Abnormality detection in ECG using hybrid feature extraction approach

2018 ◽  
Author(s):  
Ritu Singh ◽  
Navin Rajpal ◽  
Rajesh Mehta
Keyword(s):  
Feature Extraction ◽  
Abnormality Detection ◽  
Hybrid Feature Extraction

A Hybrid Feature Extraction Method for Heart Disease Classification using ECG Signals

2021 ◽  
Author(s):  
Emir Akcin ◽  
Kemal Sami Isleyen ◽  
Enes Ozcan ◽  
Alaa Ali Hameed ◽  
Erdal Alimovski ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Heart Disease ◽  
Extraction Method ◽  
Disease Classification ◽  
Feature Extraction Method ◽  
Ecg Signals ◽  
Hybrid Feature Extraction

A hybrid feature extraction approach for brain MRI classification based on Bag-of-words

2019 ◽  
Vol 48 ◽  
pp. 144-152 ◽  
Author(s):  
Wadhah Ayadi ◽  
Wajdi Elhamzi ◽  
Imen Charfi ◽  
Mohamed Atri
Keyword(s):  
Feature Extraction ◽  
Brain Mri ◽  
Bag Of Words ◽  
Hybrid Feature Extraction

scholarly journals A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)

2020 ◽  
Author(s):  
Ahmed Abdullah Farid ◽  
Gamal Ibrahim Selim ◽  
Hatem Awad A. Khater
Keyword(s):  
Feature Extraction ◽  
Virus Disease ◽  
Real Data ◽  
Ct Images ◽  
Learning Tools ◽  
Corona Virus ◽  
Novel Approach ◽  
Hybrid Classification ◽  
Hybrid Feature Extraction ◽  
First Time

The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

EPILEPTIC EEG IDENTIFICATION BASED ON HYBRID FEATURE EXTRACTION

2020 ◽  
Vol 20 (06) ◽  
pp. 2050025 ◽  
Author(s):  
XIAOCHEN LIU ◽  
JIZHONG SHEN ◽  
WUFENG ZHAO
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Energy Operator ◽  
Typical Feature ◽  
Frequency Domain Analysis ◽  
Identification System ◽  
Eeg Signals ◽  
Hybrid Features ◽  
Interictal Eeg ◽  
Hybrid Feature Extraction

Electroencephalogram (EEG) signals are widely used as an effective method for epilepsy analysis and diagnosis. For the establishment of an accurate and efficient epilepsy EEG identification system, it is very important to properly extract the features of EEG signals and select appropriate combination features. This paper proposes an automatic epileptic EEG identification method based on hybrid feature extraction. It uses temporal and frequency domain analysis, nonlinear analysis and one-dimensional local pattern recognition method to extract epileptic EEG features. Gradient energy operator and local speed pattern are proposed to better reflect typical feature in the active EEG signals measured during seizure-free intervals. The genetic algorithm is used to select the obtained hybrid features; then the AdaBoost classifier is used to classify epileptic EEG under various classification conditions. Classification results on the dataset developed by University of Bonn show that the proposed method can be used to classify normal EEG, interictal EEG and seizure activity with only a few features. Compared with related researches using the same dataset, the proposed method can obtain an equally satisfactory classification accuracy while the feature amount is reduced by 61–95%. In particular, the classification accuracy of the interictal and normal EEG can reach 99%.

Hybrid Features Extraction for Adaptive Face Images Retrieval

2020 ◽  
Vol 11 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Adel Alti
Keyword(s):  
Feature Extraction ◽  
Recognition Accuracy ◽  
Face Image ◽  
Human Face ◽  
Gradient Vector ◽  
Facial Image ◽  
Hybrid Features ◽  
Face Images ◽  
Hybrid Feature Extraction ◽  
Face Emotion Recognition

Existing methods of face emotion recognition have been limited in performance in terms of recognition accuracy and execution time. It is highly important to use efficient techniques for improving this performance. In this article, the authors present an automatic facial image retrieval combining the advantages of color normalization by texture estimators with the gradient vector. Starting from a query face image, an efficient algorithm for human face by hybrid feature extraction provides very interesting results.

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