A Robust Approach for a Real-time Accurate Screening of ST Segment

Anomalies

Giovanni Rosa

1 a

, Marco Russodivito

, Gennaro Laudato

1 b

, Angela Rita Colavita

Simone Scalabrino

1 c

and Rocco Oliveto

1 d

STAKE Lab, University of Molise, Pesche (IS), Italy

ASREM – Regione Molise, Italy

Keywords:

ST Anomalies, Temporal Window, Decision Support System, Machine Learning.

Abstract:

Nowadays, Computerized Decision Support Systems (CDSS) play an important role in medical support and

preventative care. In those scenarios, the monitoring of biomedical data, such as the ECG signal, is funda-

mental. The ECG signal may reveal a variety of abnormalities or pathological conditions. Some examples are

Ischemia and Myocardial Infarction (MI), with a signiﬁcant impact on the world’s population. Both these con-

ditions can be diagnosed by observing changes in speciﬁc sections of the ECG, such as the ST segment and/or

T-wave of heartbeats. Much effort was devoted by the scientiﬁc community to aim at automatically identifying

ST anomalies. The main drawback of such approaches is often a trade-off between the accuracy in the clas-

siﬁcation, the robustness to noise, and the real-time responsiveness. In this work, we present RAST, a robust

approach for a Real-time Accurate screening of ST segment anomalies. RAST takes as input a sequence

of 10 successive heartbeats extracted from an ECG recording and provides as output the classiﬁcation of the

ST segment trend. We evaluated two versions of RAST, namely RAST-BINARY, and RAST-TERNARY:

the ﬁrst capable of distinguishing only between an ST anomaly and Normal Sinus Rhythm and the second

able to distinguishing between ST elevation, ST depression, and normal rhythm. Moreover, we conducted an

extensive study by experiment also (i) the validation within the intra- and inter-patient strategies and (ii) the

ideal number of successive heartbeats in which to observe an anomalous episode of change in the ST segment.

As a result, both RAST-BINARY and RAST-TERNARY can achieve an F1 score of 0.94 with a window of

4 heartbeats in the inter-patient validation. For the intra-patient validation, both versions achieve an F1 score

of 0.73 using a longer observation window.

1 INTRODUCTION

Decision Support Systems (DSS) have been estab-

lished as tools for applying guidelines and support

medical decisions in the industrialized world. Such

systems can be deﬁned as ”any intervention that pro-

vides physicians with clinical knowledge and patient-

speciﬁc information to enhance patient care deci-

sions” (Berner, 2007). Many works, focused on the

review of the scientiﬁc literature, have found that

Computerized Decision Support Systems (CDSS) im-

prove preventative care, clinical performance and in-

https://orcid.org/0000-0002-5241-1608

https://orcid.org/0000-0002-3776-2848

https://orcid.org/0000-0003-1764-9685

https://orcid.org/0000-0002-7995-8582

ﬂuence clinical decision making (Kawamoto et al.,

2005; Balas et al., 1996). This is the case where such

systems are used in a computerized system for medi-

cal support. Indeed, when used in this context, CDSS

considerably improve decision quality (Sintchenko

et al., 2004).

ECG is an important signal to investigate since it

is both noninvasive and suggestive of a variety of ab-

normalities. Ischemia and Myocardial Infarction (MI)

are two of the most severe of these abnormalities.

According to recent research (Khan et al., 2020),

ischemic heart disease affects roughly 126 million

people worldwide therefore around 1.72 percent of

the world’s population. On the other hand, by con-

sidering only the United States, roughly 1.5 million

instances of myocardial infarction occur each year,

with a yearly incidence rate of around 600 cases per

Rosa, G., Russodivito, M., Laudato, G., Colavita, A., Scalabrino, S. and Oliveto, R.

A Robust Approach for a Real-time Accurate Screening of ST Segment Anomalies.

DOI: 10.5220/0010824000003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 69-80

ISBN: 978-989-758-552-4; ISSN: 2184-4305

100,000 persons

. Changes in the ST segment (and/or

T-wave) of ECG heartbeats are indicative of both of

these abnormalities (Hadjem et al., 2016).

Therefore it would be extremely important to have

accurate and rapid automatic ECG analysis tools in

order to detect these events and thus allow the medical

team to manage them in a timely manner. Consider-

ing that digital clinical data are now well-established

and available in large amounts, this procedure would

also avoid the burden on the medical staff of having

to manually analyze the various electrocardiographic

recordings.

