# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 6 Conclusions  
Although the ECG test is a 100-year-old technology, it remains scientifically challenging and attractive to research to unleash the full potential of information technology and IoT in this domain. We demonstrate the need for a compact wearable ECG monitoring system from the literature. We introduce XBeats a novel platform for continuous real-time monitoring. The ECG patch prototype proposed XBeats supports dynamic modes of operations that are actively configured when the heart conditions of the patient change. The device carries out all primary operations: data acquisition, logging, and transmission at an acquisition rate of up to 441 samples per second with significantly low latency. The proposed platform integrates an on-chip binary ECG signal classification implemented on edge devices. The classification algorithm achieved a maximum detection accuracy of 95.30% based on the Extra Trees machine learning classifier. This accuracy is accepted in our proposed platform as an initial phase of classification to support in-time notifications to the patient/healthcare providers. Our system demonstrates exemplary performance and can send immediate messages to patients or healthcare providers when irregular heartbeats are detected. It can also support long-term medical diagnosis for ECG signals in real-time. The results achieved in the prototype development allow us to conclude that high-quality real-time remote 12-lead ECG monitoring is achievable through our robust platform design  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Power Consumption Results** & **Value** \\ \hline Mean, Min, and Max & 157.73 mW, 91.69 mW, and 364.133 mW \\ Average voltage & 3.3 V \\ Battery Capacity & 2000 mAh \\ Total Operation time & 1 Day, 13 h approximately \\ \hline \hline \end{tabular}
\end{table}
Table 8: Analytical power consumption of the ECG patch hardware prototype.  
and selected hardware components. As a future direction, we plan to integrate high-performance streaming engines at the backend like Kafka or NATS. Additional future work includes extending the binary classification module to further analyze the abnormal class severity. We also consider modifying the classification technique to work with aggregated ECG data in real-time as they arrive with predefined windows for the aggregation process. The platform shall avoid false positives regarding the patient's heart conditions when classifying the heartbeats individually. Moreover, the system can be configured to call emergency response in extreme cases when the patient is at imminent risk or irresponsive.  
Conceptualization, A.B. ([PERSON]), K.E. and A.R.; methodology, A.B. ([PERSON]), K.E. and A.R.; software, A.B. ([PERSON]) and A.B. ([PERSON]); validation, A.B. ([PERSON]), A.B. ([PERSON]); K.E. and A.R.; formal analysis, A.B. ([PERSON]) and A.B. ([PERSON]); investigation, A.B. ([PERSON]), A.B. ([PERSON]), K.E. and A.R.; resources, K.E. and A.R.; data curation, A.B. ([PERSON]) and A.B. ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]) and A.B. ([PERSON]); writing--review and editing, A.B. ([PERSON]), K.E. and A.R.; visualization, A.B. ([PERSON]) and A.B. ([PERSON]); supervision, K.E. and A.R.; project administration, K.E.; funding acquisition, K.E. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), grant number: CRC-2017-00170. The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest.