Unmasking Concealed Arrhythmia Syndromes
Conditions: Brugada ECG Patterns, Brugada Syndrome (BrS)
Sponsor: Imperial College London

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UNMASKING CONCEALED ARRHYTHMIA SYNDROMES 
 
 
 
 
 
MAIN SPONSOR: Imperial College London  
FUNDERS: British Heart Foundation, Imperial Biomedical Research Centre  
STUDY COORDINATION CENTRE: Dr Keenan Saleh  
IRAS Project ID: 330619 
 
Version 4 – 02/04/2024 
 
 
Protocol authorised by: 
 
Name & Role 
           Date 
    Signature 
Dr Zachary Whinnett 
Investigator 
          10/07/2023 
    Dr Zachary 
Whinnett  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Study Management Group 
 
Chief Investigator: Dr Zachary Whinnett 
 
Co-investigators: Dr Keenan Saleh, Dr Ahran Arnold 
 
Statistician: Prof Darrel Francis 
 
Study Management: Dr Keenan Saleh 

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Study Coordination Centre  
 
For general queries, supply of study documentation, and collection of data, please contact: 
 
Study Coordinator: Dr Keenan Saleh 
Registration: MBBS BSc MRCP 
Address: NHLI, B Block, Hammersmith Hospital, Du Cane Road, W12 0HS 
 
 
    
 
Tel: 02033133000  
 
 
 
 
E-mail: ks1310@imperial.ac.uk  
Fax:  n/a 
 
 
 
 
 
 
 
 
 
Clinical Queries 
 
Clinical queries should be directed to Dr Keenan Saleh who will direct the query to the 
appropriate person 
 
 
Sponsor 
 
Imperial College London is the main research Sponsor for this study.  For further information 
regarding the sponsorship conditions, please contact the Head of Regulatory Compliance at: 
 
 
 
Head Research Governance and Integrity 
Imperial College London and Imperial College Healthcare NHS Trust 
Room 215, Level 2, Medical School Building 
Norfolk Place 
London, W2 1PG 
Tel: 0207 594 1862 
https://www.imperial.ac.uk/research-and-innovation/research-office/research-governance-
and-integrity/ 
 
 
Funder 
 
This study is funded by the Imperial Biomedical Research Centre.  
 
This protocol describes the study and provides information about procedures for entering 
participants.  Every care was taken in its drafting, but corrections or amendments may be 
necessary. These will be circulated to investigators in the study.  Problems relating to this 
study should be referred, in the first instance, to the Chief Investigator.  
 
This study will adhere to the principles outlined in the UK Policy Frame Work for Health and 
Social Care Research. It will be conducted in compliance with the protocol, the Data 
Protection Act and other regulatory requirements as appropriate.  
 
 
 
 

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GLOSSARY OF ABBREVIATION 
 
AUC 
 
Area under the curve 
ARVC  
Arrhythmogenic right ventricular cardiomyopathy 
DCM  
Dilated cardiomyopathy 
ECG                Electrocardiogram 
Echo               Echocardiogram 
HCM  
Hypertrophic cardiomyopathy 
ICC 
 
Inherited cardiac conditions 
LQTS  
Long QT syndrome 
NN 
 
Neural network 
RASE  
Rare Arrhythmia Syndrome Evaluation 
RBBB  
Right bundle branch block 
UHF-ECG 
Ultra-high frequency electrocardiography 
VT 
 
Ventricular tachycardia 
VF 
 
Ventricular fibrillation 
 
 
 
 
 
 
 
 
 
 