Harun-Ar-Rashid et al. (2020) proposed a method

to identify ﬁve categories of ST segment using the

correlation algorithm. Their approach embedded the

following steps (i) ECG ﬁlter and detrend (ii) R and

S waves identiﬁcation (iii) detection of ST segment

start and endpoint (iv) comparison between this signal

with supervised data (v) classiﬁcation of the ST seg-

ment based on the correlation value. This approach

allowed to reach an overall accuracy of 92,1 % but, as

stated by the same authors, it presented some limita-

tions, such as the strict dependency on the ECG pre-

processing and annotation stages (for R and S waves

with low amplitudes, it is too complicated the identiﬁ-

cation of the ST segment) and the computational cost

(for long term ECG signal this method took time to

elaborate).

In this paper, we present RAST (a robust ap-

proach for a Real-time Accurate screening of ST seg-

ment anomalies), an approach for the real-time iden-

tiﬁcation of ST anomalies. RAST does not provide

much information on the speciﬁc ST-change, but—in

its most accurate version—it provides only the dis-

tinction between an ST change and a Normal rhythm.

In this case, RAST outperforms state of the art.

Speciﬁcally, the baseline work proposed by Harun-

Ar-Rashid et al. (2020) was kept as reference due to

its recent release and high accuracy.

RAST has been an experiment on a well-

consolidated dataset in the scientiﬁc community,

namely the Physionet European ST-T Database

(Goldberger et al., 2000).

RAST is part of the DSS embedded in ATTICUS

(Laudato et al., 2021), an innovative system aimed

at improving healthcare services thanks to the adop-

tion of a wearable device (De Vito et al., 2021) which

is in charge of acquiring several vital signals, such as

MEDSCAPE ”What is the incidence of myocardial

infarction (MI, heart attack) in the US?” accessed on

8-sep-2021 https://www.medscape.com/answers/155919-

15093/what-is-the-incidence-of-myocardial-infarction-mi-

heart-attack-in-the-us

https://physionet.org/content/edb/1.0.0/

ECG, respiration waves, body temperature, and dy-

namics) and a strong Artiﬁcial Intelligence oriented

software solution (Balestrieri et al., 2019).

With respect to this state of the art method, RAST

shows the following advantages:

• it is independent of ECG annotation algorithms,

which are not very robust to noise due to the de-

tection of waves characterized by very low electri-

cal amplitudes (Tateno and Glass, 2000; Lake and

Moorman, 2011; Sun and Thakor, 2015)

• it has higher computational efﬁciency

• it is (near) real-time

• it provides better global accuracy

As a disadvantage, RAST provides less reﬁne-

ment in the classiﬁcation because we propose a bi-

nary or ternary classiﬁcation while the baseline work

is able to distinguish ST changes in 5 classes.

For this reason, we believe that RAST is more

for use in high-precision rapid screening applications,

and then leave the work to algorithms with more re-

ﬁned classiﬁcations or directly to specialized medical

staff.

The rest of the paper is structured as follows: Sec-

tion 2 describes the background and related works on

the ECG anomalies due to ST change and a brief re-

view of the scientiﬁc literature dedicated to the au-

tomatic detection of ST change episodes. Section 3

provides details on the proposed approach, and Sec-

tion 4 describes the details of the design of the study,

in particular for the experimental procedure adopted.

Section 5 reports on the results obtained by RAST

and Section 6 contains a discussion on the limitations

of this study. Finally, Section 7 concludes the paper.

2 BACKGROUND AND RELATED

WORKS

In this section, ﬁrst is reported a background on the

ST anomalies in terms of clinical features and inci-

dence of the connected pathologies. The second sub-

section is focused on the state-of-the-art works dedi-

cated to the automatic analysis of ST anomalies, with

a particular focus on the chosen baseline.

2.1 ST Anomalies

The oxygen requirement in the heart muscle varies de-

pending on the state of the body. The deviation of

the ST segment in the ECG is caused by an imbal-

ance between oxygen demand and supply. Myocar-

dial ischemia is a cardiac function problem caused by

HEALTHINF 2022 - 15th International Conference on Health Informatics

a lack of oxygen delivery to the heart muscle. Silent

ischemia is a type of transitory ischemia that occurs

without causing any symptoms, such as an unpleasant

feeling in the breast. If this imbalance frequently oc-

curs over time without appropriate therapy, the dam-

aged heart tissue dies and the damage becomes per-

manent, resulting in cardiac infarction (Jeong and Yu,

2006).

Hyperacute or Inverted T-wave are typical signs of

ischemia, which is usually the initial stage. The next

step is MI—which is marked by ST segment elevation

Figure 1 C—which is usually iso-electric in healthy

people. ST segment depression (Figure 1 B) can also

be a symptom of a MI (Hadjem et al., 2016).

2.2 Automatic Detection of ST

Anomalies

Monitoring the endpoint of the S wave to the start

point of the T wave identiﬁes myocardial ischemia.

