and latent space analysis (as done for VRAE) for com-
plementary gains in performance. Finally, an even-
tual detection of out-of-domain patterns (e.g. unde-
sired signals corruption, noise, artifacts) could be per-
formed making the VAE learn both normal and ab-
normal domain-patterns, keeping the class of interest
(anomaly) on those kinds of oscillations.
ACKNOWLEDGEMENTS
The project OPERATOR - Digital Transformation in
Industry with a Focus on the Operator 4.0 (OPERA-
TOR - NORTE-01-0247-FEDER-045910) leading to
this work is co-ﬁnanced by the ERDF - European Re-
gional Development Fund through the North Portugal
Regional Operational Program and Lisbon Regional
Operational Program and by the Portuguese Foun-
dation for Science and Technology - FCT under the
MIT Portugal Program (2019 Open Call for Flagship
projects - Digital Transformation in Industry).
REFERENCES
Braei, M. and Wagner, S. (2020). Anomaly detection in
univariate time-series: A survey on the state-of-the-
art.
Chalapathy, R. and Chawla, S. (2019). Deep learning for
anomaly detection: A survey.
Chandola, V., Banerjee, A., and Kumar, V. (2009).
Anomaly detection: A survey. ACM Comput. Surv.,
41(3).
da S. Luz, E. J., Schwartz, W. R., C
´
amara-Ch
´
avez, G., and
Menotti, D. (2016). ECG-based heartbeat classiﬁca-
tion for arrhythmia detection: A survey. Computer
Methods and Programs in Biomedicine, 127:144 –
164.
Dau, H. A., Bagnall, A., Kamgar, K., Yeh, C.-C. M., Zhu,
Y., Gharghabi, S., Ratanamahatana, C. A., and Keogh,
E. (2019). The ucr time series archive.
He, Z., Xu, X., and Deng, S. (2003). Discovering cluster-
based local outliers. Pattern Recognition Letters,
24(9):1641 – 1650.
Karim, F., Majumdar, S., Darabi, H., and Chen, S. (2018).
LSTM fully convolutional networks for time series
classiﬁcation. IEEE Access, 6:1662–1669.
Kingma, D. P. and Welling, M. (2019). An introduction to
variational autoencoders. Foundations and Trends
R
in Machine Learning, 12(4):307–392.
Lee, M., Song, T.-G., and Lee, J.-H. (2020). Heart-
beat classiﬁcation using local transform pattern fea-
ture and hybrid neural fuzzy-logic system based on
self-organizing map. Biomedical Signal Processing
and Control, 57:101690.
Lei, Q., Yi, J., Vaculin, R., Wu, L., and Dhillon, I. S. (2017).
Similarity preserving representation learning for time
series analysis. CoRR, abs/1702.03584.
Li, D., Chen, D., Shi, L., Jin, B., Goh, J., and Ng, S.-K.
(2019). MAD-GAN: Multivariate anomaly detection
for time series data with generative adversarial net-
works.
Liu, Y., Chen, J., Wu, S., Liu, Z., and Chao, H. (2018).
Incremental fuzzy C medoids clustering of time se-
ries data using dynamic time warping distance. PLOS
ONE, 13(5):1–25.
Llamedo, M. and Mart
´
ınez, J. P. (2011). Heartbeat classiﬁ-
cation using feature selection driven by database gen-
eralization criteria. IEEE transactions on bio-medical
engineering, 58:616–25.
Lucas, J., Tucker, G., Grosse, R., and Norouzi, M. (2019).
Don’t blame the ELBO! a linear VAE perspective on
posterior collapse.
Ma, J. and Perkins, S. (2003). Time-series novelty detection
using one-class support vector machines. volume 3,
pages 1741 – 1745 vol.3.
Malhotra, P., TV, V., Vig, L., Agarwal, P., and Shroff, G.
(2017). TimeNet: Pre-trained deep recurrent neural
network for time series classiﬁcation.
McInnes, L., Healy, J., and Melville, J. (2020). UMAP:
Uniform manifold approximation and projection for
dimension reduction.
Moody, G. and Mark, R. (2001). The impact of the
MIT-BIH arrhythmia database. IEEE engineering in
medicine and biology magazine : the quarterly mag-
azine of the Engineering in Medicine & Biology Soci-
ety, 20:45–50.
Pereira, J. and Silveira, M. (2019). Unsupervised repre-
sentation learning and anomaly detection in ECG se-
quences. International Journal of Data Mining and
Bioinformatics, 22:389–407.
Philip de Chazal, O’Dwyer, M., and Reilly, R. B.
(2004). Automatic classiﬁcation of heartbeats us-
ing ECG morphology and heartbeat interval fea-
tures. IEEE Transactions on Biomedical Engineering,
51(7):1196–1206.
Pincombe, B. (2005). Anomaly detection in time series of
graphs using ARMA processes. ASOR Bull, 24.
Ramaswamy, S., Rastogi, R., and Shim, K. (2000). Efﬁ-
cient algorithms for mining outliers from large data
sets. volume 29, pages 427–438.
Ruopp, M., Perkins, N., Whitcomb, B., and Schisterman, E.
(2008). Youden index and optimal cut-point estimated
from observations affected by a lower limit of detec-
tion. Biometrical journal. Biometrische Zeitschrift,
50:419–30.
Schlegl, T., Seeb
¨
ock, P., Waldstein, S. M., Schmidt-Erfurth,
U., and Langs, G. (2017). Unsupervised anomaly de-
tection with generative adversarial networks to guide
marker discovery.
Xu, H., Feng, Y., Chen, J., Wang, Z., Qiao, H., Chen, W.,
Zhao, N., Li, Z., Bu, J., Li, Z., and et al. (2018).
Unsupervised anomaly detection via variational auto-
encoder for seasonal kpis in web applications. Pro-
ceedings of the 2018 World Wide Web Conference on
World Wide Web - WWW ’18.
Zhang, C., Li, S., Zhang, H., and Chen, Y. (2020). VELC:
A new variational autoencoder based model for time
series anomaly detection.
Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C.,
Cheng, W., Ni, J., Zong, B., Chen, H., and Chawla,
N. V. (2018). A deep neural network for unsupervised
anomaly detection and diagnosis in multivariate time
series data.
BIOSIGNALS 2021 - 14th International Conference on Bio-inspired Systems and Signal Processing
102