# A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility

arXiv: https://arxiv.org/abs/2503.07276 · HTML: https://mlsanity.com/paper/2503.07276/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 168 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 18 of 18)

- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [A White Paper on Neural Network Quantization](https://mlsanity.com/paper/2106.08295/) (2021)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [On-Device Training Under 256KB Memory](https://mlsanity.com/paper/2206.15472/) (2022)
- [Self-supervised representation learning from 12-lead ECG data](https://mlsanity.com/paper/2103.12676/) (2021)
- [On-Device Machine Learning: An Algorithms and Learning Theory Perspective](https://mlsanity.com/paper/1911.00623/) (2019)
- [ReckOn: A 28nm Sub-mm2 Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescales](https://mlsanity.com/paper/2208.09759/) (2022)
- [Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI](https://mlsanity.com/paper/2105.12497/) (2021)
- [LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices](https://mlsanity.com/paper/1812.04818/) (2018)
- [Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks](https://mlsanity.com/paper/2110.02215/) (2021)
- [LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification](https://mlsanity.com/paper/2207.12381/) (2022)
- [Global ECG Classification by Self-Operational Neural Networks with Feature Injection](https://mlsanity.com/paper/2204.03768/) (2022)
- [Ultra Low-Power and Real-time ECG Classification Based on STDP and R-STDP Neural Networks for Wearable Devices](https://mlsanity.com/paper/1905.02954/) (2019)
- [Inter-Patient ECG Classification with Convolutional and Recurrent Neural Networks](https://mlsanity.com/paper/1810.04121/) (2018)
- [Arrhythmia Classifier Using Convolutional Neural Network with Adaptive Loss-aware Multi-bit Networks Quantization](https://mlsanity.com/paper/2202.12943/) (2022)
- [Energy-Efficient Real-Time Heart Monitoring on Edge-Fog-Cloud Internet-of-Medical-Things](https://mlsanity.com/paper/2112.07901/) (2021)

## Built on by (most cited first, 2 of 2)

- [RhythmBERT: A Self-Supervised Language Model Based on Latent Representations of ECG Waveforms for Heart Disease Detection](https://mlsanity.com/paper/2602.23060/) (2026)
- [FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis](https://mlsanity.com/paper/2509.08961/) (2025)

## Abstract (arXiv)

The classification of electrocardiogram (ECG) signals is crucial for early detection of arrhythmias and other cardiac conditions. However, despite advances in machine learning, many studies fail to follow standardization protocols, leading to inconsistencies in performance evaluation and real-world applicability. Additionally, hardware constraints essential for practical deployment, such as in pacemakers, Holter monitors, and wearable ECG patches, are often overlooked. Since real-world impact depends on feasibility in resource-constrained devices, ensuring efficient deployment is critical for continuous monitoring. This review systematically analyzes ECG classification studies published between 2017 and 2024, focusing on those adhering to the E3C (Embedded, Clinical, and Comparative Criteria), which include inter-patient paradigm implementation, compliance with Association for the Advancement of Medical Instrumentation (AAMI) recommendations, and model feasibility for embedded systems. While many studies report high accuracy, few properly consider patient-independent partitioning and hardware limitations. We identify state-of-the-art methods meeting E3C criteria and conduct a comparative analysis of accuracy, inference time, energy consumption, and memory usage. Finally, we propose standardized reporting practices to ensure fair comparisons and practical applicability of ECG classification models. By addressing these gaps, this study aims to guide future research toward more robust and clinically viable ECG classification systems.