Effect of photic stimulation for migraine detection using random forest and discrete wavelet transform. (March 2019)

• Record Type: Journal Article
• Authors: Subasi, Abdulhamit; Ahmed, Aysha; Aličković, Emina; Rashik Hassan, Ahnaf
• Is Part Of: Biomedical signal processing and control. Volume 49(2019)
• Page Start: 231
• Page End: 239
• Publication Date: 2019-03
• Subjects: Medical decision support system -- Migraine -- Electroencephalogram (EEG) -- Discrete wavelet transform (DWT) -- Machine learning algorithms; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.12.011
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: This study investigates the effects of flash stimulation and window length on the EEG signal classification. Different machine learning algorithms and discrete wavelet transform utilized for EEG signal classification. Our tests on the real-world dataset show that the flash stimulation can improve the classification accuracy for more than 10%. The selection of the proper window length is crucial for the accurate migraine identification. Abstract: Migraine is a neurological disorder characterized by persisting attacks, underlined by the sensitivity to light. One of the leading reasons that make migraine a bigger issue is that it cannot be diagnosed easily by physicians because of the numerous overlapping symptoms with other diseases, such as epilepsy and tension-headache. Consequently, studies have been growing on how to make a computerized decision support system for diagnosis of migraine. In most laboratory studies, flash stimulation is used during the recording of electroencephalogram (EEG) signals with different frequencies and variable (seconds) time windows. The main contribution of this study is the investigation of the effects of flash stimulation on the classification accuracy, and how to find the effective window length for EEG signal classification. To achieve this, we tested different machine learning algorithms on the EEG signals features extracted by using discrete wavelet transform. Our tests on the real-world dataset, recorded in the laboratory, show that the flash stimulation can improve the classification accuracy for more than 10%. Not surprisingly, it is seen that the same holds for the selection of time window length, i.e. the selection of the proper window length is crucial for the accurate migraine identification.