AIMC Topic: Signal Processing, Computer-Assisted

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Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM.

IEEE transactions on bio-medical engineering
For people with Type 1 diabetes (T1D), accurate blood glucose (BG) forecasting is crucial for the effective delivery of insulin by Artificial Pancreas (AP) systems. Deep learning frameworks like Long Short-Term-Memory (LSTM) have been widely used to ...

EEG detection and recognition model for epilepsy based on dual attention mechanism.

Scientific reports
In the field of clinical neurology, automated detection of epileptic seizures based on electroencephalogram (EEG) signals has the potential to significantly accelerate the diagnosis of epilepsy. This rapid and accurate diagnosis enables doctors to pr...

Abnormal heart sound recognition using SVM and LSTM models in real-time mode.

Scientific reports
Cardiovascular diseases are non-communicable diseases that are considered the leading cause of death worldwide accounting for 17.9 million fatalities. Auscultation of heart sounds is the most common and valuable way of diagnosing heart diseases. Norm...

Extraction of fetal heartbeat locations in abdominal phonocardiograms using deep attention transformer.

Computers in biology and medicine
Assessing fetal health traditionally involves techniques like echocardiography, which require skilled professionals and specialized equipment, making them unsuitable for low-resource settings. An emerging alternative is Phonocardiography (PCG), which...

Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis.

Scientific reports
The analysis of cognitive patterns through brain signals offers critical insights into human cognition, including perception, attention, memory, and decision-making. However, accurately classifying these signals remains a challenge due to their inher...

Automated detection of arrhythmias using a novel interpretable feature set extracted from 12-lead electrocardiogram.

Computers in biology and medicine
The availability of large-scale electrocardiogram (ECG) databases and advancements in machine learning have facilitated the development of automated diagnostic systems for cardiac arrhythmias. Deep learning models, despite their potential for high ac...

A deep Bi-CapsNet for analysing ECG signals to classify cardiac arrhythmia.

Computers in biology and medicine
- In recent times, the electrocardiogram (ECG) has been considered as a significant and effective screening mode in clinical practice to assess cardiac arrhythmias. Precise feature extraction and classification are considered as essential concerns in...

Dual-pathway EEG model with channel attention for virtual reality motion sickness detection.

Journal of neuroscience methods
BACKGROUND: Motion sickness has been a key factor affecting user experience in Virtual Reality (VR) and limiting the development of the VR industry. Accurate detection of Virtual Reality Motion Sickness (VRMS) is a prerequisite for solving the proble...

Machine learning analysis of integrated ABP and PPG signals towards early detection of coronary artery disease.

Scientific reports
Every year, Coronary Artery Disease (CAD) claims lives of over a million people. CAD occurs when the coronary arteries, responsible for supplying oxygenated blood to the heart, get occluded due to plaque deposits on their inner walls. The most critic...

Reconstruction of ECG from ballistocardiogram using generative adversarial networks with attention.

Biomedical physics & engineering express
Electrocardiogram (ECG) is widely used to provide early warning signals for cardiovascular diseases. However, traditional twelve-lead ECG monitoring methods and smartwatch-based home solutions are unable to achieve daily long-term monitoring. Therefo...