Combining Machine Learning and Bayesian Networks for ECG

Interpretation and Explanation

Tezira Wanyana

1,3 a

, Mbithe Nzomo

1,3 b

, C. Sue Price

2,3 c

and Deshendran Moodley

1,3 d

University of Cape Town (UCT), Cape Town, South Africa

University of KwaZulu-Natal (UKZN), Durban, South Africa

Centre for Artiﬁcial Intelligence Research (CAIR), South Africa

Keywords:

Agent Architecture, Machine Learning, Bayesian Networks, ECG, Atrial Fibrillation, Wearable Devices.

Abstract:

We explore how machine learning (ML) and Bayesian networks (BNs) can be combined in a personal health

agent (PHA) for the detection and interpretation of electrocardiogram (ECG) characteristics. We propose a

PHA that uses ECG data from wearables to monitor heart activity, and interprets and explains the observed

readings. We focus on atrial ﬁbrillation (AF), the commonest type of arrhythmia. The absence of a P-wave in

an ECG is the hallmark indication of AF. Four ML models are trained to classify an ECG signal based on the

presence or absence of the P-wave: multilayer perceptron (MLP), logistic regression, support vector machine,

and random forest. The MLP is the best performing model with an accuracy of 89.61% and an F1 score

of 88.68%. A BN representing AF risk factors is developed based on expert knowledge from the literature

and evaluated using Pitchforth and Mengersen’s validation framework. The P-wave presence or absence as

determined by the ML model is input into the BN. The PHA is evaluated using sample use cases to illustrate

how the BN can explain the occurrence of AF using diagnostic reasoning. This gives the most likely AF risk

factors for the individual.

1 INTRODUCTION

Wearable devices have become increasingly

ubiquitous. They are equipped with a range of

sensors, allowing people to monitor their health

continuously outside clinical settings without

interfering with their regular activities (Dias and

Cunha, 2018). These devices generate signiﬁcant

amounts of data which can be interpreted, analysed

and explained using Artiﬁcial Intelligence (AI).

Machine learning (ML) algorithms can rapidly

identify properties and patterns in the data, while

knowledge representation and reasoning (KRR)

techniques can draw novel inferences from the

data. A combination of ML and KRR techniques

can enhance prediction, interpretation, explanation,

diagnosis, discovery and therapy selection (Johnson

et al., 2018).

As an application of this concept, we propose

https://orcid.org/0000-0002-5139-8421

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https://orcid.org/0000-0002-4340-9178

a personal health agent (PHA) that incorporates

ML techniques and a Bayesian Network (BN)

for monitoring heart activity using commercially

available wearable devices. We focus on detecting

and explaining the most likely causes of arrhythmia,

a cardiac condition characterized by irregularities in

the rhythm of the heart. The prevalence of arrhythmia

is rising globally (Kornej et al., 2020) and yet in

many individuals, it remains undetected. Moreover,

if left untreated, it can lead to heart failure and stroke

(Weimann and Conrad, 2021). In about 20% of

individuals who experience stroke due to arrhythmia,

speciﬁcally Atrial Fibrillation (AF), the occurrence of

AF was not detected until the time of stroke or shortly

afterwards (Steinhubl et al., 2018). Monitoring a

patient with a home-based wearable ECG sensor

patch increases the rate of AF diagnosis after four

months (Steinhubl et al., 2018).

AF is the most common sustained and clinically

signiﬁcant cardiac arrhythmia (Chugh et al., 2014;

Nguyen et al., 2013; Wasmer et al., 2017), and is

a growing public health problem in many countries,

including developing ones (Nguyen et al., 2013). The

gold standard in the diagnosis of AF is by use of an

Wanyana, T., Nzomo, M., Price, C. and Moodley, D.

Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation.

DOI: 10.5220/0011046100003188

In Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2022), pages 81-92

ISBN: 978-989-758-566-1; ISSN: 2184-4984

electrocardiogram (ECG) (Hagiwara et al., 2018), a

medical test that provides a record of the electrical

activity of the heart. The efﬁcient and accurate

interpretation of large amounts of ECG data using

technology can reduce the burden on the healthcare

system and result in early detection of AF.

The proposed PHA will use ECG data from

wearable devices to monitor heart activity, and

interpret and explain the observed readings. We draw

from the agent architecture for knowledge discovery

and evolution (KDE) (Wanyana and Moodley, 2021)

to determine the components of the PHA. We focus

on two components from the KDE architecture: the

AI service which is the ML component; and the

BN for capturing causal knowledge and providing

plausible explanations. We constrain the PHA to

detect a speciﬁc type of arrhythmia, i.e. AF. However,

the agent can be extended to determine, interpret

and explain other types of arrhythmia. The novel

contributions of this paper are:

1. A ML model that classiﬁes an ECG signal

based on its characteristics, speciﬁcally whether

a P-wave is present or not.

