Bag-of-words representation for biomedical time series classification

Journal article


Wang, Jin, Liu, Ping, She, Mary F. H., Nahavandi, Saeid and Kouzani, Abbas. (2013). Bag-of-words representation for biomedical time series classification. Biomedical Signal Processing and Control. 8(6), pp. 634 - 644. https://doi.org/10.1016/j.bspc.2013.06.004
AuthorsWang, Jin, Liu, Ping, She, Mary F. H., Nahavandi, Saeid and Kouzani, Abbas
Abstract

Automatic analysis of biomedical time series such as electroencephalogram (EEG) and electrocardiographic (ECG) signals has attracted great interest in the community of biomedical engineering due to its important applications in medicine. In this work, a simple yet effective bag-of-words representation that is originally developed for text document analysis is extended for biomedical time series representation. In particular, similar to the bag-of-words model used in text document domain, the proposed method treats a time series as a text document and extracts local segments from the time series as words. The biomedical time series is then represented as a histogram of codewords, each entry of which is the count of a codeword appeared in the time series. Although the temporal order of the local segments is ignored, the bag-of-words representation is able to capture high-level structural information because both local and global structural information are well utilized. The performance of the bag-of-words model is validated on three datasets extracted from real EEG and ECG signals. The experimental results demonstrate that the proposed method is not only insensitive to parameters of the bag-of-words model such as local segment length and codebook size, but also robust to noise.

KeywordsBag of words; Codebook construction; k-Means clustering; EEG; ECG
Year2013
JournalBiomedical Signal Processing and Control
Journal citation8 (6), pp. 634 - 644
PublisherElsevier B.V.
ISSN1746-8094
Digital Object Identifier (DOI)https://doi.org/10.1016/j.bspc.2013.06.004
Scopus EID2-s2.0-84880311136
Page range634 - 644
Research GroupInstitute for Learning Sciences and Teacher Education (ILSTE)
Publisher's version
File Access Level
Controlled
Place of publicationUnited Kingdom
Permalink -

https://acuresearchbank.acu.edu.au/item/8545x/bag-of-words-representation-for-biomedical-time-series-classification

Restricted files

Publisher's version

  • 84
    total views
  • 0
    total downloads
  • 2
    views this month
  • 0
    downloads this month
These values are for the period from 19th October 2020, when this repository was created.

Export as

Related outputs

Teachers' ratings of social skills and problem behaviors as concurrent predictors of students' bullying behavior
Elliott, Stephen N., Hwang, Yoon-Suk and Wang, Jin. (2019). Teachers' ratings of social skills and problem behaviors as concurrent predictors of students' bullying behavior. Journal of Applied Developmental Psychology. 60, pp. 119 - 126. https://doi.org/10.1016/j.appdev.2018.12.005
Why choose teaching? A matter of choice : Evidence from the field
Wyatt-SmithWyatt-Smith, Claire, C., Wang, Jin, Alexander, Colette, Du Plessis, Anna, Hand, Kirstine and Colbert, Peta. (2017). Why choose teaching? A matter of choice : Evidence from the field Australia: Institute for Learning Sciences and Teacher Education, Australian Catholic University.
Probabilistic latent semantic analysis for multichannel biomedical signal clustering
Wang, Jin and She, Mary. (2016). Probabilistic latent semantic analysis for multichannel biomedical signal clustering. IEEE Signal Processing Letters. 23(12), pp. 1821 - 1824. https://doi.org/10.1109/LSP.2016.2623801
An incremental algorithm for discovering routine behaviours from smart meter data
Wang, Jin, Cardell-Oliver, Rachel and Liu, Wei. (2016). An incremental algorithm for discovering routine behaviours from smart meter data. Knowledge-Based Systems. 113, pp. 61 - 74. https://doi.org/10.1016/j.knosys.2016.09.016
Patient admission prediction using a pruned fuzzy min--max neural network with rule extraction
Wang, Jin, Lim, Chee Peng, Creighton, Douglas, Khorsavi, Abbas, Nahavandi, Saeid, Ugon, Julien, Vamplew, Peter, Stranieri, Andrew, Martin, Laura and Freischmidt, Anton. (2014). Patient admission prediction using a pruned fuzzy min--max neural network with rule extraction. Neural Computing and Applications. 26(2), pp. 277 - 289. https://doi.org/10.1007/s00521-014-1631-z
Sparse representation with multi-manifold analysis for texture classification from few training images
Sun, Xiangping, Wang, Jin, She, Mary F. H. and Kong, Lingxue. (2014). Sparse representation with multi-manifold analysis for texture classification from few training images. Image and Vision Computing. 32(11), pp. 835 - 846. https://doi.org/10.1016/j.imavis.2014.07.001
Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis
Wang, Jin, Sun, Xiangping, Nahavandi, Saeid, Kouzani, Abbas, Wu, Yuchuan and She, Mary. (2014). Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis. Computer Methods and Programs in Biomedicine. 117(2), pp. 238 - 246. https://doi.org/10.1016/j.cmpb.2014.06.014
Human identification from ECG signals via sparse representation of local segments
Wang, Jin, She, Mary, Nahavandi, Saeid and Kouzani, Abbas. (2013). Human identification from ECG signals via sparse representation of local segments. IEEE Signal Processing Letters. 20(10), pp. 937 - 940. https://doi.org/10.1109/LSP.2013.2267593
Scale invariant texture classification via sparse representation
Sun, Xiangping, Wang, Jin, She, Mary F. H. and Kong, Lingxue. (2013). Scale invariant texture classification via sparse representation. Neurocomputing. 122, pp. 338 - 348. https://doi.org/10.1016/j.neucom.2013.06.016
Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model
Wang, Jin, Liu, Ping, She, Mary F. H., Nahavandi, Saeid and Kouzani, Abbas. (2013). Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model. Computer Methods and Programs in Biomedicine. 111(3), pp. 629 - 641. https://doi.org/10.1016/j.cmpb.2013.05.022
Sparse representation of local spatial-temporal features with dimensionality reduction for motion recognition
Wang, Jin, Sun, Xiangping, Liu, Ping, She, Mary F. H. and Kong, Lingxue. (2013). Sparse representation of local spatial-temporal features with dimensionality reduction for motion recognition. Neurocomputing. 115, pp. 150 - 160. https://doi.org/10.1016/j.neucom.2013.01.012
Unsupervised mining of long time series based on latent topic model
Wang, Jin, Sun, Xiangping, She, Mary FH, Kouzani, Abbas and Nahavandi, Saeid. (2013). Unsupervised mining of long time series based on latent topic model. Nurocomputing. 103, pp. 93 - 103. https://doi.org/10.1016/j.neucom.2012.09.008
Supervised learning probabilistic latent semantic analysis for human motion analysis
Wang, Jin, Liu, Ping, She, Mary F. H., Kouzani, Abbas and Nahavandi, Saeid. (2013). Supervised learning probabilistic latent semantic analysis for human motion analysis. Neurocomputing. 100, pp. 134 - 143. https://doi.org/10.1016/j.neucom.2011.10.033
Intelligent clothing for automated recognition of human physical activities in free-living environment
Wu, Yuchuan, Chen, Ronghua, Wang, Jin, Sun, Xiangping and She, Mary F. H.. (2012). Intelligent clothing for automated recognition of human physical activities in free-living environment. Journal of the Textile Institute. 103(8), pp. 806 - 816. https://doi.org/10.1080/00405000.2011.611641