Hearables: Ear Electrocardiography (ECG) and Photoplethysmography (PPG) for Real-time Detection of Cardiac Arrhythmias
Conditions: Arrhythmia
Sponsor: Imperial College London

 
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Hearables 
 
 
Ear Electrocardiography (ECG) and 
Photoplethysmography (PPG) for real-time detection of 
cardiac arrhythmias 
 
 
Version 1.0 30/10/2023 
 
 
 
 
MAIN SPONSOR: Imperial College London  
FUNDERS: UKRI award for the Centre for Doctoral Training in AI for Healthcare 
STUDY COORDINATION CENTRE: ICTEM Building 4th Floor, Hammersmith Hospital, London W12 0HS 
 
IRAS Project ID: 318229 
REC reference: xxx 
 
 
 
 
Protocol authorised by: 
 
Name and Role 
Signature  
Date  
Professor Nicholas 
Peters 
 
30/10/2023 
 
 
 
Dr Patrik Bachtiger 
 
30/10/2023 
 
Edoardo Occhipinti 
 
 
30/10/2023 
 
 
 
 
 
 
 

 
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Study Management Group 
 
Chief Investigator: Prof Nicholas S Peters (National Heart & Lung Institute)      
 
Co-investigators: Edoardo Occhipinti (Faculty of Engineering), Dr Patrik Bachtiger 
(National Heart & Lung Institute), Prof Danilo Mandic (Faculty of Engineering).  
 
Statistician: Edoardo Occhipinti 
 
Study Management: ICTEM 4 TH Floor, NHLI, Hammersmith Hospital, London W12 
0HS 
 
Study Coordination Centre  
 
For general queries, supply of study documentation, and collection of data, please 
contact: 
 
Study Coordinator: Edoardo Occhipinti 
Address: ICTEM 4 TH Floor, NHLI, Hammersmith Hospital, London W12 0HS 
Tel: +447842826570  
 
E-mail:edoardo.occhipinti16@imperial.ac.uk 
 
Clinical Queries 
 
Clinical queries should be directed to Professor Nicholas S. Peters 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: 
 
 
 
Research Governance and Integrity Team 
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 
Imperial College - Research Governance and Integrity Team (RGIT) Website 
 
 
Funder 
 
This work is supported by the UKRI award for the Centre for Doctoral Training in AI for 
Healthcare (Grant No. EP/S023283/1). 
 

 
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This protocol describes the Hearables 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.  
 
Table of Contents                                                                              
Page No 
1. INTRODUCTION 
5 
1.1 BACKGROUND 
5 
1.2 RATIONALE FOR CURRENT STUDY 
7 
2.  STUDY OBJECTIVES 
8 
3.  STUDY DESIGN 
9 
3.1 RECORDING PROTOCOL 
Error! Bookmark not defined. 
3.2 STUDY OUTCOME MEASURES 
11 
4. 
PARTICIPANT ENTRY  
12 
4.1 PRE-REGISTRATION EVALUATIONS 
12 
4.2 INCLUSION CRITERIA 
12 
4.3 EXCLUSION CRITERIA 
12 
4.4 WITHDRAWAL CRITERIA 
12 
5. 
ADVERSE EVENTS 
12 
5.1 DEFINITIONS 
13 
5.3 REPORTING PROCEDURES 
13 
6. 
ASSESSMENT AND FOLLOW-UP 
14 
7. 
STATISTICS AND DATA ANALYSIS 
14 
8. 
REGULATORY ISSUES 
16 
8.1 ETHICS APPROVAL 
17 
8.2 CONSENT 
17 
8.3 CONFIDENTIALITY 
17 
8.4 INDEMNITY 
18 
8.5 SPONSOR 
18 
8.6 FUNDING 
19 
8.7 AUDITS 
19 
9. 
STUDY MANAGEMENT 
19 
10. 
PUBLICATION POLICY 
19 
11. 
REFERENCES 
19 
 
GLOSSARY OF ABBREVIATIONS 
ACM 
Accelerometer 
BP 
Blood Pressure 
COPD 
Chronic Obstructive Pulmonary Diseases 
CVD 
Cardiovascular Disease 
ECG 
Electrocardiogram 