2. A prototype BN model of AF risk factors.

3. An approach for combining ML and BNs into a

PHA for ECG interpretation and explanation.

The rest of the paper is organised as follows:

Section 2 presents the background and related

work, covering the reference architecture used, ECG

analysis and BNs. Section 3 describes the ECG

dataset and the development of the ML models

to classify ECG signals based on the presence

or absence of the P-wave. Section 4 explains

the perception module in relation to the PHA.

Section 5 presents the use of the AF BN in the

deliberation modules of the PHA and also discusses

the development of the prototype BN and its

parameters. In Section 6, we evaluate and discuss the

ML models, the BN and the PHA. We conclude and

present future work in Section 7.

2 BACKGROUND AND RELATED

WORK

2.1 The KDE Agent Architecture

The KDE architecture (Wanyana and Moodley, 2021)

is a recent agent architecture for designing agents that

perform pattern analysis and knowledge discovery

from sensor observations emanating from dynamic

physical systems. It provides a mechanism for

integrating ML and KRR techniques to detect,

interpret and explain patterns in data. The architecture

speciﬁes the components of a KDE agent and how

they interact. It can accommodate both top-down

knowledge representation and reasoning techniques

and bottom-up ML and data mining techniques

(Wanyana and Moodley, 2021; Wanyana et al., 2020).

The KDE architecture has two exogenous

modules: the AI service and the domain expert.

The AI service incorporates data driven techniques

which are used to build models from data. It also

consists of a pattern detection service which detects

patterns in new incoming data. The architecture also

originally has three endogenous modules: perception,

deliberation and theory construction. Some of the

architectural modules and components in the original

architecture, speciﬁcally the theory construction

module and the rules and the ontology which are part

of the deliberation module, were left out in order to

show only the components applied in this work.

2.2 The PHA Architecture

The KDE architecture is used to guide the design of

the PHA. Figure 1 shows a simpliﬁed version of this

architecture in relation to AF detection, interpretation

and explanation. Experts (e.g. clinicians) have

to oversee and participate in the model building

activities (arrow 2), such as data labelling. The

experts also guide the acquisition and representation

of domain knowledge captured in the BN (arrow 4).

The development of the BN can also be supported

by learning BN parameters, i.e. the conditional

probability tables (CPTs) from the data (arrow 3).

A ML model is trained to classify an ECG signal.

As the agent continuously monitors an individual’s

ECG, the model is used to detect the pattern present

in the incoming ECG data: whether the P-wave is

present or not. The detected pattern, i.e. the presence

or absence of a P-wave based on the nature of the

ECG, serves as input to perception module (arrow 5).

It then interprets the observed sinus rhythm pattern

to determine the situation (condition) of which it is

indicative, i.e. the presence or absence of AF and

whether it is as expected or not. With the help of

its existing knowledge stored in the BN, the agent

deliberates to determine potential explanations. These

are risk factors of AF to look out for in the individual.

2.3 ECG Analysis

The ECG records a series of heartbeats, with a normal

heartbeat consisting of a P-wave, the QRS complex,

and the T-wave (Wasilewski and Polonski, 2012). The

ICT4AWE 2022 - 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health

Figure 1: A simpliﬁed version of the KDE Architecture, based on Wanyana and Moodley (2021).

R-peak, which is part of the QRS complex, represents

the maximum amplitude in the heartbeat. The R-R

interval is the difference between the R-peaks in two

consecutive beats. Figure 2 shows the different parts

of an ECG.

Figure 2: Parts of an ECG (Wasilewski and Polonski, 2012).

2.3.1 Machine Learning in ECG Analysis

ML has been widely used for classiﬁcation and

prediction of cardiac conditions using ECG data,

with many recent efforts focused on AF (Olier

et al., 2021). Deep learning in particular has

proven promising in recent years, primarily because

feature extraction can be performed automatically

without the need for human experts (Hong et al.,

2020). However, deep learning models have two main

disadvantages. Firstly, they have been criticised as

being black box models with limited interpretability

(Hong et al., 2020). Secondly, they typically require

large amounts of data and tend to be computationally

expensive. Traditional ML algorithms such as

tree-based methods and linear models have been

widely used and shown to produce good results in

ECG analysis, without the disadvantages of deep

neural networks (Olier et al., 2021).

