Table 2.

Studies Using HRV as a Tool for Diagnosis in Diabetics

Author and Year	Sample	Analysis	Parameters and Methods	Sensibility (%)	Specificity (%)	Conclusion
Takasae et al. (1992)27	Diabetes: N = 25; DMT2; with CAN (13) 58 ± 11 years; without CAN (12) 49 ± 16 years	ECG 24 hours	Mean of SD of iRR (SDANN)	72	92	SDANN < 30 ms produced a better sensitivity and specificity for diabetics with CAN
Ziegler et al. (2001)28	Diabetes: N = 108; DMT1 (89); DMT2 (19), CAN (3 stages); 45.3 ± 1.4 years Control: N = 37, 41.1 ± 2.0 years	Finapress 10 minutes	LF, HF	–	–	HF is more sensitive to detect early autonomic dysfunction to a cutoff value of 0.892
Balcioglu et al. (2007)29	Diabetes: N = 90; DMT2; with CAN (35) 56 ± 9 years; CAN borderline (55) 56 ± 9 years	ECG 24 hours	SDNN, SDANN, RMSSD, triangular index, TFC of start and TFC tilt	97	71	All indices were reduced in individuals with CAN. TFC of tilt is better sensitivity and specificity for detection of CAN at a cutoff value of 3.32.
Khandoker et al. (2009)30	Diabetes: N = 17; DMT2; with CAN (9) 52 ± 12 years; without CAN (8) 56 ± 14 years	ECG 20 minutes	SDNN, RMSSD, LFun, HFun, LF/HF, SD1, SD2, SD1/SD2 and SampEn	100	75	SampEn and SD1/SD2 produced better sensitivity and specificity to distinguish between diabetics with and without CAN
Acharya et al. (2011)31	Diabetes: N = 15; 58.5 ± 6.42 years, CAN unknown Control: N = 15; 50 ± 8.8 years	ECG 60 minutes	Correlation dimension, Poincaré geometry, Recurrence plot/AdaBoost and SVM	87.5	84.6	The AdaBoost classifier obtained better accuracy to diagnose DM. The index DII was proposed based on the results to discriminate diabetic neuropathy.
Seyd et al. (2012)32	Diabetes: N = 70; DMT2; NAC unknown Control: N = 65 Age varied from 40 to 72 years for both groups	ECG 60 minutes	RMSSD, NN50, TINN, triangular index, SDNN, PNN50, LF, HF, VLF/artificial neural network	89.23	96.92	Training of artificial neural network using as base indices of HRV has good sensitivity and specificity to distinguish DM and healthy individuals
Acharya et al. (2013)33	Diabetes: N = 15; 58.5 ± 6.42 years, CAN unknown Control: N = 15; 50 ± 8.8 years	ECG 60 minutes	Recurrence plot, ApEn, DFA, Lyapunov exponet/AdaBoost, DT, FSC, k‐NN, PNN and SVM	92.52	88.73	The AdaBoost classifier obtained better performance than other classifiers to diagnose DM
Swapna et al. (2013)34	Diabetes: N = 15; 58.5 ± 6.42 years, CAN unknown Control: N = 15; 50 ± 8.8 years	ECG 60 minutes	Higher order spectra features/GMM, SVM, NBC, k‐NN, Fuzzy classifier, DT	85.7	95.2	The GMM classifier showed better accuracy than other classifiers to diagnose DM

N = sample; DMT2 = type 2 diabetes mellitus; DMT1 = type 1 diabetes mellitus; CAN = cardiac autonomic neuropathy; ECG = electrocardiogram; SDANN = standard deviation of mean of normal RR intervals every 5 minutes for a period of time, expressed in ms; LF = low‐frequency component; HF = high‐frequency component; SDNN = standard deviation of all normal RR intervals recorded at an interval of time expressed in ms; RMSSD = square root of the mean of the square of differences between adjacent normal RR intervals, at an interval of time expressed in ms; TFC = heart rate turbulence; SD1 = standard deviation of the instantaneous variability beat to beat; SD2 = standard deviation of the long‐term variability; SampEn = Sample Entropy; NN50 = adjacent RR intervals with difference of duration of 50 ms; TINN = triangular interpolation of RR intervals; PNN50 = percentage of adjacent RR intervals with difference of duration greater than 50 ms; VLF = very low‐frequency components; SVM = support vector machine; ApEn = approximate entropy; DFA = detrended fluctuation analysis; DT = decision tree; FSC = fuzzy Sugeno classifier; k‐NN = k‐nearest neighbor algorithm; PNN = probabilistic neural network; GMM = Gaussian Mixture Model; NBC = NaïveBayes classifier; DII = diabetes integrated index.