Research Directions in Cloud-Based Decision Support Systems for Health Monitoring Using Internet-of-Things Driven Data Acquisition

Abstract

The Digital Health (D-Health) era is expected to be the " next big thing " since the invention of the internet, characterized by inexpensive and widespread medical data acquisition devices, widespread availability of identity-removed health data, and analytics algorithms that provide remote health monitoring feedback to doctors in realtime. Recent years have brought incremental developments in three key technological areas towards the realization of the D-Health era: data acquisition, secure data transmission/storage, and data analytics. i) For data acquisition, the emerging Internet-of-Things (IoT) devices are becoming a viable technology to enable the acquisition of remote health monitoring data. ii) For data storage, emerging system-level and cryptographic mechanisms provide secure and privacy-preserving transmission, storage, and sharing of the acquired data. iii) For data analytics, emerging decision support algorithms provide a mechanism for healthcare professionals to base their clinical diagnoses partially on machine-suggested statistical inferences that rely on a wide corpus of accumulated data. The D-Health era will create new business opportunities in all of these areas. In this paper, we propose a generalized structure for a D-Health system that is capable of remote health monitoring and decision support. We formulate our proposed structure around potential business opportunities and conduct technical feasibility studies.

Full text

International Journal of Services Computing (ISSN 2330-4472) Vol 4, No 4, October-December2016
18
RESEARCH DIRECTIONS IN CLOUD-BASED DECISION
SUPPORT SYSTEMS FOR HEALTH MONITORING USING
INTERNET-OF-THINGS DRIVEN DATA ACQUISITION
Alex Page, Shurouq Hijazi
Dogan Askan, Burak Kantarci
Tolga Soyata
University of Rochester
Clarkson University
SUNY Albany
{apage4,shijazi}@ece.rochester.edu
{askand,bkantarc}@clarkson.edu
tsoyata@albany.edu
Abstract
The Digital Health (D-Health) era is expected to be the “next big thing” since the invention of the internet,
characterized by inexpensive and widespread medical data acquisition devices, widespread availability of identity-
removed health data, and analytics algorithms that provide remote health monitoring feedback to doctors in realtime.
Recent years have brought incremental developments in three key technological areas towards the realization of the
D-Health era: data acquisition, secure data transmission/storage, and data analytics. i) For data acquisition, the
emerging Internet-of-Things (IoT) devices are becoming a viable technology to enable the acquisition of remote
health monitoring data. ii) For data storage, emerging system-level and cryptographic mechanisms provide secure
and privacy-preserving transmission, storage, and sharing of the acquired data. iii) For data analytics, emerging
decision support algorithms provide a mechanism for healthcare professionals to base their clinical diagnoses
partially on machine-suggested statistical inferences that rely on a wide corpus of accumulated data. The D-Health era
will create new business opportunities in all of these areas. In this paper, we propose a generalized structure for a D-
Health system that is capable of remote health monitoring and decision support. We formulate our proposed structure
around potential business opportunities and conduct technical feasibility studies.
Keywords: remote health monitoring; medical decision support; Internet of Things (IoT); visualization; analytics;
__________________________________________________________________________________________________________________
1. INTRODUCTION
The unprecedented growth in the Internet of Things
(IoT) technologies makes it possible to talk about 50 billion
connected devices through the internet by 2020 (Fernandez
& Pallis, 2014). Among these devices are body-worn
sensors that monitor personal health conditions. There has
been a growing interest in wearable sensors in recent years
and an emerging set of new products are commercially
available (Jawbone, 2016; FitBit Inc., 2016; Apple Inc.,
2016) for activity recognition, personal health monitoring,
and fitness. For clinical use, long-term patient monitoring
and management has also been considered by researchers
(Pantelopoulos & Bourbakis, 2010; Son & et al., 2014;
Page, Kocabas, Soyata, Aktas, & Couderc, 2014; Paradiso,
Loriga, & Taccini, 2005; Milenkovi, Otto, & Jovanov,
2006; Istepanian, Sungoor, Faisal, & Philip, 2011; Soyata T.
, 2015). IoT-based data collection and cloud-based analytics
are the driving factors of this technology as detailed in
(Hassanalieragh, et al., 2015). A doctor can prescribe a 2–3
day period of continuous physiological monitoring of a
patient using low-cost wearable devices before a patient’s
periodic physical examination. This monitoring data can be
transmitted to the database, linked with the health records of
the patient. Statistical inference algorithms can compare this
patient’s data to a large database of other patients and
provide the doctor with a rich set of suggestions. These
machine-inferred suggestions are invaluable tools which use
technology for the benefit of human health.
The Digital Health (D-Health) vision described in the
preceding paragraph promises to be a disruptive technology
for human healthcare. In addition to saving the hospitals
money, this type of decision support could improve
diagnostic accuracy and might create third party business
opportunities. However, before this vision can be fully
realized, a set of challenges that need to be addressed are: (i)
The privacy and security of the acquired data need to be
ensured during its acquisition, storage, and processing. (ii)
A large dataset for specific health conditions takes time to
build and the accuracy of many decision support algorithms
depend on the size of the database, thereby creating a
natural vicious cycle. (iii) Despite being full aware of its
potential, hospitals will be slow in embracing the D-Health
concept due to the risks implied in basing decisions that can
effect human lives on machine suggestions. (iv) It is not
clear how this technology can turn into business
opportunities. (v) The IoT technology is still in its infancy
and it is not clear whether this technology will enable a
secure and reliable sensing platform. (vi) Even if the data
can be acquired reliably, it is not certain whether this data
can be visualized in a non-overwhelming summarized
format to be useful to the doctors and be embraced by them.
(vii) Since large databases for many diseases are proprietary
or simply do not exist, it is not clear whether statistical

