{"type":"ScholarlyArticle","title":"Discrete Biorthogonal Wavelet Transform Based Convolutional Neural Network for Atrial Fibrillation Diagnosis from Electrocardiogram","authors":[{"name":"Qingsong Xie","slug":"qingsong-xie","given":"Qingsong","family":"Xie","affiliation":"Department of Micro-Nano Electronics , Shanghai Jiao Tong University , China; Centre for Cognitive Machines and Computational Health (CMaCH) , School of SEIEE , Shanghai Jiao Tong University , China"},{"name":"Shikui Tu","slug":"shikui-tu","given":"Shikui","family":"Tu","affiliation":"Department of Computer Science and Engineering , Shanghai Jiao Tong University , China; Centre for Cognitive Machines and Computational Health (CMaCH) , School of SEIEE , Shanghai Jiao Tong University , China"},{"name":"Guoxing Wang","slug":"guoxing-wang","given":"Guoxing","family":"Wang","affiliation":"Department of Micro-Nano Electronics , Shanghai Jiao Tong University , China; Centre for Cognitive Machines and Computational Health (CMaCH) , School of SEIEE , Shanghai Jiao Tong University , China"},{"name":"Yong Lian","slug":"yong-lian","given":"Yong","family":"Lian","affiliation":"Department of Micro-Nano Electronics , Shanghai Jiao Tong University , China; Centre for Cognitive Machines and Computational Health (CMaCH) , School of SEIEE , Shanghai Jiao Tong University , China"},{"name":"Lei Xu","slug":"lei-xu","given":"Lei","family":"Xu","affiliation":"Department of Computer Science and Engineering , Shanghai Jiao Tong University , China; Centre for Cognitive Machines and Computational Health (CMaCH) , School of SEIEE , Shanghai Jiao Tong University , China"}],"abstract":"For the problem of early detection of atrial fibrillation (AF) from electrocardiogram (ECG), it is difficult to capture subject-invariant discriminative features from ECG signals, due to the high variation in ECG morphology across subjects and the\r\nnoise in ECG. In this paper, we propose an Discrete Biorthogonal Wavelet Transform (DBWT) Based Convolutional Neural Network (CNN) for AF detection, shortly called DBWT-AFNet. In  DBWT-AFNet, rather than directly feeding ECG into CNN, DBWT is used to separate sub-signals in frequency band  of heart beat from ECG, whose output is fed to CNN for AF diagnosis. Such sub-signals are better than the raw ECG for subject-invariant CNN representation learning because noisy information irrelevant to human beat has been largely filtered out. To strengthen the generalization ability of CNN to discover subject-invariant pattern in ECG, skip connection is exploited to propagate information well in neural network and channel attention is designed to adaptively highlight informative channel-wise features. Experiments show that the proposed DBWT-AFNet outperforms the state-of- the-art methods, especially for ECG segments classification across different subjects, where no data from testing subjects have been used in training.","doi":"10.24963/ijcai.2020/607","url":"https://ijcai.org/proceedings/2020/607","pdf":"https://ijcai.org/proceedings/2020/0607.pdf","markdown":"https://ijcai.org/proceedings/2020/607.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/607","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special track on AI for CompSust and Human well-being","track":"Special track on AI for CompSust and Human well-being","number":607,"pages":{"first":4403,"last":4409},"keywords":["Machine Learning Applications: Bio/Medicine","Multidisciplinary Topics and Applications: Biology and Medicine","Data Mining: Mining Data Streams","Machine Learning: Time-series;Data Streams"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25136/file_25136.m3u8","length_sec":287,"provider":"hls"}],"references":[{"position":1,"raw":"[Acharya et al., 2017] U. Rajendra Acharya, Hamido Fujita, Oh Shu Lih, Yuki Hagiwara, Jen Hong Tan, and Muham- mad Adam. Automated detection of arrhythmias using dif- ferent intervals of tachycardia ECG segments with convo- lutional neural network. Information Sciences, 405:81-90, 2017.","title":"Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network","authors":"U Rajendra Acharya, Hamido Fujita, Oh Shu Lih, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam","year":"2017","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Andersen et al., 2019] Rasmus S. Andersen, Abdolrahman Peimankar, and Sadasivan Puthusserypady. A deep learn- ing approach for real-time detection of atrial fibrillation. Expert Systems with