2.3.2 P-wave Detection and Classiﬁcation

The P-wave is particularly important in the detection

of AF from an ECG. The hallmark characteristic of

AF is the absence of a P-wave, which is replaced

by either ﬁbrillatory waves or oscillations (Hagiwara

et al., 2018; Couceiro et al., 2008). Previous work

in P-wave analysis has focused on P-wave detection.

Mar

anov

a et al. (2019) developed a method for

P-wave detection using the phasor transform, while

Hossain et al. (2019) developed an algorithm to

identify P-waves automatically in an ECG signal and

classify and differentiate between different P-wave

types.

Beyond P-wave detection, previous work has

also explored P-wave characteristics for arrhythmia

classiﬁcation. Liu et al. (2018) proposed a support

vector machine (SVM) to distinguish different rhythm

types in an ECG signal based on statistical features of

the ECG, including the P-wave. The rhythm types

which the proposed algorithm identiﬁed are normal

rhythm, AF rhythm, and other rhythm.

The approach proposed in this paper creates a new

dataset consisting of ECG signals labelled as either

having a P-wave or not. A ML model is trained on

this dataset to classify new ECG observations based

on presence or absence of the P-wave. The model can

then be used to indicate whether an individual is likely

to have AF or not.

2.4 Bayesian Networks

Bayesian networks (BNs) are graphical models in the

form of directed acyclic graphs (DAGs) for reasoning

under uncertainty in a given domain. The nodes in a

Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation

BN represent a set of random variables X= X

...X

with each variable having a ﬁnite set of mutually

exclusive states (Korb and Nicholson, 2010). The

directed arcs between pairs of nodes represent causal

dependencies between the variables. If a variable

is certain to be in a particular state, this is entered

into the network’s node as evidence. The beliefs

of all the nodes of the network are then updated,

based on Bayes’ rule (belief propagation) presented

in Equation 1. BNs can be used to represent causal

relationships between variables under uncertainty in

a compact way (Darwiche, 2010). BNs offer an

appropriate technology for modelling medical and

health problems, which also includes personalised

healthcare (Velikova et al., 2014).

P(h|e) =

P(e|h)P(h)

P(e)

(1)

The design of the BN consists of two major

steps i.e. i) determining the structure or topology of

the network and ii) obtaining the parameters of the

network which involves determining the conditional

probabilities (CPTs), given the topology of the

network. These components of a BN can be learned

from data or obtained from expert knowledge.

Kyrimi et al. (2020) describe medical reasoning

patterns for aiding in the development of medical

BNs. Nodes are classiﬁed, among others, as

conditions (e.g. AF) and risk factors for that

condition. Fuster-Parra et al. (2016) used ML to

develop the structure of their BN of cardiovascular

risk, and populate the CPTs. Their BN structure

considers risk factors which inﬂuence other

risk factors, which in turn affect the patient’s

cardiovascular risk score and metabolic syndrome.

Velikova et al. (2014) propose a methodology for

building a functional model for syndrome progression

from medical principles and use it to construct a

preeclampsia BN. They model the relationship

between syndrome/disease (having two states:

present and absent) and the associated evidence

i.e. signs and symptoms via the functioning of a

particular organ. Risk factors such as pre-existing

diseases, age, gender and genetics may affect the

functioning of the organ. The probability of the risk

factors is usually obtained from population statistics

(Velikova et al., 2014).

Reasoning in BNs happens when we observe

the value of some variable and we would like

to incorporate the new information in the network

using Bayes’ rule (equation 2). This allows us to

answer questions that are predictive, diagnostic or

inter-causal (Korb and Nicholson, 2010). In this

work, we are speciﬁcally interested in diagnostic

reasoning from effect to cause, which happens in the

opposite direction to the arcs in the BN.

3 THE ML SERVICE

To detect possible AF, we propose a ML model that

classiﬁes an ECG signal based on whether the P-wave

is present or absent. Signals classiﬁed as having an

absent P-wave suggest the presence of AF.

3.1 The ECG Dataset

To create a dataset for P-wave classiﬁcation, we

use two-channel ECG records from two databases

that are publicly available on PhysioNet (Goldberger

et al., 2000). The details for each database are

summarised in Table 1. For the negative class

(P-wave absent), we use the widely used MIT-BIH

Atrial Fibrillation Database (AFDB) (Moody and

Mark, 1983). This database contains 25 records;

however only 23 of these contain the necessary

ECG signals. The two incomplete records are

therefore not included in this study. For the

positive class (P-wave present), we use an expert

annotated database (PWDB) (Mar

anov

a et al., 2019)

containing reference P-wave annotations for 12

complete records from the MIT-BIH Arrhythmia

Database. PWDB contains arrhythmia pathologies

that make the detection of P-waves more difﬁcult.

