  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 able to capture both local and global structure similarity information is
proposed 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.
