Affective computing systems has a great potential in applications for biofeedback systems and cognitive conductual therapies. Here, by analyzing the physiological behavior of a given subject, we can infer the affective state of an emotional process. Since, emotions can be modeled as dynamic manifestations of these signals, a continuous analysis in the valence/arousal space, brings more information of the affective state related to an emotional process. In this paper we propose a method for dynamic affect recognition from multimodal physiological signals. Our model is based on learning a latent space using Gaussian process latent variable models (GP-LVM), which maps high dimensional data (multimodal physiological signals) in a low dimensional latent space. We incorporate the dynamics to the model by learning the latent representation, with associated dynamics. Finally, a support vector classifier is implemented to evaluate the relevance of the latent space features in the affective recognition process. The results show that the proposed method can efficiently model a physiological time-series and recognize with high accuracy an affective process.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/EMBC.2016.7590834 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.
PLoS Comput Biol
January 2025
Department of Biomedical Informatics, University of Colorado Anschutz School of Medicine, Aurora, Colorado, United States of America.
While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance).
View Article and Find Full Text PDFECG signal generation using feature disentanglement auto-encoder.
Physiol Meas
January 2025
Harbin Institute of Technology, Harbin Institute of Technology, Harbin, 150001, CHINA.
Objective: The demand for ECG datasets, particularly those containing rare classes, poses a significant challenge as deep learning becomes increasingly prevalent in ECG signal research. While Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are widely adopted, they encounter difficulties in effectively generating samples for classes with limited instances.
Approach: To address this issue, we propose a novel Feature Disentanglement Auto-Encoder (FDAE) designed to dissect various generative factors under a contrastive learning framework within ECG data to facilitate the generation of new ECG samples.
A multi-constraint representation learning model for identification of ovarian cancer with missing laboratory indicators.
Nan Fang Yi Ke Da Xue Xue Bao
January 2025
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Objectives: To evaluate the performance of a multi-constraint representation learning classification model for identifying ovarian cancer with missing laboratory indicators.
Methods: Tabular data with missing laboratory indicators were collected from 393 patients with ovarian cancer and 1951 control patients. The missing ovarian cancer laboratory indicator features were projected to the latent space to obtain a classification model using the representational learning classification model based on discriminative learning and mutual information coupled with feature projection significance score consistency and missing location estimation.
Anomaly detection in multidimensional time series for water injection pump operations based on LSTMA-AE and mechanism constraints.
Sci Rep
January 2025
College of computer science and technology, China University of Petroleum (East China), No.66 Changjiang West Road, Huangdao, Qingdao, 266580, Shandong, China.
Addressing the issues of inadequate information exchange among subsequences in the operational time series of water injection pumps, leading to low accuracy and high false alarm rates in anomaly detection, this paper proposes a multidimensional time series anomaly detection method for water injection pump operations, leveraging Long Short-Term Memory Autoencoder augmented with Attention Mechanism (LSTMA-AE) and mechanistic constraints. The LSTMA-AE framework encompasses three primary modules: a Time Feature Extraction Module (Encoder), an Attention Layer, and a Data Reconstruction Module (Decoder). The Encoder captures temporal dependencies and features within the input sequences, mapping the input data into a higher-dimensional space.
View Article and Find Full Text PDFVariational graph autoencoder for reconstructed transcriptomic data associated with NLRP3 mediated pyroptosis in periodontitis.
Sci Rep
January 2025
Department of Basic Sciences, Faculty of Dentistry, Universidad de Antioquia U de A, Medellín, 050010, Colombia.
The NLRP3 inflammasome, regulated by TLR4, plays a pivotal role in periodontitis by mediating inflammatory cytokine release and bone loss induced by Porphyromonas gingivalis. Periodontal disease creates a hypoxic environment, favoring anaerobic bacteria survival and exacerbating inflammation. The NLRP3 inflammasome triggers pyroptosis, a programmed cell death that amplifies inflammation and tissue damage.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!