Performance measurements estimated on the test set (hold-out estimation) of the best classifiers based on HRV features.

<p>Class.: Classifier</p><p>AB: Adaboost</p><p>MLP: Multilayer Perceptron</p><p>NB: Naïve Bayes classifier</p><p>RF: Random Forest</p><p>SVM: Support Vector Machine</p><p>NI: number of iteration</p><p>ML: minimum number of instances per leaf.</p><p>CF: confidence factor for pruning</p><p>LR: learning rate</p><p>M: momentum</p><p>NE: number of epoch</p><p>NT: number of trees</p><p>NF: number of randomly chosen features</p><p>G: gamma</p><p>Χ²-FS: chi squared feature selection algorithm (a subset of 10 HRV features)</p><p>CFS: correlation-based feature selection algorithm (a subset of 8 HRV features)</p><p>AUC: area under the curve</p><p>ACC: accuracy</p><p>CI: confidence interval</p><p>SEN: sensitivity</p><p>SPE: specificity.</p><p>Performance measurements estimated on the test set (hold-out estimation) of the best classifiers based on HRV features.</p

Performance measurement (10-fold-crossvalidation estimation) of the proposed algorithms based on HRV features.

<p>CFS: correlation-based feature selection algorithm (a subset of 8 HRV features)</p><p>Χ²-FS: chi-squared feature selection algorithm (a subset of 10 HRV features)</p><p>NI: number of iteration</p><p>ML: minimum number of instances per leaf.</p><p>CF: confidence factor for pruning</p><p>LR: learning rate</p><p>M: momentum</p><p>NE: number of epoch</p><p>NT: number of trees</p><p>NF: number of randomly chosen features</p><p>G: gamma</p><p>AUC: area under the curve</p><p>CI: confidence interval</p><p>ACC: accuracy</p><p>SEN: sensitivity</p><p>SPE: specificity</p><p>In bold: the best performances of each classifier.</p><p>Performance measurement (10-fold-crossvalidation estimation) of the proposed algorithms based on HRV features.</p

Patient baseline characteristics.

<p>Data are expressed as mean and standard deviation for continuous variables (e.g. age) and as count and percentage of patients per each group for categorical variables (e.g. gender).</p><p>Patient baseline characteristics.</p

Receiver-operator characteristic curves for predicting vascular events by HRV-based classifiers and echographic parameters.

<p>The HRV-based classifiers are able to predict vascular events with higher sensitivity and specificity rate than echographic parameters. Sensitivity is determined from the proportion of patient developing a vascular event identified as high risk; specificity is determined from the proportion of patient free of vascular events identified as low risk. Solid lines represent classifier based on HRV features, dash-dot lines represent classifications based on echographic parameters. AB: Adaboost. MLP: Multilayer Perceptron. NB: Naïve Bayes classifier. RF: Random Forest. SVM: Support Vector Machine. LVMi.: Left ventricular mass index. IMT MAX: maximum of intima media thickness.</p

Performance measurements of classification based on echographic parameters.

<p>LVMi.: Left ventricular mass index</p><p>IMT MAX: maximum of intima media thickness</p><p>AUC: area under the curve</p><p>ACC: accuracy</p><p>CI: confidence interval</p><p>SEN: sensitivity</p><p>SPE: specificity.</p><p>Performance measurements of classification based on echographic parameters.</p

Feature importance computed by using Random Forest algorithm.