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STUDY SUMMARY 
 
TITLE 
Unmasking Concealed Arrhythmia Syndromes 
DESIGN 
Proof-of-concept studies to develop ECG AI algorithms, followed by case-control 
diagnostic accuracy study and prospective cohort studies 
AIMS 
1. To develop machine learning algorithms for 12-lead ECGs to detect 
diagnostic features of Brugada syndrome. 
2. To test and validate these algorithms prospectively in participants 
wearing ambulatory ECG recording devices. 
3. To ascertain the role of UHF-ECG for risk stratification in patients with 
inherited arrhythmogenic conditions 
OUTCOME 
MEASURES 
• 
Brugada ECG algorithm sensitivity, specificity and AUC value for both 12 
lead ECGs and 12 lead vest ECGs 
• 
Yield of extended duration multi-electrode ambulatory ECG monitoring to 
detect the Brugada ECG pattern in concealed Brugada syndrome 
• 
The utility of extended duration multi-electrode ambulatory ECG 
monitoring to detect a single case of underlying Brugada or LQTS 
manifestation in idiopathic VF 
POPULATION 
Healthy volunteer controls, patients with a diagnosis of Brugada syndrome, 
patients with a diagnosis of idiopathic VF syndrome and patients with other 
inherited arrhythmogenic conditions 
ELIGIBILITY 
Participant sets (Appendix 1): 
 
• 
Group A – healthy volunteers 
• 
Group B – concealed arrhythmia syndrome patients (patients with a 
normal ECG with a known underlying arrhythmic diagnosis) 
• 
Group C – manifest arrhythmia syndrome patients (patients with an 
arrhythmic syndrome with an abnormal ECG) 
DURATION 
November 2023 – November 2026 (3 years) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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1. 
INTRODUCTION  
1.1 
BACKGROUND 
 
Sudden cardiac arrest is often the first clinical presentation of an inherited arrhythmia 
syndrome (1). In up to 37% of young sudden cardiac arrest survivors, no clear cause is 
identified despite comprehensive clinical evaluation (2). These patients and their relatives, 
who are likely to harbour a genetic predisposition for life-threatening arrhythmias, can remain 
undiagnosed for years as phenotypic changes may not be apparent at the time of their 
assessment. Patients with these concealed arrhythmia syndromes can also present with 
syncope or palpitations with apparently normal cardiac investigations at the time of 
presentation. If a diagnosis cannot be made, they cannot be protected from their potential 
risk of sudden arrhythmic death. 
 
Concealed arrhythmia syndromes may manifest themselves transiently during day-to-day 
physiological provocations such as peaks, troughs and sudden changes in cardiac 
autonomic tone. For example, long QT syndrome can manifest immediately after exercise, 
while Brugada ECG patterns can emerge during fever and deep sleep (3). It is not currently 
practicable to conduct sufficiently prolonged and detailed ECG assessment to sensitively 
detect these transient electrical manifestations. Instead, we are currently obliged to conduct 
short term provocation tests, using exercise, orthostasis, and drugs, which have low 
diagnostic yield and (in the case of ajmaline) can induce fatal arrhythmias (4). Invasive 
electrophysiology studies and genetic testing can identify some rare cases but are most 
often non-contributory (5–7). 
 
We postulate that novel non-invasive ECG-based diagnostic techniques and long term 
ambulatory monitoring may further augment our ability to detect these concealed arrhythmia 
syndromes. 
 
2.  
STUDY OBJECTIVES 
 
Primary objective: To combine novel non-invasive ECG techniques and long term 
ambulatory ECG monitoring to unmask concealed arrhythmia syndromes 
 
Secondary objective: To explore the utility of non-invasive ECG techniques for arrhythmic 
risk stratification 
 
 
3. 
STUDY DESIGN 
3.1  
RECRUITMENT 
 
Brugada ECG AI training 
We will collate anonymised Brugada ECG data from UK and international centres. We will 
collaborate with site-specific representatives to coordinate secure ECG data transmission 
and acquisition. All ECG data will be anonymised at the point of collection. Consent will not 
be sought as only anonymised ECGs will be obtained. There will be no access to patient-
identifiable data from the research team and all data will be fully anonymised prior to 
transfer. These ECGs will be acquired from pre-existing hospital digital infrastructure, 
namely the electronic health record or ECG data management system. Data will be 
transmitted securely through trusted secure data transfer systems where available. Where 
this option is not available, ECG data will be physically retrieved via a researcher visiting the 
centre and copying anonymised data onto an encrypted hard drive. 
 

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We will acquire digital ECG recordings as well as scanned paper ECGs of patients with 
established Brugada syndrome. Paper ECGs will be scanned and imported onto local secure 
storage. We will also gather anonymised ECG data from Imperial College Healthcare NHS 
Trust patients. We aim to acquire around 3000 patient ECGs in total for AI training. Where 
available, corresponding clinical data will also be collected alongside participants’ ECGs, 
including results of any relevant genetic testing and other investigations including ajmaline 
provocation testing. 
 
