VLSI Design of LSTM‐Based ECG Classification for Continuous Cardiac Monitoring on Wearable Devices
<p dir="ltr">A portable and efficient electrocardiogram (ECG) classification system is essential for continuous cardiac monitoring in wearable healthcare devices. This paper presents a highly efficient very large scale integration architecture optimized for real‐time ECG classificati...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , |
| منشور في: |
2025
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| الموضوعات: | |
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| الملخص: | <p dir="ltr">A portable and efficient electrocardiogram (ECG) classification system is essential for continuous cardiac monitoring in wearable healthcare devices. This paper presents a highly efficient very large scale integration architecture optimized for real‐time ECG classification. The proposed system integrates a feature extraction module that utilizes a four‐level daubechies discret wavelet transform and a classification module comprising multiple long‐short‐term memory recurrent neural networks, fully connected layers, and a multilayer perceptron. The design achieves a classification accuracy of . The hardware architecture demonstrates low resource utilization and operates at a power consumption of 41 mW with a clock frequency of 54 MHz, ensuring real‐time classification. The presented design is verified on a Xilinx field‐programmable gate array and tested using the publicly available ECG data set. Compared to state‐of‐the‐art implementations, our approach achieves a superior balance between classification accuracy, power efficiency, and hardware resource optimization, making it suitable for wearable cardiac monitoring applications.</p><h2>Other Information</h2><p dir="ltr">Published in: Electronics Letters<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1049/ell2.70269" target="_blank">https://dx.doi.org/10.1049/ell2.70269</a></p> |
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