AIMC Topic: Signal Processing, Computer-Assisted
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Effects of precise cardio sounds on the success rate of phonocardiography.
PloS one
Jul 15, 2024
This work investigates whether inclusion of the low-frequency components of heart sounds can increase the accuracy, sensitivity and specificity of diagnosis of cardiovascular disorders. We standardized the measurement method to minimize changes in si...
Identification and diagnosis of schizophrenia based on multichannel EEG and CNN deep learning model.
Schizophrenia research
Jul 14, 2024
This paper proposes a high-accuracy EEG-based schizophrenia (SZ) detection approach. Unlike comparable literature studies employing conventional machine learning algorithms, our method autonomously extracts the necessary features for network training...
Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities.
Scientific reports
Jul 12, 2024
The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and bio...
Enhancing ECG Heartbeat classification with feature fusion neural networks and dynamic minority-biased batch weighting loss function.
Physiological measurement
Jul 10, 2024
This study aims to address the challenges of imbalanced heartbeat classification using electrocardiogram (ECG). In this proposed novel deep-learning method, the focus is on accurately identifying minority classes in conditions characterized by signif...
Automatic semantic segmentation of EHG recordings by deep learning: An approach to a screening tool for use in clinical practice.
Computer methods and programs in biomedicine
Jul 5, 2024
BACKGROUND AND OBJECTIVE: Preterm delivery is an important factor in the disease burden of the newborn and infants worldwide. Electrohysterography (EHG) has become a promising technique for predicting this condition, thanks to its high degree of sens...
Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches.
Medical engineering & physics
Jul 5, 2024
Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal...
A Novel Instruction Driven 1-D CNN Processor for ECG Classification.
Sensors (Basel, Switzerland)
Jul 5, 2024
Electrocardiography (ECG) has emerged as a ubiquitous diagnostic tool for the identification and characterization of diverse cardiovascular pathologies. Wearable health monitoring devices, equipped with on-device biomedical artificial intelligence (A...
DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model.
Medical & biological engineering & computing
Jul 4, 2024
Continuous blood pressure (BP) provides essential information for monitoring one's health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introd...
Temporal-spatial cross attention network for recognizing imagined characters.
Scientific reports
Jul 4, 2024
Previous research has primarily employed deep learning models such as Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for decoding imagined character signals. These approaches have treated the temporal and spatial features ...
EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning.
Journal of neuroscience methods
Jul 3, 2024
Brain-computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite the...
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