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.

Offline handwritten Chinese recognition is an important research area of pattern recognition, including offline handwritten Chinese character recognition (offline HCCR) and offline handwritten Chinese text recognition (offline HCTR), which are closely related to daily life. With new deep learning techniques and the combination with other domain knowledge, offline handwritten Chinese recognition has gained breakthroughs in methods and performance in recent years. However, there have yet to be articles that provide a technical review of this field since 2016. In light of this, this paper reviews the research progress and challenges of offline handwritten Chinese recognition based on traditional techniques, deep learning methods, methods combining deep learning with traditional techniques, and knowledge from other areas from 2016 to 2022. Firstly, it introduces the research background and status of handwritten Chinese recognition, standard datasets, and evaluation metrics. Secondly, a comprehensive summary and analysis of offline HCCR and offline HCTR approaches during the last seven years is provided, along with an explanation of their concepts, specifics, and performances. Finally, the main research problems in this field over the past few years are presented. The challenges still exist in offline handwritten Chinese recognition are discussed, aiming to inspire future research work.

The present study sets out to investigate how multilingual youth perceive and represent their linguistic repertoires. To achieve this goal, we introduced a computer-vision-aided analytical method to deal with the obtained visual data, which comprised digital images of language portraits created by a group of young multilingual speakers. An OpenCV module is used to build and complete the graphic data processing, enabling quantitative evaluations of participants’ colored clusters and linguistic codes that express their language repertoires. In combination with oral narratives provided in their language portraits, the findings demonstrate that Macanese heritage speakers show a higher degree of “scope” than the Chinese mainland sojourners in Macao but a lower degree of “access”. Follow-up interviews further corroborated the self-perceptions of their linguistic resources across different registers. Overall, the computer-vision-aided analysis of language portraits enhances the current understanding of the “scope” and “access” of multilingual repertoires in lived experience.

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