Wavelet-Based Convolutional Recurrent Neural Network for the Automatic Detection of Absence Seizure

  • Kamal Basha NihaEmail author
  • Wahab Aisha Banu
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


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.


Absence 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.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science and EngineeringB S Abdur Rahman Crescent Institute of Science and TechnologyChennaiIndia

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