This section of the ECG signal is known as the ST

segment.

The scientiﬁc literature has done many efforts to

contribute the research dedicated to the automatic de-

tection of ST changes.

One of the ﬁrst papers that took into account the

automatic analysis of ST change is the one proposed

by Maglaveras et al. (1998). The ﬁnal aim of this

work was to develop an automatic approach—based

on an adaptive Backpropagation Neural Network—

for real-time ischemia episodes detection. Their re-

sults showed that the average ischemia episode detec-

tion sensitivity was 88.62 % while the ischemia dura-

tion sensitivity is 72.22 %. The method employed in

this study was different from prior algorithms in that it

did not rely on the J-point—which could be difﬁcult

to identify—and instead relied on information from

the whole ST pattern. By averaging the ST segments

of the ﬁrst 10 beats, the average template offered an

initial assessment of each patient’s physiological ST

depression (or elevation). For the same patient, in-

deed, the authors stated that this estimate did not ap-

pear to alter signiﬁcantly over time.

Xiao et al. (2018) proposed a study in which

introduced an image-based method combined with

a deep learning technique for the detection of is-

chemic ST change from an ECG. A CNN model was

trained using a transfer learning technique and eval-

uated on independent sessions in the Long Term ST

database

utilizing 24-hour ambulatory ECG record-

ing sessions. The suggested CNN model was able to

identify testing images in real-time with an AUC of

https://physionet.org/content/ltstdb/1.0.0/

89.6 %. At the 10-second sample level, their model

achieved an average sensitivity of 84.4 % at selected

optimum cutoff levels.

Wang et al. (2018) developed a beat-by-beat clas-

siﬁcation method based on multiple feature extrac-

tion. The ST section was found ﬁrst. The ST

segment’s morphological and Poincar

e characteristics

were then retrieved and merged with the global fea-

ture. Finally, the ST segment change was classiﬁed as

normal, high, or depressed using random forest. The

algorithm was tested on the European ST-T Database,

with average sensitivity of 85.2 %, 86.9 %, and 88.8

% for normal, depressed, and high ST segments, re-

spectively. The results demonstrate that the proposed

method was effective in identifying ST segment ele-

vation and depression automatically, revealing addi-

tional information about the ischemia condition.

From the above review, we opted for adopting the

preprocessing stage as the one proposed in Maglav-

eras et al. (1998), which will be further detailed in

Section 3.

Harun-Ar-Rashid et al. (2020) automatically iden-

tiﬁed ST segments and classiﬁed those into ﬁve

classes. The authors, according to a set of rules they

designed, opted for using ﬁve classes (Concave, Con-

vex, Elevation, Depression, Normal) instead of the

three ones provided by Physionet (Elevation, Depres-

sion, Normal) within the annotations of the dataset

The work proposed in this paper embedded ini-

tial denoising preprocessing based on the application

of the Savitzky-Golay Smoothing ﬁlter and then the

ECG signal’s detrend to get rid of linear and non-

linear trends.

After completing the above steps, to identify an

ST segment, this method embedded two stages of

ECG annotations:

1. a method to detect R waves;

2. depending on the outcome of the previous R peak

detector, the next steps depended on the full anno-

tation of an ECG segment to have available S and

T waves and J point.

Finally, this approach used cross correlation based

on supervised data: indeed, for each ST segment, the

cross correlation with a supervised ST segment was

performed to measure the similarity between the iden-

tiﬁed ST segment and the category of ST change.

The authors experimented their approach on two

datasets: (i) the Physionet European ST-T change

database where they obtained 95,58 %, 95,92 %,

97,86 %, 95,18 % and 96,36 % and (ii) the Physionet

MIT-BIH ST change database where they achieved

https://physionet.org/content/edb/1.0.0/annotations.shtml

A Robust Approach for a Real-time Accurate Screening of ST Segment Anomalies

Figure 1: (a) Normal heartbeat, (B) ST segment depression, (C) ST segment elevation.

98,77 %, 97,47 %, 90,35 %, 85,03 %, 69,42 % re-

spectively for the ﬁve categories of ST shapes Con-

cave, Convex, Up slope, Down slope and Horizontal.

In the discussion of their paper, the authors stated

that there are some threats to validity and limitations.

For example, they conﬁrmed that the approach was

too dependent on the outcome of the preprocessing

stage. Indeed, without proper ECG preprocessing, ST

segments could not be correctly identiﬁed. One of the

biggest problem with their method is that the annota-

tion of R and S waves is often complicated due to their

typical low amplitude (Tateno and Glass, 2000; Lake

and Moorman, 2011; Sun and Thakor, 2015). Finally,

the authors commented on the computational perfor-

mance of their method by reporting that for long-term

ECG signals, the approach took time to evaluate.

