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\begin{document}

\title{Cardiac age detected by machine learning applied to the surface ECG of
healthy subjects: Creation of a benchmark}



\author[1]{Hein E.C. van der Wall}%
\author[1]{Gerardus J. Hassing}%
\author[1]{Robert-Jan Doll}%
\author[2]{Gerard J.P. van Westen}%
\author[1]{Adam Cohen}%
\author[3]{Jasper Selder}%
\author[4]{Michiel Kemme}%
\author[1]{Jacobus Burggraaf}%
\author[1]{Pim Gal}%
\affil[1]{Centre for Human Drug Research}%
\affil[2]{Leiden Academic Centre for Drug Research}%
\affil[3]{Amsterdam UMC Locatie VUmc}%
\affil[4]{Amsterdam UMC - Locatie VUMC}%


\vspace{-1em}



  
  \date{March 14, 2025}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Objective The aim of the present study was to develop a neural network
to characterize the effect of aging on the ECG in healthy volunteers.
Moreover, the impact of the various ECG features on aging was evaluated.
Methods \& Results A total of 6228 healthy subjects without structural
heart disease were included in this study. A neural network regression
model was created to predict age of the subjects based on their ECG; 577
parameters derived from a 12-lead ECG of each subject were used to
develop and validate the neural network; A ten fold cross-validation was
performed, using 118 subjects for validation eacht fold. Using SHapley
Additive exPlanations values the impact of the individual features on
the prediction of age was determined. Of 6228 subjects tested, 1808
(29\%) were females and mean age was 34 years, range 18 -- 75 years.
Physiologic age was estimated as a continuous variable with an average
error of 6.9\selectlanguage{ngerman}±5.6 years (R2= 0.72 ± 0.04) . The correlation was slightly
stronger for men (R2= 0.74) than for women (R2= 0.66). The most
important features on the prediction of physiologic age were T wave
morphology indices in leads V4 and V5, and P wave amplitude in leads AVR
and II. Conclusion The application of artificial intelligence to the ECG
using a neural network regression model, allows accurate estimation of
physiologic cardiac age. This technique could be used to pick up subtle
age-related cardiac changes, but also estimate the reversing of these
age-associated effects by administered treatments.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Cardiac age detected by machine learning applied to the surface
ECG of healthy subjects: Creation of a benchmark}

Hein E.C. van der Wall MSc\textsuperscript{a,b}, Gerardus J. Hassing
MSc\textsuperscript{a}, Robert-Jan Doll PhD \textsuperscript{a}, Gerard
J.P. van Westen PhD,\textsuperscript{b}, Adam Cohen MD
PhD\textsuperscript{a,b,c}, Jasper Selder MD PhD \textsuperscript{d},
Michiel Kemme MD PhD \textsuperscript{d}, Jacobus Burggraaf MD
PhD\textsuperscript{a,b,c}, Pim Gal MD PhD \textsuperscript{a,c}

\subsection*{\texorpdfstring{\emph{a. Centre for Human Drug
Research}}{a. Centre for Human Drug Research}}

{\label{a.-centre-for-human-drug-research}}

\subsection*{\texorpdfstring{\emph{b. Leiden Academic Centre for Drug
Research}}{b. Leiden Academic Centre for Drug Research}}

{\label{b.-leiden-academic-centre-for-drug-research}}

\subsection*{\texorpdfstring{\emph{c. Leiden University Medical
Center}}{c. Leiden University Medical Center}}

{\label{c.-leiden-university-medical-center}}

\emph{d. Amsterdam University Medical Center}

\subsection*{}

{\label{section}}

Corresponding author:

Hein E.C. van der Wall, MSc

Centre for Human Drug Research

Zernikedreef 8, 2333 CL Leiden, The Netherlands

Hvdwall@chdr.nl

Funding: (None)

Disclosures: (None)

\subsection*{Abstract}

{\label{abstract}}

Objective

The aim of the present study was to develop a neural network to
characterize the effect of aging on the ECG in healthy volunteers.
Moreover, the impact of the various ECG features on aging was evaluated.

