Software Design and Optimization of ECG Signal Analysis and Diagnosis for Embedded IoT Devices

2016 ◽  
pp. 299-322 ◽  
Author(s):  
Vasileios Tsoutsouras ◽  
Dimitra Azariadi ◽  
Konstantina Koliogewrgi ◽  
Sotirios Xydis ◽  
Dimitrios Soudris
Keyword(s):  
Software Design ◽  
Signal Analysis ◽  
Ecg Signal ◽  
Design And Optimization ◽  
Iot Devices

scholarly journals Design and optimization of differential capacitive micro accelerometer for vibration measurement

2021 ◽  
Vol 30 (1) ◽  
pp. 19-27
Author(s):  
Kumar Gomathi ◽  
Arunachalam Balaji ◽  
Thangaraj Mrunalini
Keyword(s):  
Software Design ◽  
Proof Mass ◽  
Vibration Measurement ◽  
Design Parameters ◽  
Mechanical Sensitivity ◽  
Design And Optimization ◽  
Capacitive Accelerometer ◽  
Cross Axis ◽  
Micro Accelerometer

Abstract This paper deals with the design and optimization of a differential capacitive micro accelerometer for better displacement since other types of micro accelerometer lags in sensitivity and linearity. To overcome this problem, a capacitive area-changed technique is adopted to improve the sensitivity even in a wide acceleration range (0–100 g). The linearity is improved by designing a U-folded suspension. The movable mass of the accelerometer is designed with many fingers connected in parallel and suspended over the stationary electrodes. This arrangement gives the differential comb-type capacitive accelerometer. The area changed capacitive accelerometer is designed using Intellisuite 8.6 Software. Design parameters such as spring width and radius, length, and width of the proof mass are optimized using Minitab 17 software. Mechanical sensitivity of 0.3506 μm/g and Electrical sensitivity of 4.706 μF/g are achieved. The highest displacement of 7.899 μm is obtained with a cross-axis sensitivity of 0.47%.

A novel feature extraction-based ECG signal analysis

2021 ◽  
Author(s):  
Varun Gupta ◽  
Monika Mittal ◽  
Vikas Mittal ◽  
Arvind Kumar Sharma ◽  
Nitin Kumar Saxena
Keyword(s):  
Feature Extraction ◽  
Signal Analysis ◽  
Ecg Signal

ECG Signal Analysis for Classification of Congenital Heart Defects

2020 ◽  
Author(s):  
Muhammad Umar Khan ◽  
Sumair Aziz ◽  
Mumtaz Ch. Javeria ◽  
Anber Shahjehan ◽  
Zohaib Mushtaq ◽  
...  
Keyword(s):  
Congenital Heart Defects ◽  
Signal Analysis ◽  
Congenital Heart ◽  
Heart Defects ◽  
Ecg Signal

scholarly journals Arrhythmia ECG Signal Analysis using Non Parametric Time-Frequency Techniques

2012 ◽  
Vol 41 (4) ◽  
pp. 25-30 ◽  
Author(s):  
A. Dliou ◽  
R. Latif ◽  
M. Laaboubi ◽  
F. M. R. Maoulainine
Keyword(s):  
Signal Analysis ◽  
Ecg Signal ◽  
Time Frequency ◽  
Non Parametric

scholarly journals A study on ECG Signal Analysis and ECG databases

2019 ◽  
Vol 7 (4) ◽  
pp. 188-195 ◽  
Author(s):  
J.S. Karnewar ◽  
Dr.V.K. Shandilya ◽  
M.D. Tambakhe
Keyword(s):  
Signal Analysis ◽  
Ecg Signal

Pattern Recognition of Multiscale Entropy Curve for ECG Signal Analysis

2012 ◽  
Vol 195-196 ◽  
pp. 603-607 ◽  
Author(s):  
Chien Chih Wang ◽  
Cheng Ding Chang ◽  
Bernard C. Jiang
Keyword(s):  
Pattern Recognition ◽  
Control Chart ◽  
Signal Analysis ◽  
Self Determination ◽  
Multiscale Entropy ◽  
Ecg Signal ◽  
Physiological System ◽  
Environment Change ◽  
Experiment Analysis

Higher complexities of multiscale entropy (MSE) curve present the physiological system has the better ability to adapt under environment change. Traditional way to distinguish different complexity groups of MSE curves according to the area under MSE curves (AUC) by human self-determination, but that would be difficult to judge when some curves had similar AUC or had overlapped. This paper proposed a combination clustering and MR control chart to calculate the group distances as the response to assessment the clustering result of different MSE curves combination. From the experiment analysis result for ECG signal, using the four features considered in this paper could provide a good recognition in cluster MSE curves.

Signal Analysis for ECG Interpretation

1990 ◽  
Vol 29 (04) ◽  
pp. 317-329 ◽  
Author(s):  
J. H. van Bemmel ◽  
Chr. Zywietz ◽  
J. A. Kors
Keyword(s):  
Feature Selection ◽  
Data Reduction ◽  
Signal Analysis ◽  
Digital Filtering ◽  
Data Transformation ◽  
Diagnostic Classification ◽  
Ecg Signal ◽  
Boundary Recognition ◽  
Transformation Stage ◽  
Ecg Interpretation

AbstractIn ECG interpretation usually two main areas are discerned: the signal analysis and the diagnostic classification. This article reviews the major developments in the first area. ECG signal analysis itself is subdivided into the stages data acquisition, data transformation, feature selection, and data reduction. These stages are consecutively reviewed, while in the data transformation stage digital filtering, detection, wave typing, beat selection, and boundary recognition are discussed.

ECG Signal Analysis

2013 ◽  
pp. 15-49
Author(s):  
Rajarshi Gupta ◽  
Madhuchhanda Mitra ◽  
Jitendranath Bera
Keyword(s):  
Signal Analysis ◽  
Ecg Signal
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