Applications, 115:465-473, 2019.","title":"A deep learning approach for real-time detection of atrial fibrillation","authors":"S Rasmus, Abdolrahman Andersen, Sadasivan Peimankar, Puthusserypady","year":"2019","venue":"Expert Systems with Applications","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Asgari et al., 2015] Shadnaz Asgari, Alireza Mehrnia, and Maryam Moussavi. Automatic detection of atrial fibrilla- tion using stationary wavelet transform and support vector machine. Computers in Biology & Medicine, 60:132-142, 2015.","title":"Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine","authors":"Asgari","year":"2015","venue":"Computers in Biology & Medicine","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Averbuch et al., 2014] Amir Z Averbuch, Pekka Neit- taanmäki, and Valery A Zheludev. Spline and spline wavelet methods with applications to signal and image processing. Springer, 2014.","title":"Spline and spline wavelet methods with applications to signal and image processing","authors":"Pekka Amir Z Averbuch, Valery A Neittaanmäki, Zheludev","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cao et al., 2019] Xin-Cheng Cao, Bin Yao, and Bin-Qiang Chen. Atrial fibrillation detection using an improved multi-scale decomposition enhanced residual convolu- tional neural network. IEEE Access, 7:89152-89161, 2019.","title":"Atrial fibrillation detection using an improved multi-scale decomposition enhanced residual convolutional neural network","authors":"Xin-Cheng Cao, Bin Yao, Bin-Qiang Chen","year":"2019","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dang et al., 2019] Hao Dang, Muyi Sun, Guanhong Zhang, Xingqun Qi, Xiaoguang Zhou, and Qing Chang. A novel deep arrhythmia-diagnosis network for atrial fibrillation classification using electrocardiogram signals. IEEE Ac- cess, 7:75577-75590, 2019.","title":"A novel deep arrhythmia-diagnosis network for atrial fibrillation classification using electrocardiogram signals","authors":"Hao Dang, Muyi Sun, Guanhong Zhang, Xingqun Qi, Xiaoguang Zhou, Qing Chang","year":"2019","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Dash et al., 2009] S. Dash, K. H. Chon, S. Lu, and E. A. Raeder. Automatic real time detection of atrial fibril- lation. Annals of Biomedical Engineering, 37(9):1701- 1709, 2009.","title":"Automatic real time detection of atrial fibrillation","authors":"S Dash, K H Chon, S Lu, E A Raeder","year":"2009","venue":"Annals of Biomedical Engineering","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Fan et al., 2018] Xiaomao Fan, Qihang Yao, Yunpeng Cai, Fen Miao, Fangmin Sun, and Ye Li. Multiscaled fu- sion of deep convolutional neural networks for screen- ing atrial fibrillation from single lead short ECG record- ings. IEEE Journal of Biomedical and Health Informatics, 22(6):1744-1753, 2018.","title":"Multiscaled fusion of deep convolutional neural networks for screening atrial fibrillation from single lead short ECG recordings","authors":"Xiaomao Fan, Qihang Yao, Yunpeng Cai, Fen Miao, Fangmin Sun, Ye Li","year":"2018","venue":"IEEE Journal of Biomedical and Health Informatics","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Fujita and Cimr, 2019] Hamido Fujita and Dalibor Cimr. Computer aided detection for fibrillations and flutters us- ing deep convolutional neural network. Information Sci- ences, 486:231-239, 2019.","title":"Computer aided detection for fibrillations and flutters using deep convolutional neural network","authors":"Cimr Fujita, Hamido Fujita, Dalibor Cimr","year":"2019","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Goldberger et al., 2000] AL Goldberger, LAN Amaral, L Glass, JM Hausdorff, PCh Ivanov, RG Mark, JE Mi- etus, GB Moody, C-K Peng, and HE Stanley. Physiobank, physiotoolkit, and physionet: components of a new re- search resource for complex physiologic signals. Circu- lation, 101(23):E215-E220, 2000.","title":"Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals","authors":"Goldberger","year":"2000","venue":"Circulation","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Guidera and Steinberg, 1993] Steven A Guidera and Jonathan S Steinberg. The signal-averaged P wave duration: A rapid and noninvasive marker of risk of atrial fibrillation. Journal of the American College of Cardiology, 21(7):1645-1651, 1993.","title":"The signal-averaged P wave duration: A rapid and noninvasive marker of risk of atrial fibrillation","authors":"A Steven, Jonathan S