Methods \& Results

A total of 6228 healthy subjects without structural heart disease were
included in this study. A neural network regression model was created to
predict age of the subjects based on their ECG; 577 parameters derived
from a 12-lead ECG of each subject were used to develop and validate the
neural network; A ten fold cross-validation was performed, using 118
subjects for validation eacht fold. Using SHapley Additive exPlanations
values the impact of the individual features on the prediction of age
was determined. Of 6228 subjects tested, 1808 (29\%) were females and
mean age was 34 years, range 18 -- 75 years. Physiologic age was
estimated as a continuous variable with an average error of 6.9\selectlanguage{ngerman}±5.6
years (R\textsuperscript{2}= 0.72 ± 0.04) . The correlation was slightly
stronger for men (R\textsuperscript{2}= 0.74) than for women
(R\textsuperscript{2}= 0.66). The most important features on the
prediction of physiologic age were T wave morphology indices in leads V4
and V5, and P wave amplitude in leads AVR and II.

Conclusion

The application of machine learning to the ECG using a neural network
regression model, allows accurate estimation of physiologic cardiac age.
This technique could be used to pick up subtle age-related cardiac
changes, but also estimate the reversing of these age-associated effects
by administered treatments.

Keywords: Aging, ECG, Machine Learning, Healthy Volunteers, Artificial
Intelligence

\subsection*{Introduction}

{\label{introduction}}

Surface electrocardiograms (ECGs) are used frequently in routine
clinical care, but also in investigational studies examining the effects
of pharmacological and non-pharmacological treatments on the heart.
Readout measures include the RR interval, PR interval, QRS duration and
(corrected) QT interval. Typically, the pharmacological treatment
effects are mediated by recognized channels on the cardiac surface.
{[}1{]}However, there are cardiac effects that require a longer period
of time to become visible on the surface ECG, such as aging induced
cardiac fibrosis, and it is largely unknown if these subtle effects can
be visualized on a surface ECG. {[}2, 3{]}

There has been a number of recent investigations regarding the
prediction of physiological age using medical records, vital signs and
laboratory data, or epigenetic changes. {[}4-6{]} These investigations
indicated the existence of a gap between predicted physiological age and
actual chronological age. Exploration of this gap is clinically
important as a serious gap difference has been shown to be associated
with higher risks of all-cause mortality, cardiovascular disease,
obesity, earlier menopause, and frailty. {[}5, 7-11{]} Various previous
studies have already shown that the 12-lead ECG can be a reliable tool
to estimate physiological aging. {[}5, 7-16{]}

Previous studies have applied artificial intelligence to the raw ECG
data, allowing estimation of physiologic ECG age, which was found to
reflect aging and comorbidities. {[}17{]} However, these algorithms were
based on large hospital datasets, thus including patients that may have
disease-induced abnormalities in their ECGs, which makes the outcome
difficult to interpret when applied to a healthy volunteer. Therefore,
the aim of the present analysis was to develop a neural network in
healthy volunteers to characterize the effect of aging on the ECG.



\textbf{Methods and Materials}

Population

All data were collected at the Centre for Human Drug Research in Leiden,
the Netherlands, a clinical research organization specialized in early
phase drug development studies. Data collected during the mandatory
medical screening to verify study eligibility for enrolment in the early
phase drug development studies as a volunteer between 2010 and 2019 were
included in the present analysis. Ethical approvals from the Medical
Ethical Review Committee for the included studies were acquired and
informed consent documents were signed by the volunteers prior to any
data collection. The present study was performed in accordance to local
regulations. All activities were performed in accordance with applicable
standard operating procedures.

The medical screening consisted of a single visit to the clinical unit
where a detailed history, a physical examination, vital signs including
blood pressure, temperature, weight and height measurement, body mass
index (BMI) calculation, and a 12-lead ECG were recorded. Additionally,
haematology and chemistry blood panels, urine dipstick, and a urine drug
test were analysed.