 
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ECM 
Electret Condenser Microphone 
EEG 
Electroencephalogram 
PAT 
Pulse Arrival Time 
PPG 
Photoplethysmogram  
PTT 
Pulse Transit Time 
ACM 
Accelerometer 
BP 
Blood Pressure 
CVD 
Cardiovascular Disease 
ECG 
Electrocardiogram 
ECM 
Electret Condenser Microphone 
EEG 
Electroencephalogram 
PAT 
Pulse Arrival Time 
PPG 
Photoplethysmogram  
PTT 
Pulse Transit Time 
 
KEYWORDS 
Arrhythmias; electrocardiography; photoplethysmography; hearables; cuffless blood 
pressure, continuous real-time monitoring 
 
STUDY SUMMARY 
 
TITLE Hearables: 
Ear 
Electrocardiogram 
(ECG) 
and 
Photoplethysmogram (PPG) for real-time detection of cardiac 
arrhythmias 
DESIGN Prospective observational study  
AIMS To evaluate qualitative and quantitative performance of ear ECG 
and PPG for real-time detection of arrhythmias against gold 
standard investigations. 
OUTCOME 
MEASURES 
Primary:  
- 
Arrhythmia detection and classification from Ear-ECG and 
Ear-PPG.  
Secondary:  
- 
Signal quality of Ear-ECG in different configurations, 
evaluated against 1-lead 8Rhythm strip9 ECG at rest 
- 
Ear-ECG and Ear-PPG artefacts removal for continuous 
real-time monitoring. 
- 
Feasibility of estimating blood pressure from features 
extracted from simultaneous ear-ECG and ear-PPG 
signals  
POPULATION 50 healthy patients and 50 patients with an established diagnosis 
of cardiac arrhythmias.  

 
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ELIGIBILITY 
 
Inclusion Criteria  
Aged > 18, no upper age limit 
Able to give informed consent; 
Healthy and diagnosed with cardiac 
arrhythmias.  
Exclusion Criteria  
No abnormal ear anatomy 
DURATION 
9 months  
 
1. INTRODUCTION  
 
1.1  
BACKGROUND 
 
Gold standard diagnosis of Cardiac Arrythmias:  
 
Cardiac arrythmias, also known as abnormal heart rhythms, such as atrial fibrillation 
are a leading cause of morbidity and mortality worldwide. Left untreated the effects of 
arrythmias can be detrimental to an individual9s quality of life and become a significant 
risk factor for stroke, heart failure, cognitive decline, hospitalisation, and death [1]. The 
lifetime risk and risk factor burden of arrythmias increases with an aging population 
meaning the increased risk of mortality and morbidities poses an ever-expanding 
burden to patients and healthcare systems worldwide. In 2019 around 18 million 
people died from CVDs and 85% of those deaths were due to heart attack or stroke 
[2], a large proportion of which was contributed by cardiac arrythmias. However, if 
detected early most people with an abnormal heart rhythm can lead a normal life.  
 
The impact of cardiac arrythmias on patients is based on multiple factors, resulting in 
a wide variation of clinical presentation varying from palpitations, fatigue, light-
headedness, chest discomfort, and alerted consciousness to no symptoms at all. In 
addition to this the duration of each arrythmia is dependent on individual physiology 
and anatomy, meaning some arrythmias occur randomly i.e., paroxysmal [3].  
 
Gold standard techniques to detect arrhythmias revolve around 12-lead ECG 
recording, either in the form of a single 12-lead ECG or via continuous ECG recording 
device known as a Holter monitor for 24 hours or longer. However due to the variability 
in presentation and symptoms of cardiac arrythmias per an individual, the success in 
obtaining a clinical diagnostic ECG evidencing an arrythmia is low. In this instance 
more invasive diagnostic tools such as a cardiac event recorder can be implanted into 
patients to record electrical cardiac activity over a longer period. There is therefore a 
need for better non-invasive portable continuous ECG monitoring solutions. 
 
Extensive research has been conducted on mobile self-monitoring ECG devices, such 
as KardiaMobile [4]. However, although these devices allow for remote monitoring, 

 
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they are heavily reliant on patient self-administration to be manually initiated. There is 
therefore an ongoing need for passive and continuous real-time arrhythmia detection 
systems. 
 