Prospective studies 
200 patients will be recruited in total. The direct care team will screen the records and 
identify patients followed up at Imperial College Healthcare Trust that are suitable for the 
study. Patients will also be identified at several national ICC centres, via our collaborators, 
and invited to attend the Imperial College London Pert Rose Research Unit for recruitment 
into our study. 
 
We will recruit 3 groups of participants: 
 
• 
Group A – healthy volunteers 
• 
Group B – patients with a normal ECG and an underlying inherited arrhythmic 
syndrome 
• 
Group C – patients with an arrhythmic syndrome with an abnormal ECG 
 
We will be aiming for an approximately even distribution of participants to be recruited to 
each group. 
 
The direct care team will obtain consent to share their information with the research care 
team. Once the patient has consented to their information being shared, a member of the 
research team will contact the patient and explain the details of the research study. Written 
consent will be gained prior to any tests taking place. 
3.2  
SPECIFIC TECHNIQUES USED IN STUDY 
 
Ambulatory ECG monitoring: Participants will be fitted with various ECG monitoring devices 
to capture prolonged ECG recordings. These will include commercially used Holter monitors 
up to 12-leads, research grade CE-marked cardiac monitoring devices and wearable ECGs 
which can be applied for days to weeks at a time to capture long term rhythm recordings. 
 
Ultra High Frequency ECG: Our centre is a pioneer in using Ultra-High Frequency ECG 
(UHF-ECG) to detect, isolate and amplify high frequency components that are filtered out in 
conventional ECGs. These frequencies (1-2kHz) do not travel far: each chest lead measures 
only activation of its nearest ventricular wall. We will perform UHF-ECG recordings in our 
participants to better characterise changes in QRS morphology and activation for diagnosis 
and risk stratification of arrhythmia syndromes. The UHF-ECG machine is CE-marked. 
3.3  
METHODOLOGY 
 
Study A: Brugada ECG AI development 
The research team will collate at least 3000 ECGs, representing the 3 different categories 
previously mentioned. All ECGs will be either digital or converted to a digital format using in-
house developed software. ECGs will subsequently be processed to enable direct extraction 
of the ECG complexes from the recordings. The resulting ECG data will be segmented to 
enable labelling by experts in Brugada ECG analysis. 
 

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The anonymous ECG data will be reviewed and annotated by experts. Each ECG will be 
labelled by 5 individual experts from a larger pool of experts, who will collectively share the 
task of ECG labelling for our entire training set. Experts will be comprised from the ICC 
specialists at Imperial College Healthcare NHS Trust, as well as our research collaborators 
within the UK and internationally. The categorisation of each case will be determined by 
expert majority agreement.  
 
The labelled ECG data will be divided into the training set (75% of ECG data) and a separate 
hold-out validation set (25% of ECG data). The training set will be used to train separate 
neural networks (Appendix 2). Once the neural networks have been developed, they will be 
tested for their accuracy against a hold-out validation dataset. 
 
Study B: Remote arrhythmia diagnostics 
In this study, we will undertake validation studies to assess the robustness, accuracy and 
applicability of the aforementioned AI algorithms (i.e. neural networks) on ECG recordings 
from several different ambulatory monitoring modalities. This protocol will encompass 
participants recruited in Groups A-C. 
 
Protocol 
Participants will undergo the consent process and testing during a single research visit. The 
steps will include the following: 
 
