scholarly journals System Classification by Using Discriminant Functions of Time-Frequency Features

2006 ◽  
pp. 920-928
Author(s):  
Miguel Mendoza Reyes ◽  
Juan V. Lorenzo-Ginori ◽  
A. Taboada-Crispí ◽  
Yakelin Luna Carvajal
Keyword(s):  
Discriminant Functions ◽  
Time Frequency ◽  
Frequency Features

scholarly journals Fall Detection Based on Sequential Modeling of Radar Signal Time-Frequency Features

2013 ◽  
Author(s):  
Meng Wu ◽  
Xiaoxiao Dai ◽  
Yimin D. Zhang ◽  
Bradley Davidson ◽  
Moeness G. Amin ◽  
...  
Keyword(s):  
Fall Detection ◽  
Radar Signal ◽  
Time Frequency ◽  
Sequential Modeling ◽  
Frequency Features ◽  
Signal Time

Fault diagnosis for analog circuits utilizing time-frequency features and improved VVRKFA

2018 ◽  
Vol 29 (4) ◽  
pp. 045004 ◽  
Author(s):  
Wei He ◽  
Yigang He ◽  
Qiwu Luo ◽  
Chaolong Zhang
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Time Frequency ◽  
Frequency Features

VENTRICULAR TACHYCARDIA AND FIBRILLATION DETECTION USING DWT AND DECISION TREE CLASSIFIER

2019 ◽  
Vol 19 (03) ◽  
pp. 1950008
Author(s):  
MONALISA MOHANTY ◽  
PRADYUT BISWAL ◽  
SUKANTA SABUT
Keyword(s):  
Ventricular Tachycardia ◽  
Decision Tree ◽  
Ventricular Arrhythmias ◽  
Ventricular Tachyarrhythmia ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Time Frequency ◽  
Tree Classifier ◽  
Frequency Features

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the life-threatening ventricular arrhythmias that require treatment in an emergency. Detection of VT and VF at an early stage is crucial for achieving the success of the defibrillation treatment. Hence an automatic system using computer-aided diagnosis tool is helpful in detecting the ventricular arrhythmias in electrocardiogram (ECG) signal. In this paper, a discrete wavelet transform (DWT) was used to denoise and decompose the ECG signals into different consecutive frequency bands to reduce noise. The methodology was tested using ECG data from standard CU ventricular tachyarrhythmia database (CUDB) and MIT-BIH malignant ventricular ectopy database (VFDB) datasets of PhysioNet databases. A set of time-frequency features consists of temporal, spectral, and statistical were extracted and ranked by the correlation attribute evaluation with ranker search method in order to improve the accuracy of detection. The ranked features were classified for VT and VF conditions using support vector machine (SVM) and decision tree (C4.5) classifier. The proposed DWT based features yielded the average sensitivity of 98%, specificity of 99.32%, and accuracy of 99.23% using a decision tree (C4.5) classifier. These results were better than the SVM classifier having an average accuracy of 92.43%. The obtained results prove that using DWT based time-frequency features with decision tree (C4.5) classifier can be one of the best choices for clinicians for precise detection of ventricular arrhythmias.

Daubechies Wavelets Based Robust Audio Fingerprinting for Content-Based Audio Retrieval

2012 ◽  
Vol 4 (2) ◽  
pp. 49-69
Author(s):  
Wei Sun ◽  
Zhe-Ming Lu ◽  
Fa-Xin Yu ◽  
Rong-Jun Shen
Keyword(s):  
Wavelet Transforms ◽  
Wavelet Decomposition ◽  
Experimental Results ◽  
Daubechies Wavelet ◽  
Linear Speed ◽  
Time Frequency ◽  
Audio Fingerprinting ◽  
Audio Retrieval ◽  
Audio Clip ◽  
Frequency Features

Audio fingerprinting is the process to obtain a compact content-based signature that summarizes the essence of an audio clip. In general, existing audio fingerprinting schemes based on wavelet transforms are not robust against large linear speed changes. The authors present a novel framework for content-based audio retrieval based on the audio fingerprinting scheme that is robust against large linear speed changes. In the proposed scheme, 8 levels Daubechies wavelet decomposition is adopted for extracting time-frequency features and two fingerprint extraction algorithms are designed. The experimental results from this study are discussed further into the article.

Time-frequency features of grapevine’s xylem acoustic emissions for detection of drought stress

2020 ◽  
Vol 178 ◽  
pp. 105797
Author(s):  
Dinko Oletic ◽  
Sabine Rosner ◽  
Monika Zovko ◽  
Vedran Bilas
Keyword(s):  
Drought Stress ◽  
Acoustic Emissions ◽  
Time Frequency ◽  
Frequency Features

Prediction of Failure in Lubricated Surfaces Using Acoustic Time–Frequency Features and Random Forest Algorithm

2017 ◽  
Vol 13 (4) ◽  
pp. 1541-1553 ◽  
Author(s):  
Sergey A. Shevchik ◽  
Fatemeh Saeidi ◽  
Bastian Meylan ◽  
Kilian Wasmer
Keyword(s):  
Random Forest ◽  
Random Forest Algorithm ◽  
Time Frequency ◽  
Frequency Features

Arrhythmia detection based on time–frequency features of heart rate variability and back-propagation neural network

2019 ◽  
Vol 2 (4) ◽  
pp. 245-257 ◽  
Author(s):  
Ram Sewak Singh ◽  
Barjinder Singh Saini ◽  
Ramesh Kumar Sunkaria
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Arrhythmia Detection ◽  
Time Frequency ◽  
Frequency Features
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