AI driven ECG arrhythmia diagnosis
The accurate and timely diagnosis of cardiac arrhythmias is crucial for effective patient management and improved health outcomes. However, the precise identification of arrhythmias in electrocardiogram (ECG) data often requires specialized medical expertise, leading to potential delays and errors i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01149.pdf |
| _version_ | 1827311620724883456 |
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| author | Manohar Udutha Rangha Vardhan G. Shireen Mohammad Ramya T. |
| author_facet | Manohar Udutha Rangha Vardhan G. Shireen Mohammad Ramya T. |
| author_sort | Manohar Udutha |
| collection | DOAJ |
| description | The accurate and timely diagnosis of cardiac arrhythmias is crucial for effective patient management and improved health outcomes. However, the precise identification of arrhythmias in electrocardiogram (ECG) data often requires specialized medical expertise, leading to potential delays and errors in diagnosis. To address these challenges, this project introduces an AI-driven system for ECG arrhythmia diagnosis. Employing advanced deep learning techniques, the proposed system leverages a comprehensive dataset of annotated ECG recordings to train a robust model capable of detecting and classifying various types of arrhythmias. The model is designed to process raw ECG signals, extract relevant features, and generate clinically meaningful insights, enabling automated and rapid identification of arrhythmic patterns. Through a user-friendly interface, medical professionals can upload ECG data for real-time analysis, allowing for prompt decision-making and personalized patient care. Furthermore, the system offers interpretable results, highlighting key indicators and providing detailed explanations to aid clinicians in understanding the diagnostic outcomes. |
| first_indexed | 2024-04-24T20:21:55Z |
| format | Article |
| id | doaj.art-9f23392752d64578bff53f2bfe37b2c7 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-04-24T20:21:55Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-9f23392752d64578bff53f2bfe37b2c72024-03-22T08:05:18ZengEDP SciencesMATEC Web of Conferences2261-236X2024-01-013920114910.1051/matecconf/202439201149matecconf_icmed2024_01149AI driven ECG arrhythmia diagnosisManohar Udutha0Rangha Vardhan G.1Shireen Mohammad2Ramya T.3Department of AI&ML, CBITDepartment of AI&ML, CBITDepartment of AI&ML, CBITDepartment of AI&ML, CBITThe accurate and timely diagnosis of cardiac arrhythmias is crucial for effective patient management and improved health outcomes. However, the precise identification of arrhythmias in electrocardiogram (ECG) data often requires specialized medical expertise, leading to potential delays and errors in diagnosis. To address these challenges, this project introduces an AI-driven system for ECG arrhythmia diagnosis. Employing advanced deep learning techniques, the proposed system leverages a comprehensive dataset of annotated ECG recordings to train a robust model capable of detecting and classifying various types of arrhythmias. The model is designed to process raw ECG signals, extract relevant features, and generate clinically meaningful insights, enabling automated and rapid identification of arrhythmic patterns. Through a user-friendly interface, medical professionals can upload ECG data for real-time analysis, allowing for prompt decision-making and personalized patient care. Furthermore, the system offers interpretable results, highlighting key indicators and providing detailed explanations to aid clinicians in understanding the diagnostic outcomes.https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01149.pdfecgmachine learningdeep learningconcurrent neural networkecgsignalsarrhythmia. |
| spellingShingle | Manohar Udutha Rangha Vardhan G. Shireen Mohammad Ramya T. AI driven ECG arrhythmia diagnosis MATEC Web of Conferences ecg machine learning deep learning concurrent neural network ecg signals arrhythmia. |
| title | AI driven ECG arrhythmia diagnosis |
| title_full | AI driven ECG arrhythmia diagnosis |
| title_fullStr | AI driven ECG arrhythmia diagnosis |
| title_full_unstemmed | AI driven ECG arrhythmia diagnosis |
| title_short | AI driven ECG arrhythmia diagnosis |
| title_sort | ai driven ecg arrhythmia diagnosis |
| topic | ecg machine learning deep learning concurrent neural network ecg signals arrhythmia. |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01149.pdf |
| work_keys_str_mv | AT manoharudutha aidrivenecgarrhythmiadiagnosis AT ranghavardhang aidrivenecgarrhythmiadiagnosis AT shireenmohammad aidrivenecgarrhythmiadiagnosis AT ramyat aidrivenecgarrhythmiadiagnosis |