Current wearable technologies:  
 
Current wearable technologies have focused on recording cardiac activity either 
electrically through ECG or optically through PPG. So far, these technologies have 
been implemented in chest straps, smartwatches, and handheld devices [5]. These 
digital devices provide recreational recordings which must be manually initiated and 
require the subjects to be still at the time of recording. Moreover, most of these devices 
need a clinician to analyse the recordings to assess the presence of arrhythmias.  
 
Recent developments in wearable technology have opened up the potential for 
identifying various physiological signals (including EEG, ECG, and PPG) by placing 
standard medical grade sensors in the ears, referring to them as "Hearables" [6]-[7]. 
Considering that people are largely used to wearing earplugs and the head being in a 
more stable position than the arms in everyday activities, the ears provide a unique 
location for AI-enabled 24/7 cardiovascular monitoring.  
 
Hearables: Ear-ECG and Ear-PPG 
 
Previous works [6]-[7] have discussed the biophysics and feasibility of recording ECG 
from the ears. Different head locations and the ear canal were validated against 
standard Lead I ECG (ECG from the arms) in terms of the morphology (amplitude and 
duration) of the characteristic waves (P, QRS and T) relative to the R-peak [7]. Ear-
ECG was also used to detect an abnormal heartbeat caused by ventricular bigeminy 
[8], a type of arrhythmia where each heartbeat is quickly followed quickly by another 
twin (ectopic beat), generated by premature ventricle contraction. In all these cases 
however, the identification of the characteristic waves was possible only after 
averaging across multiple cardiac cycles [7], thus not allowing real-time monitoring. 
This is because the amplitude of ear-ECG is about 50 times smaller than a standard 
Lead I ECG, thus resulting in a much smaller Signal to Noise Ratio (SNR).  
 
Ear PPG has also been shown to be far more sensitive than finger PPG to amplitude 
variations that arise from respiration, thus allowing for a better measurement of 
respiratory rate [9]. Furthermore, a significant delay has been evidenced between 
earlobe pulse oximetry and pulse oximetry on the hand or the foot for detection of 
hypoxemia (low levels of blood oxygen) [10]. Previous work showed that in a study of 
14 healthy volunteers in-ear PPG compared with the current standard of care - the 
finger clip - the ear sensor shows non-inferiority and potential superiority with regard 
to detection of a mean blood oxygen delay (the time it took from detecting minimal 

 
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blood oxygen change in the ear to detecting minimal blood oxygen in the finger) 
reduction of 12.4 seconds, thus being faster at identifying acute desaturation [11]. 
Furthermore, ear PPG breathing waveforms have also been used to identify breathing 
disorders such as chronic obstructive pulmonary disease (COPD) and train a model 
able to distinguish those subjects against healthy and subjects with pulmonary fibrosis 
[12]. 
 
The estimation of blood pressure from the ears has been attempted in some 
preliminary works, by employing in-ear cuff-based systems with inflatable balloons 
[13], microphone and PPG signal from a single ear [14], as well as by positioning two 
PPG sensors, one in each ear and exploiting the PTT (i.e. the time necessary for the 
blood pressure wave to travel between two arterial sites) between the left and the right 
ear  [15]. 
 
1.2 
RATIONALE FOR CURRENT STUDY 
 
In the future sensors in the ear-region either as a standalone system or integrated with 
other third parties9 earbuds, may provide the opportunity to monitor the heart health in 
an unobtrusive, comfortable, and continuous way. This project sets out to explore the 
feasibility of recording ECG from the ears in real-time and use features extracted from 
ear-ECG and ear-PPG waveforms to detect cardiac arrhythmias. As discussed above, 
characteristic waves from ear-ECG could only be extracted after averaging across 
multiple cardiac cycles. From this work we aim to test both in-ear and outside of the 
ear (mastoid) electrodes to identify which configuration allows to consistently extract 
timings of the characteristic waves in real-time, and capture morphological changes. 
From the simulated cardiac electrical potentials on the head described in [7], and from 
the heart rate variability study in [16], we hypothesise that a signal recorded from the 
mastoid will be stronger in amplitude, and less prone to artefacts, compared to the 
signal recorded from inside the ear-canal.  
 
