Epileptic MEG Spikes Detection Using Amplitude Thresholding and Dynamic Time Warping
Epilepsy is a brain disorder that may strike at different stages of life. Patients' lives are extremely disturbed by the occurrence of sudden unpredictable epileptic seizures. A possible approach to diagnose epileptic patients is to analyze magnetoencephalography (MEG) signals to extract useful...
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IEEE
2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/7954764/ |
| _version_ | 1818619250570428416 |
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| author | Muhammad Imran Khalid Turky N. Alotaiby Saeed A. Aldosari Saleh A. Alshebeili Majed Hamad Alhameed Vahe Poghosyan |
| author_facet | Muhammad Imran Khalid Turky N. Alotaiby Saeed A. Aldosari Saleh A. Alshebeili Majed Hamad Alhameed Vahe Poghosyan |
| author_sort | Muhammad Imran Khalid |
| collection | DOAJ |
| description | Epilepsy is a brain disorder that may strike at different stages of life. Patients' lives are extremely disturbed by the occurrence of sudden unpredictable epileptic seizures. A possible approach to diagnose epileptic patients is to analyze magnetoencephalography (MEG) signals to extract useful information about subject's brain activities. MEG signals are less distorted than electroencephalogram signals by the intervening tissues between the neural source and the sensor (e.g., skull, scalp, and so on), which results in a better spatial accuracy of the MEG. This paper aims to develop a method to detect epileptic spikes from multi-channel MEG signals in a patient-independent setting. Amplitude thresholding is first employed to localize abnormalities and identify the channels where they exist. Then, dynamic time warping is applied to the identified abnormalities to detect the actual epileptic spikes. The sensitivity and specificity of proposed detection algorithm are 92.45% and 95.81%, respectively. These results indicate that the proposed algorithm can help neurologists to analyze MEG data in an automated manner instead of spending considerable time to detect MEG spikes by visual inspection. |
| first_indexed | 2024-12-16T17:34:30Z |
| format | Article |
| id | doaj.art-d356f4ab074a4750a6e4fd92bba03cc8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:34:30Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d356f4ab074a4750a6e4fd92bba03cc82022-12-21T22:22:51ZengIEEEIEEE Access2169-35362017-01-015116581166710.1109/ACCESS.2017.27180447954764Epileptic MEG Spikes Detection Using Amplitude Thresholding and Dynamic Time WarpingMuhammad Imran Khalid0https://orcid.org/0000-0002-0770-695XTurky N. Alotaiby1https://orcid.org/0000-0002-0924-1746Saeed A. Aldosari2Saleh A. Alshebeili3Majed Hamad Alhameed4Vahe Poghosyan5Department of Electrical Engineering and KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), College of Engineering, King Saud University, Riyadh, Saudi ArabiaKing Abdulaziz City for Science and Technology, Riyadh, Saudi ArabiaDepartment of Electrical Engineering and KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), College of Engineering, King Saud University, Riyadh, Saudi ArabiaDepartment of Electrical Engineering and KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), College of Engineering, King Saud University, Riyadh, Saudi ArabiaDepartment of Neurology, National Institute of Neuroscience, King Fahad Medical City, Riyadh, Saudi ArabiaDepartment of Neurology, National Institute of Neuroscience, King Fahad Medical City, Riyadh, Saudi ArabiaEpilepsy is a brain disorder that may strike at different stages of life. Patients' lives are extremely disturbed by the occurrence of sudden unpredictable epileptic seizures. A possible approach to diagnose epileptic patients is to analyze magnetoencephalography (MEG) signals to extract useful information about subject's brain activities. MEG signals are less distorted than electroencephalogram signals by the intervening tissues between the neural source and the sensor (e.g., skull, scalp, and so on), which results in a better spatial accuracy of the MEG. This paper aims to develop a method to detect epileptic spikes from multi-channel MEG signals in a patient-independent setting. Amplitude thresholding is first employed to localize abnormalities and identify the channels where they exist. Then, dynamic time warping is applied to the identified abnormalities to detect the actual epileptic spikes. The sensitivity and specificity of proposed detection algorithm are 92.45% and 95.81%, respectively. These results indicate that the proposed algorithm can help neurologists to analyze MEG data in an automated manner instead of spending considerable time to detect MEG spikes by visual inspection.https://ieeexplore.ieee.org/document/7954764/Epileptic spikes detectionMEGdynamic time warpingamplitude thresholding |
| spellingShingle | Muhammad Imran Khalid Turky N. Alotaiby Saeed A. Aldosari Saleh A. Alshebeili Majed Hamad Alhameed Vahe Poghosyan Epileptic MEG Spikes Detection Using Amplitude Thresholding and Dynamic Time Warping IEEE Access Epileptic spikes detection MEG dynamic time warping amplitude thresholding |
| title | Epileptic MEG Spikes Detection Using Amplitude Thresholding and Dynamic Time Warping |
| title_full | Epileptic MEG Spikes Detection Using Amplitude Thresholding and Dynamic Time Warping |
| title_fullStr | Epileptic MEG Spikes Detection Using Amplitude Thresholding and Dynamic Time Warping |
| title_full_unstemmed | Epileptic MEG Spikes Detection Using Amplitude Thresholding and Dynamic Time Warping |
| title_short | Epileptic MEG Spikes Detection Using Amplitude Thresholding and Dynamic Time Warping |
| title_sort | epileptic meg spikes detection using amplitude thresholding and dynamic time warping |
| topic | Epileptic spikes detection MEG dynamic time warping amplitude thresholding |
| url | https://ieeexplore.ieee.org/document/7954764/ |
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