TY - GEN
T1 - Deep Learning for Haemodialysis Time Series Classification
AU - Leonardi, Giorgio
AU - Montani, Stefania
AU - Striani, Manuel
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - In this paper, we propose a deep learning approach to deal with time series classification, in the domain of haemodialysis. Specifically, we have tested two different architectures: a Convolutional Neural Network, which is particularly suitable for time series data, due to its ability to model local dependencies that may exist between adjacent data points; and a convolutional autoencoder, adopted to learn deep features from the time series, followed by a neural network classifier. Our experiments have proved the feasibility of the approach, which has outperformed more classical techniques, based on the Discrete Cosine Transform and on the Discrete Fourier Transform for features extraction, and on Support Vector Machines for classification.
AB - In this paper, we propose a deep learning approach to deal with time series classification, in the domain of haemodialysis. Specifically, we have tested two different architectures: a Convolutional Neural Network, which is particularly suitable for time series data, due to its ability to model local dependencies that may exist between adjacent data points; and a convolutional autoencoder, adopted to learn deep features from the time series, followed by a neural network classifier. Our experiments have proved the feasibility of the approach, which has outperformed more classical techniques, based on the Discrete Cosine Transform and on the Discrete Fourier Transform for features extraction, and on Support Vector Machines for classification.
UR - http://www.scopus.com/inward/record.url?scp=85078440556&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-37446-4_5
DO - 10.1007/978-3-030-37446-4_5
M3 - Conference contribution
AN - SCOPUS:85078440556
SN - 9783030374457
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 50
EP - 64
BT - Artificial Intelligence in Medicine
A2 - Marcos, Mar
A2 - Juarez, Jose M.
A2 - Lenz, Richard
A2 - Nalepa, Grzegorz J.
A2 - Nalepa, Grzegorz J.
A2 - Nowaczyk, Slawomir
A2 - Peleg, Mor
A2 - Stefanowski, Jerzy
A2 - Stiglic, Gregor
PB - SPRINGER
T2 - 7th Joint Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care, KR4HC/ProHealth 2019 and the 1st Workshop on Transparent, Explainable and Affective AI in Medical Systems, TEAAM 2019 held in conjunction with the Artificial Intelligence in Medicine, AIME 2019
Y2 - 26 June 2019 through 29 June 2019
ER -