Some Importantant links below
with reports.just view the link below. if u want any project report just
search any project on our search box
Arduino interesting projects:
Arduino 30 simple and good projects
Atmega projects lists
Android Electronics projects lists
Rf based Projects with report
engineering study notes
GSM GPS based projects with report
Bluetooth based projects with reports
systems development focusing on the following aspects: Health-Care systems
general overview, sensors, devices communication, data processing. A separate
part will analyze a usability and reliability of the fuzzy logic approach and show
various number of examples where this particular type of logic was successfully
implemented in medicine.
Monitoring has three objectives, which are to reduce time loss due to lag, reduce
the medium cost, and reduce inaccuracy in traditional medical flow 1. In
addition, it has to fully realize that events and changes in the real environment
must be reflected in UHIS, and changes and decisions made in UHIS must be
forwarded to the real life. In other words, ubiquitous health-care consumers
will send out data from various sources, receive real-time medical information,
knowledge, and relevant expertise and search out relevant and useful information,
especially for remote monitoring of physiological parameters. All the
systems developed and currently used in this area can be categorized by several
aspects: type of sensors, type of connection between sensors, monitoring/processing
device and signal processing.
The choice of sensors in many cases is the most essential step while creating
a monitoring systems. Implementing different scenarios(indoor or outdoor
monitoring) demands deploying different types of sensors. However, there are
two major categories: wearable and non-wearable. The first includes all devices
which require contact with the patient’s body in order to provide measurements.
They can often be attached to a different body parts or integrated with
some clothes and represent, for instance, a common article of T-shirt [24]. A
main reason for close communication with person’s body is peculiarity of the
medical data, measured by wearable sensors. The most considerable parameters
are: heart rate, oxygen saturation, blood pressure and sugar rate. It is
possible in some cases to avoid using wearable devices for measuring this data,
and use , for instance, sensors embedded in pillows or blankets [34], however
this approach is highly limited in terms of usage and can only be employed for
monitoring at night.
The second category contains all the sensors that can be appropriately used
to provide information about surrounding environment. In many situations this
data can make a huge impact for the overall analyzes and play a vital role in
maintaining patient’s health condition. One of the examples could be CO2 sensor
[32] which was incorporated into a wireless platform and provide user with
information on carbon dioxide level in the room. In this particular project it is
embedded into the user’s piece of equipment (e.g. firefighter boot), however
other ways of usage are not excluded. There is also a several number of techniques
for posture recognition and activity measurements implemented with
cameras integrated in the living environment[8]. Usually non-wearable sensors
are combined into a sensor network which provides all kind of information,
appropriate for overall analysis, but at the same time can significantly reduce
patient’s mobility and monitoring area. At the same time, one of the major
aims of the project is to provide a person with a sophisticated system which
will maintain his/her activity level including outdoor activities (like shopping
or taking a promenade). The second type of sensors are not capable of working
in terms of permanently changing environment and therefore can not be used.
Another important issue is the number of measuring devices. The more sensors
we use the wider spectrum of possible scenarios and events we are able to
cover. However, overloading patient with electronics can cause inconvenience
and affect his mental condition. Moreover, a power consumption problem is a
serious consequence in this case, which was previously addressed and leads to
unnecessary complexity of the system [5] [38].
At the next stage, sensor layer of every remote monitoring system is typically
connected to the processing device. This communication can be either
wired [14] or wireless, depending on initial goal of the application. The wireless
communication is usually provided by Bluetooth or ZigBee confection [42].
However, there are some alternative approaches, and one of them was described
in IEEE journal on selected areas in communications [13]. Systems, designed in
this project are capable of integrating different types of sensors with Radio Frequency
Identification into a Radio Frequency board through a programmable
interface chip. This chip can use a comprehensive signal processing to extract
bio-signal feature parameters and only transmit them. These systems, however,
have an additional aggregation node for signal processing and data converting.
In our case it is replaced with a mobile phone device, which provides more
computational power and wider range of functionality.
The opposite to wireless is a wired sensor networks, where each sensor in
the system is connected to a processing device. Normally, it is a wearable sensornetwork which integrates sensors, electrodes and wired means of communication
in order to monitor patients health [14].