It is also important to assess the quality of the signals during normal daily activities to 
fully exploit the potential for continuous ear ECG/PPG monitoring. Previous work has 
shown the presence of artefacts caused by jaw clenches [6], jaw-movements 
(chewing, talking) and walking [17]. The proposed denoising method [18] has been 
evaluated on ear-EEG signals and on a small cohort (12 subjects). In this study, we 
aim to further improve a series of different artefacts related to both head movements, 
jaw movements (talking and chewing) as well as full body movement (walking). The 
artefact signals will also be captured by some external sensors, such as an ECM and 
a 3-axis ACC, as described in [18]. Multiple approaches in the literature have been 
investigated to remove motion artefacts in standard ECG signals, such as adaptive 
noise cancellation (ANC), independent component analysis (ICA) and empirical mode 
decomposition (EMD) as well as deep learning architectures in more recent years. 

 
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Ear-ECG is a novel method to record ECG with a much lower SNR, further 
investigation is required to select the optimal artefacts removal solution. Our 
hypothesis here is that artefacts which cause more internal motion within the ear-canal 
(such as talking and chewing) will be better captured by the ECM, while head and full 
body movements will be better characterised by ACC.  
 
As part of the clinical study, we will also investigate the feasibility of estimating blood 
pressure from ear ECG and PPG signals using information such as Pulse Arrival Time 
(PAT). ECG signals are electrical and hence arrive instantaneously to the 
measurement site, while PPG signals are related to the blood flow and will naturally 
have a delay. This delay (i.e. PAT) has been found to be negatively correlated with 
blood pressure. Previous works discussed above [13]-[15] have attempted to estimate 
blood pressure from the ears, however the current approaches were often tested on 
small healthy cohorts, and lack generalisation to unseen and pathological subjects. 
There is therefore scope in the literature to exploit the multimodality of the Hearables 
and extract information from both ear ECG and PPG signals to estimate BP. As part 
of the protocol, patients will be asked to perform some slow deep breaths, which result 
in the activation of the parasympathetic nervous system, dilation of blood vessels, and 
consequent reduction of the overall BP [19]; as well as immersing their hand in cold 
water < 4 ºC (cold pressor test), which on the contrary triggers a vascular sympathetic 
activation, thus increasing BP [20].  BP will also be monitored intermittently with a 
standard medical grade cuff-based device (already in use in the hospital) and those 
values will be used as ground truth and for calibration. Then, domain-specific features 
will be engineered to encode information related to blood pressure. 
 
The data generated in this pilot feasibility study would allow scaling to prepare for 
larger research projects, aiming to move to remote monitoring of patients. Beyond the 
use for monitoring already diagnosed patients, the algorithms developed in this study 
will also allow to diagnose arrhythmias at the time of onset, providing both clinical and 
health economic benefits, expanding the clinical and consumer market of wearable 
health technology towards more personalised health.  
 
2.  STUDY OBJECTIVES 
 
Objectives: 
Primary:  
- 
Detection and classification of arrhythmias from Ear-ECG and Ear-PPG 
waveforms 
Secondary:  
- 
Evaluate qualitative and quantitative performance of different configurations of 
ear-ECG against a gold standard 1-lead 8Rhythm strip9 ECG. 

 
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- 
Removal of artefacts caused by common daily activities (head movements, 
talking, chewing, walking) towards a continuous ECG/PPG monitoring solution. 
- 
Feasibility of estimating BP from features extracted from simultaneous ear-ECG 
and ear-PPG signals. 
 
Hypotheses: 
 
When tested on patients in real-world settings: 
- 
Physiological signals recorded from the ears (Ear-ECG, Ear-PPG) can be used 
for arrythmia detection and diagnosis. 
- 
Signals from ECM and ACC sensors can be used to remove artefacts in Ear-
ECG and Ear-PPG signals, thus paving the way towards continuous monitoring 
solutions. 
- 
Relevant features such as PAT can be extracted from simultaneous Ear-ECG 
and Ear-PPG signals and used to estimate BP non-invasively in real-time. 
 
