Cardiovascular disease has become one of the main diseases threatening human life and health. This disease is very common and troublesome, and the existing medical resources are scarce, so it is necessary to use a computer-aided automatic diagnosis to overcome these limitations. A computer-aided diagnostic system can automatically diagnose through an electrocardiogram (ECG) signal. This paper proposes a novel deep-learning method for ECG classification based on adversarial domain adaptation, which solves the problem of insufficient-labeled training samples, improves the phenomenon of different data distribution caused by individual differences, and enhances the classification accuracy of cross-domain ECG signals with different data distributions. The proposed method includes three modules: multi-scale feature extraction F, domain discrimination D, and classification C. The module F, constitutive of three different parallel convolution blocks, is constructed to increase the breadth of features extracted from this module. The module D is composed of three convolutional blocks and a fully connected layer, which is to solve the problem of low model layers and low-feature abstraction. In the module C, the time features and the deep-learning extraction features are concatenated on the fully connected layer to enhance feature diversity. The effectiveness of the proposed method is verified by experiments, and the classification accuracy of the experimental electrical signals reaches 92.3%.

ECG anomaly detection plays an important role in clinical medicine. So far, a number of ECG recognition technologies have emerged in this field, but most often suffer from slow training and instability. Considering that the Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) algorithm have the advantages of fast learning speed and strong generalization ability, this paper integrates multiple independent PSO-ELM model and proposes a novel ensemble learning framework termed as E-PSO-ELM to realize ECG signals recognition. More specifically, the individual PSO-ELM adopts the input weight and hidden layer deviation of ELM as the particles in the PSO algorithm, and takes the root mean square error of ELM training sample as the adaptive value of the particles, so as to enhance the stability of the network and realize high ECG recognition rate. The simulation results on MIT-BIH Arrhythmia Database show that E-PSO-ELM has a high classification accuracy rate of 98.23 %. In addition, compared with other algorithms, the stability of E-PSO-ELM is more prominent, which can reduce the probability of operating errors. Therefore, E-PSO-ELM has a high practical value.

A gait energy image contains much gait information, which is one of the most effective means to recognize gait characteristics. The accuracy of gait recognition is greatly affected by covariates, such as the viewing angle, occlusion of clothing, and walking speed. Gait features differ somewhat by angles. Therefore, how to improve the recognition accuracy of a cross-view gait is a challenging task. This study proposes a new gait recognition algorithm structure. A Gabor filter is used to extract gait features from gait energy images, since it can extract features of different directions and scales. We use linear discriminant analysis (LDA) to tackle the problem that the feature dimension restricts the process. Finally, the improved local coupled extreme learning machine based on particle swarm optimization is used for the classification process of the extracted features of the gait. The proposed method and other current mainstream algorithms are compared in terms of the recognition accuracy based on the CASIA-A and CASIA-B datasets, and the simulation results show that the proposed algorithm has good performance and performs well at cross-view gait recognition.