## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Dom**} & \multirow{2}{*}{**Feature**} & \multicolumn{2}{c}{**Normalization**} \\ \cline{4-5}  & & & **HOW** & **WHY** \\ \hline
[50] & T & QT variability index & \(\frac{\text{variance(QT)}/\text{mean(QT)}^{2}}{\text{variance(RR)}/\text{mean( RR)}^{2}}\) & To make QT interval variations \\ \hline
[51] & T & QT interval & QT preceding RR & To compensate for RR-interval duration \\ \hline
[52] & T & Entropy & \(\frac{\text{entropy}}{\text{power}}\) & To account for the effect of the system’s energy \\ \hline
[53] & T & HR & HR at walk test & To evaluate if cardiac responses \\ \hline
[54] & T & ECG leads & \(\frac{\text{PVCV}_{\text{d}}/\text{V}_{\text{a}}}{\text{sims}\text{V}_{ \text{a}}/\text{V}_{\text{s}}}\) & To see if the normalized index \\ \hline
[55] & F & \(\bullet\) & Low-frequency (LF) HRV component & \(\frac{\text{LF}}{\text{TP}-\text{VLF}}\cdot 100\) & To compensate for the effect of \\  & \(\bullet\) & High-frequency (HF) HRV component & \(\frac{\text{HF}}{\text{TP}-\text{VLF}}\cdot 100\) & aging-related processes \\ \hline
[56] & T & Total cosine R-to-T (TCRT) & \(\frac{\text{TCRT}}{\text{mean(HR)}}\) & To compensate for the fact that the \\ \hline
[57] & T & \(\bullet\) & Baseline TWA (bTWA) Stimulation TWA (sTWA) Post-stimulation TWA (psTWA) & \(Z\Big{(}\frac{\text{sTWA}}{\text{BIWA}}\Big{)}\) & To assess the effect of stimulation on \\ \hline  & & & \(Z\Big{(}\frac{\text{bTWA}}{\text{BIWA}}\Big{)}\) & TWA \\ \hline  & & & Ref: reference; Dom: domain; T: time; F: frequency; TP: total power; VLF: very low frequency; PA: premature activation; TA: T-wave amplitude computed as the mean value of the first principal component in the ST-T complex; PVC: premature ventricular complexes. \\ \end{tabular}
\end{table} 
Table 3: _Cont._interest in open-access publishing. Moreover, the upward trend continues in the most recent years, suggesting an ongoing research activity. With regard to the general characteristics of the populations studied in the included documents, almost 80% of the studies considered adult populations. The median size of the studied populations was 53 subjects, with few studies (30%) involving more than 100 subjects, and very few (13%) involving more than 200 subjects. Excluding studies that did not specify the population sex, the studied populations were predominantly male. Moreover, most studies enrolled diseased subjects and, as expected, since the data analyzed were ECG data, the patients affected by cardiac pathologies were the most commonly studied. However, the pie chart (Figure 3) helps highlight that ECG-derived measures are computed to evaluate also populations not directly affected by cardiac pathologies, rather by epilepsy, diabetes, mental health issues, or even healthy populations. The targets of the studies conducted in the included documents reported in Table 2 were provided to contextualize each study, and pointed out that, even if normalization was included in their methodologies (due to this review's design), none of the studies had the primary purpose of assessing its effect. Indeed, most studies aimed at evaluating changes in HRV that reflect cardiac autonomic modulation, changes in QT interval variability that reflect ventricular repolarization lability, or the effects of some factors (such as drugs, age, physical activity, external stimulations) on them. Among the included documents, 54% analyzed the ECG in the frequency domain, with almost all presenting normalized HRV indices, while the remaining 46% analyzed the ECG in the time domain, with half of them presenting normalized QT interval variability measures or normalized TWA measures. These results suggest that there is a significant trend towards the established use of normalization in the computation of HRV indices, likely indicating its recognized importance and effectiveness in this field.