Since the overall mobility and unobtrusiveness of the system together with
patients active life are among the highest priorities of the project, we have to
consider wireless communication as the only appropriate approach.
initial choice can vary depending on the application purposes. Generally, medical
personnel requires collection and displaying the information measured by
sensors in order to make decisions about patient’s health conditions. This requirement
can be fulfilled with PDA (Personal Data Assistant), which can serve
as an aggregation node in these cases [13]. This approach has been successfully
implemented in the project by Fei Hu and Yang Xioa where they used a special
device to integrate all sensor data into database records and send it out.
Moreover, several attempts to perform on-line analyzes were made before
the data was sent further to the assisting group [14]. In the project by Zhengand
Zhang, system provides real-time visualization, memorizing, analysis, diagnosis
and three degrees of alarm. It was implemented through the PPU (Portable
Patient Unit) device which has a wireless connection to the wearable sensor
network.
At the same time, an approach where a mobile device is used as processing
node has not been popular in healthcare project. However, mobile service
was involved in designing of systems described in ”U-Health system with use
of smart phone and sensor network”[42] and practical case of ”MyHeart”
project[24]. In first case, a signal from each sensor is transferred to the the base
mote, which is connected to Client PC. At this stage, a body-information data
is extracted and sent further to the Server PC. In case of abnormality, Server PC
transfers a text message and patient’s condition to family and medical center. It
furthermore offers a web and mobile service to confirm patient’s condition.
The second project considers a cellular phone as a transmitter of the signal,
acquired from the sensors and processed by on-body electronics to a monitoring
center. It allows professional care-givers to consult the patient’s data and
interact with him[24].
create a reliable platform for the future extensive research towards on-line
monitoring system. This implies collecting a continuous dataset and perform
a certain level of analysis, based on received measurements, which can lead to
serious restrictions in terms of algorithm complexity. In other words, we are not
interested in computationally expensive methods which might slow down the
process. Thus, for processing purposes, we employ particular methods: change
point and anomaly detection. Both procedures are commonly used in data mining
area and have been studied before [21][17][19][15][20][43].
a data sequence[2]. In other words every rapid drop/jump in time series is represented
by a certain point, which plays an important role in processing of this
data. Therefore, on-line detection of change points is useful in modeling and
prediction of time series in application areas such as finance, biometrics, and
robotics. In our case, this procedure indicates a situation, which can potentially
be dangerous for a patient, and, hence should be an integral part of data processing.
Popular approach, which incorporates several relative studies[20][43] is
based on Bayesian on-line change point detection and focused on the retrospective
segmentation problem[2][10]. In project by Ryan Prescott Adams and
David J.C.MacKay a particular case is examined: the model parameters before
and after the change point are independent. Later, an on-line algorithm for exact
inference of the most recent change point is derived. Authors compute the
probability distribution of the length of the current “run,” or time since the
last change point, using a simple message-passing algorithm. This implementation
is highly modular so that the algorithm may be applied to a variety of
types of data[2]. The whole procedure is based on different calculations and
can be combined into the following steps:
(1) Initialize,
(2) Observe New Datum,
(3) Evaluate Predictive Probability,
(4) Calculate Growth Probabilities,
(5) Calculate Changepoint Probabilities,
(6) Calculate Evidence,
(7) Determine Run Length Distribution,
(8) Update Sufficient Statistics,
(9) Perform Prediction,
(10) Return to step 2.
The concern is to estimate the posterior distribution over the current ”run
length” or time since the last change point, given the data so far observed [2].
It is implemented in step seven:
P(rtjx1:t) = P(rt, x1:t)=P(x1:t)
With the last step a marginal predictive distribution is calculated using the following
equation.
P(xt+1jx1:t) =
X
i=1
P(xt+1jxt, rt)P(rtjx1:t)
in both cases x refers to observations and r is a length of the current run. The
result after applying described algorithm are expected to look as it shown on
figure 2.1 bellow.
Another method was proposed by Allen B. Downey in his work ”Changepoint
detection in network measurements” [10]. The main idea behind this
work is also based on Bayesian theorem and implies an algorithm for simultaneous
detection and location of the change points in a time series, and a
framework for predicting the distribution of the next point in the series. The
kernel of the algorithm is a system of equations that computes, for each index
i, the probability that the last (most recent) change point occurred at i [10].
This algorithm is later evaluated by applying it to the change point detection
problem and comparing it to the generalized likelihood ratio (GLR) algorithm.
However, there is one serious drawback, considered by author in the conclusion
section of the paper: the proposed algorithm requires time proportional
to n2 at each time step, where n is the number of steps since the second-to-last
change point. If the time between change points is more than a few thousand
steps, it may be necessary to desample the data to control run time [10]. Assuming
the specific requirement, announced in the beginning of this section, the
proposed algorithm is computationally expensive for the current project.
