Cardio-Edge: Hardware-Software Co-design Implementation of LSTM Based ECG Classification for Continuous Cardiac Monitoring on Wearable Devices

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.