2018
DOI: 10.14311/nnw.2018.28.015
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Patient-Adapted and Inter-Patient Ecg Classification Using Neural Network and Gradient Boosting

Abstract: Abstract:As an improved algorithm of standard extreme learning machine, online sequential extreme learning machine achieves excellent classification and regression performance. However, online sequential extreme learning machine gives the same weight to the old and new training samples, and fails to highlight the importance of the new training samples. At the same time, the algorithm updates the network weights after obtaining the new training samples. This network weight updating mode lacks flexibility and in… Show more

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Cited by 7 publications
(5 citation statements)
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“…Feature extraction is the key step of ECG signal classification and recognition, and the extracted feature quality will affect the accuracy of ECG signal classification and recognition [ 6 ]. Generally, the features of ECG signals extracted by researchers mainly include morphological features [ 7 ], interphase features [ 8 , 9 ], wavelet transform features [ 10 ], higher-order statistics (HOS) [ 9 , 11 ], Hermite basis function (HBF) [ 12 ], QRS amplitude vector [ 13 ], and QRS composite wave area [ 14 ]. Then machine-learning algorithms are used for classification, such as the KNN algorithm [ 15 ], support vector machine (SVM) [ 7 ], and random forest [ 9 ].…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Feature extraction is the key step of ECG signal classification and recognition, and the extracted feature quality will affect the accuracy of ECG signal classification and recognition [ 6 ]. Generally, the features of ECG signals extracted by researchers mainly include morphological features [ 7 ], interphase features [ 8 , 9 ], wavelet transform features [ 10 ], higher-order statistics (HOS) [ 9 , 11 ], Hermite basis function (HBF) [ 12 ], QRS amplitude vector [ 13 ], and QRS composite wave area [ 14 ]. Then machine-learning algorithms are used for classification, such as the KNN algorithm [ 15 ], support vector machine (SVM) [ 7 ], and random forest [ 9 ].…”
Section: Related Workmentioning
confidence: 99%
“…Formula (7) indicates that the current moment memory unit C t is adjusted by the current candidate unit 􏽥 C t and its own state C t−1 as well as the input gate and the forgetting gate. Finally, formula (8) indicates that the output at the current moment, that is, the hidden state at the current moment, is determined by the current memory unit C t and the output gate.…”
Section: Bilstm Neural Networkmentioning
confidence: 99%
“…Precision: Represent the ratio of the correctly positive to the total positive perdition . [12], [14] Acc = T P F P + T P…”
Section: ) Design Training and Testing 1-d Modelmentioning
confidence: 99%
“…F1-score: This factor represent normalize average of recall and precision. [14,15] Acc = 2 ⇤ P recision ⇤ recall P recision + recall (4)…”
Section: ) Design Training and Testing 1-d Modelmentioning
confidence: 99%
“…CNN in computer vision mainly focuses on biometric recognition [3], change detection in remote sensing data [4], object detection and human behavior tracking [5], subject classification [6], and medical image analysis [7] fields. The work of the microwave and optical image fusion is restricted to the following: In [8], SAR and optical images were fused using Atrous wavelet transform and applied fusion rule in pixel to obtain the high spatial image missing boundary characteristics covering the regional properties.…”
Section: Introductionmentioning
confidence: 99%