Tao Wang1, Changhua Lu1, Mei Yang2, Feng Hong1, Chun Liu3. 1. School of Computer and Information, Hefei University of Technology, Hefei, Anhui, China. 2. Beijing Huaru Technology Co., Ltd. Hefei Branch, Hefei, Anhui, China. 3. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui, China.
Abstract
BACKGROUND: Heart arrhythmia, as one of the most important cardiovascular diseases (CVDs), has gained wide attention in the past two decades. The article proposes a hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss. METHODS: In the method, a convolution neural network is used to extract the morphological features. The reason behind this is that the morphological characteristics of patients have inter-patient variations, which makes it difficult to accurately describe using traditional hand-craft ways. Then the extracted morphological features are combined with the RR intervals features and input into the multilayer perceptron for heartbeat classification. The RR intervals features contain the dynamic information of the heartbeat. Furthermore, considering that the heartbeat classes are imbalanced and would lead to the poor performance of minority classes, a focal loss is introduced to resolve the problem in the article. RESULTS: Tested using the MIT-BIH arrhythmia database, our method achieves an overall positive predictive value of 64.68%, sensitivity of 68.55%, f1-score of 66.09%, and accuracy of 96.27%. Compared with existing works, our method significantly improves the performance of heartbeat classification. CONCLUSIONS: Our method is simple yet effective, which is potentially used for personal automatic heartbeat classification in remote medical monitoring. The source code is provided on https://github.com/JackAndCole/Deep-Neural-Network-For-Heartbeat-Classification.
BACKGROUND: Heart arrhythmia, as one of the most important cardiovascular diseases (CVDs), has gained wide attention in the past two decades. The article proposes a hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss. METHODS: In the method, a convolution neural network is used to extract the morphological features. The reason behind this is that the morphological characteristics of patients have inter-patient variations, which makes it difficult to accurately describe using traditional hand-craft ways. Then the extracted morphological features are combined with the RR intervals features and input into the multilayer perceptron for heartbeat classification. The RR intervals features contain the dynamic information of the heartbeat. Furthermore, considering that the heartbeat classes are imbalanced and would lead to the poor performance of minority classes, a focal loss is introduced to resolve the problem in the article. RESULTS: Tested using the MIT-BIH arrhythmia database, our method achieves an overall positive predictive value of 64.68%, sensitivity of 68.55%, f1-score of 66.09%, and accuracy of 96.27%. Compared with existing works, our method significantly improves the performance of heartbeat classification. CONCLUSIONS: Our method is simple yet effective, which is potentially used for personal automatic heartbeat classification in remote medical monitoring. The source code is provided on https://github.com/JackAndCole/Deep-Neural-Network-For-Heartbeat-Classification.
Authors: Tanis Mar; Sebastian Zaunseder; Juan Pablo Martínez; Mariano Llamedo; Rüdiger Poll Journal: IEEE Trans Biomed Eng Date: 2011-02-10 Impact factor: 4.538
Authors: David S Howland; Jian Liu; Yijin She; Beth Goad; Nicholas J Maragakis; Benjamin Kim; Jamie Erickson; John Kulik; Lisa DeVito; George Psaltis; Louis J DeGennaro; Don W Cleveland; Jeffrey D Rothstein Journal: Proc Natl Acad Sci U S A Date: 2002-01-29 Impact factor: 11.205
Authors: Awni Y Hannun; Pranav Rajpurkar; Masoumeh Haghpanahi; Geoffrey H Tison; Codie Bourn; Mintu P Turakhia; Andrew Y Ng Journal: Nat Med Date: 2019-01-07 Impact factor: 53.440