A Survey on Technologies Used During out of Hospital Cardiac Arrest

Gaurav Rao

1 a

, David W. Savage

2 b

, Vijay Mago

3 c

and Pawan Lingras

Saint Mary’s University, Halifax, Nova Scotia, Canada

Northern Ontario School of Medicine, Thunder Bay, Ontario, Canada

Lakehead University, Thunder Bay, Ontario, Canada

Keywords:

Out of Hospital Cardiac Arrest (OHCA), Automated External Deﬁbrillator, Cardiopulmonary Resuscitation

(CPR), CPR Feedback, Fall Detection, Agonal Breathing, OHCA Workﬂow, Responder Network System.

Abstract:

Out of hospital cardiac arrest (OHCA) causes close to 400,000 deaths every year in North America, and

it is also a leading cause of death among young athletes. OHCA is a treatable medical condition, and the

patient’s survival chances can be increased if immediate treatment is provided to the patient. However, non-

treatment of the patient leads to a dramatic decline in survival chances at 10% per minute. Currently, various

technologies are being used, and many more are being researched to reduce the time to provide early treatment

to the patient. This survey focuses on summarizing various available technologies for use during OHCA. This

survey focuses on evaluating technologies used in each step of the OHCA process. In this survey, articles were

searched using the term “ohca” on Google Scholar and more than 18,000 articles were found. The articles

were further ﬁltered using keywords for each stage of the OHCA process, ﬁnally, 112 articles were used in

this survey. The technologies that exist today work independently and are not linked with the other steps of the

OHCA process. Integration between these technologies could help in reducing time and increase the survival

chances of the patient.

1 INTRODUCTION

Sudden Cardiac Arrest (SCA) is a medical condi-

tion in which a patient’s heart either stops or beats

irregularly with little or no cardiac perfusion (Med-

lineplus.gov, 2021). Every year more than 350,000

deaths occur in the U.S. due to out of hospital car-

diac arrest (OHCA), and more than 40,000 deaths in

Canada (Heart.org, 2021a; Research, 2019). OHCA

is the leading cause of death among young athletes,

which means that SCA can occur to anyone irrespec-

tive of their health or age (Landry et al., 2017). Dur-

ing OHCA, the patient collapses and the blood circu-

lation to organs either stops or is insufﬁcient to pre-

vent damage to organs (Medlineplus.gov, 2021). In

this condition, the patient’s survival rate decreases at

a signiﬁcant rate of 10% per minute (Heart.org, 2014;

Valenzuela et al., 1997). With the rapid decline in

survival after each minute, early identiﬁcation and re-

sponse by both bystanders and emergency medical

services (EMS) is required. The recommended treat-

https://orcid.org/0000-0003-1092-9617

https://orcid.org/0000-0003-2837-3127

https://orcid.org/0000-0002-9741-3463

ment with the greatest evidence for improving sur-

vival in OHCA is performing cardiopulmonary resus-

citation (CPR) and the application of the automated

external deﬁbrillator (AED) (Heart.org, 2014; Nord

et al., 2017; Folke et al., 2021). Studies conﬁrm that

the survival rate of the patient increases if the recom-

mended treatment is started within the ﬁrst few min-

utes of the OHCA occurrence (Sanko et al., 2020; Pi-

jls et al., 2016).

During an OHCA, several steps are performed

prior to the arrival of the EMS and if executed swiftly,

the survival chances of the patient increases. Figure 1

shows the workﬂow followed during the emergency.

The OHCA workﬂow includes steps taken by either

the bystanders or the dispatcher to assist the patient

until the emergency medical services arrive at the pa-

tient’s location. Figure 1, shows a graphical represen-

tation of the workﬂow. The ﬁrst step of the OHCA

workﬂow is when a patient collapses or losses of con-

sciousness. This step can be observed by a bystander

or an intelligent device such as a smartphone, smart-

watch, or camera. The witnessing of the patient’s col-

lapse is essential as a delay in identifying the patient

suffering an OHCA delays the workﬂow and rapidly

Rao, G., Savage, D., Mago, V. and Lingras, P.

A Survey on Technologies Used During out of Hospital Cardiac Arrest.