3.  STUDY DESIGN 
 
Prospective observational study of 50 healthy patients, and 50 with a pre-existing 
diagnosis of by cardiac arrhythmias. The study sample size of n = 100 is based entirely 
on feasibility. Patients with an already diagnosed cardiac arrythmia will be approached 
either on the hospital ward or during their outpatient visit at Imperial College NHS Trust 
by a clinical member of the research team. Patients will first be asked if they are happy 
to be approached for participation in this research study by the clinical team directly 
involved in their care. Identified participants will be provided with a clear explanation 
and patient information sheet detailing the study. Only after patients express having 
had adequate time to read and understand the study, they will be asked to provide 
written informed consent in person. Please see full details of obtaining informed 
consent in sub-heading 88.2 consent9 below. After informed consent has been 
completed, participants will then be set up with Ear-ECG and Ear-PPG sensors.  
 
Hearables: Ear-ECG and Ear-PPG sensors  
 
Some off-the-shelf medical grade electrical and optical sensors will be placed in and 
around the ears, as shown in Figure 1. Two small silver-cup current standard-of-care 
EEG electrodes are placed in each mastoid with medical-grade gel to allow 
connectivity. Both the conductive gels and electrodes are already used in the NHS for 
standard of care EEG. In addition to this, two in-ear earpieces with a cloth electrode 
attached to a self-expanding foam [6] (the one used for commercial earplugs) are 
placed inside each ear canal. On the left ear, external sensors (ECM and ACC) are 
attached to the foam [18], and an additional PPG sensor (MAX30101 from Maxim 
Integrated) is attached to the back of the ear with adhesive medical tape.  
 

 
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ECG and PPG waveforms will be sent via Bluetooth to a secure laptop and visualised 
in real-time to spot any potential disconnections at the time of recording. At the end of 
each recording, the signals will be immediately uploaded to a secure repository. The 
in-ear sensor will be single-use and disposed of after use on a patient, thereby posing 
no infection control risk.  
 
3.1 
RECORDING PROTOCOL 
 
The estimated setup time to place the ear sensors and electrodes is approximately 5 
mins. In addition to being connected to the Ear-ECG and Ear-PPG sensors, the patient 
will also be connected to a gold standard single-lead ECG monitoring equipment, and 
a BP cuff in the arm, already in use on the hospital ward as part of their standard of 
care. 
 
After setup, each patient will start the recording protocol following instructions 
visualised on a screen. The participants are required to be sitting up for the duration 
of the recording and follow the instructions on the screen, apart from the <walking= 
activity (if able and willing), where they will walk freely closely followed by a doctor and 
walking assistance if needed. In the event a patient is not mobile or has been medically 
deemed not safe to walk, this step will be skipped.  
 
The instruction on the screen will be both written and visualised in a video for which 
the participants will be asked to mimic as closely as possible. The protocol lasts for a 
total of 12 mins plus breaks (reaching a maximum of 15 mins), with the activities 
described in Table 1. Patients will have allocated breaks between each activity. This 
has not been fixed, but rather left up to each person to ensure longer breaks if needed. 
However, a minimum break of 10 seconds will be guaranteed between tasks. If at any 
point the patient is uncomfortable or wishes to cease the activity early, the sensors will 
be safely removed, and the study ceased.   
 
All data recorded will be sent via Bluetooth to a research laptop. All data will be 
pseudo-anonymised, with individual Study ID. There will be no linkage to any 
identifiable patient information.  
 

 
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Figure 1. (a) Electrodes and sensors placement in the left ear including behind the ear 
PPG, silver-cup electrodes in mastoid and in-ear memory-foam earpiece, where 
accelerometer (ACC) and microphone (ECM) are attached. In the right ear the silver-
cup electrode and another earpiece with ECG cloth electrode will be placed. 
 
 
Table 1. Recording protocol. Total duration: 12 mins with breaks. The thick black lines 
correspond to instances where the blood pressure is measured from the cuff. 
 
