scholarly journals Model-Based Segmentation and Analysis of Knee Sound Signals

2021 ◽  
Author(s):  
Farhana Parveen
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Knee Joint ◽  
Classification Accuracy ◽  
Signal Analysis ◽  
Early Stage ◽  
Sound Signal ◽  
Noninvasive Diagnosis ◽  
Model Based ◽  
Leave One Out

The motivation of the work is to develop a signal processing methodology for noninvasive diagnosis of knee osteoarthritis in an early stage. The sound signal that is emitted from knee when it moves is called Vibroathrographic (VAG) signal. Analysis of this sound signal will help in diagnosis of the knee joint problems. In this project a model based approach for sementing the VAG signals, followed by feature extraction and classification is proposed. This could be used to get some indication whether the signal is from a normal knee or from an abnormal knee. The proposed scheme also has the capability for finding the depth of severity of the damage and it can also localize the angle range of the knee swing, where the damage has occurred. As a result, the project gave an accuracy of 70.4% with leave-one-out method. After doing the classification using the segments, finally it has been calculated how many segments from each signal has been correctly identified. A total of 30 knee sound signals from normal and abmoraml knees has been used in this work and out of that 26 signals has been classified properly (either normal or abnormal) and 4 signals got misclassified with a successful classification accuracy of 86.7%.

scholarly journals Model-Based Segmentation and Analysis of Knee Sound Signals

2021 ◽  
Author(s):  
Farhana Parveen
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Knee Joint ◽  
Classification Accuracy ◽  
Signal Analysis ◽  
Early Stage ◽  
Sound Signal ◽  
Noninvasive Diagnosis ◽  
Model Based ◽  
Leave One Out

The motivation of the work is to develop a signal processing methodology for noninvasive diagnosis of knee osteoarthritis in an early stage. The sound signal that is emitted from knee when it moves is called Vibroathrographic (VAG) signal. Analysis of this sound signal will help in diagnosis of the knee joint problems. In this project a model based approach for sementing the VAG signals, followed by feature extraction and classification is proposed. This could be used to get some indication whether the signal is from a normal knee or from an abnormal knee. The proposed scheme also has the capability for finding the depth of severity of the damage and it can also localize the angle range of the knee swing, where the damage has occurred. As a result, the project gave an accuracy of 70.4% with leave-one-out method. After doing the classification using the segments, finally it has been calculated how many segments from each signal has been correctly identified. A total of 30 knee sound signals from normal and abmoraml knees has been used in this work and out of that 26 signals has been classified properly (either normal or abnormal) and 4 signals got misclassified with a successful classification accuracy of 86.7%.

Milling Process Monitoring Based on Vibration Analysis Using Hilbert-Huang Transform

2018 ◽  
Vol 12 (5) ◽  
pp. 688-698 ◽  
Author(s):  
Agus Susanto ◽  
Chia-Hung Liu ◽  
Keiji Yamada ◽  
Yean-Ren Hwang ◽  
Ryutaro Tanaka ◽  
...  
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Process Monitoring ◽  
Signal Analysis ◽  
Vibration Analysis ◽  
Milling Process ◽  
Machining Process ◽  
Hilbert Huang Transform ◽  
Thin Walled ◽  
Tool Damage

Vibration analysis is one method of machining process monitoring. The vibration obtained in machining is often nonlinear and of a nonstationary nature. Therefore, an appropriate signal analysis is needed for signal processing and feature extraction. In this research, vibrations obtained in the milling of thin-walled workpieces were analyzed using the Hilbert-Huang transform (HHT). The features obtained by the HHT served as machining-state indicators for machining process monitoring. Experimental results showed the effectiveness of the HHT method for detecting chatter and tool damage.

scholarly journals Vehicle Type Recognition in Sensor Networks Using Improved Time Encoded Signal Processing Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yan Wang ◽  
Xi Wu ◽  
Xiaohua Li ◽  
Jiliu Zhou
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Sensor Networks ◽  
Spectral Feature ◽  
Sound Signal ◽  
Sensor Nodes ◽  
Support Vector ◽  
Signal Processing Algorithm ◽  
Feature Extraction Method ◽  
Vehicle Type