DOI: 10.5220/0011749800003414

In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF, pages 477-488

ISBN: 978-989-758-631-6; ISSN: 2184-4305

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

477

RNS Responder

Dispatcher

Bystander

Figure 1: Emergency workﬂow.

decreases the patient’s survival chances (Chan et al.,

2019; Rea et al., 2021; Lu et al., 2019). The second

step in the OHCA workﬂow is to alert the emergency

services, the bystander can perform this by calling the

emergency services through a telephone. Depending

on the smart device, this step can be performed in var-

ious ways: 1) Send an alert to the patient’s emergency

contact and then they call the emergency services. 2)

The smart device sends an alert to its service provider

and then the service provider calls the emergency ser-

vices (Rao et al., 2020; Berglund et al., 2018). 3) The

smart device itself sends an alert to the emergency ser-

vices. The workﬂow’s third step includes dispatching

the emergency resources to the patient’s location, the

dispatcher performs this action. The emergency re-

sources activated for the patient depend on the ser-

vices available at the emergency center. The services

include: 1) dispatching emergency medical services,

2) dispatching ﬁre and police units, and 3) activating

a Responder Network System (RNS) (Stieglis et al.,

2020; Rao et al., 2020). The fourth step is to re-

suscitate the patient by performing CPR. This step is

performed by either a bystander who witnessed the

OHCA or by a bystander who is responding after re-

ceiving the alert from RNS. At this step, the bystander

can be assisted by a feedback device or the dispatcher

to perform high-quality CPR (White et al., 2017; Plata

et al., 2019).

This survey is structured based on the steps fol-

lowed in the OHCA workﬂow and the technologies

used within each step of the workﬂow. Section 2 de-

scribes the search criteria used to select articles for

the survey. Section 3 explains the technologies used

for identifying a patient experiencing an OHCA. The

techniques used to conﬁrm cardiac arrest in the pa-

tient are discussed in Section 4. Section 5 details

technologies used to assist a bystander to perform

high-quality CPR. In Section 6 technologies related

to AED assistance are discussed. Section 7 provides

a summary and discussion of all the technologies used

during the OHCA.

2 METHODOLOGY

OHCA is a broad topic for research as it includes top-

ics such as: 1) the training of the general public and

medical professionals in the use of technologies for

the management of cardiac arrest patients; 2) proto-

cols, procedures and technologies used by bystanders,

emergency dispatch centres, and emergency medical

services during cardiac arrest; 3) the treatment and

protocols used by the doctors and nurses at the hos-

pital after the arrival of the patient. The focus of this

survey is to summarize the technologies used by by-

standers or dispatch centres during an OHCA event.

The OHCA event can be divided into four steps: 1)

witness OHCA, 2) conﬁrm OHCA, 3) CPR assis-

tance, and 4) AED assistance. The search strategy for

article selection for each of these steps is discussed

below and also shown in Figure 2.

To obtain the articles for this survey, a search was

performed with the term “OHCA” on Google Scholar

and PubMed, the query resulted in 8,950 and 1,937

matches, respectively (LLC, 2021; NIH, 2022). A

further comparison was performed on the results and

it was found that the Google Scholar results cov-

ered more than 90% of the articles found on Pubmed.

Therefore, all search queries for this survey were per-

formed on Google Scholar. For each step of the

OHCA workﬂow, a more comprehensive search query

was generated using a publication date ﬁlter set to ﬁnd

articles from 2017 onward to review the latest tech-

nologies. The results from these queries were ﬁltered

using a semi-automated ﬁltering process to select only

the relevant article for each step.

Witness OHCA: To select articles for this section,

three different searches were aggregated, which are:

a) “OHCA detection smart watch”, b) “OHCA ago-

nal breathing identiﬁcation”, and c) “OHCA fall de-

tection”. The ﬁlter of publishing date since 2017 was

also applied, and these queries resulted in 93, 276, and

367 articles, respectively. The number of duplicate

articles in each of these queries were 8, 7, 10, respec-

tively, and were removed from further analysis. The

next step for ﬁltration was performed using a python

script called “QuickSearcher” or “QS”, pesudocode is

presented in Algorithm 1. The authors developed QS

to search for keywords with logical operations (AND,

OR) in the title or abstract of the article. For each

of the three queries, the following ﬁltering query was

HEALTHINF 2023 - 16th International Conference on Health Informatics

478

Manual filtering

Removing duplicates

18,000+

8,950

93 276 367

705

62364

Google Scholar search query “ohca”

Filter since 2017

Witnessing OHCA CPR Assistance AED Assistance

"ohca detection

smart watch"

Confirming OHCA

QS filter : (smartwatch)

AND (identify OR

recognize OR detect)

"ohca agonal breathing

identification”

269

QS filter : (agonal)

AND (identify OR

recognize or detect)

"ohca fall detection”

357

QS filter: (fall AND

detect AND cardiac)

"ohca agonal

breathing call taker”

"ohca cpr feedback

technology”

603

142

QS filter :

(cpr or

compression)

"ohca aed AND find

OR locate”

689

QS filter: (aed OR aeds) AND (locate

OR search OR find OR near OR

close OR deliver OR transport OR

carry OR bring)

QS filter refers to the filtering performed using the “QuickSearcher”.