Baseline 
Artefacts 
Baseline 
Blood pressure 
Relax 
Head 
motion 
Talking 
Chewing 
Walking 
Relax 
Slow 
breathing 
Cold-
pressor 
test 
 
Up-down 
and left-
right head 
swing 
Counting 
aloud up 
to 60 
Chewing 
gum or 
simulate 
chewing 
Walking 
freely 
(with 
assistance 
if needed) 
 
Deep 
slow 
breaths 
Immerse 
hand in 
4ºC cold 
water 
2 min 
1 min 
1 min 
1 min 
1 min 
2 min 
2 min 
2 min 
 
3.2 
STUDY OUTCOME MEASURES 
 
Primary:  
Arrhythmia detection and classification from Ear-ECG and Ear-PPG: 
- 
HRV and morphological features extraction; 
- 
Identification of the timings of the characteristic waves (P, QRS, T); 
- 
Arrhythmias classification accuracy.  
 
Secondary: 
1) Performance of different ear-ECG waveforms in different placements of the ear 
against a gold standard 1-lead 8Rhythm strip9 ECG 
- 
SNR in different ear configurations; 
- 
Correlation of estimated heart rate (HR) and heart rate variability (HRV) features; 
PPG
ECG
Cloth electrode
Foam
ACC
Silver-cup electrode
Ear PPG
Mastoid ECG
In-ear ECG
ECM

 
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- 
Correlation of RR, PR and ST intervals; 
 
2) Artefacts removal evaluation in Ear-ECG and Ear-PPG:  
- 
Power reduction from artefact-corrupted signal to denoised signal; 
- 
Recovery of ECG characteristic waves (P, QRS and T) masked by presence of 
artefacts. 
 
3) Blood pressure continuous monitoring from ear-ECG and ear-PPG 
- 
Expected trend of increasing and decreasing blood pressure depending on the 
haemodynamic state;  
- 
Mean Absolute Error between estimated and BP cuff measures. 
 
4. PARTICIPANT ENTRY  
 
4.1 
PRE-REGISTRATION EVALUATIONS  
 
No pre-registration evaluations are required.  
 
4.2 
INCLUSION CRITERIA 
 
- 
Age >= 18 (no upper limit) 
- 
Able to give informed consent  
- 
Healthy control patients and patients with a diagnosed cardiac arrythmia  
 
4.3 
EXCLUSION CRITERIA 
 
- 
Abnormal ear canal anatomy 
 
4.4 
WITHDRAWAL CRITERIA 
 
Participants are free to withdraw their consent from the study at any point.  
 
5. ADVERSE EVENTS 
 
This is a minimal risk study. There are no 8side effects9 or contact hazards. Each ear 
sensor is for use by a single patient. All electrodes and contact gels have been 
medically graded and are already used in the NHS.  
 
The electrodes will not interfere with any implanted medical devices the participant 
may have or cause any harm to the participant during the study. Patients are free to 
stop the study at any time during their participation. 
 

 
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5.1 
DEFINITIONS   
Adverse Event (AE): any untoward medical occurrence in a patient or clinical study 
subject.   
 
Serious Adverse Event (SAE): any untoward and unexpected medical occurrence or 
effect that: 
 
ï Results in death 
ï Is life-threatening 3 refers to an event in which the subject 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 
 
ï Requires hospitalisation, or prolongation of existing inpatients9 
hospitalisation 
ï Results in persistent or significant disability or incapacity 
ï Is a congenital anomaly or birth defect 
 
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 subject or may require intervention to 
prevent one of the other outcomes listed in the definition above, should also be 
considered serious. 
 
5.3 
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.   
 
5.3.1 Non serious AEs 
 
All such events, whether expected or not, should be recorded.   
 
5.3.2 Serious AEs 
An SAE form should be completed and emailed to the Chief Investigator within 24 
hours.  However, relapse and death due to established medical conditions, and 
hospitalisations for elective treatment of a pre-existing condition do not need reporting 
as SAEs. 
 
All SAEs should be reported to the <name of REC> where in the opinion of the Chief 
Investigator, the event was: 

 
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ï 8related9, ie resulted from the administration of any of the research 
procedures; and 
ï 8unexpected9, 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 
CI email (and contact details below) 
n.peters@imperial.ac.uk 
Fax: xxx, attention xxx 
Please send SAE forms to: xxx 
Tel: 07946579930 (Mon to Fri 09.00 3 17.00) 
 
6. ASSESSMENT AND FOLLOW-UP  
 
The recruitment for the study will end when the requisite number of 50 healthy 
participants and 50 patients with diagnosed arrhythmias has been achieved. The study 
will end when the last trial participant performs the last data collection (i.e. "last subject, 
last assessment=). 
 
