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March 21, 2026

Embedded AI for ECG monitoring overcomes power and computational limits via Tiny ML and hybrid processing.

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Why the study?

Embedded AI in ECG monitoring faces limitations regarding real-time analysis, power consumption, memory storage, compute intensity, battery life, and interference.

Design

Review paper

Key result

Embedded AI for ECG monitoring is overcoming power and computational limitations through emerging technologies like Tiny ML, hybrid processing, and hardware-software co-design.

Authors

HEHanane ElFerdaoussiWJWissam JenkalMLMostafa Laaboubi

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Overview

Enables AI-driven real-time ECG in low-power wearables for clinicians; extends feasibility of embedded AI in cardiovascular monitoring devices.

Key Points

  • The research aims to identify current limitations in the use of embedded AI for ECG monitoring and suggest future improvements.
  • Analyzed current state of embedded AI in ECG monitoring systems.
  • Evaluated challenges in real-time analysis, power consumption, and on-chip performance.
  • Discussed advancements in algorithms and hardware design such as neural networks and hybrid processing.
  • Identified significant limitations including high compute intensity and power consumption.
  • Highlighted emerging technologies like Tiny ML and adaptive learning strategies for better performance.
  • Discussed hardware-software co-design as a method to enhance real-time analysis and reduce energy usage.

Cite This Study

ElFerdaoussi et al. (2026) studied this question. Embedded AI for ECG monitoring is overcoming power and computational limitations through emerging technologies like Tiny ML, hybrid processing, and hardware-software co-design.

synapsesocial.com/papers/69be37f16e48c4981c677fc7https://doi.org/10.1051/e3sconf/202669801015/pdf
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention2025 · 30 citations
  2. 2Investigating the Efficacy of AI-Powered Innovations in ECG Analysis and Continuous Heart Monitoring: A Comprehensive Narrative Review2025
  3. 3Electrocardiogram-Based Artificial Intelligence for Detection of Low Ejection Fraction: A Contemporary Review2025 · 2 citations
  4. 4The Evolving Role of Artificial Intelligence and Machine Learning in the Wearable Electrocardiogram: A Primer on Wearable-Enabled Prediction of Cardiac Dysfunction2026
  5. 5Advances in the Interpretation of the Electrocardiogram by Artificial Intelligence2026