Guidera, Steinberg","year":"1993","venue":"Journal of the American College of Cardiology","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. In European Conference on Computer Vision, pages 630-645, 2016.","title":"Identity mappings in deep residual networks","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hu et al., 2018] Jie Hu, Li Shen, and Gang Sun. Squeeze- and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7132-7141, 2018.","title":"Squeezeand-excitation networks","authors":"Jie Hu, Li Shen, Gang Sun","year":"2018","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Huang et al., 2011] Chao Huang, Shuming Ye, Hang Chen, Dingli Li, Fangtian He, and Yuewen Tu. A novel method for detection of the transition between atrial fibrillation and sinus rhythm. IEEE Transactions on Biomedical En- gineering, 58(4):1113-1119, 2011.","title":"A novel method for detection of the transition between atrial fibrillation and sinus rhythm","authors":"Chao Huang, Shuming Ye, Hang Chen, Dingli Li, Fangtian He, Yuewen Tu","year":"2011","venue":"IEEE Transactions on Biomedical Engineering","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kingma and Ba, 2015] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv:1412.6980, 2015.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Ladavich and Ghoraani, 2015] Steven Ladavich and Behnaz Ghoraani. Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity. Biomedical Signal Processing & Control, 18:274-281, 2015.","title":"Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity","authors":"Steven Ladavich, Behnaz Ghoraani","year":"2015","venue":"Biomedical Signal Processing & Control","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Lai et al., 2019] Dakun Lai, Xinshu Zhang, Yuxiang Bu, Ye Su, and Chang-Sheng Ma. An automatic system for real-time identifying atrial fibrillation by using a lightweight convolutional neural network. IEEE Access, 7:130074-130084, 2019.","title":"An automatic system for real-time identifying atrial fibrillation by using a lightweight convolutional neural network","authors":"Lai","year":"2019","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Larburu et al., 2011] N Larburu, T Lopetegi, and I Romero. Comparative study of algorithms for atrial fibrillation de- tection. In Computing in Cardiology, pages 265-268, 2011.","title":"Comparative study of algorithms for atrial fibrillation detection","authors":"Larburu, Lopetegi, Romero","year":"2011","venue":"Computing in Cardiology","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lee et al., 2012] Jinseok Lee, Bersain A. Reyes, D Mc- Manus, David D., Oscar Maitas, and Ki H. Chon. Atrial fibrillation detection using an iphone 4s. IEEE Transac- tions on Biomedical Engineering, 60:203-206, 2012.","title":"Atrial fibrillation detection using an iphone 4s","authors":"Lee","year":"2012","venue":"IEEE Transactions on Biomedical Engineering","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Martis et al., 2013] Roshan Joy Martis, U. Rajendra Acharya, Hari Prasad, Chua Kuang Chua, Choo Min Lim, and Jasjit S Suri. Application of higher order statistics for atrial arrhythmia classification. Biomedical Signal Processing & Control, 8(6):888-900, 2013.","title":"Application of higher order statistics for atrial arrhythmia classification","authors":"Roshan Joy Martis, U Rajendra Acharya, Hari Prasad, Chua Kuang Chua, Choo, Min Lim, Jasjit S Suri","year":"2013","venue":"Biomedical Signal Processing & Control","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Minggang et al., 2018] Shao Minggang, Bin Guangyu, Wu Shuicai, Bin Guanghong, Huang Jiao, and Zhou Zhuhuang. Detection of atrial fibrillation from ECG recordings using decision tree ensemble with multi-level features. Physiological Measurement, 39(9):094008, 2018.","title":"Detection of atrial fibrillation from ECG recordings using decision tree ensemble with multi-level features","authors":"Bin Shao Minggang, Wu Guangyu, Bin Shuicai, Huang Guanghong, Zhou Jiao, Zhuhuang","year":"2018","venue":"Physiological Measurement","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Moody and Mark, 1983] Geoge B. Moody and Roger G. Mark. A new method for detecting atrial fibrillation using R-R intervals. In Computers in Cardiology, pages 227- 230, 1983.","title":"A new method for detecting atrial fibrillation using R-R intervals","authors":"B Geoge, Roger G Moody, Mark","year":"1983","venue":"Computers in