This mitigates the fact that absence of the P-wave may

be indicative of types of arrhythmia other than AF.

AFDB contains a larger number of records and a

longer record duration than PWDB. To address this,

we randomly sampled 12 records from AFDB so that

the number of records from each database was the

same. Additionally, at a later stage, we selected the

same number of heartbeats from each database to

ensure a balanced dataset. We combined the records

from the two databases to create one dataset. For

each record in the new dataset, we separated the two

channels for easier processing. For each channel in

each record, the following steps were performed:

1. The R peaks in each signal were identiﬁed

2. The signals were segmented into heartbeats using

the R peaks

3. The ﬁrst and last beats were excluded to ensure

the strongest beats were captured

4. Signals from the P-wave database were resampled

from 360 Hz to 250 Hz to ensure a uniform

frequency in the dataset

5. 1,115 beats of equal length were randomly

sampled from each signal

ICT4AWE 2022 - 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health

Table 1: Databases used in the creation of the dataset.

Database Records Duration Frequency Channels Channel Conﬁguration

AFDB 23 10 hrs 250 Hz 2 Not reported

PWDB 12 30 mins 360 Hz 2

Upper signal: MLII (12 records)

Lower signal: V1 (9), V2 (2), V5 (1)

The subsequent ECG dataset is evenly balanced

between the two classes and contains a total of 53,520

beats from 24 records, ordered sequentially.

3.2 Model Building and Pattern

Detection

Four ML classiﬁcation algorithms were implemented

using Scikit-learn (Pedregosa et al., 2011): multilayer

perceptron (MLP), logistic regression, SVM, and

random forest.

To train and test the models, we opted

for a stratiﬁed 10-fold cross-validation approach.

Cross-validation is a widely used data resampling

method that is effective in accurately assessing the

generalisation performance of ML models (Hastie

et al., 2009). Rather than a single random train-test

split, the dataset was divided into training and testing

sets using different partitions for each of the 10

rounds. The stratiﬁed approach maintained the

distribution of each class. After the 10 rounds of

cross-validation, we obtained the average accuracy,

average F1 score, and summed confusion matrix for

each model. The evaluation and results are discussed

in Section 6.1.

The best performing technique (in this case the

MLP, see Section 6.1) from the ML experiments is

used for ECG pattern detection, i.e. to determine

whether a P-wave is present or absent.

4 PERCEPTION

The perception module in Figure 1 consists of two

sub-modules, i.e. situation detection and situation

analysis. As opposed to using rules, as suggested in

the KDE architecture (Wanyana and Moodley, 2021),

situation detection in this work is carried out by

the BN. This is because the applicable domain rules

are few and the absence of a P-wave, for instance,

does not indicate with 100% certainty that AF is

present. The BN allows us to cater for the degree of

uncertainty that the rules cannot incorporate.

This work concentrates on an absent or present

P-wave, which most probably implies the presence

or absence of AF. However it can be extended in

order to accommodate other types of arrhythmia. As

soon as a pattern (i.e. present or absent P-wave)

is detected, it is captured in the BN as evidence.

The situation (condition) that this pattern represents

is then determined by propagating the probabilities

in the network. For example, if the detected pattern

indicates that a P-wave is absent and this evidence is

entered into the BN, then the BN beliefs are updated,

depicting that AF is probably present. The detected

situation is then analysed to check whether it is an

unexpected situation and should be followed up. For

example, for an individual who is not known to have

AF, if no P-wave is detected, indicating that AF

is probably present, the BN is used to explain this

situation.

5 DELIBERATION

Deliberation in the architecture (Figure 1) occurs

using a BN. In this section, we describe the

development of a prototype AF-BN. The BN is

used to determine the factors that contribute to an

individual having AF. The explanation we seek and

intend to follow up is in answer to the question: what

factors affect an individual’s chances of having AF

and to what extent?

The BN was developed using the iterative

knowledge engineering approach (Korb and

Nicholson, 2010) with the help of the Netica

tool. A condition C can be distinguished as a disease,

a disorder or a syndrome (Kyrimi et al., 2020). We

identiﬁed the condition (AF) and its risk factors by

using the risk factor idiom (Kyrimi et al., 2020).

