Bag-of-words representation for biomedical time series classification
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2013/11/01
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Indian UGC (journal)
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Refrences47
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Citations71
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Jin Wang
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Ping Liu
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Mary F.H. She
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Saeid Nahavandi
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Abbas Kouzani
Cite
Wang, Jin, et al. “Bag-of-Words Representation for Biomedical Time Series Classification”. Biomedical Signal Processing and Control, vol. 8, no. 6, 2013, pp. 634-4, https://doi.org/10.1016/j.bspc.2013.06.004.
Wang, J., Liu, P., She, M. F., Nahavandi, S., & Kouzani, A. (2013). Bag-of-words representation for biomedical time series classification. Biomedical Signal Processing and Control, 8(6), 634-644. https://doi.org/10.1016/j.bspc.2013.06.004
Wang, Jin, Ping Liu, Mary F.H. She, Saeid Nahavandi, and Abbas Kouzani. “Bag-of-Words Representation for Biomedical Time Series Classification”. Biomedical Signal Processing and Control 8, no. 6 (2013): 634-44. https://doi.org/10.1016/j.bspc.2013.06.004.
Wang J, Liu P, She MF, Nahavandi S, Kouzani A. Bag-of-words representation for biomedical time series classification. Biomedical Signal Processing and Control. 2013;8(6):634-4.
Journal Categories
Medicine
Medicine (General)
Medical technology
Technology
Engineering (General)
Civil engineering (General)
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Semi-supervised detection of intracranial pressure alarms using waveform dynamics | 17 | 2013 | ||
| Increasing the accuracy of ECG based biometric analysis by data modelling | Measurement |
| 18 | 2012 |
| Supervised learning of Gaussian mixture models for visual vocabulary generation | Pattern Recognition |
| 14 | 2012 |
| Intracranial hypertension prediction using extremely randomized decision trees | Medical Engineering & Physics |
| 30 | 2012 |
| Automated diagnosis of epileptic EEG using entropies | Biomedical Signal Processing and Control |
| 355 | 2012 |
Refrences Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 22
is the most frequently represented among the references in this article. It primarily includes studies from Pattern Recognition and Biomedical Signal Processing and Control. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Bag of states: a non-sequential approach to video-based engagement measurement | Multimedia Systems |
| 2024 | |
| Domain Adaptation of Time Series Classification | IEEE Access |
| 2024 | |
| Quantifying the Activities of Local Assembly Members in Japan: Recent Advances and an Approach Using the BERTopic Model | Interdisciplinary Information Sciences |
| 2024 | |
| Sentiment analysis and topic modeling of COVID-19 tweets of India | International Journal of System Assurance Engineering and Management |
| 1 | 2023 |
A Deep Convolutional Neural Network for Time Series Classification with Intermediate Targets | SN Computer Science | 2023 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 34
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Invariant time-series factorization and was published in 2014. The most recent citation comes from a 2024 study titled Quantifying the Activities of Local Assembly Members in Japan: Recent Advances and an Approach Using the BERTopic Model. This article reached its peak citation in 2019, with 11 citations. It has been cited in 51 different journals, 23% of which are open access. Among related journals, the Expert Systems with Applications cited this research the most, with 6 citations. The chart below illustrates the annual citation trends for this article.