Data collection for the model

ECG parameters of 6228 subjects with an age between 18 and 75 years were
included in the present study. From each subject ECG, 574 features were
extracted by the MUSE system. Additionally, gender was used as a
feature. The age of the subjects was rounded in whole years. At least
ten EGGs were available for each age.

Data pre-processing and selection

As validation set two subjects of each age were kept apart as final test
set. The rest of the data was used as the training set.

To create a balanced training set the Synthetic Minority Oversampling
Technique (SMOTE) algorithm was applied on the training set to create
`synthetic' subjects for the less populated age groups based on the
values in the concerning age groups. {[}18{]}

Machine learning

A neural network was used as a machine learning model. The keras module
v. 2.4.3 in python 3.8.5 was used to build a model. Before training,
internal cross validation (three-fold) within the training set was used
to optimize the model. The network was optimized for number of layers,
number of nodes per layer, activation function per layer for each layer
and learning rate. A batch size of 300 was used. The number of epochs
(defined as the number of cycles through the full training dataset) for
internal validation was determined based on validation performance in
the internal validation set. The number of epochs for final validation
was based on the median of the optimal number of epochs for the internal
cross validations. This process of optimization, training, and
validation was repeated 10 times with different training and test sets.
The optimal models were evaluated on the test set with the
R\textsuperscript{2} score and mean absolute error. We also evaluated
the model performance with respect to gender.

To investigate how such a model could perform in groups of subjects or
patients, the mean absolute error in a group of 1 to 50 randomly
selected subjects in the test sets was evaluated. This was repeated 10
times for each fold (i.e. 100 times for each number of subjects).

To gain insight into the impact of the individual features on the
predicted age, each fold SHapley Additive exPlanations (SHAP) values
were calculated {[}19{]} based on the training set. The importance of
the features was validated by means of permutation importance (defined
as the decrease in a model score when a single feature value is randomly
shuffled).{[}20{]}

Results

Table 1 shows the clinical characteristics of the 6228 included
subjects. The study population was divided into ten chronological age
groups of 6 years, starting from the age of 18 years. Each age group
contained at least 194 subjects, and younger age groups comprised up to
2282 subjects. A total of 1808 (29 \%) volunteers were female.

In Figure 1 ECG examples of a young 18 year old male (1A) and an elderly
74 year old male (1B) are shown. Figure 1C shows an ECG of a young 19
year old female and figure 1D shows an ECG of an elderly 74 year old
female subject. Several differences between the young and older healthy
subjects were discernable. In elderly persons the heart rate was lower,
the T wave had a lower (absolute) amplitude in leads I,II,III,AVR, and
AVL and the P-wave duration seemed shorter. However, these ECG
differences showed considerable variations in the healthy population.

In supplementary Tables 1 and 2, 54 features present in most leads and
other ECG features used for the machine learning model are shown,
respectively. In addition, gender of each subject was also included in
the model.

The relation between the (predicted) physiologic age and the
chronological age was assessed in 10 sets of 116 subjects. In Figure 2a,
the relation between predicted physiologic age and chronological age of
all 10 test sets is shown. The average relationship of the models showed
an R\textsuperscript{2} of 0.72 ± 0.04 (mean ± SD). The mean absolute
error of all predictions was 6.9 ± 5.5 years.

On average, the predicted physiologic age was 0.3 years younger than the
chronological age of the subjects. The median deviation of all predicted
ages was 5.6 years from the actual age, indicating that half of the
predictions was within the range of 5.6 years of chronical age.

The average prediction line is presented in figure 2b. The average
predicted age of the 20 subjects per chronological age had a mean
absolute error of 3.4 ± 3.0 years (R\textsuperscript{2}= 0.93). For
subjects between 30 and 60 years old the mean absolute error of the
average predicted age per chronological age was 1.6 ± 1.1 years.

Figure 3 shows how such models could perform in new patient groups. It
can be seen that the average absolute prediction error is declining fast
when multiple subjects are tested. For example, a cohort of 10 healthy
subjects with age ranging from 18 to 75 years would have an average
absolute error of 2.7 ± 2.1 years. The mean absolute error of a test
group of 30 subjects would be only 1.7 ± 1.2 years.