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inference is possible for a wide variety of diseases that can
be detected through remote health monitoring.
In the rest of this paper, we aim at providing answers to
challenges (iv)–(vii). Towards that end, we introduce a
generalized system structure for remote health monitoring
based on recent research directions, as well as our
predictions in Section 2. In Section 3, we address challenge
(iv) and identify a clear list of existing business
opportunities. In Section 4, we identify the technical
components of D-Health. In the rest of the paper, we
provide a technical feasibility study for these technical
components. A technical feasibility study for challenge (v)
is provided in Section 5, followed by technical feasibility
studies for challenges (vi) and (vii) in Sections 6 and 7,
respectively.
2. PROPOSED SYSTEM ARCHITECTURE
We define a remote health monitoring and management
system as a system that provides the interface between a
patient and a doctor, as shown in Fig. 1. The system
acquires, stores, and analyzes patient health data along this
transition. Although a much finer grain sub-layering of a
typical remote health monitoring system is possible, our
proposed system consists of two super-layers: Front End
and Back End. These two super-layers contain similar
technical functionality and business opportunities, hence our
rationale for this layering. Details of each layer are provided
in the rest of this section. Section 2.1 details the Front End,
which is the interface between “the patient” and “the
system.” Section 2.2 details the Back End, which is the
interface between “the system” and “the doctor.”
2.1 Front End
The front end of the system is responsible for acquiring
healthcare data from the patient and transmitting it to the
back end securely and in a privacy-preserving fashion.
There are well-established standards for the acquisition of
health data, such as ISO/IEEE 11703-20601:2010
(Fernandez & Pallis, 2014). The connection of this layer to
the back end is usually through the internet (Hassanalieragh,
et al., 2015), making it necessary to ensure data privacy
during acquisition and transmission. The functions of the
front end are detailed in this subsection.
IoT-based Acquisition infrastructure: Although the
IoT concept is in its infancy, a particular radio
communication technology to improve the active bandwidth
by deflecting IoT traffic from the internet through a special
ultra-high-bandwidth and energy efficient cellular network
(900 MHz) has been created by the French company
SIGFOX (SIGFOX, 2016). The SIGFOX IoT network will
be first deployed in San Francisco. For general IoT
networks, three widely available wireless technologies are:
i) 3G/4G cellular wide area networks, ii) Wi-Fi local area
networks, and iii) Bluetooth Smart personal area networks.
A dedicated IoT network has also been proposed as a
research topic (Fernandez & Pallis, 2014).
Privacy of the acquired data: In addition to assuring
data privacy at a cryptographic and system level (Kocabas
& Soyata, 2016; Kocabas & Soyata, 2015), security
concerns arising from sensor tampering (Page, et al., 2015b)
and sensor data trustworthiness (Kantarci & Mouftah, 2014;
Pouryazdan, Kantarci, Soyata, & Song, 2016) must be taken
into account in this layer. To create a secure overall system,
an adversary model must be defined. The most common
Figure 1. Layers of the proposed remote patient monitoring system that is based on an IoT-Cloud architecture. Based on
the challenges described in Section 1, as well as the available business opportunities that will be described in Section 3,
it suffices to conceptualize the system as two super layers: The Front End represents the hardware and software,
necessary for the secure acquisition of the patient health data. The Back End represents the cloud infrastructure to store
and process the data, as well as the visualization and analytics algorithms running in the cloud.

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adversary model that we will adopt is the honest but curious
adversary model (Cao, Wang, Li, Ren, & Lou, 2014;
Goldreich, 2004), in which a given part of the system is
assumed to perform its duties correctly (i.e., “honestly”), but
is capable of intentionally or unintentionally observing other
parties’ data (i.e., “curious”). Such a system is also
vulnerable to side channel attacks from parties that observe
the peripherals (i.e., “sides”) of the system and attempt to
guess the underlying data. Among the many, a few
examples of these attacks include power analysis attacks
(Kocher, Jaffe, & Jun, 1999), timing attacks (Kocher P. C.,
1996), fault-based attacks (Boneh, DeMillo, & Lipton,
1997), and cache attacks (Bernstein, 2005).
Preprocessing of the acquired data: The amount of
the acquired data could be unmanageable in terms of
storage, transmission, or processing. Therefore, it is
necessary to apply preprocessing algorithms to the acquired
data to reduce its size (Soyata T. , Muraleedharan, Funai,
Kwon, & Heinzelman, 2012; Soyata T. , et al., 2012). These
algorithms are applied to a set of aggregated data, rather
than the raw data. The hardware components that aggregate
the data from the IoT-based sensors are concentrators
(Zhao, Wang, & Nakahira, 2011; Hu, Xie, & Shen, 2013).
The purpose of a concentrator is to reduce the power
consumption of the individual IoT devices by directly
receiving the sensor data from them at a short distance and
transmitting the aggregated data over much longer
distances. While this data concentration is a much higher
workload than what the individual IoT devices can handle,
concentrators are not necessarily the destinations where the
pre-processing takes place. For pre-processing, cloudlets are
used that are substantially more computationally capable
than concentrators and have dedicated WAN links (Soyata,
Ba, Heinzelman, Kwon, & Shi, 2013; Powers, et al., 2015).
The pre-processing of the data turns raw data into a much
more summarized format, such as the computation of the
QT and heart rate information from raw ECG signals (Page,
et al., 2015c).
2.2 Back End
The back end of the system is responsible for storing
and processing the data securely. The functions of the back
end are detailed in this subsection.
Secure Storage: The data is acquired in a time series
fashion. To store, retrieve, and query time series data, REST
APIs are provided within (Zhang, et al., 2013). One of the
concerns about handling the data in the cloud is identifying
the attack patterns. One example solution, Zachman
Framework for enterprise architecture modeling, identifies
attacks patterns by checking six characteristics (who, what,
where, when, why and how). The patterns of access in the
cloud are compared against an independently-running
“plane” to determine whether each access is normal or
malicious (Blackwell & Zhu, 2014).
Secure Computation: While static storage of data is
feasible by using well-known secure storage standards such
as SSAE16, this data cannot be operated on. If computation
has to be performed on the data that is stored in an untrusted
cloud, emerging cryptographic mechanisms such as Fully
Homomorphic Encryption (FHE) are required (Kocabas O. ,
et al., 2013). These algorithms allow the cloud to perform
“blind-folded computation” without observing the
underlying medical data, thereby eliminating concerns
regarding data privacy (Page, Kocabas, Soyata, Aktas, &
Couderc, 2014), however, computations using FHE are
orders of magnitude slower than their AES-based
“traditional” cryptographic counterpart (Page, Kocabas,
Soyata, Aktas, & Couderc, 2014; Kocabas & Soyata, 2015).
Database Sharing: Much like the concentrator in the
front end, a portion of the back end is responsible for
aggregating databases and sharing them across many
applications or other clouds. The key element of this
functionality is to aggregate the databases in an identity-
removed fashion. Data obfuscation and identity removal are
well-established techniques (IBM, 2016) that obfuscate the
data in a way that makes the data un-identifiable even if
compromised. This functionality of the back end is
important since the accuracy of the analytics engine
improves as the database sizes grow, thereby improving the
statistical inference related to disease detection.
Visualization: The visualization engine can be thought
of as being the “visual aggregator.” This engine turns an
enormous amount of data into a format that is easily
comprehensible and understandable by a human, i.e., the
doctor. Despite occupying mega- or gigabytes of storage,
the information content in the acquired raw data is very low.
The visualization engine is necessary to turn the raw data
into a highly summarized format, potentially occupying
many orders-of-magnitude less physical space for the same
(or higher) information content.
Analytics Engine: Although strictly visualizing the
data in a summarized format allows the doctor to access
patient information much faster, this visualized information
can be further augmented with machine learning (ML)
algorithms. The function of the analytics engine is to run a
standard set of machine learning and statistical inference
algorithms to determine the likelihood of certain diseases
for a given set of acquired data. These statistical inferences
can be included in the summarized data provided by the
visualization engine. The inferences provided by the ML
algorithms are much simpler than the visualized data. For
example, while a 24-hour visualization (plot) of the patient
ECG information could provide the doctor extremely useful
and summarized synopsis of the patient’s heart condition, it
is still a lot of data to browse through. This plot could be
augmented with a single statistically-inferred value (e.g.,
87% probability that the patient has the LQT1 heart
condition (Page, et al., 2015c). While the initial plot allows
the doctor to use his/her experience and knowledge to
potentially reach the same decision, augmenting the plot
with such a suggestion provides at least a “machine-based
second opinion” to the doctor. In the best case, it provides a