INCIDENTAL FINDINGS 
Incidental findings are highly unlikely during this study, as ECG/PPG signals are being 
recorded from patients with already diagnosed arrythmias. In the unlikely event of the 
study team making an incidental finding, they will notify the team in charge of the 
patient9s care immediately.  
 
There will be no follow-up for patients once they have participated in the study. 
 
7. STATISTICS AND DATA ANALYSIS 
 
Data will be analysed using statistical libraries and packages in Python. Analysis will 
be conducted by E.O. on a daily basis, in collaboration with cardiology clinical research 
fellows with appropriate access privileges (including honorary Imperial NHS contract).  

 
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In order to assess the accuracy of arrhythmia classification, we will extract features 
from the ear ECG and PPG recordings, specifically pertaining to HRV and 
morphological characteristics. These extracted features will be used to train various 
classifiers, including Decision Trees, Support Vector Machines, Bayesian models, and 
Neural Networks. 
 
To evaluate the effectiveness of the two ear-ECG recording positions (mastoid and in-
ear) in comparison to the standard-of-care Lead I configuration, two primary metrics 
will be employed. The initial metric involves assessing the precision, recall, and F1 
score for accurately identifying the R-peak's location [16]. The second metric is the 
Signal-to-Noise Ratio (SNR), where the noise is due to both the smaller amplitude of 
the ear-ECG as well as the additional physiological signals such as EEG, EMG and 
more, which are inevitably measured when recording ECG from the ears. Since in this 
study we are interested in the ECG information only, we will estimate the SNR by 
comparing these ear signals with standard of care simultaneously recorded Lead I 
ECG. 
 
With regards to artefacts removal, two metrics will be used: recovery of the 
characteristic waves (previously masked by the presence of the artefact) and reduction 
in power spectral density of the corrupted signal in the frequency of the artefacts. 
Indeed, we expect the artefacts to increase the power spectrum of the signal in specific 
frequency ranges, overlapping with the ones of the characteristic waves [15].  
 
Finally, to assess the accuracy of the blood pressure estimation model we will 
calculate the mean absolute error (MAE) between the predicted blood pressure 
derived from ear-ECG/PPG features and the actual measurements obtained from the 
cuff. We will also investigate whether a personalised calibration helps in reducing this 
error. We will also look at the expected trends of blood pressure changes with respect 
to the baseline, depending on the current haemodynamic state.  
 
Data Protection, Security and Compliance 
 
Prior to commencement this study be reviewed and obtain approval from the Imperial 
College Healthcare NHS Trust Data Protection Office. Data and all appropriate 
documentation will be stored for a minimum of 10 years after the completion of the 
study, including the follow-up period.   
 
REDCap (Research Electronic Data Capture) 
 

 
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All nonidentifiable patient study data will be collected using a REDCap (Research 
Electronic Data Capture) database, with a unique nonidentifiable study ID for each 
participant.  
 
During enrolment, the following data (where available) will be captured by the 
investigator from the electronic health record and stored on the REDCap database: 
 
- 
Age 
- 
Ethnicity 
- 
Comorbidities 
- 
Past medical history including cardiac arrhythmias  
- 
BMI (height and weight)  
- 
Medication  
- 
Electrocardiogram monitoring  
- 
Blood pressure 
 
Hearables Data Security  
 
The only data recorded will be digital ECG and PPG waveforms. Data will not be 
identifiable to the patient (i.e. not associated with an NHS number, date of birth or 
other identifier). Data recorded will be sent via Bluetooth to a password protected 
research laptop stored in a locked cabinet in a restricted building, only assessable to 
the study team. Information will then be periodically uploaded to securely store the 
data onto an encrypted folder on Imperial College NHS trust servers and computers. 
Data will be stored for a minimum of 10 years once the study is completed, in 
accordance with Imperial College policy.  
 
Missing Data 
 
The study design and nature of the variables collected makes missing data unlikely. 
To avoid introducing bias into the dataset by removal of participants with any missing 
data, all participants will be retained; missingness any variable will entail the affected 
participant not being included only in the analyses specifically relevant to that variable. 
Access to shared primary and secondary care electronic health records should make 
missing data retrievable in most cases. Details of any missing data will be explicitly 
described in any reports or publications.  
 
