{"type":"ScholarlyArticle","title":"BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series","authors":[{"name":"Bin Zhou","slug":"bin-zhou","given":"Bin","family":"Zhou","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences"},{"name":"Shenghua Liu","slug":"shenghua-liu","given":"Shenghua","family":"Liu","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences; CAS Key Laboratory of Network Data Sci- ence and Technology and University of Chinese Academy of Sci- ences , Beijing , China"},{"name":"Bryan Hooi","slug":"bryan-hooi","given":"Bryan","family":"Hooi","affiliation":"School of Computer Science , National University of Singapore"},{"name":"Xueqi Cheng","slug":"xueqi-cheng","given":"Xueqi","family":"Cheng","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences"},{"name":"Jing Ye","slug":"jing-ye","given":"Jing","family":"Ye","affiliation":"Department of Anesthesiology , Nanfang Hospital , Southern Medical University"}],"abstract":"Given a large-scale rhythmic time series containing mostly normal data segments (or `beats'), can we learn how to detect anomalous beats in an effective yet efficient way? For example, how can we detect anomalous beats from electrocardiogram (ECG) readings? Existing approaches either require excessively high amounts of labeled and balanced data for classification, or rely on less regularized reconstructions, resulting in lower accuracy in anomaly detection. Therefore, we propose BeatGAN, an unsupervised anomaly detection algorithm for time series data. BeatGAN outputs explainable results to pinpoint the anomalous time ticks of an input beat, by comparing them to adversarially generated beats. Its robustness is guaranteed by its regularization of reconstruction error using an adversarial generation approach, as well as data augmentation using time series warping. Experiments show that BeatGAN accurately and efficiently detects anomalous beats in ECG time series, and routes doctors' attention to anomalous time ticks, achieving accuracy of nearly 0.95 AUC, and very fast inference (2.6 ms per beat). In addition, we show that BeatGAN accurately detects unusual motions from multivariate motion-capture time series data, illustrating its generality.","doi":"10.24963/ijcai.2019/616","url":"https://ijcai.org/proceedings/2019/616","pdf":"https://ijcai.org/proceedings/2019/0616.pdf","markdown":"https://ijcai.org/proceedings/2019/616.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/616","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":616,"pages":{"first":4433,"last":4439},"keywords":["Machine Learning: Time-series;Data Streams","Machine Learning Applications: Bio;Medicine","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[AAMI, 1998] AAMI. Testing and reporting performance results of cardiac rhythm and st segment measurement al- gorithms. ANSI/AAMI EC38, 1998.","title":"AAMI. Testing and reporting performance results of cardiac rhythm and st segment measurement algorithms","authors":"Aami","year":"1998","venue":"ANSI/AAMI EC","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Akcay et al., 2018] Samet Akcay, Amir Atapour- Abarghouei, and Toby P Breckon. Ganomaly: Semi- supervised anomaly detection via adversarial training. arXiv preprint arXiv:1805.06725, 2018.","title":"Ganomaly: Semisupervised anomaly detection via adversarial training","authors":"Samet Akcay, Toby P Amir Atapour-Abarghouei, Breckon","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[An and Cho, 2015] Jinwon An and Sungzoon Cho. Varia- tional autoencoder based anomaly detection using recon- struction probability. Special Lecture on IE, 2:1-18, 2015.","title":"Jinwon An and Sungzoon Cho. Variational autoencoder based anomaly detection using reconstruction probability","authors":"An, Cho","year":"2015","venue":"Special Lecture on IE","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Baum and Petrie, 1966] Leonard E Baum and Ted Petrie. Statistical inference for probabilistic functions of finite state markov chains. The annals of mathematical statis- tics, 37(6):1554-1563, 1966.","title":"Statistical inference for probabilistic functions of finite state markov chains","authors":"E Leonard, Ted Baum, Petrie","year":"1966","venue":"The annals of mathematical statistics","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"Carreiras