Cardiology","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Munger et al., 2014] T M Munger, L-Q Wu, and W K Shen. Atrial fibrillation. Journal of Biomedical Research, 28(1):1-17, 2014.","title":"Atrial fibrillation","authors":"T M Munger, L-Q Wu, W K Shen","year":"2014","venue":"Journal of Biomedical Research","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Rizwan et al., 2018] Muhammed Rizwan, Bradley M Whitaker, and David V Anderson. AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning. Phyisological Measurement, 39(12):124007, 2018.","title":"AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning","authors":"Bradley M Muhammed Rizwan, David V Whitaker, Anderson","year":"2018","venue":"Phyisological Measurement","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Simona et al., 2006] Petrutiu Simona, Ng Jason, M. Nijm Grace, Al-Angari Haitham, Swiryn Steven, and V. Sa- hakian Alan. Atrial fibrillation and waveform character- ization. IEEE Engineering in Medicine & Biology Maga- zine, 25(6):24-30, 2006.","title":"Atrial fibrillation and waveform characterization","authors":"Simona","year":"2006","venue":"IEEE Engineering in Medicine & Biology Magazine","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Teijeiro et al., 2018] Tomás Teijeiro, Constantino A García, Daniel Castro, and Paulo Félix. Abductive reasoning as a basis to reproduce expert criteria in ecg atrial fibrillation identification. Physiological Measurement, 39(8):084006, 2018.","title":"Abductive reasoning as a basis to reproduce expert criteria in ecg atrial fibrillation identification","authors":"Tomás Teijeiro, A Constantino, Daniel García, Paulo Castro, Félix","year":"2018","venue":"Physiological Measurement","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Warrick and Homsi, 2018] Philip A Warrick and Ma- sun Nabhan Homsi. Ensembling convolutional and long short-term memory networks for electrocardio- gram arrhythmia detection. Physiological Measurement, 39(11):114002, 2018.","title":"Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection","authors":"Homsi ; Warrick, A Philip, Masun Warrick, Homsi Nabhan","year":"2018","venue":"Physiological Measurement","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Xia et al., 2018] Yong Xia, Naren Wulan, Kuanquan Wang, and Henggui Zhang. Detecting atrial fibrillation by deep convolutional neural networks. Computers in Biology & Medicine, 93:84-92, 2018.","title":"Detecting atrial fibrillation by deep convolutional neural networks","authors":"Yong Xia, Naren Wulan, Kuanquan Wang, Henggui Zhang","year":"2018","venue":"Computers in Biology & Medicine","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Xie et al., 2019] Qingsong Xie, Shikui Tu, Guoxing Wang, Yong Lian, and Lei Xu. Feature enrichment based convolutional neural network for heartbeat classification from electrocardiogram. IEEE Access, 7:153751-153760, 2019.","title":"Feature enrichment based convolutional neural network for heartbeat classification from electrocardiogram","authors":"Qingsong Xie, Shikui Tu, Guoxing Wang, Yong Lian, Lei Xu","year":"2019","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Xiong et al., 2018] Zhaohan Xiong, Martyn P Nash, Eliza- beth Cheng, Vadim V Fedorov, Martin K Stiles, and Jichao Zhao. Ecg signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural net- work. Physiological Measurement, 39(9):094006, 2018.","title":"Ecg signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network","authors":"Xiong","year":"2018","venue":"Physiological Measurement","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Xu, 2018] Lei Xu. Deep bidirectional intelligence: Al- phaZero, deep IA-search, deep IA-infer, and tpc causal learning. Applied Informatics, 5(1), 2018.","title":"Deep bidirectional intelligence: Al-phaZero, deep IA-search, deep IA-infer, and tpc causal learning","authors":"Lei Xu","year":"2018","venue":"Applied Informatics","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Zihlmann et al., 2017] Martin Zihlmann, Dmytro Perekrestenko, and Michael Tschannen. Convolu- tional recurrent neural networks for electrocardiogram classification. In 2017 Computing in Cardiology (CinC), pages 1-4. IEEE, 2017.","title":"Convolutional recurrent neural networks for electrocardiogram classification","authors":"Martin Zihlmann, Dmytro Perekrestenko, Michael Tschannen","year":"2017","venue":"Computing in Cardiology (CinC)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}