An Energy Efficient ECG Ventricular Ectopic Beat Classifier Using Binarized CNN for Edge AI Devices
| العنوان: | An Energy Efficient ECG Ventricular Ectopic Beat Classifier Using Binarized CNN for Edge AI Devices |
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| المؤلفون: | David Liang Tai Wong, Yongfu Li, John Deepu, Weng Khuen Ho, Chun-Huat Heng |
| المصدر: | IEEE Transactions on Biomedical Circuits and Systems. 16:222-232 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Electrocardiography, Artificial Intelligence, Biomedical Engineering, Conservation of Energy Resources, Humans, Neural Networks, Computer, Electrical and Electronic Engineering, Ventricular Premature Complexes |
| الوصف: | Wearable Artificial Intelligence-of-Things (AIoT) requires edge devices to be resource and energy-efficient. In this paper, we design and implement an efficient binary convolutional neural network (bCNN) algorithm utilizing function-merging and block-reuse techniques to classify between Ventricular and non-Ventricular Ectopic Beat images. We deploy our model into a low-resource low-power field programmable gate array (FPGA) fabric. Our model achieves a classification accuracy of 97.3%, sensitivity of 91.3%, specificity of 98.1%, precision of 86.7%, and F1-score of 88.9%, along with dynamic power dissipation of only 10.5-μW. |
| تدمد: | 1940-9990 1932-4545 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b6805a3e744970f802579eb2586333ab https://doi.org/10.1109/tbcas.2022.3152623 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi.dedup.....b6805a3e744970f802579eb2586333ab |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19409990 19324545 |
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