AIMC Topic: Signal Processing, Computer-Assisted

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A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition.

PloS one
The surface electromyography (sEMG)-based gesture recognition with deep learning approach plays an increasingly important role in human-computer interaction. Existing deep learning architectures are mainly based on Convolutional Neural Network (CNN) ...

A Machine-Learning Approach for Detection and Quantification of QRS Fragmentation.

IEEE journal of biomedical and health informatics
OBJECTIVE: Fragmented QRS (fQRS) is an accessible biomarker and indication of myocardial scarring that can be detected from the electrocardiogram (ECG). Nowadays, fQRS scoring is done on a visual basis, which is time consuming and leads to subjective...

Ambient Intelligence Environment for Home Cognitive Telerehabilitation.

Sensors (Basel, Switzerland)
Higher life expectancy is increasing the number of age-related cognitive impairment cases. It is also relevant, as some authors claim, that physical exercise may be considered as an adjunctive therapy to improve cognition and memory after strokes. Th...

Exploiting the heightened phase synchrony in patients with neuromuscular disease for the establishment of efficient motor imagery BCIs.

Journal of neuroengineering and rehabilitation
BACKGROUND: Phase synchrony has extensively been studied for understanding neural coordination in health and disease. There are a few studies concerning the implications in the context of BCIs, but its potential for establishing a communication chann...

A Machine Learning Framework for Automatic and Continuous MMN Detection With Preliminary Results for Coma Outcome Prediction.

IEEE journal of biomedical and health informatics
Mismatch negativity (MMN) is a component of the event-related potential (ERP) that is elicited through an odd-ball paradigm. The existence of the MMN in a coma patient has a good correlation with coma emergence; however, this component can be difficu...

Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches.

Journal of healthcare engineering
INTRODUCTION: Blood pressure (BP) has been a potential risk factor for cardiovascular diseases. BP measurement is one of the most useful parameters for early diagnosis, prevention, and treatment of cardiovascular diseases. At present, BP measurement ...

Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification.

IEEE transactions on bio-medical engineering
Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and, subsequent...

On the robustness of real-time myoelectric control investigations: a multiday Fitts' law approach.

Journal of neural engineering
OBJECTIVE: Real-time myoelectric experimental protocol is considered as a means to quantify usability of myoelectric control schemes. While usability should be considered over time to assure clinical robustness, all real-time studies reported thus fa...

ECG Signal Classification Using Various Machine Learning Techniques.

Journal of medical systems
Electrocardiogram (ECG) signal is a process that records the heart rate by using electrodes and detects small electrical changes for each heat rate. It is used to investigate some types of abnormal heart function including arrhythmias and conduction ...