- 
Participants will be counselled regarding the research study and written informed 
consent will be obtained. 
- 
All participants will undergo a 12-lead ECG and ultra-high-frequency ECG recording. 
- 
Group A participants will additionally undergo an echocardiogram to ensure that they 
have a structurally and electrically normal heart. 
- 
All participants will undergo testing with different ambulatory ECG modalities, 
depending on availability and resources. This will include Holter monitoring up to 12 
leads, wearable cardiac monitoring devices (namely the Boston Scientific 
BodyGuardian MINI) and other wearable ECG technologies. These devices will be 
fitted by a member of the research team. 
- 
Participants will be instructed how to maintain continuous ambulatory ECG recording 
via their device as far as possible, including during day-to-day life and including 
during sleep. Provisions of additional adhesive patches and devices may be required 
to facilitate long term monitoring up to 3 months. 
- 
Participants will be provided with a digital symptom and activity diary via email to 
record their daily activity diary, the occurrence of any arrhythmia-related symptoms 
and gather real-time feedback on tolerability of their monitoring device. In patients 
who do not have an email, a paper symptom/activity diary will be provided. 
- 
Data will be remotely uploaded onto the cloud via secure digital infrastructure or 
housed securely on the cardiac monitoring device. 
- 
Participants will be provided with pre-paid parcels with courier collection for retrieval 
of the ambulatory ECG devices and wearable ECGs at the end of the 3 month study 
period. 
 
Application of ECG AI to ECG recordings 
ECG recordings will be downloaded onto local storage. The digital ECGs will be processed 
and denoised using specialized algorithms developed by the research team. The previously 
developed ECG AI algorithms for Brugada syndrome will subsequently be applied to the 
cleaned ECG recordings for all patient groups. 
 
We will establish the sensitivity for the Brugada classification algorithms on recordings from 
patients with manifest Brugada syndrome (Group C) and their specificity using the 
recordings from the healthy volunteers (Group A). The algorithms will also be applied to 
Commented [KS1]: Added for clarity 

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patients with concealed arrhythmia syndromes (Group B) to determine the diagnostic yield of 
different ambulatory ECG recording modalities for different time durations. We will apply both 
the in-house developed Brugada ECG AI algorithms as well as existing ECG AI algorithms 
developed by external academic or industry collaborators, to investigate for underlying 
concealed arrhythmias. 
 
Ultra-high-frequency ECG analysis 
High frequency bandwidth ECG data obtained via ambulatory ECG recordings will also be 
processed to extract high frequency components for signal averaged UHF-ECG analysis. 
This will be used to investigate for characteristic UHF-ECG profiles synonymous with 
underlying Brugada syndrome. 
 
Manual ECG review with physiological and activity data 
We will also manually analyse ECG recordings coinciding with day to day physiological 
stressors using activity diaries to look for transient manifestations of concealed arrhythmia 
syndromes. 
 
Study C: Arrhythmic risk stratification using UHF-ECG 
This protocol will encompass participants recruited to Groups A-C 
 
Protocol 
Participants will undergo the consent process and testing during their initial research visit. 
The steps will include the following: 
 
- 
Participants will be counselled regarding the research study and written informed 
consent will be obtained. 
- 
All participants will undergo a 12-lead ECG and ultra-high-frequency ECG recording 
for up to 5 minutes in duration. 
- 
Patients will be followed up prospectively for instances of ventricular arrhythmias, 
aborted cardiac arrest or sudden cardiac death. 
 
Ultra-high-frequency ECG analysis 
We will utilise computational modelling to determine the degree of QRS fractionation within 
the UHF-ECG profile. We will also investigate the role of other metrics and parameters to 
analyse UHF-ECG fragmentation. These data will be used to assess the value of QRS 
fractionation as a surrogate for arrhythmic risk in patients with underlying inherited 
arrhythmic or arrhythmogenic conditions. 
 
Burdens/Risks:  
 
There are no significant risks entailed within this non-invasive study. A potential burden will 
be the requirement to wear an ambulatory ECG monitor semi-continuously over a 3 month 
period, as participants must remain motivated and engaged. We expect that participants will 
not always be wearing the monitoring devices and as part of the study, we will be assessing 
acceptability, user-friendliness, practicality and participant comfort using feedback from 
research participants. The adhesives used for the ECG electrodes can cause skin irritation 
or redness in some participants and non-allergic adhesive preparations may be required.  
3.4 
STUDY OUTCOME MEASURES 
 
Primary Outcome Measures 
• 
Sensitivity, specificity and AUC value for neural networks 1 and 2 to detect the 
Brugada ECG pattern in 12-lead ECGs. 
Commented [BK2]: From this description I think this should 
be a Pre-clinical device development or performance 
testing  medical device study (no MHRA review required) 
Commented [KS3R2]: I have ameded project filter q2 on 
IRAS to reflect 
Commented [BK4]: Outcome measures differ to IRAS 
a57/58. No mention of AI alogrithm here 
Commented [KS5R4]: Sorry, neural network and AI 
algorithm used interchangeably. Neural network is jargon 

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• 
Yield of extended duration ambulatory ECG monitoring to detect the Brugada ECG 
pattern in patients with concealed Brugada syndrome, using neural networks 1 and 2. 
• 
Sensitivity, specificity and AUC value for neural network 3 to diagnose concealed 
Brugada syndrome from 12-lead ECGs 
• 
Sensitivity, specificity and AUC value for neural network 4 to diagnose an SCN5a 
mutation from 12-lead ECGs. 
• 
Yield of extended duration ambulatory ECG monitoring to detect the Brugada ECG 
pattern (neural networks 1 and 2) and LQTS manifestation in patients with idiopathic 
VF. 
 
Secondary Outcome Measures 
• 
Yield of UHF-ECG to detect underlying Brugada syndrome in patients with a 
concealed ECG phenotype. 
• 
Utility of UHF-ECG for arrhythmia risk stratification in patients with inherited 
arrhythmogenic conditions 
• 
Beta angle value with highest predictive accuracy for Brugada syndrome based on 
expert consensus, using regression modelling. 
• 
Identification of novel electrical manifestations that may represent underlying 
concealed arrhythmia syndromes during day-to-day physiological stressors. 
• 
Clinical endpoints such as ICD therapies, all cause mortality and any clinical events 
such as hospitalisations or clinical deteriorations. 
 
4.  
PARTICIPANT ENTRY  
4.1 
PRE-REGISTRATION EVALUATIONS 
Participants will be recruited at Imperial College Healthcare NHS Trust. Participants 
may be based at other institutions and will be identified via research collaborators. 
4.2 
INCLUSION CRITERIA 
 
FOR ALL PARTICIPANTS: 
1. Adults willing to take part (ages 18 – 100 years old)   
2. Able to give consent 
 
SPECIFIC CRITERIA BY PARTICIPANT SET: 
• 
Group A – healthy volunteers 
• 
Group B – patients with a normal ECG and an underlying inherited arrhythmic 
syndrome 
• 
Group C – patients with an arrhythmic syndrome with an abnormal ECG 
 
Further details in Appendix 1. 
4.3 
EXCLUSION CRITERIA 
- 
Unable to give consent 
- 
Children age < 18 years and adults > 100 years old 
4.4 
WITHDRAWAL CRITERIA 
The research protocol will be terminated early if: 
1. Patients lose their capacity to consent or become clinically unstable  
2. The patient chooses to withdraw from the study 
3. The sponsor, the chief investigator or the research team review the data and decide 
to stop the study 

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4.5  
CONSENT 
 
Study A 
ECG data will be obtained from the host institution (Imperial College Healthcare NHS Trust) 
as well as external centres both in the UK and internationally. This data will derive from 
patients identified by the site-specific representatives, who will coordinate secure ECG data 
transmission and acquisition. All ECG data will be collected anonymously or anonymised at 
the point of collection. Consent will not be sought as only anonymised ECGs will be 
obtained. There will be no access to patient-identifiable data from the research team and all 
data will be fully anonymised prior to transfer. 
 
Studies B and C 
Patients will be identified by members of their direct care team. They may be identified when 
they attend clinic appointments. Patient records will be reviewed to assess suitability and this 
will be performed by members of the direct care team. Participation in the study will be 
discussed with the patients by their direct care team and information will only be passed on 
to the research team with the patients consent. Verbal consent will be gained by the direct 
care team for personal contact information to be shared with the research team. This will be 
documented in the patient notes. The direct care team who discussed the study will make 
patients aware that participation is voluntary and that if they do not want to participate it will 
not affect their usual care. 
 