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“good starting point” or even “the solution that was not
obvious to the doctor initially, but was mathematically the
best inference.”
3. BUSINESS OPPORTUNITIES
In this section, we will identify the business opportunities in
the front end and back end layers.
3.1 Categorizing the Business Opportunities
While a third party business entity can offer the
entirety of the services encompassing our system in Fig. 1
as a remote health management and monitoring service,
separating the front end from the back end makes sense due
to the major characteristic differences that each layer
represents. Based on the structure we introduced in Section
2, the front end of the system can be thought of as the “data
acquisition” layer, while the back end can be thought of as
the “data handling layer.”
Acquisition of the data implies a direct physical
connection to the patient; This “physical” connotation
significantly limits the location of the third party companies
that can provide these services. Alternatively, the back end
services could be completely “virtual,” since the offered
services are generally “software” in nature. In the following
subsections, we provide a detailed list of the potential
services in each layer.
3.2 Front End Business Opportunities
In this subsection, we will identify the business
opportunities related to the front end. Due to the “physical”
nature of the front end, most of the services that can be
offered involves making a physical contact with patient at
some point.
IoT Hardware and Communications: IEEE
standards form a basis for common wireless technologies
which are the main component of the front end layer.
Relevant sensor networks as LANs are: Wi-Fi 2.40 GHz &
5 GHz (IEEE 802 n ac); and low-power 900MHz (IEEE
802.110ah); ZigBee & ZigBee PRO 2.4 GHz & 900 MHz
(IEEE 802.15.4) and 6LoWPAN (for IoT); as PANs are:
Bluetooth 2.4 GHz (IEEE 802.15.1) and Bluetooth Low
Energy (BLE); UWB (IEEE 802.15.4a); RFID (IEEE
802.15.4f) (Soyata, Copeland, & Heinzelman, 2016) and
Low Rate WPAN (IEEE 802.15.6), which are identified by
the IEEE standards for the body area network (BAN). As
very low-power: DASH7 based on 433 MHz (ISO/IEC
18000-7). GSM, GPRS, UMTS, HSPA and LTE are the
current standards in mobile cellular networks (Fernandez &
Pallis, 2014). Wired and wireless communications working
on same infrastructure will concentrate with the 5th
generation communication technology (5G) for people and
IoT. The future networked society will run on this
omnipresent communication technology which has ultra-
high bandwidth (EC Horizon 2020, n.d.). Enabling
breakthrough user controlled privacy, wireless connection to
over 7 trillion devices for over 7 billion people, better
optimization for storage, processing and big data analytics,
90% energy saving per service and 100 times higher
wireless bandwidth compared to 2010 are expected to be
allowed by the 5G technology (Fernandez & Pallis, 2014).
These technologies are considered to support the
communication between the devices used for health
monitoring. Thus, network & communication providers such
as Verizon, AT&T or Cisco are expected to serve with one
or more aforementioned wireless communication
technologies in this layer.
Sharing (renting) databases: The necessity of
physical contact to the patient doesn’t necessarily mean that
each contact creates a single business opportunity. Although
the initial data must be acquired by making a physical
contact to the patient, say, patient A, this data can be used to
provide a data sample for patient B through database
sharing. As described in Section 1, data analytics algorithms
will work more accurately when the information from
patient A+B is available, as opposed to only patient A.
Therefore, this creates a business opportunity for the
company that acquired the data from patient A. With proper
user consent, the third party can anonymize the database
using the obfuscation software described in Section 2.2. The
obfuscated data provides a business opportunity to be
“rented” to other third parties, or, corporations such as the
insurance companies for use in data analytics.
Self Data Acquisition: One of the important
implications of technology is that users do not have to have
deep knowledge of the inner workings of the devices to use
them. Data acquisition for routine monitoring tasks, such as
personal ECG monitoring, can be done without the
intervention of a healthcare professional. However, this
doesn’t mean that no business opportunity exists for simple
data acquisition tasks like this. Smartphone applications that
are approved by healthcare organizations can be sold to
allow users to acquire their own data. The purpose of the
smartphone app is to significantly simplify the user’s job by
providing visual instructions, whether static or interactive
and calibrate the sensors by directing the user through
multiple steps. These smartphone apps can be sold either
with the sensors through pharmacies, or separately.
Professionally-Assisted Data Acquisition: When the
level of complexity to acquire the data exceeds a level that
no longer allows a simple smartphone app to be used,
healthcare companies specialized in data acquisition can sell
their services to acquire the medical data. This can involve
bringing concentrators, cloudlets, and sensors to the user’s
home and attaching them in proper order and ensuring
proper communication with the cloud. In many cases, using
the professional services might be legally necessary due to
the legal implications involved in the well-being of the
individual.
Invasive Data Acquisition: In the extreme case, the
data acquisition might require a surgery, such as the
implanting of a defibrillator. Clearly, this operation might

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be only feasible in a hospital environment or an approved
company with such expertise. The important note to make
here is that, such a service is in fact a separate component of
the overall system and does not necessarily have to be
provided by the provider of the rest of the services.
3.3 Back End Business Opportunities
In this subsection, we will identify the business
opportunities related to the back end. Since this layer does
not represent a “physical” contact with the patient, it can be
provided virtually anywhere.
Infrastructure as a Service: The infrastructure to
store and manipulate medical data can be rented through the
widely-accepted Infrastructure as a Service (IaaS) concept.
Rather than a generalized infrastructure, a more specialized
infrastructure provides much better business opportunities
(Powers & Soyata, 2015). For example, the databases that
store medical information could be optimized to handle
medical data, potentially incorporating privacy preserving
storage and data obfuscation methods as built-in features.
Companies such as IBM, Oracle, Microsoft and Teradata
are the potential service and technology providers for this
business opportunity.
Disease detection (Analytics) algorithms: Although
well-known standard algorithms exist for detecting certain
diseases, a one-size-fits-all algorithm is not possible due to
the sophisticated biological processes involved in different
diseases. Therefore, a new algorithm that achieves a higher
detection rate using the same database could provide a
significant business opportunity to a healthcare organization
that wants to use it for patient monitoring.
Visualization Algorithms: Visualization algorithms
can be thought of being a sub-category of Software as a
Service (SaaS). As will be exemplified in Section 6, the
only difference from SaaS is that the visualized data could
be displayed with either static limits, which do not depend
on a database, or dynamic limits, which do. In the specific
case of ECG visualization that we will show in Section 6,
the knowledge of the specific disease that is being displayed
is crucial. Therefore, the provider of the visualization
services is not just renting the software, but the database and
disease expertise too. So, it is highly likely that, depending
on the disease that is being visualized, the visualization
algorithms and their operation change dramatically.
Prediction and Analytics Services: In addition to
providing the algorithms as a service, the results of the
algorithms also provide an opportunity to rent as statistics in
certain diseases. Parties interested in such information are
organizations like CDC, or insurance companies that want
to compare the disease occurrence rates in certain
geographical regions.
4. BACKGROUND AND RELATED WORK
In this section, we will introduce the technical details of
different sub-layers. In the following sections, we will
perform a technical study of some of these layers. A three
tier architecture can be considered for most proposed
frameworks in terms of health monitoring as follows: 1)
Wireless Body Area Network (WBAN) for wearable sensors
to gather the data, 2) Communication and networking, and
3) The service layer (Pantelopoulos & Bourbakis, 2010;
Paradiso, Loriga, & Taccini, 2005; Milenkovi, Otto, &
Jovanov, 2006; Bazzani, Conzon, Scalera, Spirito, &
Trainito, 2012; Benharref & Serhani, 2014). Various
physiological parameters such as blood pressure and body
temperature can be measured by the wearable sensors as
proposed as a system model in (Babu, Chandini, Lavanya,
Ganapathy, & Vaidehi, 2013). A Bluetooth connection is
used for relaying the acquired information to a gateway
server by sensors. The gateway server converts the data to
an Observation and Measurement file and keeps it on a
remote server to be acquired by clinicians via internet. In
(Rolim, et al., 2010), a health monitoring system is
presented to illustrate medical staff reaching the stored data
online through content service implementation to utilize a
similar cloud based medical data storage. To supervise
patients with high risk of heart failure, WANDA (Lan, et al.,
2012), an end to end remote health monitoring and analytics
system is presented by aiming at a particular medical
implementation.
4.1 Data Acquisition and Sensing
Wearable devices which combine a communications
platform to convey the measured data, hardware for minor
preprocessing and miniature sensors that measure various
physiological parameters acquire physiological data.
Wearable sensors that are or will be available to measure
some biomarkers encapsulated in Table I. Those biomarkers
that can diagnose four common disease categories have the
applicability levels which are also indicated in the table.
Wearable sensors have some physical limitations due
to wearability requirements which are being lightweight and
small and not blocking patients’ maneuverability. Moreover,
energy efficiency is essential for those because of the
limited place for the batteries in the wearable package.
TABLE I
A list of advanced sensors and their potential
application potential to the monitoring of certain
diseases. ** indicates excellent application potential,
while * indicates some potential for application.
Biomarker
CVD
COPD
Parkinson’s
Gait (posture)
**
**
**
ECG
**
**
*
Respiratory rate
**
**
*
EMG
*
*
*
Blood pressure
**
*
*
Title volume
**
**
*
Body movements
*
*
**