This idiom models the risk factors which increase

someone’s likelihood of having a certain condition

(in this case, AF). The risk factors form the basis of

explanation of the presence of the condition in an

individual. The structure of the BN was inﬂuenced

by articles showing the modelling of risk factors, e.g.

Velikova et al. (2014), and the relationships found

among the variables found in Fuster-Parra et al.

(2016). Important to note is the fact that this is just

one possible structure that can be used to model AF.

The prototype BN is shown in Figure 3. The labels

of the risk factor nodes are preﬁxed with “RF: ”,

and the condition, AF, with “C: ”. The prototype

https://www.norsys.com/netica.html

Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation

AF-BN shows some traditional and representative

AF health risk factors, viz. hypertension which

carries the largest population attributable risk for

AF development in the world (Kornej et al., 2020;

Wasmer et al., 2017), valvular heart disease and

ischemic heart disease. The three factors are

identiﬁed in Nguyen et al. (2013) as the most common

conditions in a developing context. Diabetes mellitus

was added as it presents a 40% increased risk of

AF development (Kornej et al., 2020). Lifestyle risk

factors captured in the prototype network include

alcohol abuse, obesity and smoking. The identiﬁed

non-modiﬁable risk factors captured in the prototype

AF BN are age and sex: males and older people

have a higher risk of suffering from AF than females

and younger people (Kornej et al., 2020). As far

as age is concerned, increase in AF is a reﬂection

of comorbidities and cardiovascular risk factors

in addition to other factors like lifestyle changes

(Wasmer et al., 2017).

The CPT values were determined from the

literature (Feinberg et al., 1995; Nguyen et al., 2013;

Pritchett, 1992; Kornej et al., 2020; Wasmer et al.,

2017). The CPT values of the P-wave node in the BN

were selected to be P(P-wave=Absent | AF=Present)

= 98% and P(P-wave=Present | AF=Absent) = 95%.

The likelihood values used for the P-wave node are

the probabilities obtained from the ML model using

predict proba(), a method from Scikit-learn which

returns the probability estimates for each class. The

authors reviewed the structure, CPTs and working of

the BN during its development.

Other lifestyle factors that affect AF development

identiﬁed in Kornej et al. (2020), but not modelled

here, are extreme physical activity, psychological

stress and psycho-social factors. Health risk factors

identiﬁed in the literature, but not represented in

the BN are rheumatic heart disease, heart failure,

hyperthyroidism, pulmonary disease and coronary

heart disease. The risk factors are what may have

led to an onset of AF and these are used to explain

the presence of AF in an individual. Checking

for the presence of some of these risk factors that

would make one susceptible to AF may help medical

practitioners to manage their patient’s AF better. At

this point, the agent aims to inform the practitioner

that since AF is present, some other underlying

risk factors should be checked for and managed.

However, the ways of managing and treating the

speciﬁc conditions is out of scope of the network.

Using the BN to reason diagnostically presents the

risk factors which should be looked for when AF is

conﬁrmed in the ECG.

We consider speciﬁc evidence (Korb and

Nicholson, 2010), where the evidence is in the form

of P-wave = Absent. The context nodes in the BN

are age and sex, and lifestyle factors, i.e. alcohol

abuse, smoking and obesity. These factors are all

observed, and will be entered into the BN as evidence

by the individual or the medical practitioner. When

the state of the P-wave is entered into the BN along

with the state of some context nodes e.g. age and

sex, the condition that it is indicative of is obtained

e.g. P-wave = Absent indicates that AF is most likely

present. The traditional risk factors that have the

highest conditional probabilities are then obtained

from the network. These give tentative reasons for

the existence of AF in an individual and should be

followed up.

6 EVALUATION AND

DISCUSSION

In this section, we discuss the evaluation and

validation of both the ML and BN components of the

PHA and then show how we evaluate the combined

components of the PHA using a use case.

6.1 Evaluation of the ML Models

The ML models were evaluated using three metrics:

confusion matrix, accuracy, and F1 score. The

confusion matrix



T P FP

FN T N



shows the number of true

positives (TP), false negatives (FN), false positives

(FP), and true negatives (TN) in the predictions.

Accuracy refers to the percentage of correct

predictions for the test data, as seen in Equation 2.

T P + T N

T P + T N + FP + FN

(2)

The F1 score is a computation of the harmonic

mean of the precision (Equation 3) and recall

(Equation 4). Its formula is shown in Equation 5.

