Abstract: Cardiac arrhythmias should be detected at an early stage so that clinical intervention can take place and continuous patient monitoring can be established in a timely manner. In this study, we present Cardio-Edge, a hardware-software co-design implementation of an LSTM-based ECG classification system optimized for real-time use on wearable devices. Proposed architecture comprises discrete wavelet transform (DWT) and principal component analysis (PCA) for efficient feature extraction followed by multiple parallel LSTM networks and a multi-layer perceptron (MLP) for classification. Implemented on a Xilinx ZYNQ-7000 SoC, our system leverages FPGA-based hardware acceleration alongside ARM Cortex-A9 for preprocessing tasks. Compared to software-only implementation on the same ARM processor, our co-design achieves a 10× improvement in execution speed with 99% classification accuracy trained and verified on the MIT-BIH arrhythmia dataset. The hardware-efficient implementation employs resource-optimized architectures for LSTM, activation functions, and fully connected layers making it appropriate for low-power, patient-specific wearable healthcare devices. This real-time, on-chip solution eliminates dependence in-cloud connectivity and ensures data privacy hence suitable for continuous cardiac monitoring applications.

Nousheen Akhtar, Abdul Rehman Buzdar, Jiancun Fan and Muhammad Umair Khan. “Cardio-Edge: Hardware-Software Co-design Implementation of LSTM Based ECG Classification for Continuous Cardiac Monitoring on Wearable Devices”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.7 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160786

@article{Akhtar2025,
title = {Cardio-Edge: Hardware-Software Co-design Implementation of LSTM Based ECG Classification for Continuous Cardiac Monitoring on Wearable Devices},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160786},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160786},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {7},
author = {Nousheen Akhtar and Abdul Rehman Buzdar and Jiancun Fan and Muhammad Umair Khan}
}