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Highly durable batteries are eminently preferable to provide
convenience and to ensure that data is not lost during
recharging or replacement despite the rechargeable or
replaceable features.
A challenge for the quality of the data captured in
terms of the achievable signal to noise ratio can also be
presented by the energy efficiency requirements. The closer
contact with the skin enables measurement of relatively
more physiological parameters and with better accuracy,
therefore recent flexible sensor designs (Son, et al., 2014;
Xu, et al., 2014; Kim, Ghaffari, Lu, & Rogers, 2012) that
can be located in contact with the skin in various body parts
are especially alluring for medical implementations.
Additionally, there have been attempts to make the
operational lifetime of wearable sensors longer by
combining low power device and circuit level techniques
(Olorode & Nourani, 2014; Park, Chou, Bai, Matthews, &
Hibbs, 2006) and energy harvesting method (Torfs, Leonov,
Van Hoof, & Gyselinckx, 2006). Furthermore, the
operational durability can be improved more by utilizing
smart sensing methods on system level.
There have been studies about energy efficient sensing
mechanisms in the related background of wireless sensor
networks (WSNs) that are accustomed to sense physical
phenomenon in a distributed fashion. Current WSNs
methods can be referred again to fulfill our requirements
despite the more concentrated sensor deployment, compared
to WSNs, in our health monitoring system. The suggested
energy efficient sensing approaches hinge on appointing
sensing duties to the nodes formed on their relative distance
so as to sense the maximum amount of physical information
while improving energy efficiency by abolishing probable
unnecessary sensing duties (Madhani, Tauil, & Zhang,
2005; Chou, Rana, & Hu, 2009) and by distribution of
duties formed on the energy availability at each sensor
(Zhang & Hou, 2005; Huang & Tseng, 2005; Chen & Zhao,
2005; Cardei & Wu, 2006; Yu & Sharma, 2010). By
construing and operating an active context which is formed
on energy availability and the patients’ health condition, our
system can employ akin mechanisms. For instance, as
demonstrated in Table I, separately sensed biomarkers have
distinctive levels of applicability for particular health
conditions. The other sensors are turned off to enable
lifetime extension when the energy level is critical and the
patients sensitive condition forces focusing on a specific
biomarker. The application of such schemes to develop
energy efficiency adaptively by approving dynamic
utilization of sensors formed on the context is enabled by an
IoT based sensing architecture. It is hard to find such
flexibility and intelligence in the ordinary data acquisition
systems where the gathered information is transmitted
passively by sensors. More sophisticated algorithms can
also be implemented without patients’ manual intervention
to wield the sensors or the software on the data concentrator
by removing the decision making process to sense task
assignment to the cloud.
As the communication can account an important part of
the energy usage in sensing devices, appropriate low power
communication protocols usage is constrained by energy
limitation of these devices. In order to support
communication between low power devices that perform in
personal operating space (POS) of roughly 10m, ZigBee
over IEEE 802.15.4 is usually used in low rate WPANs
(LR-WPANs) (Lee, Su, & Shen, 2007). Energy efficient
dependable mesh networking is provided by ZigBee.
Another wireless communication protocol, Bluetooth low
energy (BLE), is appropriate for low power short range
communication which is advisable for the particular
necessities of implementations such as health monitoring,
sports, and home entertainment. The design purpose of the
original Bluetooth protocol (IEEE 802.15.1) is to provide
relatively short range communications for implementations
of a streaming nature, such as audio. By applying extended
sleep intervals to enable the general energy efficiency, the
framework is altered by BLE. A superior energy efficiency
in terms of number of bytes sent per Joule of energy is
accomplished by BLE (Siekkinen, Hiienkari, Nurminen, &
Nieminen, 2012). An intermediate node (data concentrator)
is required to make sensors data and control available over
internet while the preceding communication protocols in
use. IPv6 through Low Power Wireless Personal Area
Networks (6LoWPAN) has been put forward to perfectly
connect energy constrained WPAN devices to the internet to
comprehend the IoT concept additionally (Bui & Zorzi,
2011). In order to fit IPv6 datagrams into IEEE 802.15.4
limited frame size to enable IP access to low power, low
intricacy sensing devices, fragmentation techniques are
construed by 6LoWPAN.
4.2 Internet of Things (IoT)
Integration of the IoT paradigm with electronic remote
health monitoring systems can boost flexibility, intelligence
and interoperability more while being noticeable of these
systems has assured to transform the conventional health
care methods (Bazzani, Conzon, Scalera, Spirito, &
Trainito, 2012) (Ray, 2014). With the help of IoT
architecture, identifiable and uniquely addressed devices are
available through the internet at anytime and anywhere.
Devices equipped with IoT functionality in health
monitoring systems, fairly reducing the work load on set up
and administration tasks, can exchange information with
each other and health institutes in addition to their capability
of conventional sensing tasks. Providing services such as
automatic alarm to the closest healthcare institute during the
time of a critical accident for a supervised patient can be an
example for such systems (Bui & Zorzi, 2011).
4.3 Cloud Storage and Processing
Most of the research on sensors related to healthcare
monitoring deal with managing of the data on the devices,
storing the medical data directly on computer nodes, or

