Cardio-Edge: Hardware-Software Co-design Implementation of LSTM Based ECG Classification for Continuous Cardiac Monitoring on Wearable Devices
Year: 2025
Type: article
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.
Authors: Nousheen Akhtar, Abdul Rehman Buzdar, Jiancun Fan, Muhammad Umair Khan
Institutions: National University of Technology, Xi'an Jiaotong University
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4412996609