However, the overall accuracy obtained was 92,1

%. This value will be kept as a reference for the com-

parison of RAST with respect to one of the most ac-

curate and recent work from state-of-the-art.

3 RAST- A ROBUST APPROACH

FOR A REAL-TIME ACCURATE

SCREENING OF ST SEGMENT

ANOMALIES

The complete workﬂow of RAST is depicted in Fig-

ure 2.

As the ﬁrst step, the approach needs a digital ECG

pattern composed of 10 successive heartbeats. To do

so RAST buffers and evaluates the ECG until 10 R

waves are detected—this latter condition is validated

through the Pan-Tompkins method (Pan and Tomp-

kins, 1985), and it is necessary to deﬁne a pattern of

10 heartbeats. Once acquired such an ECG segment, a

multi-domain algorithm generates the features vector

for the ﬁnal classiﬁcation stage.

3.1 Preprocessing

A ﬁrst stage of preprocessing is applied to the ECG

signal as described in the method proposed by (Pan

and Tompkins, 1985). This procedure for the R waves

detection needs to apply two successive ﬁlters—low-

pass and high-pass—in order to get rid of the base-

line wander and select the frequency band where the

R peaks are contained. Then, a derivated ﬁlter has to

be applied to the signal. The remaining steps are not

strictly related to the ECG ﬁltering but are dedicated

to improving the R peak detection (e.g., squaring and

dynamic thresholding).

A second stage of preprocessing is expected in

RAST once the ECG pattern of 10 successive heart-

beats is successfully acquired. In this case, the signal

is submitted to a detrend operation.

3.2 Generation of the Features Vector

The features vector is generated through the evalu-

ation of multi-domain features calculated from the

ECG pattern of 10 heartbeat signals. Speciﬁcally, for

each pattern, the following features are evaluated:

• Energy of Maximal Overlap Discrete Wavelet

Transform (EMO-DWT) (Ghaemi et al., 2019).

• Autoregressive Model (AR) coefﬁcients of order

4 (Zhao and Zhang, 2005).

• Multifractal Wavelet (MFW) leader estimates

of the log-cumulants of the scaling exponents

(Leonarduzzi et al., 2010).

• Fast Fourier Transform (FFT).

These features have demonstrated their informa-

tion power in other scientiﬁc works (Rosa et al.,

2021a,b).

HEALTHINF 2022 - 15th International Conference on Health Informatics

Figure 2: The complete workﬂow of RAST.

3.3 Classiﬁcation Stage

The classiﬁcation stage is the ﬁnal step of RAST.

This is composed of an ML algorithm in charge of

providing information on the category for each pat-

tern of heartbeats.

To conduct exhaustive experimentation, we pro-

pose in this study a more high-level classiﬁcation

experiment where we studied the performance of

RAST in distinguishing between Normal and ST

anomaly (RAST-BINARY in Figure 2) and a more

reﬁned classiﬁcation experiment where we evaluated

RAST in the capability of identiﬁcation between Nor-

mal, ST Elevation and Depression anomalies (RAST-

TERNARY in Figure 2).

This way of studying the performances of RAST

led to the version of the tool: RAST-binary and

RAST-ternary depending on the number of outputs.

With this choice, we aimed at measuring the perfor-

mance of the tool in two different scenarios: (i) when

used in applications of rapid screening, such as when

only a rapid and accurate detection is requested and

(ii) when the medical constraints request information

also on the category of the ST segment.

More details will be reported in Section 4, focused

on the design of the study.

4 DESIGN OF THE STUDY

The goal of this paper is to study the performances

of a real-time screening algorithm for ST anomalies

detection. To do so, we implemented RAST, a tool

designed to be robust to noise because it is indepen-

dent of any ECG annotator algorithm except for the R

waves—that are the most prominent clinical features

of an ECG and the least susceptible to various kinds

of noise (Huang et al., 2010).

This study is steered by the following research

question:

: To what extent does the accuracy of

a binary or ternary detector of ST-segment

anomalies vary?

: Can a real-time and noise-robust ap-

proach outperform the accuracy of a state-of-

the-art method?

With the ﬁrst research question, we aim at inves-

tigating the capability of a Machine Learning model

in identifying ST segment in a binary problem (ST

anomaly vs Normal Rhythm) and in a more speciﬁc

problem (ST segment depression and elevation and

Normal Rhythm). With the second research ques-

tion, we aim at studying the overall accuracy of the

above methods and compare it with the updated state

of the art of reference. Therefore, as a baseline ap-

proach, we chose one of the most recent and accurate

A Robust Approach for a Real-time Accurate Screening of ST Segment Anomalies

methods from the scientiﬁc literature of reference, the

work proposed by Harun-Ar-Rashid et al. (2020).