Figure 2: The method of selection and ﬁltering of articles used in this survey, along with the count of articles selected at each

step.

made to QS, respectively: 1) smartwatch AND (iden-

tify OR recognize OR detect) 2) agonal AND (iden-

tify OR recognize OR detect) 3) fall AND detect AND

cardiac. A manual review was performed on the QS

results to identify articles relevant to this survey. The

authors performed the manual review and identiﬁed a

total of 21 articles from all three queries performed in

this section.

Algorithm 1: Pseudocode for Quick Searcher.

Input:

AllData = List of titles and abstract of the articles

AllSearch =List of combinations to search

Output:

List of articles matching search criteria

Process:

for data IN AllData do

AllMatchFound = True

newText = Join title and abstract in data

for search IN AllSearch do

if search Does Not match newText then

SET AllMatchFound = False

end if

end for

if AllMatchFound is True then

ADD article to output

end if

end for

Conﬁrm OHCA: The initial query performed in

Google Scholar for this section was “ohca agonal

breathing call taker”, and articles published since

2017, resulting in 64 articles. Due to a small num-

ber of articles found, the authors performed a manual

review of the articles’ abstracts and selected 20 most

relevant articles.

CPR Assistance: In order to ﬁnd articles for this sec-

tion, a Google Scholar search was performed using

the terms “ohca cpr feedback technology” for articles

published since 2017, which resulted in 623 articles

out of which 20 were duplicates. QS was used to ﬁl-

ter the results further by using the query “compression

OR cpr”, which narrowed the results to 142. The au-

thors then performed a manual review of the title and

abstracts and selected 37 articles.

AED Assistance: The query was performed using

Google Scholar for this section using the terms “ohca

aed AND ﬁnd OR locate”, for articles published since

2017. The query resulted in 705 articles which in-

cluded 16 duplicates. The results were further ﬁltered

using the QS query “(aed OR aeds) AND (locate OR

search OR ﬁnd OR near OR close OR deliver OR

transport OR carry OR bring)”. The QS algorithm

further reduced the number of articles to 84. A man-

ual review by authors resulted in a selection of 34 ar-

ticles for this section.

Exclusions: This survey discusses technologies cur-

rently being used during OHCA events, along with

the technologies under development for future use.

However, other technologies are being used during

OHCA workﬂow, such as mechanical CPR initi-

ated by ﬁrst responders and healthcare profession-

A Survey on Technologies Used During out of Hospital Cardiac Arrest

479

als, advanced trafﬁc light controls that allow ambu-

lances an unobstructed path to the emergency loca-

tion, advanced AED algorithms for early detection of

a shockable rhythm, and the use of audio and video

feedback in CPR training (Nguyen, 2019). These

technologies have been excluded as this survey fo-

cuses on technologies that directly affect the OHCA

workﬂow before the arrival of the emergency medical

services.

3 WITNESSING OHCA

A crucial part of the OHCA process is to witness the

occurence of OHCA event, which implies that there

should be a bystander nearby the patient who ob-

serves the patient experience OHCA symptoms and

reports the event to the emergency services. There

are a signiﬁcant number of OHCA events that remain

unwitnessed as they occur in private dwellings or in

less crowded public places such as parking lots (Chan

et al., 2019). Patients who experience an unwitnessed

OHCA, experience a delayed response and treatment,

and with each minute delay, their survival chances

are reduced by 10% per minute (Ko et al., 2018;

Heart.org, 2021c). Multiple studies have conﬁrmed

that early detection of the OHCA event increases the

survival chances of the patient (Chan et al., 2019;

Kiyohara et al., 2021). Thus, it is crucial to detect

the OHCA in the ﬁrst few minutes of its occurrence,

such that the patient can be treated immediately. Vari-

ous technologies are being developed for the early de-

tection of the OHCA event, including smartwatches,

smart wristbands, radar detection, and fall detection.

These technologies are explained in detail below.

Body Sensors: Smartwatches and wristbands are

playing an important role in the health monitoring in-

dustry (Plata et al., 2019; Lu et al., 2019). These de-

vices are small and can be worn continuously with-

out interrupting day-to-day activities. Depending on

the device, they are equipped with sensors to capture

the user’s physical activity, ECG data, blood oxygen

levels, heart rate, and are capable of detecting falls

(Jesus, 2018; Chan et al., 2019). The information

collected on the device can synchronize with other

devices such as smartphones and tablets or even to

the cloud server via a cellular or Wi-Fi connection.

Some of these devices can detect medical conditions

such as irregular heart rhythms and low oxygen levels.

