Improved Patient-Independent Seizure Detection System Using Novel Feature Extraction Techniques

2021 ◽  
pp. 879-888
Author(s):  
Durgesh Nandini ◽  
Jyoti Yadav ◽  
Asha Rani ◽  
Vijander Singh
Keyword(s):  
Feature Extraction ◽  
Detection System ◽  
Seizure Detection ◽  
Extraction Techniques

scholarly journals An optimized design of seizure detection system using joint feature extraction of multichannel EEG signals

2020 ◽  
Vol 34 (3) ◽  
pp. 191
Author(s):  
Dattaprasad Torse ◽  
◽  
◽  
Veena Desai ◽  
Rajashri Khanai ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Detection System ◽  
Seizure Detection ◽  
Optimized Design ◽  
Eeg Signals ◽  
Multichannel Eeg

scholarly journals A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System

2010 ◽  
Vol 45 (4) ◽  
pp. 804-816 ◽  
Author(s):  
Naveen Verma ◽  
Ali Shoeb ◽  
Jose Bohorquez ◽  
Joel Dawson ◽  
John Guttag ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Detection System ◽  
Seizure Detection ◽  
Micro Power

scholarly journals Machine Learning-Based Epileptic Seizure Detection Methods Using Wavelet and EMD-Based Decomposition Techniques: A Review

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8485
Author(s):  
Rabindra Gandhi Thangarajoo ◽  
Mamun Bin Ibne Reaz ◽  
Geetika Srivastava ◽  
Fahmida Haque ◽  
Sawal Hamid Md Ali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Journal Citation Report ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Detection Methods ◽  
High Dimensional ◽  
Support Vector ◽  
Extraction Techniques ◽  
Eeg Data

Epileptic seizures are temporary episodes of convulsions, where approximately 70 percent of the diagnosed population can successfully manage their condition with proper medication and lead a normal life. Over 50 million people worldwide are affected by some form of epileptic seizures, and their accurate detection can help millions in the proper management of this condition. Increasing research in machine learning has made a great impact on biomedical signal processing and especially in electroencephalogram (EEG) data analysis. The availability of various feature extraction techniques and classification methods makes it difficult to choose the most suitable combination for resource-efficient and correct detection. This paper intends to review the relevant studies of wavelet and empirical mode decomposition-based feature extraction techniques used for seizure detection in epileptic EEG data. The articles were chosen for review based on their Journal Citation Report, feature selection methods, and classifiers used. The high-dimensional EEG data falls under the category of ‘3N’ biosignals—nonstationary, nonlinear, and noisy; hence, two popular classifiers, namely random forest and support vector machine, were taken for review, as they are capable of handling high-dimensional data and have a low risk of over-fitting. The main metrics used are sensitivity, specificity, and accuracy; hence, some papers reviewed were excluded due to insufficient metrics. To evaluate the overall performances of the reviewed papers, a simple mean value of all metrics was used. This review indicates that the system that used a Stockwell transform wavelet variant as a feature extractor and SVM classifiers led to a potentially better result.

Hair Detection in Mushroom Based on Image Processing

2015 ◽  
Vol 713-715 ◽  
pp. 402-405
Author(s):  
Zhan Si Deng ◽  
Tong Qiang Li
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction ◽  
High Speed ◽  
Detection System ◽  
Target Identification ◽  
Accurate Method ◽  
Inspection System ◽  
Extraction Techniques ◽  
On Line

Nowadays,artificial recognition is widely used in the mushroom inspection system, however, it depends on subjective judgment of inspectors.Therefore,the testing personnel's experience, technology and other factors will affect the objectivity and accuracy of test results.Commodity inspection system need a high-speed, objective and accurate method for the on-line hair detection in the mushroom.On the basis of summary of domestic and foreign research, this paper studies the target identification and feature extraction techniques based on computer vision, conducts a feasibility study for the real-time hair detection system.

Deep convolutional neural networks for human movement detection using wireless signals

2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Human Detection ◽  
Two Dimensional ◽  
Dimensional Matrix ◽  
State Classification ◽  
Propagation Paths

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

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