Cardiovascular disease (CVD) stands as a prominent contributor to human mortality. Electrocardiogram (ECG) represents a widely adopted noninvasive method employed by clinicians to detect and diagnose CVDs. Nonetheless, conventional ECG‐based detection approaches for cardiac disorders tend to be time‐consuming and inefficient, necessitating the need for more effective solutions. Recent studies have highlighted the effectiveness of the echo state network (ESN) in detecting abnormal ECG patterns. However, traditional ESN models often face challenges such as unstable training and convergence difficulties due to variations in the range of reservoir state values. To address this issue, this study introduces a novel approach called the normalized echo state network (NESN). The NESN method normalizes the states of all neurons within the reservoir before applying the nonlinear activation function. In our study, we conducted performance evaluations of the proposed model using the MIT‐BIH arrhythmia database. We performed a synergistical analysis to investigate the impact of reservoir parameters on the network performance. The experimental results demonstrated promising outcomes, with an accuracy of 99.1% and an F1‐score of 96.4%. Specifically, for detecting abnormal ECG patterns, our model achieved a sensitivity of 90.2%, a positive predictive value of 96.6%, and a specificity of 99.8%. These results highlight the superior performance of our classifier compared to most traditional mainstream heartbeat detection methods and ring topology ESN model.

In pattern recognition such as face recognition, the recognition result is not only limited by the quality and quantity of samples, but also limited by the extracted principal components. For improving the quality and quantity of training samples and for extracting more efficient principal components, this paper presents a recognition method combing the increased virtual samples and kernel principal component analysis (KPCA), which doubly weakens the influence of nonlinear factors on face recognition. New database is generated with the pose-changed and the mirror-like virtual images. Then KPCA is used for dimension reduction and feature extraction. The shortest Euclidean distance is applied to measure similarity. A series of experiments are conducted in the ORL and YALE face database and the experimental results show the efficiency of the proposed method.

BackgroundElectrocardiogram (ECG) is a powerful tool for studying cardiac activity and diagnosing various cardiovascular diseases, including arrhythmia. While machine learning and deep learning algorithms have been applied to ECG interpretation, there is still room for improvement. For instance, the commonly used Recurrent Neural Networks (RNNs), reply on its previous state to update and is therefore ineffective for parallel computing. RNN also struggles to efficiently address the issue of long‐distance reliance.PurposeTo reduce computational complexity by dimensionality reduction of ECG signals we constructed a Stacked Auto‐encoders model using Transformer for ECG‐based arrhythmia detection. And overcome the challenges of long‐term dependencies and limited parallelizability in traditional RNNs when applied to ECG signal processing.MethodsIn this paper, a Transformer‐Based ECG Dimensionality Reduction Stacked Auto‐encoders model is proposed for ECG‐based arrhythmia detection. The transformer is used to encode ECG signals into a feature matrix, which is then dimensionally reduced using unsupervised greedy training through the four linear layers. This resulted in a low‐dimensional representation of ECG features, which are subsequently classified using support vector machines (SVM) to minimize overfitting.ResultsThe proposed method is benchmarked on the MIT‐BIH Arrhythmia database. In the 10‐fold cross validation of beat‐based arrhythmia detection, the average accuracy, sensitivity, specificity and F1 score of the proposed method are 99.83%, 98.84%, 99.84% and 99.13%, respectively, for the record‐based arrhythmia detection which refers to the approach where the training and testing sets use ECG data from independent recorded patients are 88.10%, 49.79%, 91.56% and 39.95%, respectively.ConclusionsCompared to other existing ECG‐based arrhythmia detection methods, our proposed approach exhibits improved detection accuracy and stronger generalization for arrhythmia beats. Additionally, the use of the record‐based data division method makes our approach more suitable for clinical practice.