These devices can be programmed to send an emer-

gency alert to emergency services or to a designated

user speciﬁed in the emergency contacts upon detect-

ing an emergency (Fakhrulddin et al., 2019; Tanaka

et al., 2017).

Fall Detection: The patients collapse is the ﬁrst ob-

served sign that a sudden cardiac arrest is occuring.

It is not always possible to have bystanders witness

and assist the patient; therefore, researchers have pro-

posed solutions to detect falls using smart devices.

Fall detection is a highly researched topic, a Google

Scholar search found over 233,000 articles using the

term “fall detection system” since 2017. A major-

ity of these proposed technologies use smartphones,

smartwatches, cameras, radar, and other IoT devices

to detect the fall (Bhattacharya and Vaughan, 2020;

King and Sarrafzadeh, 2018). Most of these systems

can send an alert when the user collapses to the reg-

istered emergency contact. Some of these devices

may use another device to send the alert, such as a

smartwatch using the paired smartphone to send the

alert. Additionally, security cameras are widely used

in ofﬁces, warehouses, parking lots, and especially in

isolated parts of a building, to address security con-

cerns. Artiﬁcial intelligence (AI) and machine learn-

ing (ML) algorithms can use the video from secu-

rity cameras to detect a person collapsing. For in-

stance, Scquizzato proposed an algorithm that can de-

tect OHCA from a security camera feed (Scquizzato,

2018).

Agonal Breathing Agonal breathing is a type of

“gasping” commonly seen in approximately half of

patients experiencing cardiac arrest (Chan et al.,

2019; Riou et al., 2018b). A recent study conﬁrms

that if a patient experiences agonal breathing dur-

ing CPR, then the patient’s survival chances are 17%

higher than a cardiac arrest patient without agonal

breathing (Adams et al., 2017). A bystander can

quickly identify agonal breathing in a patient; in cases

when a bystander is not present, researchers have pro-

posed solutions that can detect agonal breathing us-

ing microphones built-in smart devices such as mo-

bile phones and smart speakers (Jesus, 2018; Rea

et al., 2021). Studies conﬁrm that a signiﬁcant num-

ber of SCA occur in the home environment (Chan

et al., 2019; Tsukigase et al., 2019). Agonal breathing

detection solutions work optimally in quiet environ-

ments, as these solutions require minimal background

noise so that the device can record the agonal breath-

ing.

4 CONFIRMATION OF CARDIAC

ARREST

Once the emergency dispatcher receives a call about

an emergency, they need to determine if the patient

is experiencing cardiac arrest or not. If the OHCA is

conﬁrmed, the dispatcher then advises the bystander

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480

to perform CPR and communicate the conﬁrmation

information to the dispatched ambulance. The time

taken by the dispatcher to conﬁrm cardiac arrest may

cause a substantial delay in the patient’s treatment (Ko

et al., 2018; Fukushima and Bolstad, 2020). Studies

conﬁrm that if the time taken by the dispatchers to

conﬁrm OHCA is reduced, then the survival chances

of the patient increases (Adams et al., 2017; Sanko

et al., 2021). The early conﬁrmation is also beneﬁ-

cial as the dispatcher can dispatch other emergency

services (e.g., police and ﬁre services) who can ar-

rive prior to the ambulance (Heart.org, 2021b). This

issue has been recognized in the literature and multi-

ple solutions have been proposed which are discussed

in this section (Riou et al., 2018b; Breckwoldt et al.,

2020).

When OHCA incidents occur in residential loca-

tions, the caller is generally known to the patient and

is emotionally distressed. In such situations, the dis-

patcher has to spend time calming the caller ﬁrst be-

fore they can ask the caller to provide the patient’s in-

formation (Fukushima and Bolstad, 2020). When an

OHCA occurs in a public location, the caller some-

times steps away from the patient to call the emer-

gency services. The stepping away from the patient

may be due to overcrowding near the patient, cellular

reception or environmental noise (Case et al., 2018).

In such situations, when the dispatcher requests the

caller to check and provide the medical information of

the patient, the caller needs to go back to the patient

and then analyze the condition, causing the delay in

assessment (Fukushima and Bolstad, 2020).

Two signiﬁcant recommendations from various

studies related to agonal breathing and OHCA are

1) overcoming the language and linguistic issues be-

tween the caller and dispatcher will improve out-

comes (Riou et al., 2017; Fukushima and Bolstad,

2020) and 2) if the dispatcher has confusion in de-

tecting agonal breathing, then CPR should be advised

(Adams et al., 2017; Leong et al., 2020). Overall,

early conﬁrmation of cardiac arrest can help in reduc-

ing the time to resuscitate the patient.

