EVDHM-ARIMA-based time series forecasting model and its application for COVID-19 cases
The time-series forecasting makes a substantial contribution in timely decision-making. In
this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) …
this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) …
COVID-19 disease identification from chest CT images using empirical wavelet transformation and transfer learning
In the current scenario, novel coronavirus disease (COVID-19) spread is increasing day-by-
day. It is very important to control and cure this disease. Reverse transcription-polymerase …
day. It is very important to control and cure this disease. Reverse transcription-polymerase …
A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images
Coronavirus disease is a viral infection caused by a novel coronavirus (CoV) which was first
identified in the city of Wuhan, China somewhere in the early December 2019. It affects the …
identified in the city of Wuhan, China somewhere in the early December 2019. It affects the …
Deep time-frequency features and semi-supervised dimension reduction for subject-independent emotion recognition from multi-channel EEG signals
B Zali-Vargahan, A Charmin, H Kalbkhani… - … Signal Processing and …, 2023 - Elsevier
In recent years, human emotion recognition has received great attention since it plays an
essential role in human-computer interactions. Traditional methods focused on …
essential role in human-computer interactions. Traditional methods focused on …
Detecting congestive heart failure by extracting multimodal features and employing machine learning techniques
The adaptability of heart to external and internal stimuli is reflected by the heart rate
variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes …
variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes …
1D-CADCapsNet: One dimensional deep capsule networks for coronary artery disease detection using ECG signals
Purpose Cardiovascular disease (CVD) is a leading cause of death globally.
Electrocardiogram (ECG), which records the electrical activity of the heart, has been used for …
Electrocardiogram (ECG), which records the electrical activity of the heart, has been used for …
A new method to identify coronary artery disease with ECG signals and time-Frequency concentrated antisymmetric biorthogonal wavelet filter bank
M Sharma, UR Acharya - Pattern Recognition Letters, 2019 - Elsevier
The extreme deposition of plaque in the inner walls of arteries causes coronary artery
disease (CAD). It can be detected by the morphological changes in the electrocardiogram …
disease (CAD). It can be detected by the morphological changes in the electrocardiogram …
A two-stage classification model integrating feature fusion for coronary artery disease detection and classification
Abstract According to the World Health Organization, Coronary Artery Disease (CAD) is a
leading cause of death globally. CAD is categorized into three types, namely Single Vessel …
leading cause of death globally. CAD is categorized into three types, namely Single Vessel …
Arrhythmic heartbeat classification using 2d convolutional neural networks
Background Electrocardiogram (ECG) is a method of recording the electrical activity of the
heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any …
heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any …
Design and implementation of a robust noise removal system in ECG signals using dual-tree complex wavelet transform
The key deliverable for any health monitoring system that offers telecardiology services is
the recovery of the ECG signal related to cardiac diagnostics. Accurate analysis and …
the recovery of the ECG signal related to cardiac diagnostics. Accurate analysis and …