4.1 Context of the Study

The European ST-T Database is designed for use in

evaluating methods for ST and T-wave change analy-

sis. This resource contains 90 annotated ambulatory

ECG recordings from 79 individuals. The participants

included 70 males ranging in age from 30 to 84, and

8 women ranging in age from 55 to 71. (For one sub-

ject, information is absent.)

With lengths ranging from 30 seconds to several

minutes, the database contains 367 occurrences of ST

segment change and 401 episodes of T-wave change.

Each two-hour record comprises two signals sam-

pled at 250 samples per second with 12-bit resolution

across a notional 20-millivolt input range.

Two cardiologists annotated each record beat by

beat, looking for changes in ST segment and T-wave

shape, rhythm, and signal quality. The ST annotations

in this database mark transient ST changes superim-

posed on any ﬁxed elevation or depression.

We did not use the MIT-BIH ST Change DB

be-

cause the annotation ﬁles contain only beat labels;

they do not include ST change annotations, as in the

European ST-T Database.

4.2 Experimental Procedure

To conduct an exhaustive study, we experimented

with a large set of parameters within several vali-

dation schemes. This way to conduct the study is

in the perspective of research that aims at observing

the classiﬁcation performances under many points of

view. More details are provided in the next subsec-

tions.

4.2.1 Tuning of the Parameters

We opted for studying the performances of RAST as

different parameters vary, such as:

• The TWHO (Temporal Window for the Heart-

beat Observation): the literature work (Maglav-

eras et al., 1998) proposed an observation of 10

successive heartbeats. We opted to evaluate the

performances of RAST for the observation win-

dows in the set [4, 6, 8, 10, 16, 32, 64] where each

value corresponds to the number of heartbeats to

be evaluated. We opted for the typical length in

terms of powers of 2 [4, 8, 16, 32, 64] with two

more lengths of 6 and 10 heartbeats in order to as-

https://physionet.org/content/stdb/1.0.0/

sess, with more efﬁcacy, the best length close to

the one proposed in the literature.

• The SAMPLING Technique: we opted to keep

this choice as a parameter, in the sense that we

measured the performances of RAST with and

without the application of the SMOTE (Chawla

et al., 2002) technique for the balancing of the

dataset.

• The ALGORITHM: in order to assess the most

ﬁtting Machine Learning algorithm for RAST,

we experimented several classiﬁcation models.

The tuning of these parameters was performed on

the whole dataset.

This phase of tuning of the parameters was un-

dertaken to look for the best conﬁguration of RAST.

Considered that the experimentation was undertaken

for only one dataset, to avoid any data overﬁtting,

we experimented with each tuning phase under two

robust validation schemes: 80-20 random-split and

L1SO.

4.2.2 Validation Schemes

To avoid any data overﬁtting and to offer a complete

overview of the results, two validation schemes were

adopted for the assessment of the RAST’s perfor-

mances. In detail, the tool was experimented through

the:

• 80-20 random-split validation: in this scenario,

the dataset is decomposed in 80 % and 20 % of

the instances for the training and testing set, re-

spectively. With this kind of data separation, the

cardiac data related to a subject can be found both

in the training and testing dataset. To avoid any

favorable data division and, therefore, to decrease

the randomness of the results, we repeated the ex-

periment 1000 times. This validation scheme can

be interpreted as the scenario in which RAST has

to provide an outcome on a patient and its model

may have observed in the past data of the same

patient.

• L1SO (Leave 1 Subject Out): this procedure im-

plies that one person is left out of the training set

at a time, resulting in the training set containing

no data of the person being tested (the classiﬁer

was not tuned with the test data of that person).

This is possible because each data segment has

an anonymous label that corresponds to an indi-

vidual. Thus, this validation scheme can be in-

terpreted as the scenario in which RAST has to

provide an outcome on a patient and its model has

never been trained on the data of that patient.

HEALTHINF 2022 - 15th International Conference on Health Informatics

4.3 Screening Experiments

To answer RQ1, we studied the performances of

RAST under two different screening experiments:

• RAST-BINARY: in this case, the classiﬁcation

performances of RAST have been studied only

depending on its capabilities in distinguishing be-

tween an ECG segment Normal and with ST

anomaly.

• RAST-TERNARY: with this conﬁguration,

RAST provides three outcomes Therefore, it

is studied for the identiﬁcation of Normal, ST

Depression, and ST Elevation.

RAST-BINARY is an algorithm more suitable

when used in rapid and accurate screening applica-

tions because it is capable only of distinguishing be-

tween a generic ST anomaly and a Normal ECG. On

the other hand, RAST-TERNARY could be used in

applications of detection (and not simple screening)

because it is capable of providing additional informa-

tion on the ST anomaly, as in the case of depression

or elevation.