5 ASSISTED CPR

After the dispatcher conﬁrms that the patient is expe-

riencing cardiac arrest, the next step is to guide the

caller to perform CPR. CPR performed by bystanders

plays a vital role in increasing the patient’s survival

chances (White et al., 2018; Ng et al., 2021). By-

stander CPR is necessary because the ambulance dis-

patched to the patient’s location takes approximately

8-10 minutes, depending on the site of dispatch vehi-

cles (Van de Voorde et al., 2017; Chien et al., 2020).

The estimated time from the start of the emergency

to the arrival of the emergency services is estimated

to be between 8 and 15 minutes, leading to almost

100% mortality for patients who do not receive chest

compressions (Ko et al., 2018; Heart.org, 2021c; An-

delius et al., 2019). Figure 3, shows an estimate of the

time required at each step of the SCA workﬂow.

Figure 3: Figure showing an estimated timeline graph of the

steps performed during an OHCA.

The CPR quality directly affects the patient’s sur-

vival chances; high quality CPR increases a patients

chances of survival (Ng et al., 2021; Estabrooks,

2018). Medical agencies have deﬁned CPR per-

formance metrics; for instance, the American Heart

Association (AHA) deﬁnes that high-quality CPR

should have a chest compression depth between 2

and 2.4 inches at a rate of 100-120 compressions per

minute (Heart.org, 2015). The European Resusci-

tation Council recommends that chest compressions

should have a depth of 5–6 cm with a rate of 100–

120 compressions per minute (Perkins et al., 2015).

These standards are likely unknown to bystanders un-

less they are trained in CPR. Also, it is not easy for

the bystander to measure the compression depth and

the frequency during a stressful emergency scenario.

Researchers have proposed various solutions to over-

come this issue, such as guiding the bystander over

the phone, real-time CPR feedback devices to im-

prove performance, and mechanical devices to per-

form CPR automatically (Riou et al., 2018a; Case

et al., 2018). These technologies are discussed in the

following subsections.

Telephone Assisted CPR: Telephone assisted CPR

is also known as “Tele CPR”, “TCPR”, or “DA-CPR”

(Dispatcher Assisted CPR) (Hardeland et al., 2017;

Al Hasan et al., 2019). In this CPR assistance method,

the dispatcher advises the caller to perform CPR and

guides them on performing high-quality CPR (Sanko

et al., 2021; Al Hasan et al., 2019). This type of

assisted CPR is the most commonly used since it

does not require any additional hardware or software.

A Survey on Technologies Used During out of Hospital Cardiac Arrest

481

Also, the dispatcher can modify their instructions as

per the caller to help them understand and perform

high-quality CPR (Riou et al., 2018a). Assisted CPR

can encounter challenges caused by the communica-

tion between the two parties. One signiﬁcant con-

cern is the refusal by the caller to perform CPR ei-

ther due to their physical ability, emotional barriers,

or legal concerns. The patient needs to be placed on a

hard ﬂat surface such as a ﬂoor for performing CPR.

The physical movement of the patient might be chal-

lenging for the caller due to their own physical abil-

ity. Another concern is the refusal by the caller to

perform CPR, which may be due to their low conﬁ-

dence level, emotional anxiety, or legal concern that

they might hurt the patient (Fukushima and Bolstad,

2020; Takahashi et al., 2018). Linguistic differences

between the dispatcher and the caller are also a con-

cern as they may affect the delivery of the instruction

and their feedback (Sanko et al., 2021; Case et al.,

2018). Researchers have also proposed training the

dispatcher to improve communication with the caller

(Al Hasan et al., 2019; Michiels et al., 2020). Studies

have shown that the dispatchers were able to convince

38% more callers to perform CPR after receiving the

specialized training (Riou et al., 2018b). Tele CPR

is dependent on the communication between the dis-

patcher and the caller. There will be situations where

the dispatcher’s instructions are not accurately heard

and understood by the caller due to the surrounding

noise or linguistic challenges, ultimately affecting the

CPR quality and patient’s survival chances (Leong

et al., 2020; Gram et al., 2021).

