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With the acceleration of global population aging, the elderly have an increasing demand for home care and nursing institutions, and the significance of health prevention and management of the elderly has become increasingly prominent. In this context, we propose a biometric recognition method for multi-modal biomedical signals. This article focuses on three key signals that can be picked up by wearable devices: ECG, PPG, and breath (RESP). The RESP signal is introduced into the existing two-mode signal identification for multi-mode identification. Firstly, the features of the signal in the time–frequency domain are extracted. To represent deep features in a low-dimensional feature space and expedite authentication tasks, PCA and LDA are employed for dimensionality reduction. MCCA is used for feature fusion, and SVM is used for identification. The accuracy and performance of the system were evaluated using both public data sets and self-collected data sets, with an accuracy of more than 99.5%. The experimental data fully show that this method significantly improves the accuracy of identity recognition. In the future, combined with the signal monitoring function of wearable devices, it can quickly identify individual elderly people with abnormal conditions, provide safer and more efficient medical services for the elderly, and relieve the pressure on medical resources. [ABSTRACT FROM AUTHOR]

Background: The accurate classification of lung nodules is critical to achieving personalized lung cancer treatment and prognosis prediction. The treatment options for lung cancer and the prognosis of patients are closely related to the type of lung nodules, but there are many types of lung nodules, and the distinctions between certain types are subtle, making accurate classification based on traditional medical imaging technology and doctor experience challenging. This study adopts a novel approach, using computed tomography (CT) radiomics to analyze the quantitative features in CT images to reveal the characteristics of lung nodules, and then employs diversity-weighted ensemble learning to enhance the accuracy of classification by integrating the predictive results of multiple models., Methods: We extracted lung nodules from the Lung Image Database Consortium image collection (LIDC-IDRI) dataset and derived radiomics features from the nodules. For the classification tasks of seven types of lung nodules, each was split into binary classifications. Two model-building methods were employed: M1 (equal-weighted voting ensemble classifier) and M2 (diversity-weighted voting ensemble classifier). Models were evaluated using 10-fold cross-validation with metrics including the area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity., Results: Both methods effectively completed classification tasks. The M2 method outperformed M1, particularly in classifying texture, calcification, and the benign and malignant nature of lung nodules. The AUC values of the M2 method in the four subclassifications of texture types of lung nodules were 0.9913, 0.8838, 0.9525, and 0.8845, with the corresponding accuracies of 0.9651, 0.8116, 0.9000, and 0.8284, respectively. In the classification of the degree of calcification of lung nodules, the AUC value of the M2 method was 0.9775 with an accuracy of 0.9642. In the classification of the benign and malignant nature of lung nodules, the AUC value of the M2 method was 0.8953 with an accuracy of 0.8168. The combination of CT radiomics and diversity-weighted ensemble learning effectively identifies lung nodule types, providing a novel method for the precise classification of lung nodules and aiding personalized lung cancer treatment and prognosis prediction., Conclusions: The combination of CT radiomics and ensemble learning for diversity weighting can be well realized to identify the type of lung nodules., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-1315/coif). The authors have no conflicts of interest to declare., (2024 AME Publishing Company. All rights reserved.)