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utilizing intermediate nodes for storage and/or computation.
Data storage and management through the Cloud
Technology has been pointed rarely pointed out in the
related work. A sensor-oriented cloud infrastructure is
presented by the authors in (Yuriyama & Kushida, 2010).
The actual devices are not included by the early evaluation
results since the initial results are formed on simulated
sensors in the preceding principle. To store sensor-based
data in a dedicated manner, nonetheless, several Cloud-
based services are currently available (e.g., Pachube,
Nimbits, ThingSpeak, iDigi). Available services call for
solutions for data security and provisioning interface for
linkage to mobile or external implementation on latter
processes (Doukas & Maglogiannis, 2012).
Cloud Computing enables favorable, on-demand
network access to adjustable computing resources
configured as a shared group which an interaction by service
provider or slightest managing attempt is enough to
provision or release in a quick response time. Devices, like
smart phones, considered as heterogeneous thin or thick
platforms can reach resources through the network
accessing over standard mechanisms. Virtual machines,
network bandwidth, memory processing, and storage are
mentioned to be examples of resources. The dexterity which
develops with users being able to quickly and cheaply re-
provision technological infrastructure resources is a huge
asset by given the essences of Cloud Computing and the
resilience of the services which can be improved. Since
there is no need for a specific location or device, any user
can connect the system using a web browser in any location
and on any device. Allowing for unification infrastructure in
locations inexpensively is possible due to multi-tenancy
facilitating resource and costs sharing by virtue of enormous
user pool. To manage user data, many Cloud Computing
applications are accessible for both free (e.g., iCloud,
Okeanos, Pithos, Dropbox) and commercial usage (e.g.,
GoGrid, Amazon AWS, Rackspace). Building custom
applications and consolidating Cloud Computing
functionality are not supported by many of them.
Furthermore, optimization to service healthcare-based
implementation is not validated yet (Doukas &
Maglogiannis, 2012).
4.4 Analytics and Visualization
Medical data analysis and visualization are also critical
elements of remote health monitoring implementations
(aside from the technology for data acquisition, storage and
access). It is essential to analyze the medical records
containing various physiological characteristics over a long
period of time to diagnose accurately and to monitor a
patient’s medical condition. The data analysis task becomes
frustrating and error prone for clinicians who work with
multidimensional data, especially when long term (i.e. high
quantity) of data is used. Data mining and visualization
techniques have just reached the considerable level in
remote health monitoring implementations (Ukis, Tirunellai
Rajamani, Balachandran, & Friese, 2013; Rao, 2013),
despite the fact that they have been addressed as a solution
to the preceding challenge (Wei, et al., 2005; Mao, et al.,
2011).
We have proposed that decision support be performed
by a dedicated company, which may or may not be
responsible for collecting the data. This “Clinical Decision
Support as a Service” has also been described in (Weaver,
Ball, Kim, & Kiel, 2015), where they suggest standardizing
the relevant portions of healthcare records to make
computerized analysis easier. (Halamka, 2010) agrees with
this, and further suggests standardizing the decision support
rules, which we believe will be difficult when using
machine learning and in the presence of competition. An
“open” version of the cloud-based health monitoring
concept is discussed in (Li, Guo, & Guo, 2014), in which
scientists and healthcare professionals can share their data
and models. We expect that such a system may still thrive
alongside paid solutions, while the proprietary versions may
be based on specialized databases and more refined
algorithms.
5. FRONT-END FEASIBILITY STUDY
At this point, we have described all of the pieces of our
ideal remote monitoring system. We will now present case
studies which detail specific components.
The front end segment of remote health monitoring is
anticipated to be connected to the Internet of Things
architecture as this segment is responsible for data
acquisition through wearable sensors, and the sensors are to
be interfaced via front-end circuitry in nearby devices that
would offer built-in IoT sensing capability. These include
smart phones, smart tablets and other personalized devices
with communication interfaces. A typical cloud-inspired
service model to acquire data through the front-end of the
Figure 2. Front-end design by IoT sensors interfacing
wearables.

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health monitoring could be Sensing as a Service (S2aaS)
(Sheng, Tang, Xiao, & Xue, 2013). In a cloud-centric IoT
architecture, uniquely identifiable sensors push data to the
cloud platform for being aggregated, analyzed and presented
to the end user (Gubbi, Buyya, Marusic, & Palaniswami,
2013). Data acquisition through cloud-centric IoT has to
maximize the usefulness of the collected data for the
platform whereas the sensing costs of the IoT sensors may
need to be compensated. A minimalist illustration of the
concept can be seen in Fig. 2, and the following
mathematical model can be used to analyze the feasibility of
such front-end structure.
Utility of the cloud platform can be calculated as the
difference between the total usefulness of the data and the
compensation made to the IoT sensors for their sensing
costs in a certain time window. It is worthwhile noting that
we use data to denote a task of sensing a particular
phenomenon. In (1), Up denotes the utility of the cloud
platform while Vτ (Sτ) stands for the usefulness/value of the
data received for sensing tasks handled by the IoT sensors in
the set, Sτ during the time window, τ. In the same equation,
ρiτ denotes the sensing cost/compensation of sensor s of the
overall sensors set, S during the time window, τ.
Besides the utility of the platform, utility of the nearby
IoT sensors is another metric that is to be used in the
feasibility study of the front-end segment in a remote health
monitoring system. If the IoT sensor is compensated based
on the usefulness of its sensor reading, the compensation
should be no less than the sensing cost. Equation (2)
formulates the utility of an IoT sensor (Us) as the difference
between the total compensation received for participating in
the sensing tasks and total sensing cost.
As the wearable sensors are interfaced with nearby
mobile devices and their corresponding built-in IoT sensors,
the cloud platform can be misinformed due to either of the
following scenarios: 1) Built-in sensors of mobile devices
may be malfunctioning. 2) Users may be behaving
maliciously to send altered sensor data. Regardless of the
intention of the IoT sensor, misinformation/disinformation
of the cloud platform may lead to severe consequences in
patient’s health. In other words, platform utility is
significantly reduced if wrong sensor data is shared with the
cloud platform. Here, trustworthiness of the IoT sensors
introduces an important consequence impacting the platform
utility. Reputation-based models can be utilized to reduce
manipulation probability in the aggregated data at the cloud
platform. In (Kantarci & Mouftah, 2014a; Kantarci &
Mouftah, 2014b; Kantarci & Mouftah, 2014c), trustworthy
data acquisition schemes have been proposed for public
safety purposes in a cloud-centric IoT architecture. This
concept can easily be adopted by the front-end segment of
the presented remote health monitoring architecture in this
paper. In case a particular task is sensed by multiple IoT
sensors, the percentage of positive readings upon detection
of outliers can be used via an outlier detection algorithm
(Zhang, Meratnia, & Havinga, 2010). The statistical
reputation of an IoT sensor (i.e., sensor i) at the end of the
time window t (Ri(t)), can be formulated as shown in (3)
where p(t) and p(t) denote positive and negative readings,
respectively. Thus, instantaneous reputation and previous
reputation undergo a weighted sum function, and an IoT
sensor with low reputation will be less likely to be selected
and vice versa. Moreover, the usefulness of the data
provided by an IoT sensor will be scaled by the reputation
of the sensor.
Adopting the IoT-based data acquisition in the front-
end can increase the precision of sensed data as the higher
the number of sensors the better the performance of a
distributed estimation system. On the other hand, due to the
issues reported above, trustworthiness of the data acquired
through IoT sensors can be guaranteed by reputation-based
sensing. Fig. 3 illustrates the disinformation probability in a
distributed sensing environment under reputation-unaware
sensing and reputation-aware sensing in the presence of
malicious behavior and malfunctioning sensors where
sensing costs of IoT sensors vary between 1 and 5 and the
usefulness of sensor data varies between 1 and 10. In the
experimental setup, 1000 IoT sensors are deployed in a
Figure 3. Manipulation probability in the presence of
malfunctioning and malicious IoT sensors.