T P

T P + FP

(3)

T P

T P + FN

(4)

2 ∗

Precision ∗ Recall

Precision + Recall

(5)

Because the models were trained using 10-fold

cross-validation, the reported accuracy and F1 scores

are averaged across the 10 folds, while the confusion

matrix is summed. The results on the testing set

are shown in Table 2. The best performing model

is the MLP, with an average accuracy of 89.61%

ICT4AWE 2022 - 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health

Figure 3: A prototype BN for explaining causes of AF.

and an average F1 score of 88.68%. The confusion

matrix results for the MLP show a higher number

of FN than FP. This is in contrast to the logistic

regression model, where the numbers of FP and FN

are similar. This means that the MLP is more likely

to incorrectly determine an absent P-wave than to

incorrectly determine a present P-wave. Therefore,

the model may incorrectly suggest AF in a small

number of cases.

In Table 3, we compare the MLP’s performance

to that of the SVM presented by Liu et al. (2018),

which classiﬁes an ECG signal as either a normal

rhythm, AF rhythm, or other rhythm based on the

features of the ECG, including the characteristics of

the P-wave. Liu et al. (2018) used the F1 score metric

to evaluate their proposed algorithm. They report

the F1 scores for each of the rhythm types as well

as the average F1 score for all rhythms, on both the

training set and testing set. In particular, we compare

the MLP’s performance to the performance of the

SVM in classifying the AF rhythm and the average

performance for all rhythms. The results show that

the MLP that we present in this paper outperforms

the SVM proposed by Liu et al. (2018) for detecting

AF. For this reason, the MLP is applied as part of the

PHA’s ML service.

6.2 Evaluation of the Prototype BN

The prototype BN was evaluated by the authors

using Pitchforth and Mengersen’s framework for

expert-elicited BNs (Pitchforth and Mengersen,

2013). The network ﬁts within the medical and

in particular, cardiology domain, thus conﬁrming

nomological validity. The BN has the structure, node

discretisation and parameters that would be expected,

conﬁrming face validity.

The prototype BN contains the main risk factors

for AF mentioned in the literature. However,

additional nodes can be added to cover a wider range

of risk factors. In the literature, some papers state that

certain AF risk factors are more important than others.

However, this depends on the population upon which

the paper is based. The states in the nodes contain all

the values that the node can take. The input nodes’

CPT values are based on the literature. These factors

conﬁrm the BN’s content validity. It should be noted

that the CPT values in the prototype BN can change

according to the context which is being modelled. For

example, for some populations, diabetes plays a larger

role than in others; in an Ethiopian study, the levels of

obesity in patients with AF were lower than in other

contexts (Pitman et al., 2021).

To evaluate convergent validity, the structure of

the BN was inspected. At the bottom of the BN,

the measurement idiom (Neil et al., 2000; Kyrimi

et al., 2020) can be seen in the nodes C:Atrial

Fibrillation → P-wave. The Lifestyle risk

factors node summarises the RF:Alcohol abuse,

RF:Smoking and RF:Obesity nodes using the

deﬁnitional/synthesis idiom (Neil et al., 2000; Kyrimi

et al., 2020). The cause–consequence idiom (Neil

et al., 2000; Kyrimi et al., 2020) can be seen where

the nodes Age and Lifestyle risk factors cause

the four traditional risk factors (RF:Hypertension

to RF:Diabetes Mellitus) in the middle of the

BN, and where Sex causes the condition of Atrial

Fibrillation. The BN structure follows the risk

Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation

Table 2: Results of classiﬁcation on the testing set.

Algorithm Confusion Matrix (Summed) Average Accuracy Average F1 Score

MLP



24983 1777

3784 22976



89.61% 88.68%

Logistic Regression



23192 3568

3671 23089



86.47% 86.13%

SVM



22556 4204

7141 19619



78.80% 77.40%

Random Forest



22975 3785

10326 16434



73.63% 68.33%

Table 3: Comparison of MLP performance with SVM

performance from Liu et al. (2018) on the testing and

training sets.

Algorithm

F1 Score

(Testing Set)

F1 Score

(Training Set)

MLP 88.68% 99.64%

SVM

(AF rhythm)

78.56% 86.37%

SVM

(average)

80.00% 84.00%

factor idiom outlined by Kyrimi et al. (2020), e.g.

the four traditional risk factors (RF:Hypertension

to RF:Diabetes Mellitus) in the middle of the

BN to the condition C:Atrial Fibrillation. The

structure of risk factor nodes were modelled based

this risk factor idiom, and on the work of Velikova

et al. (2014) (preeclempsia) and Fuster-Parra et al.