The authors of the paper chosen as baseline

(Harun-Ar-Rashid et al., 2020) for the comparison of

RAST opted for deﬁning two more classes of ST seg-

ment categories (i.e., Convex and Concave), accord-

ing to a set of rules they designed. We opted to work

with the only annotations provided by the Physionet

cardiologists. Therefore, the comparisons between

RAST and the baseline can be mostly made in terms

of overall accuracy.

5 ANALYSYS OF THE RESULTS

The results of RAST are reported according to the

screening experiment. For the sake of space limita-

tion, we report here only part of the results. The full

report can be found at the following replication pack-

age

We used the typical classiﬁcation metrics to assess

the capability of the several conﬁgurations of RAST

in the detection of ST anomalies. These metrics are:

• Accuracy =

T P+T N

T P+T N+FP+FN

• Speciﬁcity =

T N

T N+FP

• Precision =

T P

T P+FP

• Recall =

T P

T P+FN

• F1 Score =

2T P

2T P+FP+FN

https://github.com/grosa1/healthinf2022-st-anomalies-

replication-package

Table 1: The performances of RAST-BINARY in terms

of the main classiﬁcation metrics for the experiment with

L1SO among all the heartbeat windows.

Window Acc Spec Prec Recall F1 Score

4 beats 76,31 33,09 84,57 76,31 72,79

6 beats 75,98 32,88 84,63 75,98 72,47

8 beats 76,37 33,33 85,48 76,37 72,78

10 beats 75,11 31,57 84,66 75,11 71,09

16 beats 76,49 24,94 86,21 76,49 70,17

32 beats 76,11 23,57 86,70 76,11 69,73

64 beats 75,00 30,28 83,52 75,00 70,83

Table 2: The performances of RAST-BINARY in terms of

the main classiﬁcation metrics for the experiment with 80-

20 validation scheme among all the heartbeat windows.

Window Acc Spec Prec Recall F1 Score

4 beats 92,93 81,20 92,89 92,93 92,69

6 beats 92,76 80,60 92,73 92,76 92,49

8 beats 92,76 80,68 92,72 92,76 92,49

10 beats 92,60 80,08 92,58 92,60 92,31

16 beats 92,38 79,42 92,35 92,38 92,07

32 beats 91,70 77,50 91,67 91,70 91,32

64 beats 90,97 75,35 90,96 90,97 90,50

5.1 Selecting the Most Fitting ML

Algorithm

The performances of the algorithms under validation

are depicted in Figure 3 for RAST-BINARY. This ﬁg-

ure is only illustrative because it was obtained with

a speciﬁc conﬁguration of parameters. However, the

Random Forest model was found to be the most ac-

curate model among all the parameters tuning and

validation schemes and for both RAST-BINARY and

RAST-TERNARY.

5.2 RAST-BINARY

The results of RAST-BINARY within the L1SO are

depicted in Table 1 and Figure 4. The best accuracy

found was 76,49 % obtained with a pattern of 16 suc-

cessive heartbeats.

On the other hand, RAST-BINARY when submit-

ted to a 80-20 random-split validation scheme shows

the performances reported in Table 2 and Figure 5.

The best accuracy found was 92,93 % obtained with

a pattern of 4, 6, 8 or 10 successive heartbeats (with

slightly more precision for 6 heartbeats).

Finally, the results of RAST-BINARY with the

dataset balanced according to SMOTE and within the

80-20 random-split validation scheme are reported in

Table 3 and Figure 6. In this case, the best accuracy

was 93,61 % obtained with a window of 4 heartbeats.

A Robust Approach for a Real-time Accurate Screening of ST Segment Anomalies

Figure 3: A demonstrative iteration for all the evaluated Machine Learning algorithms with the conﬁguration of 10 heartbeat

window length and 80-20 validation scheme for RAST-BINARY.

Figure 4: Boxplot of the accuracies obtained within the

L1SO validation scheme for RAST-BINARY.

Figure 5: Boxplot of the accuracies obtained within the 80-

20 random-split validation scheme for RAST-BINARY.

5.3 RAST-TERNARY

The results of RAST-TERNARY within the L1SO

are depicted in Table 4 and Figure 7. The best accu-

racy found was 77,35 % obtained with a pattern of 4,

6 or 8 successive heartbeats (with slightly more pre-

cision with 8 heartbeats).

Table 3: The performances of RAST-BINARY in terms of

the main classiﬁcation metrics for the experiment with 80-

20 random-split validation scheme and SMOTE among all

the heartbeat windows.

