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ECG signal analysis techniques using digital filter with deep learning in unbiased data
Name: Aekasak Insook
Organization :
Naresuan University. Department of Electrical Engineering
Email :
aekasaki63@nu.ac.th
Name: Settha Tangkawanit
Organization :
Naresuan University. Department of Electrical Engineering
Email :
setthat@nu.ac.th
Name: Surachet Kanprachar
Organization :
Naresuan University. Department of Electrical Engineering
Email :
surachetka@nu.ac.th
Name: Jirarat Ieamsaard
Organization :
Naresuan University. Department of Electrical Engineering
Email :
jirarati@nu.ac.th
keyword:
Biomedical signal processing
LCSH:
Signal processing
Abstract:
Electrocardiogram (ECG) can be used in various ways, especially in the classification of cardiovascular diseases with deep learning technology. Nowadays deep learning technology has been developed by many ideas through time to create an effective model. This paper presented pre-processing method by removing bias in data and filtering data before using them in deep learning to increase the classification accuracy. This deep learning process can categorize cardiovascular diseases in 5 conditions by using Lead II ECG signal in PhysioNets MIT-BIH and PTB Diagnostic ECG datasets with Median, Butterworth, Chebyshev I, Chebyshev II, and Elliptic for digital filtering. The evaluation after deep learning delivered the accuracy as follows: 97.4% in Median, 98.6% in Butterworth, 97.8% in Chebyshev I, 97.2% in Chebyshev II, and 97.2% in Elliptic. Moreover, the factor of deep learning accuracy depends on bias and configuration of digital filters that will be detailed in the result and discussion.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2024
Modified:
2026-06-05
Issued:
2026-06-05
บทความ/Article
application/pdf
BibliograpyCitation :
In Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI) Association, Thailand. 2024 21th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2024) (pp.453-456). Piscataway, NJ : IEEE
eng
©copyrights King Mongkut's University of Technology North Bangkok
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ใช้เวลา
0.052776 วินาที
Aekasak Insook
| Title | Contributor | Type |
|---|---|---|
| ECG signal analysis techniques using digital filter with deep learning in unbiased data
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Aekasak Insook;Settha Tangkawanit;Surachet Kanprachar;Jirarat Ieamsaard | บทความ/Article |
Settha Tangkawanit
| Title | Contributor | Type |
|---|---|---|
| ECG signal analysis techniques using digital filter with deep learning in unbiased data
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Aekasak Insook;Settha Tangkawanit;Surachet Kanprachar;Jirarat Ieamsaard | บทความ/Article |
Surachet Kanprachar
| Title | Contributor | Type |
|---|---|---|
| ECG signal analysis techniques using digital filter with deep learning in unbiased data
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Aekasak Insook;Settha Tangkawanit;Surachet Kanprachar;Jirarat Ieamsaard | บทความ/Article | |
| Estimation of suitable low-frequency passbands of MMFs using neural network
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Jaruwat Patmanee;Chairat Pinthong;Surachet Kanprachar | บทความ/Article |
Jirarat Ieamsaard
| Title | Contributor | Type |
|---|---|---|
| Newton\'s cube root finding data sequence
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Suchart Yammen;Jirarat Ieamsaard | บทความ/Article | |
| Deep learning-based face mask detection using YOLOv5
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Jirarat Ieamsaard;Surapon Nathanael Charoensook;Suchart Yammen | บทความ/Article |