Performance measures for ERP signal extraction methods

2002 ◽  
Author(s):  
S. Tajwar ◽  
S.G. Mason ◽  
G.E. Birch
Keyword(s):  
Performance Measures ◽  
Extraction Methods ◽  
Signal Extraction

Analysis of Electrocardiogram Data Compression Techniques

2020 ◽  
pp. 930-970
Author(s):  
Anukul Pandey ◽  
Barjinder Singh Saini ◽  
Butta Singh ◽  
Neetu Sood
Keyword(s):  
Performance Evaluation ◽  
Performance Measures ◽  
Parameter Extraction ◽  
Extraction Methods ◽  
Walsh Transform ◽  
Ecg Compression ◽  
And Performance ◽  
Electrocardiogram Ecg ◽  
Ecg Database ◽  
Ecg Data

In this Chapter, a MATLAB-based approach is presented for compression of Electrocardiogram (ECG) data. The methodology employs in three different domains namely direct, transformed and parameter extraction methods. The selected techniques from direct ECG compression methods are TP, AZTEC, Fan, and Cortes. Moreover selected techniques from transformed ECG compression methods are Walsh Transform, DCT, and Wavelet transform. For each of the technique, the basic implementation of the algorithm was explored, and performance measures were calculated. All 48 records of MIT-BIH arrhythmia ECG database were employed for performance evaluation of various implemented techniques. Moreover, based on requirements, any basic techniques can be selected for further innovative processing that may include the lossless encoding.

scholarly journals Performance Measures of Respiratory Disease Classification using Deep Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 884-892
Author(s):  
G. Rama Janani
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Performance Measures ◽  
Respiratory Infections ◽  
Respiratory Illness ◽  
Extraction Methods ◽  
Disease Classification ◽  
Deep Cnn

The paper is based on classification of respiratory illness like covid 19 and pneumonia by using deep learning. The symptoms of COVID-19 and pneumonia are similar. Due to this, it is often difficult to identify what is causing your condition without being tested for COVID-19 or other respiratory infections. To find out how COVID-19 and pneumonia differs from one another, this paper presents that a novel Convolutional Neural Network in Tensor Flow and Keras based Covid-19 pneumonia classification. The proposed system supported implements CNN using Pneumonia images to classify the Covid-19, normal, pneumonia. The knowledge from these studies can potentially help in diagnosis of the concerned disease. It is predicted that the success of the anticipated results will increase if the CNN method is supported by adding extra feature extraction methods for classifying covid-19 and pneumonia successfully thereby improving the efficacy and potential of using deep CNN to pictures.

Progress on the Fabrication of Smart Textiles Based on Soft Strain Sensors

2019 ◽  
Vol 6 (6) ◽  
pp. 1-12
Author(s):  
Rafiu King Raji ◽  
Xuhong Miao ◽  
Ailan Wan ◽  
Zhejiang ◽  
Shu Zhang ◽  
...  
Keyword(s):  
Experimental Testing ◽  
Extraction Methods ◽  
Strain Sensors ◽  
Sensor Nodes ◽  
Signal Extraction ◽  
Strain Sensing ◽  
Sensitive Material ◽  
Smart Textiles ◽  
Fabric Structure ◽  
Fabric Deformation

The focus of this study is on strain sensing research and applications in smart textiles. Strain sensing is the measurement of fabric deformation by embedding a strain-sensitive material in it and subjecting it to stress. This paper presents an extensive classification of knitted textile strain sensors. Salient knitted strain sensor production parameters, such as conductive yarn choice, fabric structure, fabric structure deformation, and its relationship to strain signal extraction are discussed. The study concludes that producing yarn-based soft strain sensors for smart textile applications is viable. However, sensitive yarns with the right conductivity, count, and structural configuration are often unavailable. Work remains in the areas of efficient fabric deformation, signal extraction methods, development of sensor nodes, and robust experimental testing systems.

Analysis of Electrocardiogram Data Compression Techniques

2017 ◽  
pp. 272-313 ◽  
Author(s):  
Anukul Pandey ◽  
Barjinder Singh Saini ◽  
Butta Singh ◽  
Neetu Sood
Keyword(s):  
Performance Evaluation ◽  
Performance Measures ◽  
Parameter Extraction ◽  
Extraction Methods ◽  
Walsh Transform ◽  
Ecg Compression ◽  
And Performance ◽  
Electrocardiogram Ecg ◽  
Ecg Database ◽  
Ecg Data

In this Chapter, a MATLAB-based approach is presented for compression of Electrocardiogram (ECG) data. The methodology employs in three different domains namely direct, transformed and parameter extraction methods. The selected techniques from direct ECG compression methods are TP, AZTEC, Fan, and Cortes. Moreover selected techniques from transformed ECG compression methods are Walsh Transform, DCT, and Wavelet transform. For each of the technique, the basic implementation of the algorithm was explored, and performance measures were calculated. All 48 records of MIT-BIH arrhythmia ECG database were employed for performance evaluation of various implemented techniques. Moreover, based on requirements, any basic techniques can be selected for further innovative processing that may include the lossless encoding.

Characterization of surfactant complex mixtures using Raman spectroscopy and signal extraction methods: Application to laundry detergent deformulation

2016 ◽  
Vol 915 ◽  
pp. 36-48 ◽  
Author(s):  
Alexandra Gaubert ◽  
Yohann Clement ◽  
Anne Bonhomme ◽  
Benjamin Burger ◽  
Delphine Jouan-Rimbaud Bouveresse ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Laundry Detergent ◽  
Extraction Methods ◽  
Complex Mixtures ◽  
Signal Extraction ◽  
Surfactant Complex
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