Video-Assisted CPR: Video-assisted CPR (V-CPR)

technology allows the dispatcher to view the CPR

performed by the caller over video and provide real-

time feedback to the CPR provider (Lee et al., 2021a;

Meinich-Bache et al., 2018). This technology is one

of the latest methods proposed for dispatchers but

it has not been implemented widely. Once the dis-

patcher conﬁrms that the patient is experiencing car-

diac arrest and another bystander can hold the phone,

the dispatcher switches the audio call to a video call

(Lee et al., 2021a). The second bystander holds the

device such that the dispatcher can view the CPR be-

ing performed and provides real-time feedback to the

bystander to perform high-quality CPR (Kim et al.,

2020). Studies conﬁrm that CPR quality improved

when video feedback was provided (Ali et al., 2019;

Lee et al., 2021b). Studies have also conﬁrmed that

the time spent explaining hand placement on the chest

was reduced during V-CPR with subsequent feedback

able to make quick corrections to improve CPR qual-

ity. (Lin et al., 2018; Ecker et al., 2020). V-CPR

technology has some limitations, implementing this

system in existing dispatch centres will require signif-

icant cost. The cost will include adding a high-speed,

secure network for streaming videos, and a software

upgrade to switch audio calls to video calls. Also,

another uncontrollable factor is that the bystander

should have a cellphone that allows video calling.

These requirements may be fulﬁlled in major cities,

but the overhead cost may be too high for rural areas

(Hambly and Rajabiun, 2021; Durish, 2020).

CPR Feedback Devices: CPR feedback devices are

designed to provide real-time feedback to the CPR

performer to provide high-quality CPR. CPR feed-

back devices are external hardware devices encapsu-

lated with various sensors to analyze current CPR per-

formance and compare it with high-quality CPR stan-

dards (i.e., compression depth of 5-6 cm and compres-

sion rate from 100-120 per minute). After compar-

ing actual CPR performing metrics with high-quality

CPR standards, these devices provide audio, visual, or

both feedback to the user for achieving high-quality

CPR.

Figure 4: Image showing Visual Feedback indications on

CPR feedback devices. Top image: Zoll Real CPR Help,

Source: https://www.zoll.com/. Bottom image: Laerdal

feedback device, Source: Skorning et al., Resuscitation

(2010).

Many CPR feedback devices are available on the

market, such as Real CPR Help and CPRmeter 2

(Zoll, 2021; Laerdal, 2021). These devices are placed

on the patient’s chest and the CPR performer places

their hands on the device and performs CPR. Most of

these devices consist of two sensors, one measuring

acceleration and another measuring force. Based on

the data collected from these sensors, the device an-

alyzes the CPR performance and provides feedback.

Depending on the device, the feedback can be visual,

showing if the parameter is in optimal range or not,

shown in Figure 4. However, these devices are ex-

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482

Table 1: A comparison of technologies used to perform CPR during an OHCA.

Technologies Availability

during

OHCA

Additional

person

required

Ease of use Time to

setup

Hardware re-

quirement

Cost Can

cause

harm

Market

avail-

ability

Tele CPR Easy No Hard None None None Yes Yes

Camera Easy Yes Easy Little Camera phone None Minor Yes

Smart

watch

Easy No Easy None Smartwatch None None Yes

Feedback

devices

Hard No Hard Hard Special device High None Yes

AR Hard No No Little Special device High None No

VR Hard No No Little Special device High None No

pensive and are not publicly available for help dur-

ing OHCA incidents. Some researchers are propos-

ing smaller devices similar to the size of a credit

card, which will help portability but still limit the use

of these devices in an actual incident (White et al.,

2018). Another type of CPR feedback device on the

market are contained in smartphone and smartwatch

devices (Sevil et al., 2021; Plata et al., 2019). As

the degree of portability increases, the probability of

these devices being used during an emergency also

increases. These devices use the built-in sensors like

accelerometer and gyroscope to evaluate the perfor-

mance of the CPR and can provide audio, visual and

haptic feedback for improving CPR performance in

real-time (Jeon et al., 2021). With technology con-

tinually improving, the number of CPR feedback de-

vices continues to expand with the latest technologies

being Virtual Reality (VR) and Augmented Reality

(AR) (Vaughan et al., 2019; Higashi et al., 2017).

At the time of writing this review, the authors are not

aware of any application of these technologies in ac-

tual OHCA. Overall, many CPR feedback devices are

available for OHCA usage and studies conﬁrm that

these devices improve CPR performance. However,

due to their high cost and limited availability, these

devices are often used for CPR training but not dur-

ing OHCA. Table 1 compares the different CPR assis-

tance technologies used during OHCA and highlights

the drawbacks of each technology.

6 AED ASSISTANCE

The American Heart Association and the European

Resuscitation Council recommend that in addition to

providing high quality CPR, the patient should also

be deﬁbrillated using an AED device if they have the

appropriate heart rhythm (Sondergaard et al., 2018;

Heart.org, 2021b). Multiple studies have conﬁrmed

an increased rate of survival for patients when AEDs

were used during an OHCA (Heart.org, 2014). AEDs

are an electronic device consisting of a central unit

and two electrode pads. For portable AEDs used dur-

ing OHCA, the central unit consists of a battery and a

mini-computer to capture and interpret the ECG and

other information needed by the AED. The electrode

pads are placed on the patient’s body to collect the

ECG information and delivers an electric shock if

needed. When a bystander calls the emergency ser-

vices during a SCA emergency, the dispatcher sends

an ambulance and provides instructions to the caller

to perform CPR on the patient. At this stage of the

SCA workﬂow, it becomes vital to apply an AED

to the patient to increase their chances of survival.

