Wavelet-Based Convolutional Recurrent Neural Network for the Automatic Detection of Absence Seizure
In this paper, the new model is proposed to automatically detect and predict absence seizure using hybrid deep learning algorithm [Convolutional Recurrent Neural Network (CRNN)] along with the Discrete Wavelet Transform (DWT) with Electroencephalography (EEG) as input. This model comprises of four steps (1) Single-channel segmentation process (2) Decomposition of segmented signal using wavelet transform (3) Extraction of relevant feature using statistical method (4) Deep learning algorithms for classification, detection, and early detection. This model enhances the feature extraction and also the overall performance by feeding the segmented data into Long Short Tern Memory (LSTM) which is one of the Recurrent Neural Network (RNN). And also the output of this network is used to calculate the extracted feature along with the classification results. The values in hidden state are used to diagnose the seizure by locating the pattern using the extracted features of time window. The proposed model achieves 100% accuracy on detection and 95% overall accuracy on early detection of normal, abnormal and absence seizure.
KeywordsAbsence seizure Convolutional Recurrent Neural Network Electroencephalography Epilepsy Discrete Wavelet Transform Long Short-Term Memory
We thank Dr. S. Velusamy, DM—Neurology, MD—Paediatrics, MBBS Neurologist, and General Physician, who has 22 years of experience for his continuous support throughout this work.
- 2.Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of advances in neural information processing systems (NIPS), Lake Tahoe, NV, pp 1097–1105Google Scholar
- 3.Ciresan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Providence, RI, pp 3642–3649Google Scholar
- 4.Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: a neural image caption generator. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Boston, MA, pp 3156–3164Google Scholar
- 5.Sainath TN, Weiss RJ, Senior A, Wilson KW, Vinyals O (2015) Learning the speech front-end with raw waveform CLDNNs. In: Proceedings of INTERSPEECH 2015, 16th Annual conference of the international speech communication association, Dresden, Germany, p 15Google Scholar
- 6.Amodei D, Anubhai R, Battenberg E, Case C, Casper J, Catanzaro BC, Chen J, Chrzanowski M, Coates A, Diamos G, Elsen E, Engel J, Fan L, Fougner C, Han T, Hannun AY, Jun B, LeGresley P, Lin L, Narang S, Ng AY, Ozair S, Prenger R, Raiman J, Satheesh S, Seetapun D, Sengupta S, Wang Y, Wang Z, Wang C, Xiao B, Yogatama D, Zhan J, Zhu Z (2015) Deep speech 2: end-to-end speech recognition in English and Mandarin. arXiv, preprint arXiv:1512.02595
- 7.Graves A, Mohamed A, Hinton GE (2013) Speech recognition with deep recurrent neural networks. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, (ICASSP), Vancouver, Canada, pp 6645–6649Google Scholar
- 8.Cho K, Van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), Doha, Qatar, pp 1724–1734Google Scholar
- 9.Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Proceedings of advances in neural information processing systems (NIPS), Montreal, Canada, pp 3104–3112Google Scholar
- 10.Zhang B, Jiang H, Dong L (2017) Classification of EEG signal by WT-CNN model in emotion recognition system. In: International conference on Informatics and cognitive computing. IEEEGoogle Scholar