AIMC Topic: Signal Processing, Computer-Assisted

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A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography.

Sensors (Basel, Switzerland)
Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiograph...

The Classification of Minor Gait Alterations Using Wearable Sensors and Deep Learning.

IEEE transactions on bio-medical engineering
OBJECTIVE: This paper describes how non-invasive wearable sensors can be used in combination with deep learning to classify artificially induced gait alterations without the requirement for a medical professional or gait analyst to be present. This a...

Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals.

Computers in biology and medicine
In motor imagery (MI) based brain-computer interface (BCI) signal analysis, mu and beta rhythms of electroencephalograms (EEGs) are widely investigated due to their high temporal resolution and capability to define the different movement-related ment...

A Multi-Layer Gaussian Process for Motor Symptom Estimation in People With Parkinson's Disease.

IEEE transactions on bio-medical engineering
The assessment of Parkinson's disease (PD) poses a significant challenge, as it is influenced by various factors that lead to a complex and fluctuating symptom manifestation. Thus, a frequent and objective PD assessment is highly valuable for effecti...

Phonocardiogram classification using deep neural networks and weighted probability comparisons.

Journal of medical engineering & technology
Cardiac auscultation is one of the most conventional approaches for the initial assessment of heart disease, however the technique is highly user-dependent and with low repeatability. Several computational approaches based on the analysis of the phon...

Design and Implementation of a Novel Subject-Specific Neurofeedback Evaluation and Treatment System.

Annals of biomedical engineering
Electroencephalography (EEG)-based neurofeedback (NF) is a safe, non-invasive, non-painful method for treating various conditions. Current NF systems enable the selection of only one NF parameter, so that two parameters cannot be feedback simultaneou...

Artificial Neural Network for in-Bed Posture Classification Using Bed-Sheet Pressure Sensors.

IEEE journal of biomedical and health informatics
Pressure ulcer prevention is a vital procedure for patients undergoing long-term hospitalization. A human body lying posture (HBLP) monitoring system is essential to reschedule posture change for patients. Video surveillance, the conventional method ...

Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach.

Biomedical engineering online
BACKGROUND: Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previously made ECG in the same individual. Here, we present a novel algorithm to construct dedicated deep-...

Proximal detection of guide wire perforation using feature extraction from bispectral audio signal analysis combined with machine learning.

Computers in biology and medicine
Artery perforation during a vascular catheterization procedure is a potentially life threatening event. It is of particular importance for the surgeons to be aware of hidden or non-obvious events. To minimize the impact it is crucial for the surgeon ...

Machine learning for MEG during speech tasks.

Scientific reports
We consider whether a deep neural network trained with raw MEG data can be used to predict the age of children performing a verb-generation task, a monosyllable speech-elicitation task, and a multi-syllabic speech-elicitation task. Furthermore, we ar...