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1000x1000 terrain with 5% malfunctioning or malicious
activity. It is worth noting that disinformation denotes the
case where an IoT sensor is recruited while it is reporting
wrong sensor data. Reporting of wrong sensor reading can
be either continuous or intermittent. Intermittent
disinformation/misinformation denotes the situation where
true sensor data is sent for a while and then wrong sensor
data is shared either due to malfunctioning or to lead to
disinformation. As seen in the figure, reputation awareness
degrades disinformation probability at the order of 75%
under various sensing task arrival rates. Malicious or
malfunctioning sensors can be identified faster if they keep
sending wrong sensor data continuously. Thus, the
reputation of a sensor that continuously sends wrong sensor
reading will be degraded continuously and converge to a
low value shortly, and the corresponding IoT sensor device
will not be recruited again due to its reasonably low
reputation. Therefore, disinformation probability under
intermittent disinformation is slightly higher, however, the
improvement over reputation-unaware sensing is still above
70% even in the presence of malicious sensors that attack
based on a strategy.
In addition to the experimental results above, Fig. 4
presents the utility of the cloud platform and the average
utility of an IoT sensor calculated by (1)-(2) in the presence
of malfunctioning and malicious IoT sensors which may
report wrong sensor data either continuously or
intermittently. The simulation setup is adopted from the
study in (Kantarci & Mouftah, 2014a). As seen in Fig. 4a,
platform utility can be improved by 12% under a lightly
arriving sensing task load and by 85% under a heavily
arriving sensing task mode. As seen in Fig. 4b,
compensation of IoT sensors is always non-zero. Note that
the compensation mechanism in these examples adopts the
auction-based payment approach in (Yang, Xue, Fang, &
Tang, 2015).
6. VISUALIZATION CASE STUDY
In order to concretely illustrate backend components,
Sections 6 and 7 will focus on a single case study: detection
and monitoring of the Long QT Syndrome (LQTS).
6.1 Background: Long-QT Syndrome
LQTS is a cardiac illness which may be congenital or
drug-induced. It is characterized by prolongation of the QT
interval on an ECG, shown in Fig. 5. This interval is a
measure of ventricular repolarization time, and its
prolongation can warn of impending arrhythmias such as
torsades de pointes (TdP), leading to syncope or death. The
congenital form of the disease is particularly dangerous, as
this risk never fully goes away.
The impact of LQTS varies widely based on gender,
age, and specific genetic mutation. It also manifests during
different activities based on genotype. Type-1 LQT (LQT1)
patients tend to have issues during exercise, while Type-2
(LQT2) patients are more at risk during sleep. Patients with
an LQT genotype, or people who are on known QT-
prolonging drugs, may benefit from — or outright require
— long term monitoring via ECG sensors, providing an
early warning to the patient, doctor, and/or EMS based on
QT interval measurements. More specifically, the physician
is interested in the length of the QT interval in relation to
heart rate; i.e., whether QT is happening quickly enough,
before the next cardiac cycle begins. It is common to look at
a “corrected” QT value, shown in Equation (4), known as
the Bazett QT correction equation (Bazett, 1920). While it is
not necessarily the best correction for all purposes, it is
perhaps the simplest and one of the most commonplace.
Figure 4. (a) Utility of the cloud platform in a distributed IoT sensing scenario, (b) Average utility of an IoT sensor node
in a distributed IoT sensing scenario.

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6.2 Decision Support
One of the most useful types of decision support is not
for a computer to generate specific recommendations, but to
simply present the data in a manner that allows the doctor to
fully understand the situation. Based on this presentation,
the doctor can make his or her own decision. The challenge
here is to condense many sensor measurements spanning a
long period of time into a very concise summary.
An important consideration in building visual aids for
decision support is knowing which features are relevant to
the condition being investigated. In the Long QT Syndrome
(LQTS), for example, many ECG measurements such as
QT, RR, or TpTe may all carry some information about the
disease, not just QT. Additionally, there are several ECG
leads (sensor locations) to choose from, and certain leads
may be better for QT measurement. We also know that
LQTS manifests differently throughout the day based on
patient genotype, so perhaps there are a few key times of
day that should be checked (as opposed to looking at an
overall average of the available data).
We are building a sizable array of factors that are
relevant to this disease, and circling back around to the
original problem: displaying it all to the doctor in a form
that can be digested very quickly. Remember that in
addition to each of these factors — ECG marker and lead,
time of day, etc. — the doctor may also have 20–30
patients. Further, the advent of long-term remote monitoring
means that each patient will be generating more data than
ever before. So we would like to create a picture that
adequately summarizes a patient’s day with only a few
seconds of viewing (Page, et al., 2015c).
The first set of techniques we will apply to LQTS
monitoring involve the removal of redundant information
from the ECG recording. For instance, while many ECG
measurements may contain some information related to the
patient’s illness, we may focus simply on QTc (which
combines two measurements, QT and RR). Further, since
many ECG leads are available, we will combine data from
all of them using e.g. a median or average (We could also
choose to look only at a single lead, perhaps the least noisy.)
Now that we are focused on a single (computed) feature
on a single (virtual) lead, our visualization problem is much
more focused. We must plot or tabulate the values of QTc
for ~120,000 heart beats per day. Again, we know that
certain times of day are more critical based on genotype.
However, as they are based on sleep and exercise patterns,
they will still vary significantly between patients. So, we
would like to show the entire day if possible.
The most obvious way to present the remaining data
would be to simply plot it. However, the scale of the plot
must be determined to ensure that short duration events are
still visible. In the case of LQTS, we are mainly interested
in events that last for several minutes. It is therefore
practical to plot a full 24 hours in a fairly “typical” plot size
(e.g. “half page”), which allows us to see with at least one-
minute resolution.
Finally, we note that for data spanning 24 hours or
more, polar axes can be beneficial. By using the angle of a
polar plot to represent time of day, and the radius to
represent the value of some feature, multi-day data can
simply continue to circle around the plot. Even with single-
day data, this representation makes it unnecessary to adjust
Figure 5. Standard ECG waveform. We will mainly
look at the QT interval (annotated), but RR — the
time of one heart beat — is also of interest. Other
metrics may provide even more detail, such as
Tpeak–Tend (TpTe). Image source:
SinusRhythmLabels.png by Anthony Atkielski.
Figure 6. Example plot of QTc over 24 hours in the
“ECG Clock” format. This patient has a relatively
normal QTc interval during the day, but it becomes
potentially dangerous at night.

