[HTML][HTML] Review of Phonocardiogram Signal Analysis: Insights from the PhysioNet/CinC Challenge 2016 Database
The phonocardiogram (PCG) is a crucial tool for the early detection, continuous monitoring,
accurate diagnosis, and efficient management of cardiovascular diseases. It has the …
accurate diagnosis, and efficient management of cardiovascular diseases. It has the …
Automated detection of left bundle branch block from ECG signal utilizing the maximal overlap discrete wavelet transform with ANFIS
Left bundle branch block (LBBB) is a common disorder in the heart's electrical conduction
system that leads to the ventricles' uncoordinated contraction. The complete LBBB is usually …
system that leads to the ventricles' uncoordinated contraction. The complete LBBB is usually …
Assessment of Multi-Layer Perceptron Neural Network for Pulmonary Function Test's Diagnosis Using ATS and ERS Respiratory Standard Parameters
The aim of the research work is to investigate the operability of the entire 23 pulmonary
function parameters, which are stipulated by the American Thoracic Society (ATS) and the …
function parameters, which are stipulated by the American Thoracic Society (ATS) and the …
Social Media Devices' Influence on User Neck Pain during the COVID-19 Pandemic: Collaborating Vertebral-GLCM Extracted Features with a Decision Tree
B Al-Naami, BEA Badr, YZ Rawash, HA Owida… - Journal of …, 2023 - mdpi.com
The prevalence of neck pain, a chronic musculoskeletal disease, has significantly increased
due to the uncontrollable use of social media (SM) devices. The use of SM devices by …
due to the uncontrollable use of social media (SM) devices. The use of SM devices by …
A Dual-tree Complex Wavelet Transform Simulation Model for Improved Noise Modeling and Prediction of Real-time Stencil-Printing Process
This article presents a dynamic simulation model for the stencil-printing process (SPP) in
surface mount technology (SMT) assembly lines, focusing on accurately replicating the real …
surface mount technology (SMT) assembly lines, focusing on accurately replicating the real …
Heart disease classification based on combination of PCA/ANFIS model
AG Shabeeb, HA Hashim, SK Gharghan - Research on Biomedical …, 2024 - Springer
Purpose This study aims to create an intelligent system for the classification of
electrocardiogram (ECG) signals using a combined approach of Principal Component …
electrocardiogram (ECG) signals using a combined approach of Principal Component …
Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable …
The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions.
This study proposes the training and testing of several classifiers based on SVMs (support …
This study proposes the training and testing of several classifiers based on SVMs (support …
Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images
Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer
treatments. As a result of the rapid development of medical imaging technology, the use of …
treatments. As a result of the rapid development of medical imaging technology, the use of …
PCG Signal Acquisition and Classification for Heart Failure Detection: Recent Advances and Implementation of Memory-Efficient Classifiers for Edge Computing …
L Spongano, R De Fazio, M De Vittorio… - … on Smart and …, 2024 - ieeexplore.ieee.org
Conventional diagnostic tools for cardiovascular diseases usually employ expensive
instrumentation and require specialized medical staff. An inexpensive and non-invasive …
instrumentation and require specialized medical staff. An inexpensive and non-invasive …
A sensorized face mask to monitor sleep and health of the astronauts: architecture definition, sensing section development and biosignals' acquisition
R De Fazio, VM Mastronardi… - … on Smart and …, 2024 - ieeexplore.ieee.org
Sleep can be classified into NREM (Non-Rapid Eye Movement) and REM (Rapid Eye
Movement) phases, forming cycles lasting 90–120 minutes. In this field, polysomnography …
Movement) phases, forming cycles lasting 90–120 minutes. In this field, polysomnography …