In order to study gender differences, the predicted physiological ages
of the male and female subjects in the test sets were separated and are
presented in Figure 4. The predicted ages of the male subjects were more
accurate (R\textsuperscript{2}= 0.74) than the predictions of the female
subjects (R\textsuperscript{2}= 0.66).

Figure 5 shows the SHAP values of the 40 most important ECG features
used in the prediction model. So, the impact of each individual feature
on the model output and physiologic aging can be seen. Some of the most
important features on the prediction of physiologic age were T top
abnormalities in leads V4 and V5, P top amplitude in leads AVR and II
and atrial rate.

An increase of P peak amplitude in lead II for example, indicates a
younger physiological age (a long red bar to the left). A longer PR
interval both indicate an older physiologic age (longer red bar to the
right). A higher atrial rate indicates a younger physiologic age ( large
red bar to the left). The impact of gender was only of minor importance
with SHAP values ranging from -1.2 to 0.9. The order of the feature
permutation importance is similar to the order of the SHAP values,
confirming the impact of the features.

Discussion

In this study we developed machine learning models that allow accurate
prediction of physiologic cardiac age of healthy subjects based on
12-lead surface ECG parameters. Using a neural network we were able to
estimate the age of a healthy subject with an error of 7 years and to
analyze the impact of the ECG features. The created models of the
present study may serve as a benchmark for testing the effects of new
pharmacological drugs on potential decline or improvement of physiologic
health of the heart.

\subsection*{Application of Machine
Learning}

{\label{application-of-machine-learning}}

Attia et al. recently sought to determine whether the application of
machine learning algorithms, including convolutional neural networks, to
a large ECG patient data set would be capable of predicting age and sex
reported by patients, independent of additional clinical data {[}17{]}.
They further investigated whether discrepancies between ECG age and
chronological age might be a marker of physiological health. When the
convolutional neural network-predicted age exceeded a patient's actual
age by at least 7 years, there was a higher incidence of cardiovascular
comorbidities, potentially suggesting that the convolutional neural
network-predicted age from 12-lead ECGs may correlate with physiological
health. Their findings suggested that physiological age is distinct from
chronological age, and may have useful clinical applications. For
example, if a patient's biologic age is 60 but their ECG age predicts
that they are 70, it may indicate underlying cardiovascular disease and
potential risk. A limitation of their study was, as also recognized by
the authors, that all individuals included were patients, and thus an
ECG was obtained for a certain clinical indication. It was questioned by
the authors whether their results are similarly accurate among an
ostensibly healthy population is unknown, and revalidation in such a
cohort will therefore be critical.

The same holds true for the study by Hirota et al., who studied
biological age, physiological age, and all-cause mortality by 12-lead
ECG in patients without structural heart disease. {[}21{]} Their data
showed that the gap between ECG-predicted physiological and biological
age allowed estimation of increased risk of all-cause mortality.
Although their study subjects were assumed to have no structural heart
diseases, it was stated by the authors that it will be necessary to
validate the results of their study in populations of healthy subjects.
In our study, we only studied healthy individuals, giving the advantage
of being a much needed benchmark study, which enables the validation of
future studies in patients versus our data.

\subsection*{Performance of the model}

{\label{performance-of-the-model}}

The relation between chronological and predicted physiologic age was
associated with an R\textsuperscript{2} of 0.72. Although with a smaller
dataset than used by Attia et al., our predictions have a similar
performance, probably because of the healthy population in our study,
which we expect reduces the variability of the association. Given the
large number of influencing factors that can affect ECG parameters the
R\textsuperscript{2} of 0.72 of our models seems sufficient to detect a
pharmacodynamic effect in a cohort of subjects. Use of the entire
dataset with a larger number of subjects may improve future performance
of the model.

