A real-time 12-lead electrocardiogram remote patient monitoring and analytics framework

This work presents XBeats: A machine learning-based framework for real-time electrocardiogram monitoring and analysis that uses edge computing and data analytics for early anomaly detection. The framework 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 for transmission 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 an edge node 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 is implemented to demonstrate the feasibility and usability of the proposed system. XBeats can achieve up to 95.30% detection accuracy for abnormal conditions while maintaining a high data acquisition rate of up to 480 samples per second. Besides a systematic energy consumption profiling criteria is provided for evaluating participating hardware components in the XBeats ECG patch. We isolate each hardware component to find power-intensive processes, discover energy consumption patterns, and measure voltage, current, power, and energy consumption for a given period. The proposed optimization techniques demonstrate significant improvements to the hardware components. The results show that optimizing the data acquisition process saves 8.2% compared to the original power consumption and 1.62% in data transmission over BLE, thus extending the lifetime of the device. Lastly, we optimize the data logging operation to save 54% of data initially written to an external drive. Moreover, the analytical results of the energy consumption profile show that the ECG patch provides up to 37 hours of continuous 12-lead ECG acquisition.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Badr, Ahmed

Advisor dc:contributor.advisor

• Elgazzar, Khalid

• Remote patient monitoring
• Electrocardiogram
• Telemedicine
• Cardiovascular diseases
• Real-time streaming

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1553

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1553