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 increases unnecessary computation. This paper proposes an adaptive online sequential extreme learning machine with an effective sample updating mechanism. The new and old samples are given different weights. The effect of new training samples on the algorithm is further enhanced, which can further improve the regression prediction ability of extreme learning machine. At the same time, an improved artificial bee colony algorithm is proposed and used to optimize the parameters of the adaptive online sequential extreme learning machine. The stability and convergence property of proposed prediction method are proved. The actual collected short-term wind speed time series is used as the research object and verify the prediction perfromance of the proposed method. Multi step prediction simulation of short-term wind speed is performed out. Compared with other prediction methods, the simulation results show that the proposed approach has higher prediction accuracy and reliability performance, meanwhile improve the performance indicators.

Combining Rhythm Information between Heartbeats and BiLSTM-Treg Algorithm for Intelligent Beat Classification of Arrhythmia

“…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 ].…”

“…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.…”

ECG signal classification based on Deep Learning by using Convolutional Neural Network (CNN)

“…Precision: Represent the ratio of the correctly positive to the total positive perdition . [12], [14] Acc = T P F P + T P…”

“…F1-score: This factor represent normalize average of recall and precision. [14,15] Acc = 2 ⇤ P recision ⇤ recall P recision + recall (4)…”

Fusion of SAR and optical images using pixel-based CNN

“…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.…”