Window Acc Spec Prec Recall F1 Score

4 beats 93,61 88,62 93,61 93,61 93,61

6 beats 93,46 88,33 93,47 93,46 93,46

8 beats 92,73 88,60 92,88 92,73 92,79

10 beats 93,36 88,13 93,37 93,36 93,37

16 beats 93,13 87,79 93,14 93,13 93,14

32 beats 92,63 86,71 92,63 92,63 92,62

64 beats 92,21 85,63 92,19 92,21 92,19

Figure 6: Boxplot of the accuracies obtained within the

80-20 random-split validation scheme for RAST-BINARY

with the application of the SMOTE technique.

On the other hand, RAST-TERNARY when sub-

mitted to an 80-20 random-split validation scheme

shows the performances reported in Table 5 and Fig-

ure 8. The best accuracy found was 92,76 % ob-

tained with a pattern of 4, 6 successive heartbeats

(with slightly more precision for 4 heartbeats).

Finally, the results of RAST-TERNARY with the

dataset balanced according to SMOTE, and within the

80-20 random-split validation scheme are reported in

HEALTHINF 2022 - 15th International Conference on Health Informatics

Table 4: The performances of RAST-TERNARY in terms

of the main classiﬁcation metrics for the experiment with

L1SO among all the heartbeat windows.

Window Acc Spec Prec Recall F1 Score

4 beats 77,04 31,79 85,33 77,04 72,58

6 beats 76,70 30,82 84,90 76,70 71,90

8 beats 77,35 31,40 86,05 77,35 72,83

10 beats 76,24 29,69 86,40 76,24 70,95

16 beats 75,78 28,33 86,36 75,78 70,15

32 beats 76,28 27,49 86,33 76,28 70,75

64 beats 75,74 27,94 86,40 75,74 69,98

Figure 7: Boxplot of the accuracies obtained within the

L1SO validation scheme for RAST-TERNARY.

Table 6 and Figure 9. In this case, the best accuracy

was 93,52 % obtained with a window of 4 heartbeats.

5.4 Discussion

One of the most noteworthy result from the experi-

ments depicted in the subsections 5.2 and 5.3 is that

for the L1SO-CV the best results are mostly spread

among the window lengths of 8 and 16 heartbeats (see

Tables 1 and 4) while for the 80-20 Random Split vali-

dation the best results obtained are obtained when in-

volving in RAST an observation window of only 4

heartbeats. This could be translated in an online sce-

nario of the detector in the following usage: when a

new patient has to be monitored, a longer observation

Table 5: The performances of RAST-TERNARY in terms

of the main classiﬁcation metrics for the experiment with

80-20 random-split validation scheme among all the heart-

beat windows.

Window Acc Spec Prec Recall F1 Score

4 beats 92,76 80,44 92,77 92,76 92,44

6 beats 92,57 79,74 92,60 92,57 92,22

8 beats 92,45 79,43 92,47 92,45 92,09

10 beats 92,32 78,91 92,37 92,31 91,93

16 beats 92,06 78,04 92,11 92,06 91,63

32 beats 91,51 76,57 91,57 91,51 91,01

64 beats 90,61 73,66 90,73 90,61 89,97

Figure 8: Boxplot of the accuracies obtained within the 80-

20 random-split validation scheme for RAST-TERNARY.

Table 6: The performances of RAST-TERNARY in terms

of the main classiﬁcation metrics for the experiment with

80-20 random-split validation scheme and SMOTE among

all the heartbeat windows.

Window Acc Spec Prec Recall F1 Score

4 beats 93,52 90,03 93,60 93,52 93,54

6 beats 93,38 89,77 93,46 93,38 93,40

8 beats 92,47 90,00 92,74 92,47 92,56

10 beats 93,29 89,47 93,35 93,29 93,30

16 beats 92,99 89,02 93,07 92,99 93,01

32 beats 92,60 88,16 92,67 92,61 92,62

64 beats 92,26 86,54 92,26 92,26 92,22

window is needed to best detect ST related anoma-

lies. On the other hand, when the data of a patient

is already available, RAST will need an observation

window of only 4 heartbeats.

In Figure 7 are highlighted the performances

of the best experiment in RAST (i.e., RAST-

TERNARY with a window of 4 heartbeats, with 80-20

random-split validation scheme and SMOTE) detailed

by class and expressed in percentage with respect to

the main classiﬁcation metrics. The metrics are aver-

Figure 9: Boxplot of the accuracies obtained within the 80-

20 random-split validation scheme for RAST-TERNARY

with the application of the SMOTE technique.

A Robust Approach for a Real-time Accurate Screening of ST Segment Anomalies

Table 7: The performances of the best experiment in RAST,

i.e., RAST-TERNARY, detailed by class and expressed in

percentage with respect to the main classiﬁcation metrics.

