Prediabetes Detection Method Based on Multi-scale Analysis of HRV

doi:10.12068/j.issn.1005-3026.2025.20240103

Abstract

Abstract:

Prediabetes is an important stage of abnormal glucose metabolism in the development of diabetes， and its early diagnosis is crucial for global diabetes prevention and control. To explore non-invasive detection methods for prediabetes， heart rate variability （HRV） signals were utilized. By introducing a multi-scale analysis strategy， the global information of the signals was revealed， as well as subtle but important changes at different scales. The CatBoost algorithm was used for classification task. The results show that this method achieves an accuracy of 88.52%， a sensitivity of 83.40%， a specificity of 91.82%， a precision of 86.73%， and an F1-score of 87.40% on the dataset. This study provides a new approach for the diagnosis of prediabetes. The results are especially suitable for wearable devices， offering a potential solution for daily self-health monitoring and disease prevention.

Key words: prediabetes, heart rate variability, multi-scale analysis, wavelet scattering network, CatBoost algorithm

CLC Number:

TP 391.5

Hong-ru LI, Tong-tong LI, Kang-kang SHI, Ying-hua YANG. Prediabetes Detection Method Based on Multi-scale Analysis of HRV[J]. Journal of Northeastern University(Natural Science), 2025, 46(12): 19-28.

Figures/Tables 13

References 31

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类型	糖尿病前期	健康
糖尿病前期	TP	FN
健康	FP	TN

类型	糖尿病前期	健康
糖尿病前期	TP	FN
健康	FP	TN

J	Q
J	(2,1)	(4,1)	(6,1)	(4,2)
2	0.72	0.74	0.73	0.72
3	0.73	0.75	0.75	0.74
4	0.75	0.76	0.76	0.77
5	0.77	0.78	0.77	0.79
6	0.80	0.81	0.79	0.80
7	0.79	0.81	0.80	0.80

J	Q
J	(2,1)	(4,1)	(6,1)	(4,2)
2	0.72	0.74	0.73	0.72
3	0.73	0.75	0.75	0.74
4	0.75	0.76	0.76	0.77
5	0.77	0.78	0.77	0.79
6	0.80	0.81	0.79	0.80
7	0.79	0.81	0.80	0.80

方法	准确率	敏感度	特异度	精确度	F1
特定生理尺度特征+CatBoost	76.71	65.96	83.61	72.09	73.74
精细化尺度特征+CatBoost	81.03	71.49	87.16	78.14	78.55
多尺度特征+CatBoost	88.52	83.40	91.82	86.73	87.40