AIMC Topic: Signal Processing, Computer-Assisted
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Machine learning-empowered sleep staging classification using multi-modality signals.
BMC medical informatics and decision making
May 6, 2024
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EO...
Detecting emotions through EEG signals based on modified convolutional fuzzy neural network.
Scientific reports
May 6, 2024
Emotion is a human sense that can influence an individual's life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability o...
Skin Conductance-Based Acupoint and Non-Acupoint Recognition Using Machine Learning.
IEEE journal of biomedical and health informatics
May 6, 2024
Acupoints (APs) prove to have positive effects on disease diagnosis and treatment, while intelligent techniques for the automatic detection of APs are not yet mature, making them more dependent on manual positioning. In this paper, we realize the ski...
Cross-Attention Enhanced Pyramid Multi-Scale Networks for Sensor-Based Human Activity Recognition.
IEEE journal of biomedical and health informatics
May 6, 2024
Human Activity Recognition (HAR) has recently attracted widespread attention, with the effective application of this technology helping people in areas such as healthcare, smart homes, and gait analysis. Deep learning methods have shown remarkable pe...
CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation.
IEEE journal of biomedical and health informatics
May 6, 2024
Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly...
Magnetoencephalography Decoding Transfer Approach: From Deep Learning Models to Intrinsically Interpretable Models.
IEEE journal of biomedical and health informatics
May 6, 2024
When decoding neuroelectrophysiological signals represented by Magnetoencephalography (MEG), deep learning models generally achieve high predictive performance but lack the ability to interpret their predicted results. This limitation prevents them f...
Deep Representation Learning With Sample Generation and Augmented Attention Module for Imbalanced ECG Classification.
IEEE journal of biomedical and health informatics
May 6, 2024
Developing an efficient heartbeat monitoring system has become a focal point in numerous healthcare applications. Specifically, in the last few years, heartbeat classification for arrhythmia detection has gained considerable interest from researchers...
EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT.
IEEE journal of biomedical and health informatics
May 6, 2024
Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of ...
Compression and Encryption of Heterogeneous Signals for Internet of Medical Things.
IEEE journal of biomedical and health informatics
May 6, 2024
Psychophysiological computing can be utilized to analyze heterogeneous physiological signals with psychological behaviors in the Internet of Medical Things (IoMT). Since IoMT devices are generally limited by power, storage, and computing resources, i...
Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data.
NeuroImage
May 3, 2024
Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA o...
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