<p>CD: Correlation dimension. SampEn: Sample entropy. LF_peak: peak frequency of LF band. SD₂: long-term variability in Poincaré Plot. LF: absolute power in low frequency band (0.04–0.15 Hz). SDNN: standard deviation of all RR intervals. HF: absolute power in high frequency band (0.15–0.4 Hz). VLF_%: relative power in very low frequency band (0–0.04 Hz). LF_%: relative power in low frequency band (0.04–0.15 Hz). HRVTi: HRV triangular index. HF_%: relative power in high frequency band (0.15–0.4 Hz). SD₁: short-term variability in Poincaré Plot. TP: total power. DET: determinism. LF/HF: the ratio between LF and HF. VLF_peak: peak frequency of VLF band. TINN: triangular interpolation of RR interval histogram. NN50: number of differences between adjacent RR intervals that are longer than 50 ms. REC: recurrence rate. L_mean: mean length of lines in recurrence plot. AppEn: Approximate Entropy. HF_peak: peak frequency of HF band. Alpha₁: short-term fluctuations in Detrended Fluctuation Analysis. RMSSD: square root of the mean of the sum of the squares of differences between adjacent RR intervals. HF_nu: power in high frequency band (0.15–0.4 Hz), expressed in normalized unit. LF_nu: power in low frequency band (0.04–0.15 Hz), expressed in normalized unit. AVNN: Average of all RR intervals. ShanEn: Shannon Entropy. DIV: Divergence. VLF: absolute power in very low frequency band (0–0.04 Hz). Alpha₂: long-term fluctuations in Detrended Fluctuation Analysis. L_max: maximal length of lines in recurrence plot. pNN50: percentage of differences between adjacent RR intervals that are longer than 50 ms.</p

Decision tree for prediction of vascular events.

<p>The decision tree shows the set of rules adopted for classify high and low risk subjects: if HRVTi is higher than 13.6, the subject is classified as low risk, otherwise if SampEn lower than 0.997 or LF% lower than 18.1%, the subject is classified as high risk. The remaining subjects (with higher SampEn and LF_%), are classified based on LF and CF: as high risk, if LF is higher than 0.001 s² and CD is lower 3.43, otherwise as low risk. HRVTi: HRV Triangular Index. SampEn: Sample Entropy. LF: Low Frequency. LF_%: Low Frequency expressed as percentage of Total Power. CD: correlation dimension.</p

Forest plots of the HR of the combined endpoint per one SD of average mean CCA-IMT (with 95% CIs).

<p>Panel I: Group A (asymptomatic individuals with three or more CVD risk factors), HR adjusted for age, sex and annual mean CCA-IMT change (model 1). Panel II: Group A (asymptomatic individuals with three or more CVD risk factors), HR adjusted for age, sex, annual mean CCA-IMT change and other CVD risk factors (model 2). Panel III: Group B (asymptomatic individuals with carotid plaques), HR adjusted for age, sex and annual mean CCA-IMT change (model 1). Panel IV: Group B (asymptomatic individuals with carotid plaques), HR adjusted for age, sex, annual mean CCA-IMT change and other CVD risk factors (model 2). Panel V: Group C (individuals with previous CVD events), HR adjusted for age, sex and annual mean CCA-IMT change (model 1). Panel VI: Group C (individuals with previous CVD events), HR adjusted for age, sex, annual mean CCA-IMT change and other CVD risk factors (model 2).</p

Forest plots of the HR of the combined endpoint per one SD of annual mean CCA-IMT change (with 95% CIs).

<p>Panel I: Group A (asymptomatic individuals with three or more CVD risk factors), HR adjusted for age, sex and average mean CCA-IMT (model 1). Panel II: Group A (asymptomatic individuals with three or more CVD risk factors), HR adjusted for age, sex, average mean CCA-IMT and other CVD risk factors (model 2). Panel III: Group B (asymptomatic individuals with carotid plaques), HR adjusted for age, sex and average mean CCA-IMT (model 1). Panel IV: Group B (asymptomatic individuals with carotid plaques), HR adjusted for age, sex, average mean CCA-IMT and other CVD risk factors (model 2). Panel V: Group C (individuals with previous CVD events), HR adjusted for age, sex and average mean CCA-IMT (model 1). Panel VI: Group C (individuals with previous CVD events), HR adjusted for age, sex, average mean CCA-IMT and other CVD risk factors (model 2).</p

Meta-regression plot for the HR (combined endpoint) per SD of annual mean CCA-IMT change (model 1), by the correlation of baseline and follow-up common CIMT.

<p>The size of each circle represents the precision of the log HR.</p