Consent for the study of the prospectively recruited patients will be obtained by a member of 
the research team. Patients will have details of the study discussed with them and any family 
members or friends the patients wish to be present. They will also be provided with written 
information (patient information sheets). Patients will be given as much time as they wish, 
with a minimum of at least 24 hours, to decide whether they wish to participate in the study 
and will be offered additional visits to further discuss the study if they wish. Patients are able 
to withdraw their consent from the study at any time. Patients will be made aware that their 
participation is voluntary and that if they do not want to take part it will not affect their usual 
care. Patients who agree to take part in the study will sign a consent form, a copy of the form 
will be given to the patients and a second copy will be kept in their study record file. A copy 
will also be kept in the site file.  
 
Conflicts of Interest: 
There are no conflicts of interest to report. 
 
5. 
ADVERSE EVENTS 
5.1 
DEFINITIONS   
 
Adverse Event (AE): any untoward medical occurrence in a patient or clinical study 
participant.   
 
Serious Adverse Event (SAE): any untoward and unexpected medical occurrence or effect 
that: 
• 
Results in death 
• 
Is life-threatening – refers to an event in which the participant was at risk of 
death at the time of the event; it does not refer to an event which hypothetically 
might have caused death if it were more severe 
• 
Requires 
hospitalisation, 
or 
prolongation 
of 
existing 
inpatients’ 
hospitalisation 
• 
Results in persistent or significant disability or incapacity 
• 
Is a congenital anomaly or birth defect 
Commented [BK6]: Please make distinction between Part A 
and B/C of study 
Commented [KS7R6]: done 

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Medical judgement should be exercised in deciding whether an AE is serious in other 
situations.  Important AEs that are not immediately life-threatening or do not result in death or 
hospitalisation but may jeopardise the participant or may require intervention to prevent one 
of the other outcomes listed in the definition above, should also be considered serious. 
5.2 
REPORTING PROCEDURES 
 
All adverse events should be reported.  Depending on the nature of the event the reporting 
procedures below should be followed.  Any questions concerning adverse event reporting 
should be directed to the Chief Investigator in the first instance.   
5.2.1  Non-serious AEs 
All such events, whether expected or not, should be recorded.   
5.2.2  Serious AEs 
An SAE form should be completed and faxed to the Chief Investigator within 24 hours.  
However, hospitalisations for elective treatment of a pre-existing condition do not need 
reporting as SAEs. 
 
All SAEs should be reported to the West of Scotland (REC 5) where in the opinion of the Chief 
Investigator, the event was: 
 
• 
‘related’, ie resulted from the administration of any of the research procedures; 
and 
• 
‘unexpected’, ie an event that is not listed in the protocol as an expected 
occurrence 
 
Reports of related and unexpected SAEs should be submitted within 15 days of the Chief 
Investigator becoming aware of the event, using the NRES SAE form for non-IMP studies.  
The Chief Investigator must also notify the Sponsor of all related and unexpected SAEs. 
 
Local investigators should report any SAEs as required by their Local Research Ethics 
Committee, Sponsor and/or Research & Development Office.  
 
Contact details for reporting SAEs 
RGIT@imperial.ac.uk 
Dr Zachary Whinnett email: z.whinnett@imperial.ac.uk 
Please send SAE forms to: Hammersmith Hospital, Du Cane road, London W12 0HS. 
Tel: 020 8383 4967 (Mon to Fri 09.00 – 17.00) 
 
 
 
6. 
ASSESSMENT & FOLLOW UP   
 
 
Patients will be followed up over a 3 month period remotely while wearing their ambulatory 
ECG. 12-lead ECG and UHF-ECG data will be collected at the index visit. Symptom diaries 
and ambulatory ECG recordings will be collected at the end of the study interval. Any 
incidental findings that we identify during the study will be reviewed by the research team 
and reported to the GP and also the cardiology team normally looking after the patient. We 
will continue to follow up all patients after their last contact with the research team through 
review of the electronic health record and any clinical cardiac device checks up until the end 
of the study on 1st April 2027, at which time all follow up will cease. The prospective studies 

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will end after 200 patients have undergone the 3 month interval of follow up or by the study 
end date of 1st April 2027. 
 
