Key result
FPGA-based 1D-CNN enables real-time ECG classification with ~99% accuracy and sub-millisecond latency.
Why the study?
Traditional software-based ECG processing methods using general-purpose processors or microcontrollers have severe constraints in real-time applicability, energy efficiency, and scalability for embedded portable healthcare applications.
Absolute Event Rate: 98.7% vs 98.2%
An FPGA-based 1D-CNN framework for real-time ECG classification provides superior accuracy, sub-millisecond latency, and higher energy efficiency compared to standard microcontrollers, demonstrating strong potential for wearable edge-AI biomedical devices.
May enable energy-efficient real-time ECG wearables; leaves open prospective clinical validation before practice adoption.
The electrocardiogram (ECG) classification is one of the key elements of the continuous cardiac monitoring systems, as through this measurement, cardiovascular disorders and arrhythmia could be diagnosed at an early stage. As the market demand of embedded portable, real-time, energy-efficient Healthcare-related applications and services such as health monitoring grows, traditional software based ECG processing methods usually using software with general-purpose processor or microcontroller platforms have severe constraints in real-time applicability, energy efficiency, and scalability. This paper proposes a reconfigurable computing system specifically to accomplish real-time ECG classification over Field-Programmable Gate Arrays (FPGAs) in Field-Programmable Gate Arrays (FPGA) presents an interesting alternative to a system based on traditional computing due to the capabilities of parallelism, dynamic adaptability, and low power draws. The designed system is organized on the basis of a lightweight 1D Convolutional Neural Network (CNN) structure of solution focused on the ECG signal processing. Using the high speed pipelined architecture and dynamic partial reconfiguration (DPR) of current generation FPGAs, the system dynamically reconfigures its hardware operating using the requirements at a specific time ensuring an efficient use of the resources and saving of energy. The architectural design is verified through the MIT-BIH Arrhythmia Database with a classification accuracy of 98.7% nevertheless ensuring that latency of inference can still be less than 1 millisecond thus proving the time-sensitive nature of this architecture. To overcome the throughput bottleneck caused by fixed-point hardware, CNN model is quantized to run on fixed-point logic platform and implemented into reconfigurable logic tiles. Experimental implementations demonstrate that FPGA-based system outperforms conventional embedded systems in time and energy efficiency, reducing power costs and inference time to a considerable extent. Moreover, the addition of DPR makes the architecture interchangeable between the high-accuracy or low-power setting, given the operational situation, making the architecture very suitable to wearable and edge healthcare devices. The case study used herein is just an illustration of the opportunities offered in reconfigurable computing to biomedical signal processing and pioneer work towards the future of FPGA-based intelligent health monitoring systems into multi-modal biosignals and edge-AI-based diagnosis. The findings highlight the importance of co-design between hardware and software that will allow effective, scalable, and accurate edge medical AI.
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Mpamije et al. (2025) studied Arrhythmia (n=47). FPGA-Based 1D-CNN ECG Classification vs. ARM Cortex-A9 and STM32 MCU was evaluated on Classification accuracy. The FPGA-based real-time 1D-CNN ECG classification system achieved 98.7% accuracy with an inference latency of 0.93 ms, outperforming conventional embedded systems in time and energy efficiency.
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