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31 December 2019 Feature extraction methods based on ECG RR intervals for diabetes detection
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In this study, we propose two feature extraction methods based on Electrocardiogram (ECG) RR intervals for diabetes Mellitus (DM) detection, respectively on the time and space dimension. Method Ⅰ is based on the pRRx sequence to detect diabetes subjects via signal recordings, which yielded the highest prediction precision value of 86%. Method Ⅱ is a new method of meshing Poincaré plot to extract the whole information entropy 𝐻𝐻(𝑋𝑋) and region information entropy 𝐻𝐻(𝑋𝑋)′ on the space dimension as features. When the grid gap of the meshing Poincaré plot is set as 50 and 400, we got the highest prediction precision value of 96%, which have better effect on the perspective of prediction accuracy comparing with method Ⅰ. In the future, we will collect more data of diabetic patients with our new improved ECG monitor to further optimize and improve the above feature extraction methods.
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Qiuping Li, Xin’an Wang, Tianxia Zhao, Ran Li, He Sun, Changpei Qiu, and Xuan Cao "Feature extraction methods based on ECG RR intervals for diabetes detection", Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 1138418 (31 December 2019);

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