A Deep Transfer Learning Approach for Seizure Detection Using RGB Features of Epileptic Electroencephalogram Signals This paper demonstrates an approach based on Deep Transfer Learning for the classification for Seizure and Nonseizure Electroencephalogram (EEG) signals. Recognizing seizure signals in intelligent way is quite important in clinical diagnosis of Epileptic seizure. Various traditional and deep machine learning techniques are employed for this purpose. However, the Epileptic seizure prediction and classification performance is not satisfactory over small EEG dataset using traditional approaches. The Transfer learning approach overcomes this by reusing the pretrained networks such as googlenet, resnet101 and vgg19 trained on large Image database. This experiment has been done in two phases: (1) RGB image dataset generated for the seizure and nonseizure EEG signals data of University of Bonn using a novel preprocessing technique, (2) we configured googlenet, resnet101 and vgg19 trained networks to learn a new pattern or features from the RGB image Dataset and finally, above mentioned networks have been used for the classification. The use of Vgg19 network shows greater accuracy among the three but takes comparatively more prediction time. We will mainly emphasize on the results obtained from the googlenet, since it provides effective accuracy taking less time for prediction. The proposed method achieved an accuracy of above 99% for a smaller number of epochs and maximum accuracy of 100% when we increase number of epochs. Experimental outcomes show the proposed approach using googlent achieved better performance w.r.t to many stateoftheart classification algorithms even on the small EEG dataset. In addition, classification performance of our proposed approach has compared with different traditional machine learning techniques over the same input data.

Deep Learning in Physiological Signal Data: A Survey Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deeplearning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deeplearning approach that we review are the input data type, deeplearning task, deeplearning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deeplearning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deeplearning concept perspective such as training architecture and dataset sources.