8. REGULATORY ISSUES 
 

 
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8.1 
ETHICS APPROVAL 
The Study Coordination Centre has obtained approval from the xxx Research Ethics 
Committee (REC) and Health Regulator 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 human subjects 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.  
 
Inpatients will be given minimum 24 hours to decide whether or not to take part in the 
study. The research team will aim to obtain full consent within 72 hours of the patient 
receiving the patient information sheet. Outpatients instead will be provided with the 
patient information sheet prior to their clinical appointment and only after having the 
time to read the study patient information sheet and ask necessary questions, the 
patient will be recruited into the study post clinic. Patients will be given a minimum of 
the duration of their appointment to decide whether or not to take part in the study. 
This alternative is provided to eliminate the necessity for patients to make an additional 
trip to the hospital solely for the purpose of participating in the study data collection. If 
patients are still not sure in that timeframe, patients can be offered a telephone 
consultation to obtain informed consent by a member of the research team to call back 
in 48h after discharge from their outpatient appointment. 
 
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 
participant9s 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 
The Chief Investigator will preserve the confidentiality of participants taking part in the 
study and is registered under the Data Protection Act. Data will be collected according 
to the data protection act 2018 in line with the general data protection regulation 
(GDPR).  
 

 
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Paper consent forms and questionnaires will be held in a secure room (swipe card 
access only) inside a locked cabinet (key only accessible to the study team) at Imperial 
College Healthcare NHS Trust (ICHNT). At the end of the study these consent forms 
will be securely collected and transported by a member of the research team and held 
in secure physical storage at ICHNT. 
 
This study involves multiple layers of data protection. First, pseudonymised login 
details will be generated for each participant by the research team, thereby mitigating 
the need for the entry of personal identifiable data. The unique study ID for each 
participant will only be identifiable by the principal investigator using an encrypted 
master key held in a password protected file on NHS servers, linking the study ID to 
the NHS numbers. Data will be automatically transferred to a secure research laptop, 
where it will be password-protected and only accessible to the research team. All data 
transferred to the research laptop via Bluetooth will be stored under the Study ID. 
When not in use, the laptop will be stored in a secure room (swipe card access only) 
inside a locked cabinet (key only accessible to principal investigator) at site. After the 
study period, data and all appropriate documentation will be stored for a minimum of 
10 years, according to Imperial College policy. 
The study data will be collected using a REDCap (Research Electronic Data Capture) 
database, with a unique study ID for each participant (only identifiable by the principal 
investigator using an encrypted master key held in a password protected file on NHS 
servers). Patients will be allocated a de-identified study ID number, derived from a key 
that processes NHS numbers algorithmically. This key will be kept secure in a 
password encrypted file (accessible only to principal investigator). Study participant 
data will therefore not be identifiable unless researchers revert study ID9s to NHS 
number. 
 
No data received by research laptop will be identifiable to the patient (i.e. not 
associated with an NHS number, date of birth or other identifier). At the end of the 
study, all data will be stored in accordance with Imperial College Healthcare NHS Trust 
policy on secure Trust computers.  
 
Upon completion of the study, data will be accessible to the co-investigators only, no 
data will be transferred to other additional third parties. 
 
8.4 
INDEMNITY 
Imperial College London holds 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.   

 
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8.6 
FUNDING 
This study is funded through an award held by Edoardo Occhipinti, supported by the 
UKRI award for the Centre for Doctoral Training in AI for Healthcare (Grant No. 
EP/S023283/1). Participants will not be paid for joining the study. Investigators will 
receive no direct or indirect payments for conducting the study. 
 
8.7 
AUDITS  
The study may be subject to 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 through the Imperial 
College & Trust, 4th Floor ICTEM, Hammersmith Hospital, London, W12 0HS. 
 
10. 
PUBLICATION POLICY 
Results from this study will be submitted for publication in peer-reviewed medical 
journals with authors as per the listed investigators. Results may also be discussed in 
internal reports and presented at scientific conferences.  
 
11. 
REFERENCES 
 
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