrhythmias. Proposed
feature extraction utilizes Daubechies as wavelet function and extracts 21 feature points which include
the QRS complex of ECG signal. The Multilayered Probabilistic Neural Network (MPNN) classifier is pro
posed as the best-suited classifier for the proposed feature. Total 1700 ECG betas were tested using MPNN
classifier and compared with other three classifiers Back Propagation (BPNN), Multilayered Perceptron
(MLP) and Support Vector Machine (SVM). The system efficiency and performance have been evaluated
using seven types of evaluation criteria: precision (PR), F-Score, positive predictivity (PP), sensitivity (SE),
classification error rate (CER) and specificity (SP). The overall system accuracy, using MPNN technique
utilizing the proposed feature, obtained is 99.53% whereas using BPNN, MLP and SVM provide 97.94%,
98.53%, and 99%. The processing time using MPNN classifier is only 3 s which show that the proposed
techniques not only very accurate and efficient but also very quick.
Keywords: Signal processing ، Artificial intelligence ، Pattern recognition ، Soft computing ، Wavelet transform
قیمت: رایگان
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