7. 
STATISTICS AND DATA ANALYSIS 
 
We expect to have access to over 3000 patients’ worth of ECG data to train and validate our 
neural networks, comprising a similar proportion of Brugada and non-Brugada ECGs. Our 
group has published extensively regarding applications of AI to ECG data (13–17) and 
includes experienced, independently-funded clinician scientists and engineers with expertise 
in machine learning. They will guide me in developing neural networks for ECG classification 
tasks and signal processing. As we will be employing a very similar methodology to our 
previous successful ECG AI projects in this experiment, we are confident that this quantity of 
ECG data will be sufficient to develop highly accurate neural networks. 
  
The accuracy of the prolonged duration ambulatory ECG recordings will be calculated using 
a binomial distribution.  Assuming an actual accuracy of 95% and a target accuracy of >85% 
for ECG analysis discriminating between healthy normal participants and manifest Brugada 
patients, 90 patients will provide greater than 80% power in demonstrating that the 
measured accuracy is significantly greater than 85% at the two-tailed 5% significance level. 
To achieve 90 patients with some loss to follow up, I will recruit 50 healthy volunteers and 50 
manifest Brugada patients. 
 
McNemar’s test will be used for paired analysis of 24-hour 12 lead Holter and prolonged 
ambulatory ECG recordings in the concealed arrhythmia patient set. Historical data report 20 
to 34% sensitivity for 24-hour Holter for detection of the Brugada ECG pattern in patients 
with concealed Brugada syndrome (5,6). Assuming 25% sensitivity for 24-hour Holter and 
60% for prolonged ambulatory ECG recordings beyond 24 hours, with the prolonged 
ambulatory ECG recordings being able to identify 80% of 24-hour Holter-identified cases, 35 
patients would be needed to provide 90% power at the 5% significance level. I will recruit 50 
patients. 
 
We are also seeking to identify at least one extra patient with Long QT or Brugada syndrome 
by performing extended duration monitoring beyond standard 12 lead 24-hour Holter. 
Registry data (7–9) suggests that between 3-4% of idiopathic VF patients eventually receive 
a specific diagnosis of Brugada syndrome through conventional diagnostic testing and 
routine clinical follow up. A similar proportion are diagnosed with long QT syndrome. If 
extended duration monitoring can identify half of these (4%), then 50 patients will provide 
87% power to detect at least one extra case (simulated through repeat sampling of a 
binomial distribution). 
 
We also plan to recruit 100 patients with other inherited arrhythmic conditions, who can be 
subdivided into high and low arrhythmic risk groups. We believe that these cohort sizes will 
be adequate to gather sufficient pilot data to enable identification of subtle changes in UHF-
ECG profiles. 
  
8. 
REGULATORY ISSUES 
8.1 
ETHICS APPROVAL 
 
The Study Coordination Centre has obtained approval from the by the North of Scotland (1) 
Research Ethics Committee and Health Research Authority (HRA). The study must also 
receive confirmation of capacity and capability from each participating NHS Trust before 
accepting participants into the study or any research activity is carried out. The study will be 
conducted in accordance with the recommendations for physicians involved in research on 

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human participants adopted by the 18th World Medical Assembly, Helsinki 1964 and later 
revisions. 
8.2 
CONSENT  
 
Consent to enter the study must be sought from each participant only after a full explanation 
has been given, an information leaflet offered and time allowed for consideration.  Signed 
participant consent should be obtained.  The right of the participant to refuse to participate 
without giving reasons must be respected.  After the participant has entered the study the 
clinician remains free to give alternative treatment to that specified in the protocol at any stage 
if he/she feels it is in the participant’s best interest, but the reasons for doing so should be 
recorded.  In these cases the participants remain within the study for the purposes of follow-
up and data analysis.  All participants are free to withdraw at any time from the protocol 
treatment without giving reasons and without prejudicing further treatment. 
8.3 
CONFIDENTIALITY OF RECORDS: 
 
The Chief Investigator will preserve the confidentiality of participants taking part in the study 
and is registered under the Data Protection Act. All data will be analysed 
pseudoanonymously.  
8.4 
INDEMNITY 
 
Imperial College London hold negligent harm and non-negligent harm insurance policies 
which apply to this study. 
8.5 
SPONSOR 
 
Imperial College London will act as the main Sponsor for this study.  Delegated responsibilities 
will be assigned to the NHS trusts taking part in this study.   
8.6 
FUNDING 
 
The Imperial Biomedical Research Centre and British Heart Foundation are funding this 
study. 
8.7 
AUDITS  
 
The study may be subject to inspection and audit by Imperial College London under their remit 
as sponsor and other regulatory bodies to ensure adherence to GCP and the UK Policy Frame 
Work for Health and Social Care Research 
 
9. 
STUDY MANAGEMENT 
 
The day-to-day management of the study will be co-ordinated by Dr Keenan Saleh. 
 
