AIMC Topic: Signal Processing, Computer-Assisted
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SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals.
Sensors (Basel, Switzerland)
Dec 5, 2024
Sleep apnea syndrome (SAS) affects about 3-7% of the global population, but is often undiagnosed. It involves pauses in breathing during sleep, for at least 10 s, due to partial or total airway blockage. The current gold standard for diagnosing SAS i...
Robust modelling of arterial blood pressure reconstruction from photoplethysmography.
Scientific reports
Dec 5, 2024
Blood pressure is a crucial indicator of cardiovascular disease, and arterial blood pressure (ABP) waveforms contain information that reflects the cardiovascular status. We propose a novel deep-learning method that converts photoplethysmogram (PPG) s...
DS-MS-TCN: Otago Exercises Recognition With a Dual-Scale Multi-Stage Temporal Convolutional Network.
IEEE journal of biomedical and health informatics
Dec 5, 2024
The Otago Exercise Program (OEP) represents a crucial rehabilitation initiative tailored for older adults, aimed at enhancing balance and strength. Despite previous efforts utilizing wearable sensors for OEP recognition, existing studies have exhibit...
Multi-Task Learning for Audio-Based Infant Cry Detection and Reasoning.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Infant cry is a crucial indicator that offers valuable insights into their physical and mental conditions, such as hunger and pain. However, the scarcity of infant cry datasets hinders the model's generalization in real-life scenarios. The varying vo...
Transfer Contrastive Learning for Raman Spectroscopy Skin Cancer Tissue Classification.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Using Raman spectroscopy (RS) signals for skin cancer tissue classification has recently drawn significant attention, because of its non-invasive optical technique, which uses molecular structures and conformations within biological tissue for diagno...
Sleep Stage Classification Via Multi-View Based Self-Supervised Contrastive Learning of EEG.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Self-supervised learning (SSL) is a challenging task in sleep stage classification (SSC) that is capable of mining valuable representations from unlabeled data. However, traditional SSL methods typically focus on single-view learning and do not fully...
MSVTNet: Multi-Scale Vision Transformer Neural Network for EEG-Based Motor Imagery Decoding.
IEEE journal of biomedical and health informatics
Dec 5, 2024
OBJECT: Transformer-based neural networks have been applied to the electroencephalography (EEG) decoding for motor imagery (MI). However, most networks focus on applying the self-attention mechanism to extract global temporal information, while the c...
ESSN: An Efficient Sleep Sequence Network for Automatic Sleep Staging.
IEEE journal of biomedical and health informatics
Dec 5, 2024
By modeling the temporal dependencies of sleep sequence, advanced automatic sleep staging algorithms have achieved satisfactory performance, approaching the level of medical technicians and laying the foundation for clinical assistance. However, exis...
Multiband Convolutional Riemannian Network With Band-Wise Riemannian Triplet Loss for Motor Imagery Classification.
IEEE journal of biomedical and health informatics
Dec 5, 2024
This paper presents a novel motor imagery classification algorithm that uses an overlapping multiscale multiband convolutional Riemannian network with band-wise Riemannian triplet loss to improve classification performance. Despite the superior perfo...
Fully Convolutional Hybrid Fusion Network With Heterogeneous Representations for Identification of S1 and S2 From Phonocardiogram.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Heart auscultation is a simple and inexpensive first-line diagnostic test for the early screening of heart abnormalities. A phonocardiogram (PCG) is a digital recording of an analog heart sound acquired using an electronic stethoscope. A computerized...
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