AIMC Topic: Signal Processing, Computer-Assisted

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Constructing a Personalized Cross-Day EEG-Based Emotion-Classification Model Using Transfer Learning.

IEEE journal of biomedical and health informatics
State-of-the-art electroencephalogram (EEG)-based emotion-classification works indicate that a personalized model may not be well exploited until sufficient labeled data are available, given a substantial EEG non-stationarity over days. However, it i...

Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals.

Computers in biology and medicine
In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obtained from electrocardiogram (ECG) data. Recent progress in deep learning has contributed significant...

A Real-Time Health 4.0 Framework with Novel Feature Extraction and Classification for Brain-Controlled IoT-Enabled Environments.

Neural computation
In this letter, we propose two novel methods for four-class motor imagery (MI) classification using electroencephalography (EEG). Also, we developed a real-time health 4.0 (H4.0) architecture for brain-controlled internet of things (IoT) enabled envi...

Laser-based classification of olive oils assisted by machine learning.

Food chemistry
Olive oil is an essential diet component in all Mediterranean countries having a considerable impact on the local economies, which are producing almost 90% of the world production. Therefore, the quality assessment of olive oil in terms of its acidit...

Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol.

Computers in biology and medicine
BACKGROUND: Major depressive disorder (MDD) is one of the leading causes of disability; however, current MDD diagnosis methods lack an objective assessment of depressive symptoms. Here, a machine learning approach to separate MDD patients from health...

Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network.

Computers in biology and medicine
Atrial fibrillation (AF) is the most common heart arrhythmia, and 12-lead electrocardiogram (ECG) is regarded as the gold standard for AF diagnosis. Highly accurate diagnosis of AF based on 12-lead ECG is valuable and remains challenging. In this pap...

Reachability Analysis of Neural Masses and Seizure Control Based on Combination Convolutional Neural Network.

International journal of neural systems
Epileptic seizures arise from synchronous firing of multiple spatially separated neural masses; therefore, many synchrony measures are used for seizure detection and characterization. However, synchrony measures reflect only the overall interaction s...

Development of a biological signal-based evaluator for robot-assisted upper-limb rehabilitation: a pilot study.

Australasian physical & engineering sciences in medicine
Bio-signal based assessment for upper-limb functions is an attractive technology for rehabilitation. In this work, an upper-limb function evaluator is developed based on biological signals, which could be used for selecting different robotic training...

A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning.

IEEE journal of biomedical and health informatics
The ballistocardiography (BCG) signal is a measurement of the vibrations of the center of mass of the body due to the cardiac cycle and can be used for noninvasive hemodynamic monitoring. The seismocardiography (SCG) signals measure the local vibrati...