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.

Evaluation of convolutional neural networks using a large multisubject P300 dataset Abstract   Deep neural networks (DNN) have been studied in various machine learning areas. For example, eventrelated potential (ERP) signal classification is a highly complex task potentially suitable for DNN as signaltonoise ratio is low, and underlying spatial and temporal patterns display a large intra and intersubject variability. Convolutional neural networks (CNN) have been compared with baseline traditional models, i.e. linear discriminant analysis (LDA) and support vector machines (SVM) for single trial classification using a large multisubject publicly available P300 dataset of schoolage children (138 males and 112 females). For single trial classification, classification accuracy stayed between 62% and 64% for all tested classification models. When applying the trained classification models to averaged trials, accuracy increased to 76xe2x80x9379% without significant differences among classification models. CNN did not prove superior to baseline for the tested dataset. Comparison with related literature, limitations and future directions are discussed.