###### Abstract  
Changes in cardiac function and morphology are reflected in variations in the electrocardiogram (ECG) and, in turn, in the cardiac risk indices derived from it. These variations have led to the introduction of normalization as a step to compensate for possible biasing factors responsible for inter- and intra-subject differences, which can affect the accuracy of ECG-derived risk indices in assessing cardiac risk. The aim of this work is to perform a scoping review to provide a comprehensive collection of open-access published research that examines normalized ECG-derived parameters used as markers of cardiac anomalies or instabilities. The literature search was conducted from February to July 2024 in the major global electronic bibliographic repositories. Overall, 39 studies were selected. Results suggest extensive use of normalization on heart rate variability-related indices (49% of included studies), QT-related indices (18% of included studies), and T-wave alternans (5% of included studies), underscoring their recognized importance and suggesting that normalization may enhance their role as clinically useful risk markers. However, the primary objective of the included studies was not to evaluate the effect of normalization itself; thus, further research is needed to definitively assess the impact and advantages of normalization across various ECG-derived parameters. normalization; cardiac risk; electrocardiography; HRV; QT; TWA +
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Age, sex, life habits, body composition and weight, environmental conditions, and ethnic origins are all factors that can affect cardiac characteristics, leading to differences among individuals. Aging is associated with many cardiac changes, such as aortic stiffening and ventricular hypertrophy, that are known to contribute to age-related increases in cardiovascular risk [8]. Cardiac function and morphology are known to differ between male and female populations, as well as among different ethnic groups. As an example, it has been shown that T-wave height differs with both ethnic origin and sex [8; 9; 10]. Moreover, life habits, including diet, physical activity, smoking, and stress management, are also known to affect heart function. To illustrate, an unhealthy diet can lead to electrolyte imbalances that can affect the electrical activity of the heart [11]. There is evidence that environmental conditions, such as high altitude, can also lead to cardiovascular changes, including ventricular hypertrophy and T-wave inversion [12; 13]. Last but not least, studies have demonstrated that changes in body composition and weight, measured in terms of bioelectrical impedance, body fat percentage, and/or body mass index, can have an impact on cardiac electrical activity [14; 15] as well. All these changes in cardiac function and morphology are inevitably reflected in variations in the ECG and, in turn, in the risk indices derived from it. Indeed, there is a large variety in the morphology, amplitude, and polarity of ECG waves among different individuals, as well as in serial ECGs taken at different times from the same individual [8; 9; 10; 11; 12; 13; 14; 15]. The variability and non-reproducibility of ECG waves have led to the introduction of normalization as a step to compensate for inter- and intra-subject differences. Specifically, it can be considered as a preprocessing step, usually applied to the ECG signal directly, or as a corrective step, usually applied to ECG-derived measures (and thus on ECG-derived risk indices). As a preprocessing step, the normalization is intended to adjust and scale data before analysis, as well as to avoid redundancy by removing unnecessary information. It is typically used to bring all the variables to the same scale; thus, it usually includes amplitude scaling, which consists of adjusting the signal amplitudes so that they fall within a specific range (which is usually \([-1,1]\)).