Vehicle type recognition is a demanding application of wireless sensor networks (WSN). In many cases, sensor nodes detect and recognize vehicles from their acoustic or seismic signals using wavelet based or spectral feature extraction methods. Such methods, while providing convincing results, are quite demanding in computational power and energy and are difficult to implement on low-cost sensor nodes with limitation resources. In this paper, we investigate the use of time encoded signal processing (TESP) algorithm for vehicle type recognition. The conventional TESP algorithm, which is effective for the speech signal feature extraction, however, is not suitable for the vehicle sound signal which is more complex. To solve this problem, an improved time encoded signal processing (ITESP) is proposed as the feature extraction method according to the characteristics of the vehicle sound signal. Recognition procedure is accomplished using the support vector machine (SVM) and thek-nearest neighbor (KNN) classifier. The experimental results indicate that the vehicle type recognition system with ITESP features give much better performance compared with the conventional TESP based features.

scholarly journals Methods for the Analysis of EEG signals: A Review

2021 ◽  
Vol 9 (9) ◽  
pp. 873-876
Author(s):  
Malika Garg
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Electrical Activity ◽  
Signal Analysis ◽  
Eeg Signal ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Processing Techniques ◽  
The Brain ◽  
Eeg Signal Analysis

Abstract: Electroencephalography (EEG) helps to predict the state of the brain. It tells about the electrical activity going on in the brain. Difference of the surface potential evolved from various activities get recorded as EEG. The analysis of these EEG signals is of utmost importance to solve the problems related to the brain. Signal pre-processing, feature extraction and classification are the main steps of the EEG signal analysis. In this article we discussed various processing techniques of EEG signals. Keywords: EEG, analysis, signal processing, feature extraction, classification

scholarly journals Multichannel Analysis of EEG Signals for Screening Alcoholics

2021 ◽  
Author(s):  
Jasmin Thevaril
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Digital Signal Processing ◽  
Spectral Estimation ◽  
Digital Signal ◽  
Eeg Signals ◽  
Accuracy Rate ◽  
Signal Processing Algorithm ◽  
Linear Discriminant ◽  
Leave One Out

Multi-channel analysis of EEG signals were analyzed in the project to detect alcoholism. A digital signal-processing algorithm that automates the classification of signals as normal or alcoholic is studied here. The method exploits the existing digital signal processing concepst sucah as signal modeling and and spectral estimation for feature extraction and classification. The mult-channel AR modeling and Cepstral theory were used for feature extraction. The EEG signals use in the project include 32 channels recorded from different portions of the brain for three minutes duration. 25 signals of each subject were taken for analysis. A classifier is developed based on Linear Discriminant Analysis (LDA) and Leave-One-Out method (LWO), the signal deatures were classified based on the norm distances to maximize the accuracy. Maximum Likelihood (MLM) or Euclidean distance is used to extract the norm distance between the signal under test and the reference vector. This was repeated for the entire training database. The classifier thus obtained gave the overall accuracy rate of the system. The accuracy rate obtained with AR coefficients is 72% and the accuracy rate with cepstral coefficients is 62%.

scholarly journals Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Suxian Cai ◽  
Shanshan Yang ◽  
Fang Zheng ◽  
Meng Lu ◽  
Yunfeng Wu ◽  
...  
Keyword(s):  
Knee Joint ◽  
Signal Analysis ◽  
Early Stage ◽  
Matching Pursuit ◽  
Characteristic Curve ◽  
Classifier Fusion ◽  
Support Vector ◽  
Data Set ◽  
Matching Pursuit Decomposition ◽  
Joint Vibration

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis.

scholarly journals Multichannel Analysis of EEG Signals for Screening Alcoholics

2021 ◽  
Author(s):  
Jasmin Thevaril
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Digital Signal Processing ◽  
Spectral Estimation ◽  
Digital Signal ◽  
Eeg Signals ◽  
Accuracy Rate ◽  
Signal Processing Algorithm ◽  
Linear Discriminant ◽  
Leave One Out

Multi-channel analysis of EEG signals were analyzed in the project to detect alcoholism. A digital signal-processing algorithm that automates the classification of signals as normal or alcoholic is studied here. The method exploits the existing digital signal processing concepst sucah as signal modeling and and spectral estimation for feature extraction and classification. The mult-channel AR modeling and Cepstral theory were used for feature extraction. The EEG signals use in the project include 32 channels recorded from different portions of the brain for three minutes duration. 25 signals of each subject were taken for analysis. A classifier is developed based on Linear Discriminant Analysis (LDA) and Leave-One-Out method (LWO), the signal deatures were classified based on the norm distances to maximize the accuracy. Maximum Likelihood (MLM) or Euclidean distance is used to extract the norm distance between the signal under test and the reference vector. This was repeated for the entire training database. The classifier thus obtained gave the overall accuracy rate of the system. The accuracy rate obtained with AR coefficients is 72% and the accuracy rate with cepstral coefficients is 62%.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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