In the present study,~the impact of physiologic aging on the various ECG
features was analyzed using SHAP values. Several changes are clearly
visible in the ECG figures. Some of these are already well known in
clinical practice, such as prolongation of PR and QT interval and
deceleration of heart rate.{[}12{]} Other changes, however, could only
be recognized by using machine learning, while these may be evenly
important Moreover, when multiple features change at the same time, it
becomes difficult to judge whether the change in the ECG is good or bad
without using machine learning. By means of machine learning techniques
a combination of various ECG changes allows a more accurate insight into
the physiologic health changes of the heart.

\subsection*{Gender differences}

{\label{gender-differences}}

The accuracy of predicting physiologic age was found to be higher in
males than in the female subjects. This may be due to the somewhat
smaller female study population, but it may also reflect the atypical
ECG repolarization patterns which are known to occur frequently in
women.{[}22{]} The SHAP values show that impact of gender on physiologic
age prediction was only of minor importance.

\subsection*{Pharmaceutical drug testing and potential
implications}

{\label{pharmaceutical-drug-testing-and-potential-implications}}

The prediction of the physiologic age for one single person is less
relevant in this model. However for larger groups or cohorts of multiple
subjects, the prediction is more accurate. For example, for a group of
30 test subjects, the average deviation is only less than two years from
average physiologic age. Therefore, our models could be particularly
suitable as benchmark for testing new pharmaceutical drugs or other
interventions which may have impact on cardiac health in the near
future. Differences between physiologic ECG age and chronological age
have been shown to predict all-cause and cardiovascular mortality and
reflect physiologic age, cardiovascular health and long term outcomes.
{[}23{]}

The proper use of a model - trained on the entire dataset - in early
drug development can provide important information that can be used to
make a go/no-go decision regarding further development of new drugs.
Similarly, this can be used to guide the decision-making process
regarding the dosage range to be used in phase II studies, determining a
therapeutic window, and even identifying the target study population
{[}24{]}. This way novel pharmacological drugs could be tested for
effect on cardiac physiologic aging in the early phase of development.

Limitations

Our population consisted of only 29\% female subjects. This may have
influenced the accuracy of the model, but SHAP value analysis showed
that gender only had a minimal impact on the predictions of physiologic
age.

ECG changes do not need to have a purely cardiac cause, but they may
also be caused by effects of age on the position of the heart in the
thorax, the presence of fat layers around the heart, and the shape of
the thorax shape. Therefore, the found relationship does not necessarily
mean older heart per se, but can also mean an older body.

Conclusion

The application of machine learning to the ECG using a neural network
regression model, allows estimation of physiologic cardiac age. This
technique could be used to pick up subtle age-related cardiac changes,
but also estimate the reversing of these age-associated effects by
administered treatments.

References

1. van Dam, P.M., et al., \emph{The relation of 12 lead ECG to the
cardiac anatomy: The normal CineECG.} Journal of Electrocardiology,
2021.

2. Biernacka, A. and N.G. Frangogiannis, \emph{Aging and cardiac
fibrosis.} Aging and disease, 2011. \textbf{2} (2): p. 158.

3. Hayashi, H., et al., \selectlanguage{english}\emph{Aging-related increase to inducible atrial
fibrillation in the rat model.} Journal of cardiovascular
electrophysiology, 2002. \textbf{13} (8): p. 801-808.

4. Wang, F., T. Syeda-Mahmood, and D. Beymer. \emph{Information
extraction from multimodal ECG documents} . in \emph{2009 10th
International Conference on Document Analysis and Recognition} . 2009.
IEEE.

5. Roetker, N.S., et al., \emph{Prospective study of epigenetic age
acceleration and incidence of cardiovascular disease outcomes in the
ARIC study (Atherosclerosis Risk in Communities).} Circulation: Genomic
and Precision Medicine, 2018. \textbf{11} (3): p. e001937.

6. Kistler, P.M., et al., \emph{Electrophysiologic and electroanatomic
changes in the human atrium associated with age.} Journal of the
American College of Cardiology, 2004. \textbf{44} (1): p. 109-116.

7. Breitling, L.P., et al., \emph{Frailty is associated with the
epigenetic clock but not with telomere length in a German
cohort.}Clinical epigenetics, 2016. \textbf{8} (1): p. 21.

