Deep Learning for Haemodialysis Time Series Classification

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine
Subtitle of host publicationKnowledge Representation and Transparent and Explainable Systems - AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Revised Selected Papers
EditorsMar Marcos, Jose M. Juarez, Richard Lenz, Grzegorz J. Nalepa, Grzegorz J. Nalepa, Slawomir Nowaczyk, Mor Peleg, Jerzy Stefanowski, Gregor Stiglic
PublisherSPRINGER
Pages50-64
Number of pages15
ISBN (Print)9783030374457
DOIs
Publication statusPublished - 2019
Event7th 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 - Poznan, Poland
Duration: 26 Jun 201929 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11979 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th 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
Country/TerritoryPoland
CityPoznan
Period26/06/1929/06/19

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