10. 
PUBLICATION POLICY 
 
Our aim to publish in a major international cardiology journal and present at international 
cardiology conferences. 
 
11.  
REFERENCES 
 

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1.  
Arnold A, Howard JP, Shun-Shin MJ, Reddy R, Wright IJ, Wyn Davies D, et al. 
Machine Learning Can Predict Ventricular Ectopic Foci Location From The 12 Lead 
Surface Ecg. Journal of interventional cardiac electrophysiology : an international 
journal of arrhythmias and pacing. 2019;55: 39.  
2.  
Chan C, Arnold A, Howard J, Shun-Shin M, Maclean E, Cullen B, et al. Explanation-
visualised deep learning model for accessory pathway localisation using 12-lead 
electrocardiography. EP Europace. 2021;23(Supplement 3). 
https://doi.org/10.1093/europace/euab116.510. 
3.  
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12.  
APPENDICES 
 
Appendix 1 – Arrhythmic syndromes 
 
Group B (concealed arrhythmia syndromes): 
• 
Concealed Brugada syndrome 
• 
Early repolarisation syndrome 
• 
Idiopathic VF 
• 
Purkinje-related arrhythmic syndrome 
• 
Concealed pre-excitation 
 
Group C (manifest arrhythmia syndromes): 
• 
Manifest Brugada syndrome 
• 
Manifest Long QT syndrome 
• 
Manifest short QT syndrome 
• 
Manifest pre-excitation / Wolff-Parkinson-White syndrome 

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Date: 02/04/2024 
• 
Catecholaminergic polymorphic VT (CPVT) 
• 
Arrhythmic cardiomyopathy (ACM) 
• 
Arrhythmogenic right ventricular cardiomyopathy (ARVC) 
• 
Arrhythmogenic left ventricular cardiomyopathy (ALVC) 
• 
Hypertrophic cardiomopathy 
• 
Dilated cardiomyopathy 
• 
Hypokinetic non-dilated cardiomyopathy 
• 
Lamin-related cardiomyopathy 
• 
Desmoplakin-related cardiomyopathy 
• 
Titin-related cardiomyopathy 
• 
Filamin C-related cardiomyopathy 
 
Appendix 2 – ECG AI algorithms / neural networks that will be developed 
 
• 
NN1 - Direct classification of the Brugada ECG pattern: This neural network will be 
trained to output whether a Brugada ECG pattern is detected based on the expert 
consensus classification. We will pool phenotype positive Brugada ECGs and non-
Brugada ECGs within our ECG dataset. 
• 
NN2 - Measurement-based classification of the Brugada ECG pattern: This neural 
network will be trained to establish the beta angle of the QRS (between the upslope 
of the S wave and downslope of the R’ wave). The beta angle can be used to 
discriminate the Brugada pattern from Brugada mimics. Experts will be tasked with 
performing manual measurements on the ECG and the consensus measurement will 
be used. We will pool ECGs from all 3 groups within our ECG dataset. 
• 
NN3 - Deep learning of concealed Brugada syndrome: This neural network will be 
trained to output whether an ECG represents underlying concealed Brugada 
syndrome. We will pool phenotype negative Brugada ECGs with a confirmed clinical 
diagnosis of Brugada syndrome alongside non-Brugada ECGs within our ECG 
dataset. 
• 
NN4 - Deep learning of an SCN5a mutation from the 12-lead ECG: This neural 
network will be trained to detect changes in the ECG which indicate an underlying 
SCN5a mutation. In ECGs for patients who have already had SCN5a genetic testing, 
these ECGs will be divided into two groups based on their SCN5a status and used to 
train the neural network.