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the axes range to view different recordings. (For example,
should the x axis start where the recording does, or at some
other time like midnight?) We have found it best to
standardize on a 24-hour polar axis. An example of the
visualization we’ve just described is given in Fig. 6.
Because our data was still fairly noisy even after all the
preceding steps, we used a median filter to smooth it.
Further uses of this visualization technique have already
been well-described in (Page, Soyata, Couderc, & Aktas,
2016) and an open source ECG visualization program is
available in (Page, Soyata, Couderc, & Aktas, 2015a).
7. ANALYTICS CASE STUDY
The objective of the visualization techniques developed in
Section 6 was to present enough data for the doctor to make
a decision. However, especially in the case of rare diseases
with which the physician may not be experienced, it would
be good for the computer to also provide some extra “hints”.
In this section, we begin to investigate ways to augment the
visualizations using machine learning (ML) algorithms. The
goal is to utilize (a subset of) the same data used to generate
plots to compute the likelihood that a patient has a particular
medical condition. In this study, we will continue to focus
on LQTS.
7.1 Background/Methods
We consider machine learning algorithms from three
general categories:
1) “Conventional” supervised learning methods, such
as SVM, decision tree, and nearest neighbors.
2) Clustering techniques such as GMM, K means, and
DBSCAN.
3) Artificial neural networks (ANN).
We will mainly discuss the first category, but will
present some formulation and results from the third. We will
also consider “ensemble” techniques such as AdaBoost and
Random Forest, which attempt to use the results from
several classifiers improve accuracy. Clustering methods
will not be discussed, as we have not yet found satisfactory
parameters to achieve good results with these.
Which ML algorithm is best to detect and classify
LQTS? This really depends on properties of our data and
our long term goals for how it will be used. For instance,
some algorithms may be lighter in terms of storage and/or
computation if we intend to continuously update the
classifier (i.e. “online” machine learning). Additionally, we
will want to keep the dimensionality of the data as low as
possible in order to improve the accuracy of many methods.
For now, we will make some assumptions — e.g. that
hourly data will be sufficient, as opposed to beat-to-beat
data, and that the database is small — and test the
performance of a variety of conventional ML algorithms on
our data. Incidentally, our database is indeed somewhat
small; we have access to 639 24-hour Holter recordings of
healthy, LQT1, and LQT2 patients. LQT2 is the smallest
cohort, with 145 recordings. LQT1 has 294 recordings, and
healthy has 200. The scikit-learn (Pedregosa, 2011) Python
library will be used to perform the tests. 70% of the samples
will be used for training, and the remaining 30% will be
used for testing. Again, the data will consist of hourly QTc
values for each patient, plus their gender (25 “dimensions”),
and a corresponding classification (0, 1, or 2, for “healthy”,
“LQT1”, or “LQT2”, respectively).
To start with, we will test one of the simplest machine
learning algorithms: nearest neighbors. This method
simply selects the “closest” training sample to the presented
sample (i.e. shortest Minkowski distance). An extension of
this takes a weighted average of the N closest samples. One
disadvantage of this technique is that you must store and
search through all previous data in order to find the nearest
match(es).
Support vector machines (SVM) are also very
common, and simple to train and interpret. Depending on
the nature of the data, they can be highly accurate. They
operate by defining hyperplanes which separate the data into
different groups. These planes are created from a subset of
the training points, known as support vectors, in a way that
maximizes the distance from the plane to the nearest data
point of any class. Additionally, the feature space may be
transformed using different kernels to allow nonlinear
classification boundaries. Regardless of the kernel, SVM
offers several advantages including memory efficiency and
effective classification in high dimensional spaces. We will
attempt to train SVMs using both linear and radial basis
kernel functions. While this (and some other) algorithm(s)
are designed for data from only two categories, the scikit-
learn implementation will internally split our three-category
data into two-category stages to bypass this limitation. One
weakness of SVM is in the ability to judge how certain we
are about a prediction; you can compute the distance from a
point to the nearest separator plane, but this doesn’t
necessarily translate well to a “confidence percentage”.
A very different method that we expect to perform well
is the random forest algorithm. This method uses training
samples to construct multiple decision trees — a forest —
using random subsets of the given features to build each
tree. It then classifies a new testing point by averaging the
results of the individual trees (a single decision tree operates
by splitting the data multiple times until “leaves” are created
of a single class. Then, to classify a new sample, we simply
traverse the tree based on the splitting criteria until we
arrive at a leaf.) By taking the mean, a random forest gets
rid of the over-fitting problem that is often encountered with
a single decision tree. Random forests offer other valuable
features such as processing large amounts of data
efficiently.
The random forest is a type of “ensemble” algorithm,
basing its output on the output of several other classifiers.
We will test two other ensemble techniques as well:

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AdaBoost, and voting. The voting classifier simply takes
the output of several other classifiers and does a majority
vote if they disagree. In a more advanced version of this, the
results of the individual classifiers will be weighted based
on their confidence in it (and/or our confidence in that
classifier). AdaBoost is somewhat different; it is a
multistage classifier where each stage is trained on the
failures of the previous stage (in our case, each stage is a
Decision Tree, but this can be changed).
Finally, we will use the NVIDIA Deep Learning GPU
Training System (NVIDIA-DIGITS, 2016) and Caffe
(CAFFE, 2016) for ANN-based classification of LQTS. We
have seen in Section 6 that proper visual
arrangement/presentation of ECG sensor data can greatly
aid the doctor’s decision in diagnosis and prescription. As
there are many ANNs designed for visual recognition tasks,
we decided to adapt our visual output (i.e. the ECG Clock)
to a form that could be directly used as input for a pre-tuned
ANN. One common vision task for ANNs is to classify
handwritten digits from the MNIST handwriting database
(LeCun, Cortes, & Burges, 1998b).
These are binary images, and are 28x28 px each. We
simply shrink our ECG clocks (the plotted lines only) down
to this size, and attempt to train an ANN to classify
“healthy”, “LQT1”, and “LQT2”, from plotted QTc values.
This format essentially restricts us to 784 data points
(28x28), and most of the image is blank (i.e. it is sparse); we
may only plot ~70 points. So it will be interesting to see if
we are providing enough data to the ANN. This technique is
shown in Fig. 7. Based on the examples in this figure, we
expect that “healthy vs. sick” will be fairly simple to
determine, but “LQT1 vs. LQT2” may be difficult.
7.2 Results
Classification of “healthy” vs. “long QT” was
relatively accurate — about 90% — as we expected. Further
differentiating between LQT1 and LQT2 was more difficult,
lowering the score of each classifier as shown in Fig. 8.
Still, an accuracy of about 70–75% was consistently
achievable with the SVM and Random Forest methods. We
should note right now that several of the recordings in our
database were noisy or incomplete, which likely degraded
our results. Missing data was replaced with average values,
but very short recordings should probably have simply been
thrown out. However, we wanted to present a “worst case”
starting point for further research, so all data was retained.
Another important consideration is that while our data is
segregated by LQTS genotype, not all LQTS subjects show
the corresponding phenotype. In other words, a handful of
the LQTS patients truly do look healthy, so even a
cardiologist would be likely to “misclassify” them.
We just saw that Random Forest and Support Vector
Machine (SVM) generally proved superior to other
algorithms. Now, we would like to see what information
they are using to arrive at their decisions. For example,
Figure 7. Classifying LQTS using QT Clocks and
ANNs. Top: typical samples for ANN handwriting
classification. Bottom: QTc clocks converted to a
similar style. The first three clocks are healthy,
followed by three LQT1 and two LQT2 clocks. While
this format may not be ideal, it allows us to test
preconfigured ANNs on our LQTS diagnosis problem.
Figure 8. Comparison of conventional ML classifiers.
The training+testing cycle was repeated 20 times with
Holter recordings randomly assigned as training or
testing each time. Here we see the average performance
of each classifier when identifying “healthy vs. sick”
(blue) or “healthy vs. LQT1 vs. LQT2” (red). Error
bars show the range from worst- to best case
performance of each classifier over the 20 trials.

