Implementation of convolution neural network using scalogram for identification of epileptic activity. (September 2022) - Kaur, Arshpreet
Abstract: Background: Inter-ictal state is a period between convolutions (seizures). Expert neurologist looks for inter-ictal activity within this period to support the diagnosis of epilepsy. The focus of this work is to automate the process of identification of inter-ictal activity from EEG and to distinguish it from the activity of a controlled patient. Also, we have worked on differentiating between different epileptic states. This work uses the Benchmark Bonn dataset and novel patient data collected from Max Hospital, Saket. Five groups are considered from Bonn database with the first four groups, with one case each and group five divided into ten cases and data collected from hospital is also considered. This study explores four cases under group 5, reporting of which is not available in the literature for bonn dataset and also reports the results obtained from data collected at Max Hospital. Two scenarios for Group 5 are presented under the first, the complete signal of length 23.6 s is converted into scalograms and in next scenario the complete signal is broken into segments of 2 s to make a comparative study with real time database. New method: In this work, Continuous Wavelet Transform (CWT) is used to convert the signals to scalograms. Scalograms are further, resized to reduce computation. A fifteen-layer novel Convolution neural network architecture is applied to these scalograms to classify these into their respective classes. Also present results obtained on Bonn data with two second segments and show a comparative performance with the real time data. Comparison and results: State of the art methods are compared with our novel methodology in context to the five out of fifteen cases considered under different groups. The performance evaluation parameters considered are accuracy, specificity, sensitivity, and F1 score. This study establishes a clear comparison and outperforms the state-of-the-art methods in context with performance measures considered. We have achieved a classification accuracy of 99.83 %, 100, 100 %, 99.833 % for cases AB-C, AB-D, CD-A, CD-B. Also, our method has outperformed pre-existing methods, achieving classification accuracy of 99.8 %, 100 %, 99.8 %, 99.75 %, and 99.75 % for AB-E, CD-E, ABCD-E, AB-CD-E, and AB-CD. For the real time data collected at Max Hospital, Saket and Benchmark dataset the model provided an accuracy of 91.7 %. Conclusion: The novel Convolution neural network architecture deployed in this work has outperformed the existing methods for the cases previously considered by researchers, which used the same benchmark dataset. Also, for the problem of identification of inter-ictal discharges, the proposed model has shown excellent performance concerning all the performance metrics with the data collected from clinical setup. Further, this study establishes the efficiency and usability of scalogram for identification of different epileptic states.