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In this study, techniques were proposed for the detection of epileptic seizures from electroencephalogram (EEG) signals using the wavelet transform (WT), peak extraction and phase–space reconstruction (PSR) based Euclidean distances. In the first step, the wavelet coefficients were extracted after eliminating the noise from the EEG signals using a WT, which is a widely used signal processing technique. In the second step, the peaks were extracted from the wavelet coefficients. In the third step, the continuous peaks that were extracted were mapped to 3D coordinates using PSR. In the fourth step, the Euclidean distances between the mapped 3D coordinates and the origin were obtained. The features of the Euclidean distances obtained were extracted using statistical techniques. The final features extracted were used as inputs to the neural network with weighted fuzzy membership (NEWFM). NEWFM contains the bounded sum of weighted fuzzy memberships (BSWFMs) that can reveal the differences in the graphic characteristics between normal EEG signals and epileptic-seizure EEG signals. The BSWFMs can easily be embedded in a portable device to detect epileptic seizures from EEG signals in real life.
Seok-Woo Jang; Sang-Hong Lee. Detection of Epileptic Seizures Using Wavelet Transform, Peak Extraction and PSR from EEG Signals. Symmetry 2020, 12, 1239 .
AMA StyleSeok-Woo Jang, Sang-Hong Lee. Detection of Epileptic Seizures Using Wavelet Transform, Peak Extraction and PSR from EEG Signals. Symmetry. 2020; 12 (8):1239.
Chicago/Turabian StyleSeok-Woo Jang; Sang-Hong Lee. 2020. "Detection of Epileptic Seizures Using Wavelet Transform, Peak Extraction and PSR from EEG Signals." Symmetry 12, no. 8: 1239.
High-speed wired and wireless Internet are one of the useful ways to acquire various types of media data easily. In this circumstance, people also can easily get media data including objects with exposed personal information through the Internet. Exposure of personal information emerges as a social issue. This paper proposes an effective blocking technique that makes it possible to robustly detect target objects with exposed personal information from various types of input images with the use of deep neural computing and to effectively block the detected objects’ regions. The proposed technique first utilizes the neural computing-based learning algorithm to robustly detect the target object including personal information from an image. It next generates a grid-type mosaic and lets the mosaic overlap the target object region detected in the previous step so as to effectively block the object region that includes personal information. Experimental results reveal that the proposed algorithm robustly detects the target object region with exposed personal information from a variety of input images and effectively blocks the detected region through grid-type mosaic processing. The object blocking technique proposed in this paper is expected to be applied to various application fields such as image security, sustainable anticipatory computing, object tracking, and target blocking.
Seok-Woo Jang; Sang-Hong Lee. Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing. Sustainability 2020, 12, 2373 .
AMA StyleSeok-Woo Jang, Sang-Hong Lee. Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing. Sustainability. 2020; 12 (6):2373.
Chicago/Turabian StyleSeok-Woo Jang; Sang-Hong Lee. 2020. "Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing." Sustainability 12, no. 6: 2373.
The aim of this study is to develop an embedded method for automatic diagnosis of ventricular fibrillation (VF) using a neuro-fuzzy system embedded in an automated external defibrillator (AED). To diagnose VF using AEDs, we use the neural network with weighted fuzzy membership functions (NEWFM), a wavelet transform (WT), a sequential increment method (SIM), and phase-space reconstruction (PSR) in order to classify normal sinus rhythm (NSR) and VF of electrocardiogram (ECG) episodes. This study has the following key points. The first contribution is the extraction of peaks from ECG episodes by the use of the WT and SIM by a time–frequency technique. The second contribution is that NSR and VF are distinguished by means of three-dimensional (3D) PSR based on a 3D graphic model. The third contribution is the identification of feature differences between NSR and VF by the use of graphical characteristics of weighted fuzzy membership functions (WFMs) supported by the NEWFM. The final contribution is the development of a neuro-fuzzy system for automatic diagnosis of VF using the WFMs embedded in the AED. The following four preprocessing steps are implemented to extract features from ECG episodes. In the first step, the WT is used for multi-scale representation and analysis and wavelet coefficients are then generated from the ECG episodes. In the second step, the SIM is used to extract peaks from the wavelet coefficients. In the third step, successive peaks are plotted in a 3D phase-space diagram by performing 3D PSR. In the final step, the distance between the origin (0, 0, 0) and the successive peaks plotted in a 3D phase-space diagram is calculated; then, 20 features are extracted from the calculated distances using statistical methods, including frequency distributions and their variability. The 20 extracted features are applied as inputs to the NEWFM, and the result is that the classification accuracy of the NEWFM is 100 %.
Sang-Hong Lee. Development of Ventricular Fibrillation Diagnosis Method Based on Neuro-fuzzy Systems for Automated External Defibrillators. International Journal of Fuzzy Systems 2016, 19, 440 -451.
AMA StyleSang-Hong Lee. Development of Ventricular Fibrillation Diagnosis Method Based on Neuro-fuzzy Systems for Automated External Defibrillators. International Journal of Fuzzy Systems. 2016; 19 (2):440-451.
