Title :
Sleep EMG analysis using sparse signal representation and classification
Author :
Shokrollahi, M. ; Krishnan, Sridhar
Author_Institution :
Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON, Canada
fDate :
Aug. 28 2012-Sept. 1 2012
Abstract :
The development of automatic sleep based abnormality detection in patient for sleep related problem is a key field in the recent research. However the sleep signals are obtained as long-time recordings and inhibit complex characteristics, making their analysis computationally challenging. As a result, recognition methods that facilitate efficient dimensionality reduction are developed to suit different applications. In recent years sparse representation schemes provide an effective means for achieving best possible data reduction by comparing the input with pre-formulated dictionaries, especially for huge datasets. Recent research proves the usability of these methods for signal classification. In this paper, a robust technique is provided for sparse representation of small dataset signal types. Here, the signal decomposition is obtained using the l1-minimization technique, following which a generalization based on the leave-one-out (LOO) is performed. The dependency of the proposed algorithm is analyzed, using a sparsity measure, in order to verify the dependency between the input data and extracted feature space. Performance measures obtained using long-term sleep data shows an average classification accuracy of 80% and further validates the usefulness of the technique for long term biomedical signal analysis.
Keywords :
electromyography; feature extraction; medical signal processing; minimisation; signal classification; signal representation; sleep; automatic sleep based abnormality detection; biomedical signal analysis; data reduction; feature extraction; l1-minimization technique; long-time recordings; recognition methods; signal decomposition; sleep EMG analysis; sleep signals; sparse signal classification; sparse signal representation; Accuracy; Approximation algorithms; Approximation methods; Electromyography; Minimization; Sleep; Training; Algorithms; Electromyography; Humans; Signal Processing, Computer-Assisted; Sleep;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-4119-8
Electronic_ISBN :
1557-170X
DOI :
10.1109/EMBC.2012.6346715