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based on our findings in (Page, Soyata, Couderc, & Aktas,
2016), we expect that data from ~3AM will be a very good
differentiator between the classes. We also expect that
afternoon QTc measurements will not help distinguish
between LQT1 and LQT2. Fortunately, we can examine the
internals of these trained classifiers quite easily. In Fig. 9,
we extract the “importance” of each feature (hour) from the
Random Forest and Linear SVM classifiers. The results are
basically what we expected — late-night data is most useful
to both classifiers — but SVM also used information from
earlier in the evening. Because of the random nature of the
training/testing data split, and the random selection of
features in Random Forest, these results will not be exactly
the same on every trial. However, we observed the same
general trend over several trials.
Finally, we attempted a very basic Artificial Neural
Network analysis of the QTc data. In this case, we did not
provide hourly data points, but (28x28 px) QTc clocks as
shown in Fig. 7 (bottom). These clocks were used to train a
LeNet network (LeCun, Bottou, Bengio, & Haffner, 1998a),
which is known to perform well on the MNIST handwriting
data set. Missing data was not “filled” in any way; we
simply passed incomplete plots to the ANN. This
implementation achieved ~70% accuracy with absolutely no
tuning (and ~90% accuracy when only classifying “healthy
vs. sick”). i.e., it was comparable to the classifiers we’ve
already discussed. However, providing QTc clocks based on
a different correction equation (Fridericia, 1920):
(as opposed to Eqn. 4) yielded a significant improvement:
three-way classification accuracy increased to ~80%. Using
this alternate QTc equation did not improve the accuracy of
any of the other classifiers, only the ANN.
7.3 Future Work
TensorFlow (TENSOR-FLOW, 2016) and Amazon
Machine Learning (AMAZON-ML, 2016) are two relatively
recent ML developments that we have yet to test. Both of
these solutions appear to be relatively simple to use and to
collaborate with. The Amazon product is promising as a
very high level solution, that will simplify the tuning
process. TensorFlow, developed at Google, is likely to be
useful for researching and training more complex neural
networks.
Another avenue of research will arise as data collection
and collaboration increases: the analysis of trends and
disease outbreaks. This analysis may be more statistical in
nature than what we’ve presented; i.e., machine learning
may not play a major role. This will also tie in with the
visualizations of Section 6. For example, the statistically
“normal” ranges must be continuously updated with new
recordings.
In Fig. 9, the low values during the daytime (about
9AM-5PM) indicate that perhaps we don’t even need to
monitor that data. In further research, we will attempt to
select only the necessary features to provide similar results.
If there are indeed several hours which don’t require
observation, it may save battery life, storage, and processing
time. We must also determine which other
features/measurements would improve performance.
Gender, for example, was used as a feature in our results
above, but its importance turned out to be quite low. We
must attempt to add other ECG markers (such as RR and
TpTe) to determine the best set of features for classifying
LQTS. Other researchers will have to do the same for other
diseases. Many optimizations remain in terms of classifier
parameters as well, but tweaking them did not affect our
performance very much at this stage. We therefore believe
that it is more important to find the correct features before
finding the optimal classifier configurations.
At this point, our ANN results are really only a very
preliminary proof of concept. We must optimize this on two
fronts: 1) neural network parameters (layers, etc.) and 2)
input data. The input data side will be beneficial to the
“conventional” algorithms as well. This is the research we
just mentioned, where we will attempt to identify other ECG
parameters to include in the input, and how to best reduce
the dimensionality of the feature space. Further, we will
attempt to hand-select only clean, complete recordings of
phenotype-positive individuals as input; from initial testing,
Figure 9. Weight of each measurement in
classification. A stronger magnitude means QTc
measurement was more “helpful” at that time.
Random Forest weights are extracted from the
classifier’s feature_importances_ array. SVM
weights are taken as the (normalized) maximum
amplitudes of the weights in coef_ across all three
possible classes (healthy, LQT1, LQT2). We see that
both classifiers focus on late-night QTc values, and
that SVM also uses information from evening (~7PM)
while Random Forest does not.

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31
this may reduce our error by an order of magnitude! We
may also try a different branch of research, where we look
at, for example, 1 hour of ECG data, and attempt to predict
if there will be a cardiac event in the following hour. This
will allow us to provide real-time warnings, rather than
being limited to disease classification.
8. SUMMARY AND CONCLUDING REMARKS
Emerging technologies such as IoT and cloud-based
machine learning have opened the door for vast
improvements to personalized health care. However, we
must understand the strengths and limitations of each
technology to assemble a system that is reliable, practical,
and provides the best possible support to both doctors and
patients. We have addressed many of the privacy and
security concerns in this system, and presented our
approaches to developing some of the key components. We
have also identified several business opportunities that
naturally arise from such a system, for instance in the realms
of data acquisition, sharing, and analytics. In our front end
feasibility study, we discussed IoT-based data acquisition in
the presence of malfunctioning/malicious nodes. In our
backend feasibility studies, we presented decision support
methods for long-term patient monitoring. The first method
involved visualization of key features from sensor data, and
the second method applied machine learning to these
measurements to identify disease states. Our future work
will focus on improving the ML-based analysis of long-term
medical data.
9. ACKNOWLEDGMENT
This work is supported in part by the National Science
Foundation grants CNS-1239423 and CNS-1464273. Tolga
Soyata was an Assistant Professor - Research at the
University of Rochester during the preparation of this
manuscript. He was the PhD adviser of Alex Page, as well
as the undergraduate adviser of Shurouq Hijazi. Before the
publication of this manuscript he joined SUNY Albany,
ECE as an Associate Professor.
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Authors
Alex Page is a PhD candidate in Dr.
Soyata’s research group. He
received his MS in Electrical
Engineering from the University of
Rochester. His current research is
focused on computer systems for
medical data analysis, including
databasing, GPU acceleration, and
machine learning techniques.
Shurouq Hijazi is an
undergraduate student majoring in
ECE with a concentration in
Wireless Communications at the
University of Rochester. She is
currently a research assistant in Dr.
Soyata’s laboratory, and is focused
on machine learning techniques.
Dogan Askan is an MS student
in the ECE department of
Clarkson University. He is a
researcher in Prof. Burak
Kantarci’s lab. His research
interests include trustworthy
sensing.
Burak Kantarci is an Assistant
professor in the Department of ECE
at Clarkson University, and the
founding director of the next
generation communications and
computing networks (NEXTCON)
research lab at Clarkson. He
received his PhD in Computer
Engineering at Istanbul Technical
University in 2009. He is an editor
for IEEE Communications Surveys and Tutorials. Dr.
Kantarci also serves as the secretary of IEEE ComSoc
Communication Systems Integration and Modeling
Technical Committee (CSIM-TC). He is a senior member of
IEEE and a member of ACM.
Tolga Soyata received his B.S.
degree in Electrical and
Communications Engineering
from Istanbul Technical
University in 1988, M.S. degree
in ECE from Johns Hopkins
University in 1992 and PhD in
ECE from University of
Rochester in 2000. He joined the
University of Rochester ECE
Department in 2008. He was an
Assistant Professor – Research at UR ECE until 2016. He
joined SUNY Albany Department of CE as an Associate
Professor in 2016. His teaching interests include CMOS
VLSI ASIC Design, FPGA-based High Performance Data
Processing System Design, and GPU Parallel Programming.
His research interests include Cyber Physical Systems,
Digital Health, and GPU-based high-performance
computing. He is a senior member of both IEEE and ACM.