AI Medical Compendium Topic:
Signal Processing, Computer-Assisted

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Eye-Movement-Controlled Wheelchair Based on Flexible Hydrogel Biosensor and WT-SVM.

Biosensors
To assist patients with restricted mobility to control wheelchair freely, this paper presents an eye-movement-controlled wheelchair prototype based on a flexible hydrogel biosensor and Wavelet Transform-Support Vector Machine (WT-SVM) algorithm. Cons...

Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review.

Biomedical engineering online
INTRODUCTION: The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities th...

Application of Physical Examination Data on Health Analysis and Intelligent Diagnosis.

BioMed research international
Analysis and diagnosis according to the collected physical data are an important part in the physical examination. Through the data analysis of the physical examination results and expert diagnoses, the physical condition of a specific physical exami...

A convolutional neural network for estimating synaptic connectivity from spike trains.

Scientific reports
The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a me...

Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals.

Scientific reports
This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning a...

Depression Diagnosis Modeling With Advanced Computational Methods: Frequency-Domain eMVAR and Deep Learning.

Clinical EEG and neuroscience
Electroencephalogram (EEG)-based automated depression diagnosis systems have been suggested for early and accurate detection of mood disorders. EEG signals are highly irregular, nonlinear, and nonstationary in nature and are traditionally studied fro...

A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation.

IEEE journal of biomedical and health informatics
Traditionally, abnormal heart sound classification is framed as a three-stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed. Som...

A Multipulse Radar Signal Recognition Approach via HRF-Net Deep Learning Models.

Computational intelligence and neuroscience
In the field of electronic countermeasure, the recognition of radar signals is extremely important. This paper uses GNU Radio and Universal Software Radio Peripherals to generate 10 classes of close-to-real multipulse radar signals, namely, Barker, C...

Signal-piloted processing and machine learning based efficient power quality disturbances recognition.

PloS one
Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of m...

A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods.

Sensors (Basel, Switzerland)
The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning metho...