Chicago/Turabian StyleSang-Hong Lee. 2016. "Development of Ventricular Fibrillation Diagnosis Method Based on Neuro-fuzzy Systems for Automated External Defibrillators." International Journal of Fuzzy Systems 19, no. 2: 440-451.
Feature selection has commonly been used to remove irrelevant features and improve classification performance. Some of features are irrelevant to the learning process; therefore to remove these irrelevant features not only decreases training and testing times, but can also improve learning accuracy. This study proposes a novel supervised feature selection method based on the bounded sum of weighted fuzzy membership functions (BSWFM) and Euclidean distances between their centers of gravity for decreasing the computational load and improving accuracy by removing irrelevant features. This study compares the performance of a neural network with a weighted fuzzy membership function (NEWFM) without and with the proposed feature selection method. The superiority of the NEWFM with feature selection over NEWFM without feature selection was demonstrated using three experimental datasets from the UCI Machine Learning Repository: Statlog Heart, Parkinsons and Ionosphere. 13 features, 22 features, and 34 features were used as inputs for the NEWFM without feature selection and these resulted in performance accuracies of 85.6%, 86.2% and 91.2%, respectively, using Statlog Heart, Parkinsons and Ionosphere datasets. 10 minimum features, 4 minimum features and 25 minimum features were used as inputs for the NEWFM with feature selection and these resulted in performance accuracies of 87.4%, 88.2%, and 92.6%, respectively, using Statlog Heart, Parkinsons and Ionosphere datasets. The results show that NEWFM with feature selection performed better than NEWFM without feature selection.
Sang-Hong Lee. Feature selection based on the center of gravity of BSWFMs using NEWFM. Engineering Applications of Artificial Intelligence 2015, 45, 482 -487.
AMA StyleSang-Hong Lee. Feature selection based on the center of gravity of BSWFMs using NEWFM. Engineering Applications of Artificial Intelligence. 2015; 45 ():482-487.
Chicago/Turabian StyleSang-Hong Lee. 2015. "Feature selection based on the center of gravity of BSWFMs using NEWFM." Engineering Applications of Artificial Intelligence 45, no. : 482-487.
This paper proposes new combined methods to classify normal and epileptic seizure EEG signals using wavelet transform (WT), phase-space reconstruction (PSR), and Euclidean distance (ED) based on a neural network with weighted fuzzy membership functions (NEWFM). WT, PSR, ED, and statistical methods that include frequency distributions and variation, were implemented to extract 24 initial features to use as inputs. Of the 24 initial features, 4 minimum features with the highest accuracy were selected using a non-overlap area distribution measurement method supported by the NEWFM. These 4 minimum features were used as inputs for the NEWFM and this resulted in performance sensitivity, specificity, and accuracy of 96.33%, 100%, and 98.17%, respectively. In addition, the area under Receiver Operating Characteristic (ROC) curve was used to measure the performances of NEWFM both without and with feature selections.
Sang-Hong Lee; Joon S. Lim; Jae-Kwon Kim; Junggi Yang; Youngho Lee. Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance. Computer Methods and Programs in Biomedicine 2014, 116, 10 -25.
AMA StyleSang-Hong Lee, Joon S. Lim, Jae-Kwon Kim, Junggi Yang, Youngho Lee. Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance. Computer Methods and Programs in Biomedicine. 2014; 116 (1):10-25.
Chicago/Turabian StyleSang-Hong Lee; Joon S. Lim; Jae-Kwon Kim; Junggi Yang; Youngho Lee. 2014. "Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance." Computer Methods and Programs in Biomedicine 116, no. 1: 10-25.
The k-Nearest Neighbor (KNN) algorithm is widely used as a simple and effective classification algorithm. While its main advantage is its simplicity, its main shortcoming is its computational complexity for large training sets. A Prototype Selection (PS) method is used to optimize the efficiency of the algorithm so that the disadvantages can be overcome. This paper presents a new PS algorithm, namely Fuzzy k-Important Nearest Neighbor (FKINN) algorithm. In this algorithm, an important nearest neighbor selection rule is introduced. When classifying a data set with the FKINN algorithm, the most repeated selection sample is defined as an important nearest neighbor. To verify the performance of the algorithm, five UCI benchmarking databases are considered. Experiments show that the algorithm effectively deletes redundant or irrelevant prototypes while maintaining the same level of classification accuracy as that of the KNN algorithm. © 2013 Springer Science+Business Media.
Zhen-Xing Zhang; Xue-Wei Tian; Sang-Hong Lee; Joon S. Lim. A Prototype Selection Algorithm Using Fuzzy k-Important Nearest Neighbor Method. Lecture Notes in Electrical Engineering 2012, 997 -1001.
AMA StyleZhen-Xing Zhang, Xue-Wei Tian, Sang-Hong Lee, Joon S. Lim. A Prototype Selection Algorithm Using Fuzzy k-Important Nearest Neighbor Method. Lecture Notes in Electrical Engineering. 2012; ():997-1001.
Chicago/Turabian StyleZhen-Xing Zhang; Xue-Wei Tian; Sang-Hong Lee; Joon S. Lim. 2012. "A Prototype Selection Algorithm Using Fuzzy k-Important Nearest Neighbor Method." Lecture Notes in Electrical Engineering , no. : 997-1001.