XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
This work presents XBeats, a novel platform for real-time electrocardiogram monitoring and analysis that uses edge computing and machine learning for early anomaly detection. The platform encompasses a data acquisition ECG patch with 12 leads to collect heart signals, perform on-chip processing, and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Signals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-6120/3/2/13 |
| _version_ | 1827656524176031744 |
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| author | Ahmed Badr Abeer Badawi Abdulmonem Rashwan Khalid Elgazzar |
| author_facet | Ahmed Badr Abeer Badawi Abdulmonem Rashwan Khalid Elgazzar |
| author_sort | Ahmed Badr |
| collection | DOAJ |
| description | This work presents XBeats, a novel platform for real-time electrocardiogram monitoring and analysis that uses edge computing and machine learning for early anomaly detection. The platform encompasses a data acquisition ECG patch with 12 leads to collect heart signals, perform on-chip processing, and transmit the data to healthcare providers in real-time for further analysis. The ECG patch provides a dynamically configurable selection of the active ECG leads that could be transmitted to the backend monitoring system. The selection ranges from a single ECG lead to a complete 12-lead ECG testing configuration. XBeats implements a lightweight binary classifier for early anomaly detection to reduce the time to action should abnormal heart conditions occur. This initial detection phase is performed on the edge (i.e., the device paired with the patch) and alerts can be configured to notify designated healthcare providers. Further deep analysis can be performed on the full fidelity 12-lead data sent to the backend. A fully functional prototype of the XBeats has been implemented to demonstrate the feasibly and usability of the proposed system. Performance evaluation shows that XBeats can achieve up to 95.30% detection accuracy for abnormal conditions, while maintaining a high data acquisition rate of up to 441 samples per second. Moreover, the analytical results of the energy consumption profile show that the ECG patch provides up to 37 h of continuous 12-lead ECG streaming. |
| first_indexed | 2024-03-09T22:30:35Z |
| format | Article |
| id | doaj.art-1209b0722fea4979b0572a6111249625 |
| institution | Directory Open Access Journal |
| issn | 2624-6120 |
| language | English |
| last_indexed | 2024-03-09T22:30:35Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Signals |
| spelling | doaj.art-1209b0722fea4979b0572a61112496252023-11-23T18:57:38ZengMDPI AGSignals2624-61202022-04-013218920810.3390/signals3020013XBeats: A Real-Time Electrocardiogram Monitoring and Analysis SystemAhmed Badr0Abeer Badawi1Abdulmonem Rashwan2Khalid Elgazzar3Department of Electrical, Computer and Software Engineering, Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaDepartment of Electrical, Computer and Software Engineering, Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaSchool of Computing, Queen’s University, Kingston, ON K7L 2N6, CanadaDepartment of Electrical, Computer and Software Engineering, Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaThis work presents XBeats, a novel platform for real-time electrocardiogram monitoring and analysis that uses edge computing and machine learning for early anomaly detection. The platform encompasses a data acquisition ECG patch with 12 leads to collect heart signals, perform on-chip processing, and transmit the data to healthcare providers in real-time for further analysis. The ECG patch provides a dynamically configurable selection of the active ECG leads that could be transmitted to the backend monitoring system. The selection ranges from a single ECG lead to a complete 12-lead ECG testing configuration. XBeats implements a lightweight binary classifier for early anomaly detection to reduce the time to action should abnormal heart conditions occur. This initial detection phase is performed on the edge (i.e., the device paired with the patch) and alerts can be configured to notify designated healthcare providers. Further deep analysis can be performed on the full fidelity 12-lead data sent to the backend. A fully functional prototype of the XBeats has been implemented to demonstrate the feasibly and usability of the proposed system. Performance evaluation shows that XBeats can achieve up to 95.30% detection accuracy for abnormal conditions, while maintaining a high data acquisition rate of up to 441 samples per second. Moreover, the analytical results of the energy consumption profile show that the ECG patch provides up to 37 h of continuous 12-lead ECG streaming.https://www.mdpi.com/2624-6120/3/2/13remote patient monitoringelectrocardiogramtelemedicinecardiovascular diseases |
| spellingShingle | Ahmed Badr Abeer Badawi Abdulmonem Rashwan Khalid Elgazzar XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System Signals remote patient monitoring electrocardiogram telemedicine cardiovascular diseases |
| title | XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System |
| title_full | XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System |
| title_fullStr | XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System |
| title_full_unstemmed | XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System |
| title_short | XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System |
| title_sort | xbeats a real time electrocardiogram monitoring and analysis system |
| topic | remote patient monitoring electrocardiogram telemedicine cardiovascular diseases |
| url | https://www.mdpi.com/2624-6120/3/2/13 |
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