An Efficient Framework for the Analysis of Big Brain Signals Data
Big Brain Signals Data (BBSD) analysis is one of the most difficult challenges in the biomedical signal processing field for modern treatment and health monitoring applications. BBSD analytics has been recently applied towards aiding the process of care delivery and disease exploration. The main purpose of this paper is to introduce a framework for the analysis of BBSD of time series EEG in biomedical signal processing for identification of abnormalities. This paper presents a data analysis framework combining complex network and machine learning techniques for the analysis of BBSD in time series form. The proposed method is tested on an electroencephalogram (EEG) time series database as the implanted electrodes in the brain generate huge amounts of time series data in EEG. The pilot study in this paper has examined that the proposed methodology has the capability to analysis massive size of brain signals data and also can be used for handling any other biomedical signal data in time series form (e.g. electrocardiogram (ECG); Electromyogram (EMG)). The main benefit of the proposed methodology is to provide an effective way for analyzing the vast amount of BBSD generated from the brain to care patients with better outcomes and also help technicians for making intelligent decisions system.
KeywordsBig data Biomedical signal EEG Complex network Machine learning Feature extraction Classification
- 8.Bache, K., Lichman, M.: UCI machine learning repository. University of California, Irvine, School of Information and Computer (2013). http://archive.ics.uci.edu/ml
- 11.Supriya, S., Siuly, S., Wang, H., Zhuo, G., Zhang, Y.: Analyzing EEG signal data for detection of epileptic seizure: introducing weight on visibility graph with complex network feature. In: Cheema, M., Zhang, W., Chang, L. (eds.) Databases Theory and Applications. LNCS, vol. 9877, pp. 56–66. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46922-5_5CrossRefGoogle Scholar
- 15.Cao, J., Wu, Z., Mao, B., Zhang, Y.: Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system. World Wide Web 16(5–6), 729–748 (2012)Google Scholar
- 17.Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques. Emerg. Artif. Intell. Appl. Comput. Eng. 160, 3–24 (2007)Google Scholar