Classification of uterine EMG signals by using Normalized Wavelet Packet Energy

2012 ◽  
Author(s):  
Mohamad O. Diab ◽  
Bassam Moslem ◽  
Mohamad Khalil ◽  
Catherine Marque
Keyword(s):  
Wavelet Packet

Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks

2020 ◽  
pp. 1328-1340
Author(s):  
Natarajan Sriraam ◽  
Leema Murali ◽  
Amoolya Girish ◽  
Manjunath Sirur ◽  
Sushmitha Srinivas ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Histogram Equalization ◽  
Developed Countries ◽  
Left Breast ◽  
Statistical Moments ◽  
Thermal Images ◽  
Life Threatening ◽  
The Right

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks

2020 ◽  
pp. 1175-1187
Author(s):  
Natarajan Sriraam ◽  
Leema Murali ◽  
Amoolya Girish ◽  
Manjunath Sirur ◽  
Sushmitha Srinivas ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Histogram Equalization ◽  
Developed Countries ◽  
Left Breast ◽  
Statistical Moments ◽  
Thermal Images ◽  
Life Threatening ◽  
The Right

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

scholarly journals Machine Fault Classification Based on Local Discriminant Bases and Locality Preserving Projections

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Qingbo He ◽  
Xiaoxi Ding ◽  
Yuanyuan Pan
Keyword(s):  
Feature Extraction ◽  
Wavelet Packet ◽  
Fault Classification ◽  
Feature Extraction Method ◽  
Locality Preserving Projections ◽  
Vibration Data ◽  
Pattern Structure ◽  
Locality Preserving ◽  
Machine Fault

Machine fault classification is an important task for intelligent identification of the health patterns for a mechanical system being monitored. Effective feature extraction of vibration data is very critical to reliable classification of machine faults with different types and severities. In this paper, a new method is proposed to acquire the sensitive features through a combination of local discriminant bases (LDB) and locality preserving projections (LPP). In the method, the LDB is employed to select the optimal wavelet packet (WP) nodes that exhibit high discrimination from a redundant WP library of wavelet packet transform (WPT). Considering that the obtained discriminatory features on these selected nodes characterize the class pattern in different sensitivity, the LPP is then applied to address mining inherent class pattern feature embedded in the raw features. The proposed feature extraction method combines the merits of LDB and LPP and extracts the inherent pattern structure embedded in the discriminatory feature values of samples in different classes. Therefore, the proposed feature not only considers the discriminatory features themselves but also considers the dynamic sensitive class pattern structure. The effectiveness of the proposed feature is verified by case studies on vibration data-based classification of bearing fault types and severities.

scholarly journals Design of Ensemble Stacked Auto-Encoder for Classification of Horse Gaits with MEMS Inertial Sensor Technology

Micromachines ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 411 ◽  
Author(s):  
Jae-Neung Lee ◽  
Yeong-Hyeon Byeon ◽  
Keun-Chang Kwak
Keyword(s):  
Mean Squared Error ◽  
Inertial Sensor ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Sensor Technology ◽  
Support Vector ◽  
Softmax Classifier ◽  
National Team ◽  
Motion Data

This paper discusses the classification of horse gaits for self-coaching using an ensemble stacked auto-encoder (ESAE) based on wavelet packets from the motion data of the horse rider. For this purpose, we built an ESAE and used probability values at the end of the softmax classifier. First, we initialized variables such as hidden nodes, weight, and max epoch using the options of the auto-encoder (AE). Second, the ESAE model is trained by feedforward, back propagation, and gradient calculation. Next, the parameters are updated by a gradient descent mechanism as new parameters. Finally, once the error value is satisfied, the algorithm terminates. The experiments were performed to classify horse gaits for self-coaching. We constructed the motion data of a horse rider. For the experiment, an expert horse rider of the national team wore a suit containing 16 inertial sensors based on a wireless network. To improve and quantify the performance of the classification, we used three methods (wavelet packet, statistical value, and ensemble model), as well as cross entropy with mean squared error. The experimental results revealed that the proposed method showed good performance when compared with conventional algorithms such as the support vector machine (SVM).

Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks

2017 ◽  
Vol 6 (2) ◽  
pp. 18-32
Author(s):  
Natarajan Sriraam ◽  
Leema Murali ◽  
Amoolya Girish ◽  
Manjunath Sirur ◽  
Sushmitha Srinivas ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Histogram Equalization ◽  
Developed Countries ◽  
Left Breast ◽  
Statistical Moments ◽  
Thermal Images ◽  
Life Threatening ◽  
The Right

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization

1998 ◽  
Vol 6 (1) ◽  
pp. 65-74 ◽  
Author(s):  
L. Pesu ◽  
P. Helistö ◽  
E. Ademovič ◽  
J.-C. Pesquet ◽  
A. Saarinen ◽  
...  
Keyword(s):  
Vector Quantization ◽  
Wavelet Packet ◽  
Learning Vector Quantization ◽  
Respiratory Sounds ◽  
Wavelet Packet Decomposition

scholarly journals Classification of lactose and mandelic acid THz spectra using subspace and wavelet-packet algorithms

2007 ◽  
Author(s):  
Xiaoxia Yin ◽  
Sillas Hadjiloucas ◽  
Bernd M. Fischer ◽  
Brian W.-H. Ng ◽  
Henrique M. Paiva ◽  
...  
Keyword(s):  
Mandelic Acid ◽  
Wavelet Packet
Sign in / Sign up

Export Citation Format

Share Document