P A Karthick1, Hideaki Tanaka2, Hui Ming Khoo3, Jean Gotman4. 1. Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; Department of Instrumentation and Control Engineering, National Institute of Technology Tiruchirappalli, India. Electronic address: pakarthick1@gmail.com. 2. Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; Department of Neurosurgery, Fukuoka University Hospital, Fukuoka City, Japan. 3. Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan. 4. Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
Abstract
OBJECTIVE: Intracranial EEG covers only a small fraction of brain volume and it is uncertain if a discharge represents a true seizure onset or results from spread. We therefore assessed if there are differences between characteristics of the ictal onset when we are likely to have a true onset, and characteristics of the discharge in regions of spread. METHODS: Wavelet based statistical features were extracted in 503 onset and 390 spread channels of 58 seizures from 20 patients. These features were used as predictors in models based on machine learning algorithms such as k-nearest neighbour, logistic regression, multilayer perceptron, support vector machine, random and rotation forest. RESULTS: Statistical features (mean, variance, skewness and kurtosis) associated with all wavelet scales were significantly higher in onset than in spread channels. The best classifier, random forest, achieved accuracy of 79.6% and precision of 82%. CONCLUSIONS: The signals associated with onset and spread regions exhibit different characteristics. The proposed features are able to classify the signals with good accuracy. SIGNIFICANCE: Using our classifier on new seizures could help clinicians gain confidence in having recorded the real seizure onset or on the contrary be concerned that the true onset may have been missed.
OBJECTIVE: Intracranial EEG covers only a small fraction of brain volume and it is uncertain if a discharge represents a true seizure onset or results from spread. We therefore assessed if there are differences between characteristics of the ictal onset when we are likely to have a true onset, and characteristics of the discharge in regions of spread. METHODS: Wavelet based statistical features were extracted in 503 onset and 390 spread channels of 58 seizures from 20 patients. These features were used as predictors in models based on machine learning algorithms such as k-nearest neighbour, logistic regression, multilayer perceptron, support vector machine, random and rotation forest. RESULTS: Statistical features (mean, variance, skewness and kurtosis) associated with all wavelet scales were significantly higher in onset than in spread channels. The best classifier, random forest, achieved accuracy of 79.6% and precision of 82%. CONCLUSIONS: The signals associated with onset and spread regions exhibit different characteristics. The proposed features are able to classify the signals with good accuracy. SIGNIFICANCE: Using our classifier on new seizures could help clinicians gain confidence in having recorded the real seizure onset or on the contrary be concerned that the true onset may have been missed.
Authors: Graham W Johnson; Leon Y Cai; Saramati Narasimhan; Hernán F J González; Kristin E Wills; Victoria L Morgan; Dario J Englot Journal: J Neurol Neurosurg Psychiatry Date: 2022-03-28 Impact factor: 13.654
Authors: Nykan Mirchi; Nebras M Warsi; Frederick Zhang; Simeon M Wong; Hrishikesh Suresh; Karim Mithani; Lauren Erdman; George M Ibrahim Journal: Front Hum Neurosci Date: 2022-06-27 Impact factor: 3.473