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Found 24 papers, 5 papers with code
Design, Implementation, and Performance Evaluation of a Fiber Bragg Gratings(FBG) based Smart Insole to Measure Plantar Pressure and Temperature
Various foot complications can be easily avoided by continuous monitoring of foot plantar temperature and pressure.
BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Data
This study uses 25 biomarkers and CXR images in predicting the risk in 930 COVID-19 patients admitted during the first wave of COVID-19 (March-June 2020) in Italy.
Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis
The highest average {\Delta}SNR and {\eta} using single-stage artifacts removal techniques (WPD) are found as 16. 11 dB and 26. 40%, respectively for all the fNIRS signals using fk4 wavelet packet.
Blind ECG Restoration by Operational Cycle-GANs
Usually, a set of such artifacts occur on the same ECG signal with varying severity and duration, and this makes an accurate diagnosis by machines or medical doctors extremely difficult.
RamanNet: A generalized neural network architecture for Raman Spectrum Analysis
Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kind of materials.
A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals
Most existing methods used in the hospitals for continuous monitoring of BP are invasive.
Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks
Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acquired from mobile electrocardiogram (ECG) sensors, such as Holter monitors.
A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Images
Several studies have reported that thermogram images may help to detect an increase in plantar temperature prior to DFU.
COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network
Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: two-class classification (Normal vs COVID-19); three-class classification (Normal, COVID-19, and Other CVDs), and finally, five-class classification (Normal, COVID-19, MI, AHB, and RMI).
COVID-19 Infection Localization and Severity Grading from Chest X-ray Images
In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images.
A Novel Non-Invasive Estimation of Respiration Rate from Photoplethysmograph Signal Using Machine Learning Model
This ML model based RR estimation can be embedded in wearable devices for real-time continuous monitoring of the patient.
EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication
In this work, we present, EDITH, a deep learning-based framework for ECG biometrics authentication system.
Detection and severity classification of COVID-19 in CT images using deep learning
Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94. 13% and IoU of 91. 85% using the FPN model with the DenseNet201 encoder.
Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network
Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99. 83% F1-score, 99. 85% recall, and 99. 82% precision.
Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning
Finally, the third study shows a precision of 98. 29% on the noisy PASCAL dataset with transfer learning approach.
Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images
The accuracy, precision, sensitivity, f1-score, and specificity in the detection of COVID-19 with gamma correction on CXR images were 96. 29%, 96. 28%, 96. 29%, 96. 28% and 96. 27% respectively.
An early warning tool for predicting mortality risk of COVID-19 patients using machine learning
The prognostic model, nomogram and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.
Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization
The accuracy, precision, sensitivity, F1-score, specificity in the detection of tuberculosis using X-ray images were 97. 07 %, 97. 34 %, 97. 07 %, 97. 14 % and 97. 36 % respectively.
Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images
All networks showed high COVID-19 detection sensitivity (>96%) with the segmented lung images.
Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques
Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP).
PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks
This motivates us to develop a method to predict the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using photoplethysmogram (PPG) signals.
An Intelligent and Low-cost Eye-tracking System for Motorized Wheelchair Control
In the 34 developed and 156 developing countries, there are about 132 million disabled people who need a wheelchair constituting 1. 86% of the world population.
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93. 3% respectively.
Can AI help in screening Viral and COVID-19 pneumonia?
The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation.