Finding an AED nearby and bringing it to the patient

are the two biggest challenges faced at this stage of

SCA workﬂow. These challenges are recognized and

have been investigated by several researchers (Murata

et al., 2021; Telec et al., 2018). The following subsec-

tions detail the various technological solutions pro-

posed by researchers for ﬁnding and getting the AED

to the patient during OHCA.

Finding AED: AEDs are small devices, generally

placed in corridors, on shelves, in cabinets and other

places. Studies show that the existing AEDs are un-

derutilized because bystanders are unable to ﬁnd them

during an emergency since many are placed in areas

with restricted access or in unmarked locations (Fred-

man, 2018; Cunningham et al., 2019). Another sig-

niﬁcant issue found in the studies is that the AEDs

are not optimally placed compared to the OHCA oc-

currence, which results in low availability of AEDs

in high OHCA prone geographical locations (Srini-

vasan et al., 2017; Leung et al., 2021). The follow-

ing solutions proposed in the literature to ﬁnd AEDs

are: 1) Mobile-based Applications, 2) Dispatcher As-

sisted, and 3) RNS. There are currently many free mo-

bile applications available on the Google Play Store

and the Apple App Store that can help the user to

ﬁnd a nearby AED, such as AED Quebec, Staying

Alive, and PulsePoint AED (Champlain, 2021; As-

sociation RMC-BFM and AEDMAP, 2021; Founda-

tion, 2021). These mobile applications generally use

crowdsourcing techniques to collect AED informa-

A Survey on Technologies Used During out of Hospital Cardiac Arrest

483

Table 2: A comparison between the technologies used to ﬁnd nearby AED devices during an OHCA event.

Finding AED using Mobile Appli-

cation

Finding AED using Dis-

patcher Assisted

Finding AED using RNS

Action by Bystander Dispatcher RNS system

Requirements Mobile App 911 system with AED listing 911 RNS integration

Advantage Can be used when 911 or RNS is

not available

Bystander can focus on CPR,

dispatcher helps ﬁnding AED

Fully automatic and con-

sider various situations

Disadvantage Pre-installed mobile application.

Latest AED information on app.

May distract dispatcher in help-

ing the bystander perform CPR.

RNS responder should

agree carrying the AED

Data Reliability Low High High

tion (Neves Briard et al., 2019; Chua et al., 2020).

In a dispatcher assisted AED location system, the dis-

patcher has access to a particular platform integrated

within the existing emergency services system to ﬁnd

the available AEDs near the patient’s location (Per-

era et al., 2020; Tsukigase et al., 2019). This sys-

tem ofﬂoads the work of ﬁnding an AED from the by-

stander to the dispatcher. The RNS system can help in

ﬁnding AEDs during an emergency and is controlled

and activated by the dispatcher (Berglund et al., 2018;

Stieglis et al., 2020). RNS are automated systems that

ﬁnd and alert registered users near the emergency to

assist the patient by ﬁnding an AED or performing

CPR. RNS have access to the AED location informa-

tion and can ﬁnd the nearest AEDs to the emergency

(Rao et al., 2019; Smith et al., 2020; Rao et al., 2020).

Table 2 summarizes various technologies used in ﬁnd-

ing the nearby AEDs during an OHCA emergency.

Delivering the AED: Once the nearest AED is found

by the methods described above, the next challenge

is to quickly bring the AED to the patient. The de-

livery mechanisms are linked to the technology used

to ﬁnd an AED. For example, if a mobile applica-

tion is used to ﬁnd the AED, then another bystander

must bring the AED to the patient (Neves Briard et al.,

2019; Chua et al., 2020). In this scenario, another by-

stander is required because the ﬁrst bystander should

not stop performing CPR to get an AED according

to the AHA guidelines (Heart.org, 2014; Heart.org,

2021c). Therefore, this technique may not work in

situations when another bystander is not available.

The second way of AED delivery is dispatcher as-

sisted, and this method is very similar to the mo-

bile application method of delivery discussed above

(Perera et al., 2020; Tsukigase et al., 2019). The

critical difference between the two methods is that

the dispatcher provides the location of the nearest

AED in this method. In this situation, there is a

high probability that the location of the AED is cor-

rect. However, this method shares the identical draw-

back: one bystander must make a round trip to attain

the AED while another bystander performs CPR. The

third method of AED delivery discussed is via RNS.