(2016) (cardiovascular risk score and metabolic

syndrome). This conﬁrms convergent validity.

To evaluate predictive validity, three aspects of

the BN’s execution need to be assessed: the BN’s

behaviour when it is executed; its sensitivity to

ﬁndings or parameters; and its modelling of extreme

conditions. Without evidence added, the prototype

AF-BN shows prevalence for the four traditional

risk factors, as they are experienced worldwide (see

Figure 3). If a person has AF (i.e. evidence in

the C:Atrial Fibrillation node is set to Present

as in Figure 4), the values of the four traditional

risk factors can be compared with literature: the

systematic review in Nguyen et al. (2013) provides

these values for developing contexts (see Table 4).

The values in the BN give acceptable results; it should

be noted that the BN’s CPT values would need to

change if the BN is to represent a different population.

For example, valvular heart disease is more prevalent

in developing countries than in developed countries

(Nguyen et al., 2013).

The sensitivity to ﬁndings for the C:Atrial

Fibrillation node can be found in Table 5. This

shows a ranking of the nodes to which evidence

should be added in order to be more certain about

the value of the C:Atrial Fibrillation node.

RF:Hypertension is the risk factor which gives the

most certainty to C:Atrial Fibrillation. This

concurs with Nguyen et al. (2013); Wasmer et al.

(2017); Kornej et al. (2020), who rank hypertension

as the most common risk factor for AF. Other AF risk

factors may have a larger or smaller effect on AF in

different contexts.

To assess extreme conditions, different values

were entered into the BN as evidence. For example,

high and low values of the Sex, Age and lifestyle risk

factor nodes were entered to verify the prediction of

the C:Atrial Fibrillation node. These extreme

conditions showed expected behaviour of the BN.

The validation showed that the BN represents the

factors causing AF suitably well. However, it is

recognised that further testing and evaluation of this

prototype BN is necessary before deployment into

industry.

6.3 Evaluation of the PHA

The PHA agent acquires its percepts by leveraging an

exogenous ML service in which an MLP algorithm

is used to classify whether the P-wave was absent or

present in the ECG signal. When new ECG signals

come in, the presence or absence of the P-wave is

detected and the situation that it is indicative of is

interpreted and explained using a BN. The BN allows

the causal relationships and uncertainties present in

expert knowledge to be captured. This knowledge is

used in ECG interpretation and explanation. To link

the results of the ML to the BN, the probabilities for

classifying the sensed input as having a P-wave or not

were used as the likelihood for the evidence entered

in the P-wave node in the BN.

To evaluate the PHA, we apply use cases which

also show its predictive validity. The intention is to

check whether the provided explanation matches the

trends mentioned in the literature, given the state of

the P-wave detected by the ML component.

Consider a man who is above 80 years of age, and

the P-wave is detected as absent with a likelihood of

ICT4AWE 2022 - 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health

Table 4: Values of the four traditional risk factor nodes, given AF, compared to Nguyen et al. (2013).

Node

AF BN value

(Figure 4)

Min value in

Nguyen et al. (2013)

Max value in

Nguyen et al. (2013)

RF:Hypertension 57.3% 10.3% 71.9%

RF:Ischemic Heart Disease 26.7% 6.4% 47%

RF:Valvular Heart Disease 5.99% 5.6% 66%

RF:Diabetes Mellitus 20.3% 3.3% 33%

Figure 4: Extract of the prototype BN showing causes of AF risk factors, given that AF is present.

96.1% i.e. P(observation | P-wave = P-wave Absent)

= 96.1%) (see Figure 5). There is 84.9% chance that

the individual has AF. Using diagnostic reasoning,

this may be attributed to the fact that the individual

suffers hypertension (54.2%). Ischemic heart disease

and diabetes may also be a cause although they

depict a low probability of only 28.6% and 30.2%

respectively.

If more context is available for the person in terms

of the lifestyle risk factors, this can be added to the

BN. For example, if this person is obese, smokes and

abuses alcohol, the probability of having AF rises to

90.3% (see Figure 6). Reasoning diagnostically, the

traditional risk factors also rise: hypertension rises to

59.6% and ischemic heart disease to 60.3%. While

the chances of the individual having valvular heart

disease and diabetes mellitus have also consequently

risen, these have not risen above 50%. The

person’s medical practitioner may want to check these

conditions as a preventative management step.