8. Perna, L., et al., \emph{Epigenetic age acceleration predicts cancer,
cardiovascular, and all-cause mortality in a German case
cohort.}Clinical epigenetics, 2016. \textbf{8} (1): p. 64.

9. Wang, Z., et al., \emph{Predicting age by mining electronic medical
records with deep learning characterizes differences between
chronological and physiological age.} Journal of biomedical informatics,
2017. \textbf{76} : p. 59-68.

10. Horvath, S., et al., \emph{Obesity accelerates epigenetic aging of
human liver.} Proceedings of the National Academy of Sciences,
2014.\textbf{111} (43): p. 15538-15543.

11. Levine, M.E., et al., \emph{Menopause accelerates biological
aging.}Proceedings of the National Academy of Sciences, 2016.
\textbf{113} (33): p. 9327-9332.

12. Rijnbeek, P.R., et al., \emph{Normal values of the electrocardiogram
for ages 16--90 years.} Journal of electrocardiology, 2014.\textbf{47}
(6): p. 914-921.

13. Macfarlane, P., et al., \emph{Effects of age, sex, and race on ECG
interval measurements.} Journal of electrocardiology, 1994. \textbf{27}
: p. 14-19.

14. Mason, J.W., E.W. Hancock, and L.S. Gettes, \emph{Recommendations
for the standardization and interpretation of the electrocardiogram:
part II: Electrocardiography diagnostic statement list: a scientific
statement from the American Heart Association Electrocardiography and
Arrhythmias Committee, Council on Clinical Cardiology; the American
College of Cardiology Foundation; and the Heart Rhythm Society: endorsed
by the International Society for Computerized
Electrocardiology.}Circulation, 2007. \textbf{115} (10): p. 1325-1332.

15. Kligfield, P., et al., \emph{Recommendations for the standardization
and interpretation of the electrocardiogram: part I: the
electrocardiogram and its technology a scientific statement from the
American Heart Association Electrocardiography and Arrhythmias
Committee, Council on Clinical Cardiology; the American College of
Cardiology Foundation; and the Heart Rhythm Society endorsed by the
International Society for Computerized Electrocardiology.} Journal of
the American College of Cardiology, 2007. \textbf{49} (10): p.
1109-1127.

16. Khane, R.S., A.D. Surdi, and R.S. Bhatkar, \emph{Changes in ECG
pattern with advancing age.} 2011.

17. Attia, Z.I., et al., \emph{Age and sex estimation using artificial
intelligence from standard 12-lead ECGs.} Circulation: Arrhythmia and
Electrophysiology, 2019. \textbf{12} (9): p. e007284.

18. Chawla, N.V., et al., \emph{SMOTE: synthetic minority over-sampling
technique.} Journal of artificial intelligence research,
2002.\textbf{16} : p. 321-357.

19. Lundberg, S.M. and S.-I. Lee. \emph{A unified approach to
interpreting model predictions} . in \emph{Advances in neural
information processing systems} . 2017.

20. Altmann, A., et al., \emph{Permutation importance: a corrected
feature importance measure.} Bioinformatics, 2010. \textbf{26} (10): p.
1340-1347.

21. Hirota, N., et al., \emph{Prediction of biological age and all-cause
mortality by 12-lead electrocardiogram in patients without structural
heart disease.} BMC Geriatrics, 2020. \textbf{21} (460).

22. Okin, P.M., \emph{Electrocardiography in women: taking the
initiative} . 2006, Am Heart Assoc.

23. Ladejobi, A., et al., \emph{ECG-DERIVED AGE AND SURVIVAL: VALIDATING
THE CONCEPT OF PHYSIOLOGIC AGE DETECTED BY ECG USING ARTIFICIAL
INTELLIGENCE.} Journal of the American College of Cardiology,
2020.\textbf{75} (11 Supplement 1): p. 3469.

24. Groeneveld, G.J., Hay, J. L., Van Gerven, J. M., \emph{Measuring
blood--brain barrier penetration using the NeuroCart, a CNS test
battery.} Drug Discovery Today: Technologies, 2016. \textbf{20} : p.
27-34.