In this method, the dispatcher activates the RNS sys-

tem, which then ﬁnds AEDs and responders in close

proximity to the patient and provides instructions to

either carry an AED or reach the emergency to as-

sist the patient depending on their locations (Stieglis

et al., 2020; Smith et al., 2020). These systems have

been implemented in limited geographical locations

in the world due to their signiﬁcant cost of setup

(Stieglis et al., 2020; Berglund et al., 2018). Another

innovative and advanced technology proposed by re-

searchers for the delivery of the AED is by drones

(Fredman, 2018; Shirane, 2020). These devices have

been tested in simulated OHCA incidents, with mul-

tiple studies conﬁrming that they can be used during

OHCA and can deliver AEDs faster than any existing

method of delivery (Sanfridsson et al., 2019; Nguyen,

2019). Table 3 summarizes various technologies used

in delivering the AEDs to the patient. Yet another way

to deliver an AED faster is to optimize their placement

such that AEDs can be accessed more quickly in loca-

tions that cover a larger geographical area. This issue

and its solutions are discussed in the following sub-

section.

AED Placement: Existing AEDs have been under-

used during OHCA incidents and one signiﬁcant rea-

son for their under utilization is the distance be-

tween the emergency and the AED (Deakin et al.,

2018; Sondergaard et al., 2018; Cunningham et al.,

2019). Multiple studies show that if the placement

of the AEDs is optimized, then their use will in-

crease and ultimately the patient’s chances of survival

will improve (Cunningham et al., 2019; Srinivasan

et al., 2017). Researchers have proposed mathemati-

cal models to determine the optimized locations of the

AEDs (Leung et al., 2021; Derevitskii et al., 2020).

These models include a mathematical formulation to

identify high-risk OHCA zones, positioning based on

historical OHCA incidents, and equally distributing

the units across levels of socioeconomic deprivation.

HEALTHINF 2023 - 16th International Conference on Health Informatics

484

Table 3: A comparison between the technologies used to deliver AED devices to the emergency location during an OHCA

event.

Mobile Application Dispatcher Assisted RNS Drone

Time to delivery Two way trip, going

and getting it

Two way trip, going

and getting it

One way trip, getting

the AED enroute

One way trip, getting

the AED enroute via air

Constraints Requries another by-

stander

Requries another by-

stander

Requires RNS respon-

der to bring the AED

Requires a nearby

drone station.

Advantage Bystander can search

for AED

Bystander focuses on

getting AED

Multiple responders

get different AEDs

Time to delivery is low-

est

Cost Low Low Low High

Currently Used Yes Yes Yes In trial

7 DISCUSSION AND

CONCLUSION

SCA patient’s survival chances depended on the by-

stander, their CPR knowledge, past CPR training, and

CPR performance. We are in a new era, various tech-

nologies have been developed to assist the patient in

receiving early resuscitation and increasing their sur-

vival chances.

Currently, technologies assist bystanders in pro-

viding early resuscitation to the patient. One of the

primary reasons for delays in response to OHCA or

no assistance being provided to the patient relates to

the location of the cardiac arrest. A large number

of OHCAs occur at home, in parking lots, or other

private locations, where bystanders are not available

to assist the patient. Researchers have proposed fall

detection, agonal breathing and camera monitoring

as solutions for detecting OHCA occurrence in these

places. The fall detection method uses sensors such

as an accelerometer, gyroscope, heart rate sensor, or

ECG sensor that are available on smartwatches, smart

bands, and smartphones. Furthermore, these devices

can also monitor heart rhythm to conﬁrm cardiac ar-

rest using advanced algorithms depending on the de-

vice. This method of OHCA detection can be used

in the community and is effective since many of the

smart devices are now part of people’s day-to-day life.

The technological solutions described in this re-

view have the potential to improve mortality for those

patients experiencing an OHCA. The feasibility of

these proposed solutions depends on the technologies

adopted either by the bystander or the dispatch cen-

ter. The use of smart devices such as smartphones

and smartwatches is one of the most practical solu-

tions as they are widely used and have become part

of the day-to-day life of people. There are indirect

ways that can help to increase the survival chances of

the SCA patient, such as better AED placement and

better CPR training. Researchers have proposed solu-

tions for these issues as well. This survey provides

an overview of the current and future technologies

that can be used during an OHCA event. This sur-

vey provides a good foundation for researchers who

intend to develop or advance technologies used dur-

ing OHCA, it will also help them integrate their own

solutions within the OHCA workﬂow.

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