A similar scenario was run with information for

the same person (male, above 80 years old), with no

known lifestyle factors. The ML algorithm detected

a P-wave (with the same likelihood value of 96.1%)

indicating that AF is absent. Results show that AF’s

presence is 3.01%. Reasoning diagnostically, the

man has a 28.3% chance of having hypertension,

12.1% ischemic heart disease, 4.18% valvular heart

disease and 15.7% diabetes mellitus. If this man is

obese, smokes and abuses alcohol, the probability of

having AF rises to 4.89%, while the traditional risk

factors rise to 36.3% (hypertension), 36.8% (ischemic

heart disease), 5.11% (valvular heart disease) and

18.7% (diabetes mellitus). This rise is what would

be expected in an older person with these lifestyle

factors.

These use cases demonstrate that the agent

behaves as expected and aligns with the trends

obtained from the literature.

7 CONCLUSION AND FUTURE

WORK

This paper demonstrates how a combination of data

driven techniques and expert elicited knowledge

can be applied in a hybrid AI approach in an

agent that provides explanations. We have shown

how data driven techniques, speciﬁcally ML, and

reasoning using BNs can be integrated into a PHA.

Wearables in the personal health domain generate

large volumes of data in a continuous manner, and

therefore data driven techniques are required to

analyse and extract useful knowledge from the data.

Scientiﬁc or expert knowledge, with the help of tools

like BNs, can be applied in the interpretation and

understanding of interesting situations obtained from

the data. The application of the KDE architecture

has enabled ML and reasoning to be combined in the

PHA for ECG interpretation and explanation. ML

and reasoning over existing knowledge have been

combined in various domains for example in the

biodiversity domain (Sen et al., 2021). However, to

our knowledge, this is the ﬁrst study that has sought

to explore the combination of ML and reasoning in

the interpretation, understanding and explanation of

ECGs using a PHA.

A P-wave annotated arrhythmia database and

an AF database were combined to create a dataset

containing ECG signals with present and absent

P-waves. Four ML algorithms were trained on

Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation

Table 5: Sensitivity to ﬁndings for the C:Atrial Fibrillation node.

Node

Entropy Value

Percent Variance of Beliefs

C:Atrial Fibrillation

0.71643 100 0.1583535

P-wave

0.52619 73.4 0.1211922

RF:Hypertension

0.07575 10.6 0.0183385

RF:Ischemic Heart Disease

0.03808 5.32 0.0101794

RF:Diabetes Mellitus

0.02155 3.01 0.0056523

Lifestyle risk factors

0.01835 2.56 0.0043785

Sex

0.00890 1.24 0.0019414

RF:Valvular Heart Disease

0.00797 1.11 0.0022011

Age

0.00445 0.621 0.0010077

RF:Smoking

0.00403 0.562 0.0009329

RF:Obesity

0.00266 0.372 0.0006217

RF:Alcohol Abuse

0.00039 0.0546 0.0000924

Figure 5: The AF BN representing an 80+ year old man who has an absent P-wave.

Figure 6: The AF BN representing an 80+ year old man with lifestyle risk factors and an absent P-wave.

ICT4AWE 2022 - 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health

the dataset to classify an ECG signal based on the

presence or absence of the P-wave. The classiﬁcation

results are promising, with the best performing

model outperforming the classiﬁer proposed by Liu

et al. (2018). The prototype BN used (Figure 3)

demonstrates how risk factors for AF can be used to

explain the occurrence of AF in an individual. The

parameters of the BN presented here can be adjusted

to represent the prevalence of different risk factors

in different populations. The BN can be extended to

accommodate other types of arrhythmia.

Despite these promising results, this study

has some limitations. The number of patients

in the dataset used to train the ML models is

quite small. This is because there is a limited

number of ECG datasets with accurate, expertly

annotated P-waves. For future work, we intend to

create a larger dataset which includes ECG signals

collected from commercially available wearable

devices. We will also explore ways to boost the

performance of the ML models, for example through

additional hyperparameter tuning. Additionally, the

generalisability of the ML models could be further

improved using leave-one-out cross-validation, in

which the number of folds corresponds to the number

of patients in the dataset. To further validate the BN,

expert clinicians need to be involved in improving

and testing the prototype BN. We also plan to

explore how new unknown situations or ECG patterns

that can lead to construction of new theories, as

suggested in Wanyana and Moodley (2021), can be

incorporated into the agent-based system towards

knowledge discovery and evolution.

ACKNOWLEDGEMENTS

This work was ﬁnancially supported by the Hasso

Plattner Institute for Digital Engineering through the

HPI Research School at UCT. The authors thank the

reviewers for suggestions made, which have helped to

improve this paper.

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