et al., 2015 ]","title":null,"authors":"Carreiras","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"Carlos Carreiras, Ana Priscila Alves, André Lourenc ¸o, Filipe Canento, Hugo Silva, Ana Fred, et al. BioSPPy: Biosignal processing in Python, 2015-. [Online; accessed 2019-1-12].","title":"BioSPPy: Biosignal processing in Python","authors":"Carlos Carreiras, Ana Priscila Alves, André Lourenc ¸o, Filipe Canento, Hugo Silva, Ana Fred","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2018] Pudi Chen, Shenghua Liu, Chuan Shi, Bryan Hooi, Bai Wang, and Xueqi Cheng. Neucast: Sea- sonal neural forecast of power grid time series. In IJCAI, pages 3315-3321, 2018.","title":"Neucast: Seasonal neural forecast of power grid time series","authors":"Pudi Chen, Shenghua Liu, Chuan Shi, Bryan Hooi, Bai Wang, Xueqi Cheng","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/460"},{"position":8,"raw":"[Davis and Goadrich, 2006] Jesse Davis and Mark Goadrich. The relationship between precision-recall and roc curves. In Proceedings of the 23rd international conference on Machine learning, pages 233-240. ACM, 2006.","title":"The relationship between precision-recall and roc curves","authors":"Goadrich Davis, Jesse Davis, Mark Goadrich","year":"2006","venue":"Proceedings of the 23rd international conference on Machine learning","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Gen- erative adversarial nets. In Advances in neural information processing systems, pages 2672-2680, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio","year":"2014","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hagiwara et al., 2018] Yuki Hagiwara, Hamido Fujita, Shu Lih Oh, Jen Hong Tan, Ru San Tan, Edward J Ciac- cio, and U Rajendra Acharya. Computer-aided diagnosis of atrial fibrillation based on ecg signals: a review. Infor- mation Sciences, 467:99-114, 2018.","title":"Computer-aided diagnosis of atrial fibrillation based on ecg signals: a review","authors":"Yuki Hagiwara, Hamido Fujita, Shu Lih Oh, Jen Hong Tan, Ru San Tan, Edward J Ciaccio, U Rajendra, Acharya","year":"2018","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hooi et al., 2017] Bryan Hooi, Shenghua Liu, Asim Smailagic, and Christos Faloutsos. BEATLEX: Sum- marizing and forecasting time series with patterns. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 3-19. Springer, 2017.","title":"BEATLEX: Summarizing and forecasting time series with patterns","authors":"Bryan Hooi, Shenghua Liu, Asim Smailagic, Christos Faloutsos","year":"2017","venue":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hooi et al., 2018] Bryan Hooi, Hyun Ah Song, Amritan- shu Pandey, Marko Jereminov, Larry Pileggi, and Christos Faloutsos. Streamcast: Fast and online mining of power grid time sequences. In Proceedings of the 2018 SIAM In- ternational Conference on Data Mining, pages 531-539.","title":"Streamcast: Fast and online mining of power grid time sequences","authors":"Bryan Hooi, Hyun Ah Song, Amritanshu Pandey, Marko Jereminov, Larry Pileggi, Christos Faloutsos","year":null,"venue":"Proceedings of the 2018 SIAM International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kiranyaz et al., 2017] Serkan Kiranyaz, Turker Ince, and Moncef Gabbouj. Personalized monitoring and advance warning system for cardiac arrhythmias. Scientific Re- ports, 7(1):9270, 2017.","title":"Personalized monitoring and advance warning system for cardiac arrhythmias","authors":"Serkan Kiranyaz, Turker Ince, Moncef Gabbouj","year":"2017","venue":"Scientific Reports","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Le Guennec et al., 2016] Arthur Le Guennec, Simon Mali- nowski, and Romain Tavenard. Data augmentation for time series classification using convolutional neural net- works. In ECML/PKDD workshop on advanced analytics and learning on temporal data, 2016.","title":"Arthur Le Guennec, Simon Malinowski, and Romain Tavenard. Data augmentation for time series classification using convolutional neural networks","authors":"Le Guennec","year":"2016","venue":"ECML/PKDD workshop on advanced analytics and learning on temporal data","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2009] Lei Li, James McCann, Nancy S Pollard, and Christos Faloutsos. Dynammo: Mining and summa- rization of coevolving sequences with missing values. In Proceedings of the 15th ACM SIGKDD international con- ference on Knowledge discovery and data mining, pages 507-516. ACM, 2009.","title":"Dynammo: Mining and summarization of coevolving sequences with missing values","authors":"Lei Li, James Mccann, Nancy S Pollard, Christos Faloutsos","year":"2009","venue":"Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Matsubara et al., 2014] Yasuko Matsubara, Yasushi Saku- rai, and Christos Faloutsos. Autoplait: Automatic mining of co-evolving time sequences. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data, pages 193-204. ACM, 2014.","title":"Autoplait: Automatic mining of co-evolving time sequences","authors":"Yasuko Matsubara, Yasushi Sakurai, Christos Faloutsos","year":"2014","venue":"Proceedings of the 2014 ACM SIGMOD international conference on Management of data","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Moody and Mark, 2001] George B Moody and Roger G Mark. The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3):45-50, 2001.","title":"The impact of the mit-bih arrhythmia database","authors":"B George, Roger G Moody, Mark","year":"2001","venue":"IEEE Engineering in Medicine and Biology Magazine","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Radford et al., 2015] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.","title":"Unsupervised representation learning with deep convolutional generative adversarial networks","authors":"Radford","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Rajpurkar et al., 2017] Pranav Rajpurkar, Awni Y Hannun, Masoumeh Haghpanahi, Codie Bourn, and Andrew Y Ng. Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv preprint arXiv:1707.01836, 2017.","title":"Cardiologist-level arrhythmia detection with convolutional neural networks","authors":"Pranav Rajpurkar, Masoumeh Awni Y Hannun, Codie Haghpanahi, Andrew Y Bourn, Ng","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Schlegl et al., 2017] Thomas Schlegl, Philipp Seeböck, Se- bastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In Inter- national Conference on Information Processing in Medi- cal Imaging, pages 146-157. Springer, 2017.","title":"Unsupervised anomaly detection with generative adversarial networks to guide marker discovery","authors":"Thomas Schlegl, Philipp Seeböck, Ursula Sebastian M Waldstein, Georg Schmidt-Erfurth, Langs","year":"2017","venue":"International Conference on Information Processing in Medical Imaging","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Schölkopf et al., 2000] Bernhard Schölkopf, Robert C Williamson, Alex J Smola, John Shawe-Taylor, and John C Platt. Support vector method for novelty detection. In Advances in neural information processing systems, pages 582-588, 2000.","title":"Support vector method for novelty detection","authors":"Bernhard Schölkopf, Alex J Robert C Williamson, John Smola, John C Shawe-Taylor, Platt","year":"2000","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Shah et al., 2014] Neil Shah, Alex Beutel, Brian Gallagher, and Christos Faloutsos. Spotting suspicious link behavior with fbox: An adversarial perspective. In Data Mining (ICDM), 2014 IEEE International Conference on, pages 959-964. IEEE, 2014.","title":"Spotting suspicious link behavior with fbox: An adversarial perspective","authors":"Neil Shah, Alex Beutel, Brian Gallagher, Christos Faloutsos","year":"2014","venue":"Data Mining (ICDM), 2014 IEEE International Conference on","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Song et al., 2017] Hyun Ah Song, Bryan Hooi, Marko Jereminov, Amritanshu Pandey, Larry Pileggi, and Chris- tos Faloutsos. Powercast: Mining and forecasting power grid sequences. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 606-621. Springer, 2017.","title":"Powercast: Mining and forecasting power grid sequences","authors":"Hyun Ah Song, Bryan Hooi, Marko Jereminov, Amritanshu Pandey, Larry Pileggi, Christos Faloutsos","year":"2017","venue":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Vintsyuk, 1968] Taras K Vintsyuk. Speech discrimination by dynamic programming. Cybernetics and Systems Anal- ysis, 4(1):52-57, 1968.","title":"Speech discrimination by dynamic programming","authors":"K Taras, Vintsyuk","year":"1968","venue":"Cybernetics and Systems Analysis","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}