AIMC Topic: Signal Processing, Computer-Assisted

Clear Filters Showing 561 to 570 of 2081 articles

Wavelet Transform, Reconstructed Phase Space, and Deep Learning Neural Networks for EEG-Based Schizophrenia Detection.

International journal of neural systems
This study proposes an innovative expert system that uses exclusively EEG signals to diagnose schizophrenia in its early stages. For diagnosing psychiatric/neurological disorders, electroencephalogram (EEG) testing is considered a financially viable,...

Elbow Gesture Recognition with an Array of Inductive Sensors and Machine Learning.

Sensors (Basel, Switzerland)
This work presents a novel approach for elbow gesture recognition using an array of inductive sensors and a machine learning algorithm (MLA). This paper describes the design of the inductive sensor array integrated into a flexible and wearable sleeve...

Wearable ECG Device and Machine Learning for Heart Monitoring.

Sensors (Basel, Switzerland)
With cardiovascular diseases (CVD) remaining a leading cause of mortality, wearable devices for monitoring cardiac activity have gained significant, renewed interest among the medical community. This paper introduces an innovative ECG monitoring syst...

High-accuracy heart rate detection using multispectral IPPG technology combined with a deep learning algorithm.

Journal of biophotonics
Image Photoplethysmography (IPPG) technology is a noncontact physiological parameter detection technology, which has been widely used in heart rate (HR) detection. However, traditional imaging devices still have issues such as narrower receiving spec...

HiRENet: Novel convolutional neural network architecture using Hilbert-transformed and raw electroencephalogram (EEG) for subject-independent emotion classification.

Computers in biology and medicine
BACKGROUND AND OBJECTIVES: Convolutional neural networks (CNNs) are the most widely used deep-learning framework for decoding electroencephalograms (EEGs) due to their exceptional ability to extract hierarchical features from high-dimensional EEG dat...

Higher-Order Spectral Analysis Combined with a Convolution Neural Network for Atrial Fibrillation Detection-Preliminary Study.

Sensors (Basel, Switzerland)
The global burden of atrial fibrillation (AFIB) is constantly increasing, and its early detection is still a challenge for public health and motivates researchers to improve methods for automatic AFIB prediction and management. This work proposes hig...

Feature evaluation for myoelectric pattern recognition of multiple nearby reaching targets.

Medical engineering & physics
Intention detection of the reaching movement is considerable for myoelectric human and machine collaboration applications. A comprehensive set of handcrafted features was mined from windows of electromyogram (EMG) of the upper-limb muscles while reac...

Clinical knowledge-based ECG abnormalities detection using dual-view CNN-Transformer and external attention mechanism.

Computers in biology and medicine
BACKGROUND: Automatic abnormalities detection based on Electrocardiogram (ECG) contributes greatly to early prevention, computer aided diagnosis, and dynamic analysis of cardiovascular diseases. In order to achieve cardiologist-level performance, dee...

EMG-based prediction of step direction for a better control of lower limb wearable devices.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Lower-limb wearable devices can significantly improve the quality of life of subjects suffering from debilitating conditions, such as amputations, neurodegenerative disorders, and stroke-related impairments. Current control...

Band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification.

Computer methods in biomechanics and biomedical engineering
The electroencephalogram-based motor imagery (MI-EEG) classification task is significant for brain-computer interface (BCI). EEG signals need a lot of channels to be acquired, which makes it difficult to use in real-world applications. Choosing the o...