Class Acc Spec Prec Recall F1 Score

NSR 93,56 87,74 96,24 95,33 95,78

ST+ 98,38 98,75 85,37 93,61 89,30

ST- 95,09 97,06 84,71 84,84 84,77

Table 8: Comparison between RAST and the baseline work

(Harun-Ar-Rashid et al., 2020) in terms of overall accuracy.

Method Binary Ternary

RAST 93,61 93,52

(Harun-Ar-Rashid et al., 2020) 92,10 92,10

Delta +1,51 +1,42

aged among the 1000 iterations. We consider this ex-

periment as the best because the accuracy for RAST-

BINARY and RAST-TERNARY are not signiﬁcantly

different for the same conﬁguration of 4 heartbeats,

SMOTE and 80-20 scheme).

As shown in Figure 8, in terms of overall accuracy,

our tool outperforms the baseline method by approx-

imately 1,51 % and 1,4 % (respectively for RAST-

BINARY and RAST-TERNARY) providing higher

noise robustness, lower computational cost and higher

prediction responsiveness. All this makes it a robust

and highly accurate tool for both screening and detec-

tion of ST segment diseases.

To provide a complete report of the results, we

measured the times of processing for the generation

of the ﬁnal features vector. With the longest window

considered, i.e., the 64 heartbeats window, we mea-

sured the generation of the ﬁnal features vector in 0,09

s while with the smallest window, with only 4 heart-

beats, we measured the same processing in 0,012 s.

For this purpose, we used a laptop running Windows

10 with a Ryzen 7 5800x CPU and 32Gb of Ram.

These measures of time are to be intended only for the

features vector generation. To these amounts, the time

for the prediction needs to be added. In RAST, the

time for the prediction is not signiﬁcant, considering

that the most ﬁtting model (Random Forest) was as-

sessed in the literature as a classiﬁer with a very small

computational cost at test time (Sol

e et al., 2014).

In addition, thanks to the large experimentation

conducted, it was possible to achieve another result.

Indeed, it was observed that for the L1SO-CV, a

longer observation window was needed. In contrast,

with a random split, the observation window is re-

duced to 4 beats to provide a highly accurate binary

or ternary prediction of ST abnormalities. This could

be because within the L1SO-CV the training model

does not have personal patient data on which to make

the ST segment prediction, while the 80-20 random-

split validation represents a scenario where subjective

data are always available for the training of the model.

Therefore, the outcome can be that if a patient has

never been assisted, the observation window to iden-

tify ST anomalies must last longer, while if the model

has had the chance to use personal data for the training

of the model, the observation can be reduced to only

4 successive heartbeats. This result is compliant with

another work in the literature (Rosa et al., 2021b).

6 THREATS TO VALIDITY

A limitation might be the fact that we worked on only

one dataset, when it would have been more useful to

test the tuning of all design parameters on one dataset

and validate it on a completely different dataset of

patients. Unfortunately, because of the way our ap-

proach is done, we could only work on European ST-

T Database. To mitigate this limitation, we opted to

introduce robust validation schemes.

7 CONCLUSIONS

In this work RAST was presented, a tool dedicated

to the screening of ST anomalies. With respect to

other state-of-the-art methods, RAST is focused on

the binary (ST and Normal) and ternary (ST+, ST-

and Normal) identiﬁcation of ST segment anomalies.

Indeed, other tools opted for the identiﬁcation of more

ST segment categories of anomaly. At the same time,

RAST is mostly focused on the triggering of the dan-

ger without providing too many details on its nature.

Therefore, RAST is more intended to be involved in

rapid and accurate screening applications where the

diagnosis is continued by a specialized medical staff.

This method was exhaustively experimented on

the European ST-T Database and showed many im-

provements: (i) a better overall accuracy (+2 % with

respect to the chosen baseline) for both versions of

RAST (ii) a more efﬁcient computational cost con-

sidering that in the baseline work Maglaveras et al.

(1998) declared a high computational cost for their

presented method.

As a part of our future agenda, we aim at evalu-

ating the performances of RAST with different Deep

Learning-based classiﬁers. Moreover, we want to ex-

periment the approach on larger and heterogeneous

datasets, but also in clinical contexts via controlled

experiments. In these cases, it will be useful to eval-

uate the importance and the impact of demographics

factors, such as age and weight, as features.

HEALTHINF 2022 - 15th International Conference on Health Informatics

ACKNOWLEDGMENT

The authors have been supported by the project PON

2014-2020—ARS01 00860 “ATTICUS: Ambient-

intelligent Tele-monitoring and Telemetry for

Incepting and Catering over hUman Sustainability”

funded by the Ministry of Education, University and

Research—RNA/COR 576347.

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