\emph{Table 1. Age characteristics of the 6228 healthy subjects}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Subject age ( years) & N & \% female\tabularnewline
\midrule
\endhead
18 - 23 & 2282 & 29\tabularnewline
24 - 29 & 1563 & 26\tabularnewline
30 - 35 & 449 & 20\tabularnewline
36 - 40 & 247 & 17\tabularnewline
41- 46 & 245 & 24\tabularnewline
46 - 52 & 339 & 35\tabularnewline
53 - 57 & 241 & 38\tabularnewline
58- 63 & 194 & 42\tabularnewline
63 - 69 & 393 & 40\tabularnewline
69 - 75 & 275 & 35\tabularnewline
\bottomrule
\end{longtable}

\emph{Figure 1. ECG samples of young and elderly male and female healthy
subjects.A: ECG of a young 18 year old male subject. B: ECG of an
elderly 74 year old male subject.C: ECG of a young 19 year old female
subject. D: ECG of an elderly 74 year old female subject}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\emph{Figure 2. Relationship between predicted) physiologic age and
chronological age for 1180 healthy adults (10 test sets, Figure 2a,
left). The average prediction line is shown in figure 2b (right).}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

a
b

\emph{Figure 3. Average absolute error with standard deviation of
subject groups ranging from size 1 to 50 test subjects. On the vertical
axis the mean absolute error ( in years) is shown, with on the
horizontal axis the number of test subjects}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\emph{Figure 4. Predictions of 819 male subjects (a, left) and 361
female subjects (b, right).}

\emph{Figure 5. SHAP values of the 40 most important features for
predicting physiologic age. High values of the features are represented
in red. Low values are represented in blue. On the x-axis, the predicted
physiologic age. Shorter bars mean less impact on physiologic aging} .\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\emph{Supplementary Table 1. ECG features present in most leads
extracted by the Muse system included in the model. P', R', S', and T'
indicate the second components of P, R, S, and T wave, respectively,
which could be positive or negative polarity.}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Features per lead} &\tabularnewline
\midrule
\endhead
max r amplitude & R PEAK TIME\tabularnewline
max s amplitude & R' AREA\tabularnewline
maximum st level & R' DURATION\tabularnewline
minimum st level & R' PEAK AMPLITUDE\tabularnewline
p area & R' PEAK TIME\tabularnewline
p area full & S AREA\tabularnewline
p duration & S DURATION\tabularnewline
p offset & S PEAK AMPLITUDE\tabularnewline
p onset & S PEAK TIME\tabularnewline
p onset amplitude & SPECIAL T\tabularnewline
p peak amplitude & ST END ST\tabularnewline
p peak time & ST J POINT\tabularnewline
p' area & ST MID ST\tabularnewline
p' duration & S' AREA\tabularnewline
p' peak amplitude & S' DURATION\tabularnewline
p' peak time & S' PEAK AMPLITUDE\tabularnewline
q area & S' PEAK TIME\tabularnewline
q duration & T AREA\tabularnewline
q peak amplitude & T AREA FULL\tabularnewline
q peak time & T DURATION\tabularnewline
qrs area & T END\tabularnewline
qrs balance & T PEAK AMPLITUDE\tabularnewline
qrs deflection & T PEAK TIME\tabularnewline
qrs intrinsicoid & T' AREA\tabularnewline
R AREA & T' DURATION\tabularnewline
R DURATION & T' PEAK AMPLITUDE\tabularnewline
R PEAK AMPLITUDE & T' PEAK TIME\tabularnewline
\bottomrule
\end{longtable}

\emph{Supplementary Table 2. Other ECG features extracted by the Muse
system included in the model.}\selectlanguage{english}
\begin{longtable}[]{@{}l@{}}
\toprule
\textbf{Features per ECG}\tabularnewline
\midrule
\endhead
atrial rate P AXIS Q OFFSET QT INTERVAL QTC BAZETT Q ONSET R AXIS Number
of qrs complexes T AXIS T OFFSET T ONSET\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{english}
\FloatBarrier
\end{document}

