Arrhythmia is irregular changes of normal heart rhythm and effective manual identifying of them require a lot of time and depends on experience of clinicians. This paper proposes deep learning-based novel 2-D convolutional neural network (CNN) approach for accurate classification of five different arrhythmia types. The performance of the proposed architecture is tested on Electrocardiogram (ECG) signals that are taken from MIT-BIH arrhythmia benchmark database. ECG signals was segmented into heartbeats and each of the heartbeats was converted into 2-D grayscale images as an input data for CNN structure. The accuracy of the proposed architecture was found as 97.42% on the training results revealed that the proposed 2-D CNN architecture with transformed 2-D ECG images can achieve highest accuracy without any preprocessing and feature extraction and feature selection stages for ECG signals.

Emotion is an important topic in different fields such as biomedical engineering, psychology, neuroscience and health. Emotion recognition could be useful for diagnosis of brain and psychological disorders. In recent years, deep learning has progressed much in the field of image classification. In this study, we proposed a Convolutional Neural Network (CNN) based LeNet architecture for facial expression recognition. First of all, we merged 3 datasets (JAFFE, KDEF and our custom dataset). Then we trained our LeNet architecture for emotion states classification. In this study, we achieved accuracy of 96.43% and validation accuracy of 91.81% for classification of 7 different emotions through facial expressions.

The emotional state of people plays a key role in physiological and behavioral human interaction. Emotional state analysis entails many fields such as neuroscience, cognitive sciences, and biomedical engineering because the parameters of interest contain the complex neuronal activities of the brain. Electroencephalogram (EEG) signals are processed to communicate brain signals with external systems and make predictions over emotional states. This paper proposes a novel method for emotion recognition based on deep convolutional neural networks (CNNs) that are used to classify Valence, Arousal, Dominance, and Liking emotional states. Hence, a novel approach is proposed for emotion recognition with time series of multi-channel EEG signals from a Database for Emotion Analysis and Using Physiological Signals (DEAP). We propose a new approach to emotional state estimation utilizing CNN-based classification of multi-spectral topology images obtained from EEG signals. In contrast to most of the EEG-based approaches that eliminate spatial information of EEG signals, converting EEG signals into a sequence of multi-spectral topology images, temporal, spectral, and spatial information of EEG signals are preserved. The deep recurrent convolutional network is trained to learn important representations from a sequence of three-channel topographical images. We have achieved test accuracy of 90.62% for negative and positive Valence, 86.13% for high and low Arousal, 88.48% for high and low Dominance, and finally 86.23% for like–unlike. The evaluations of this method on emotion recognition problem revealed significant improvements in the classification accuracy when compared with other studies using deep neural networks (DNNs) and one-dimensional CNNs.