Call for Papers:Vol.10 Issue.6

Submission
Last date		 30-Dec-2024
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-10,Issue-6. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  AUTOMATED DETECTION OF ATRIAL FIBRILLATION USING DEEP LEARNING
PaperId: :  19455
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 2 2023
DUI:    16.0415/IJARIIE-19455
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Sai Teja GanjiVasireddy Venkatadri Institute of Technology
Vattikonda AshokVasireddy Venkatadri Institute of Technology
Nandigama Sagar BabuVasireddy Venkatadri Institute of Technology

Abstract

Bio Medical and Computer Science Engineering
Atrial Fibrillation, MIT- BIH Database, Bi-Directional LSTM
The condition of atrial fibrillation (Afib) involves irregular beating of the heart's upper chambers (atria), which can increase the risk of stroke caused by a blood clot. The identification of paroxysmal AF can be improved through prolonged cardiac monitoring. To identify AF beats in Heart Beat (HR) signals, a machine learning model was employed, where the dataset is divided into sliding windows of 100-beat sequences. These sequences are then fed into a model that comprises a Bi-Directional LSTM layer, a Global max pooling layer, a Dense layer, a Dropout layer, and an output layer. The model was trained and tested using the MIT-BIH Atrial Fibrillation Database. The approach achieved high accuracy rates during training and validation, with a 98.15% accuracy rate. Additionally, the 7-fold cross-validation on 20 subjects yielded an accuracy rate of 93.43%, while testing with unknown data from 3 subjects resulted in an accuracy rate of 99.2%. The model performed well on untrained data, as demonstrated by the complete setup. The neural network architecture used in the proposed model was straightforward and consisted of simple deep-learning layers. Moreover, the proposed model demonstrated better efficiency.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE Sai Teja Ganji, Vattikonda Ashok, and Nandigama Sagar Babu. "AUTOMATED DETECTION OF ATRIAL FIBRILLATION USING DEEP LEARNING" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 2 2023 Page 641-645
MLA Sai Teja Ganji, Vattikonda Ashok, and Nandigama Sagar Babu. "AUTOMATED DETECTION OF ATRIAL FIBRILLATION USING DEEP LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 9.2(2023) : 641-645.
APA Sai Teja Ganji, Vattikonda Ashok, & Nandigama Sagar Babu. (2023). AUTOMATED DETECTION OF ATRIAL FIBRILLATION USING DEEP LEARNING. International Journal Of Advance Research And Innovative Ideas In Education, 9(2), 641-645.
Chicago Sai Teja Ganji, Vattikonda Ashok, and Nandigama Sagar Babu. "AUTOMATED DETECTION OF ATRIAL FIBRILLATION USING DEEP LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 2 (2023) : 641-645.
Oxford Sai Teja Ganji, Vattikonda Ashok, and Nandigama Sagar Babu. 'AUTOMATED DETECTION OF ATRIAL FIBRILLATION USING DEEP LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 2, 2023, p. 641-645. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/AUTOMATED_DETECTION_OF_ATRIAL_FIBRILLATION_USING_DEEP_LEARNING_ijariie19455.pdf (Accessed : 05 April 2023).
Harvard Sai Teja Ganji, Vattikonda Ashok, and Nandigama Sagar Babu. (2023) 'AUTOMATED DETECTION OF ATRIAL FIBRILLATION USING DEEP LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, 9(2), pp. 641-645IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/AUTOMATED_DETECTION_OF_ATRIAL_FIBRILLATION_USING_DEEP_LEARNING_ijariie19455.pdf (Accessed : 05 April 2023)
IEEE Sai Teja Ganji, Vattikonda Ashok, and Nandigama Sagar Babu, "AUTOMATED DETECTION OF ATRIAL FIBRILLATION USING DEEP LEARNING," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 2, pp. 641-645, Mar-App 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/AUTOMATED_DETECTION_OF_ATRIAL_FIBRILLATION_USING_DEEP_LEARNING_ijariie19455.pdf [Accessed : 05 April 2023].
Turabian Sai Teja Ganji, Vattikonda Ashok, and Nandigama Sagar Babu. "AUTOMATED DETECTION OF ATRIAL FIBRILLATION USING DEEP LEARNING." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 2 (05 April 2023).
Vancouver Sai Teja Ganji, Vattikonda Ashok, and Nandigama Sagar Babu. AUTOMATED DETECTION OF ATRIAL FIBRILLATION USING DEEP LEARNING. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : 05 April 2023]; 9(2) : 641-645. Available from: https://ijariie.com/AdminUploadPdf/AUTOMATED_DETECTION_OF_ATRIAL_FIBRILLATION_USING_DEEP_LEARNING_ijariie19455.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads


Last download on 4/5/2023 7:32:45 AM

Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
Review Coronary Heart Disease in WomenmedicineDr Prabha Chapagain Koirala Download
Heart Guard AI: Smart Detection and Classification using Neural NetworksComputer Science and EngineeringPranay Singh Download
Left atrial appendage occlusionMedical Mohammad Kabir Haroon Download
Lipid-Lowering Therapy and PCSK9 Inhibitors: The Function of PCSK9 inhibitors in Controlling High Cholesterol Level and Lowering the Possibility of StrokeMedicalSaddam Hussain Download
HEART DISEASE PREDICTION USING MACHINE LEARNING TECHNIQUESBio MedicalRAVULA BALA RANGA SAI Download
AUTOMATED DETECTION OF ATRIAL FIBRILLATION USING DEEP LEARNINGBio Medical and Computer Science EngineeringSai Teja Ganji Download
CARDIAC DISEASES PREDICTION USING SVM WITH XG BOOST ALGORITHMMachine Learning, MedicalMr.E.Loganathan Download