What type of study is this?

This is a Literature Review study.

What does this research mean for the field?

Convolutional Neural Networks (CNNs) are the most efficient architecture for ECG signal classification on FPGA platforms. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The paper aims to review the integration of machine learning models with FPGA technologies for ECG analysis.

January 10, 2026Open Access

FPGA-Accelerated ECG Analysis: Narrative Review of Signal Processing, ML/DL Models, and Design Optimizations

Key Result

Convolutional Neural Networks (CNNs) were highlighted as the most efficient architecture for ECG signal classification on FPGA platforms.

Key Points

The paper aims to review the integration of machine learning models with FPGA technologies for ECG analysis.
Reviewed various intelligent algorithms for ECG classification, including CNNs and RNNs.
Evaluated FPGA implementations focusing on performance metrics like accuracy and latency.
Analyzed different FPGA families and their reported operating characteristics.
EMphasized the superior classification accuracy of CNN models in ECG signal analysis.
Demonstrated the benefits of FPGA architectures in terms of performance and energy efficiency.
Highlighted various design optimization strategies for hardware accelerators.

Structured PICO

Intervention

FPGA-accelerated machine learning and deep learning models (SVMs, ANNs, RNNs, LSTMs, CNNs) for ECG signal classification

Outcome

Classification accuracy, operating frequency, power consumption, and latency

FPGA platforms provide flexible, high-performance, and energy-efficient hardware acceleration for real-time ECG signal classification using deep learning models, particularly CNNs.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Recent advances in deep learning have had a significant impact on biomedical applications, driving precise actions in automated diagnostic processes. However, integrating neural networks into medical devices requires meeting strict requirements regarding computing power, energy efficiency, reconfigurability, and latency, essential conditions for real-time inference. Field-Programmable Gate Array (FPGA) architectures provide a high level of flexibility, performance, and parallel execution, thus making them a suitable option for the real-world implementation of machine learning (ML) and deep learning (DL) models in systems dedicated to the analysis of physiological signals. This paper presents a review of intelligent algorithms for electrocardiogram (ECG) signal classification, including Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Convolutional Neural Networks (CNNs), which have been implemented on FPGA platforms. A comparative evaluation of the performances of these hardware-accelerated solutions is provided, focusing on their classification accuracy. At the same time, the FPGA families used are analyzed, along with the reported performances in terms of operating frequency, power consumption, and latency, as well as the optimization strategies applied in the design of deep learning hardware accelerators. The conclusions emphasize the popularity and efficiency of CNN architectures in the context of ECG signal classification. The study aims to offer a current overview and to support specialists in the field of FPGA design and biomedical engineering in the development of accelerators dedicated to physiological signals analysis.

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Cite This Study

Mihăilă et al. (Fri,) reported a other. Convolutional Neural Networks (CNNs) were highlighted as the most efficient architecture for ECG signal classification on FPGA platforms.

synapsesocial.com/papers/696321b791e05aa366cb7f4b https://doi.org/https://doi.org/10.3390/electronics15020301

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