In spite of the fact that, change point detection algorithm plays an essential
role in data processing for our particular case, it wont be able to satisfy all the
requirements. When it comes to personal health care, a large number of different
scenarios can not to be covered by a single processing technique. Therefore,
these scenarios should be considered in conjunction and processed by multiple
data mining methods.
anomaly detection provides us with a wider spectrum of information about
the signal. Normally this procedure refers to detecting patterns in a given data
set that do not conform to an established normal behavior. We assume, that
if any particular section differs from the rest of the signal, it could potentially
represent a dangerous situation and should be analyzed by a medical specialist.
At the same time, anomaly detection itself would not be able to register rapid
changes in a processed data if they repeat several times. It basically leads to
a fact, that both methods have their own benefits and compliment each other
during data analysis.
A numerous number of works provide a variety of algorithms and methods
for anomaly detection [7] [29] [31] [37]. A common procedure in many
related works presumes implementing several basic steps. As a rule, anomaly
detection algorithms learn models of normalities by fitting models to training
sets deemed as normal. With a next step, previously unseen instances are tested
by measuring their distances to the learned models, using threshold for determining
anomalies[37]. Using the previously mentioned sequence we eliminate
a clustering algorithm, commonly used in data mining, as it was claimed to be
meaningless in our case [18].
Moreover, before initiating a detection algorithm, it is considerable to determine
data representation. In many cases, this pre-step is simply eliminated
and data is processed directly [21]. However, assuming a specific nature of the
measured records (medical sensors, specific format), a choice of data representation
might play an essential role for future analysis. A possible approach is
to convert time series into a number of symbolic strings representing particular
sections of the entire data sequence [17] [19]. In the paper from ”ASME
Transaction on Mechatronics”, authors propose a novel method for anomaly
detection in mechanical systems, which make use of hidden Markov model, derived
from the time-series data of pertinent measurements[17]. In this particular
project a concept of Symbolic dynamics is used for converting the data. More
precisely, they are performing a partitioning of a compact region of the phase
space and a mapping from partitioned space into the symbol alphabet, which
becomes a representation of the system dynamics defined by the trajectories.
A rather alternative way of performing anomaly detection involves fuzzy
rules automatic extraction[30]. An idea behind this method is the following:
we split previously collected data into equal parts and extract primitive rules
about the signal behavior, based on the first part. For example,
IF pulse is High THEN oximetry is Low
We then examine the second part of the data and register every situation which
is not covered by previously formulated rules. The entire procedure is therefore
reduced to implementing a sufficient automatic fuzzy rules extraction algorithm.
This problem is common in data mining, and has been studied from
many perspectives[30][6][9][39][23].
Among the popular approaches is a rule extraction from support vector
machines [6]. Shuwei Chen, Jie Wand and Dongshu Wang are considering a
general fuzzy model with m fuzzy rules of the form
IF x1 is A1
j and x2 is A2
j ... xl is Al
j THEN y is Bj,
where xi are the input variables, y is the output variable of the fuzzy system;
and Al
j and Bj are linguistic forms characterized by fuzzy membership functions.
They later use a special fuzzy basis function as a kernel function for SVM
and perform a step by step SVM learning procedure:
(1) Assign parameters, (2)
Generate fuzzy rules from SVMs
(3) Create a combined fuzzy rule base. The
three step procedure creates a combined fuzzy rule base whose rules are from
either those generated from numerical data or linguistic rules [6].
In a similar paper [9] authors are using 3 steps procedure again. Firstly, they
obtain membership degrees by projecting previously calculated SVM into the
coordinate axis. With the second step fuzzy sets associated with each input attribute
are constructed. And, finally, each support vector generates a fuzzy rule.
The algorithm is evaluated using a benchmark classification database (Bupa
Liver Disorders) [9].
Alternative methods for automatic rule extraction are based on various approaches
including: ID3-based method (generating a fuzzy decision tree for a
specified class)[39], separation of the input space into activation rectangles,
corresponding to different output intervals [23], genetic algorithm and constrained
nonlinear optimization of membership functions [28], extraction from
typicality and membership partitions [35], extraction based on practical swarm
optimization [25] and from a trained multi layered neural network[26].
making assistance in situations when it is highly required. In our case,
reasoning platform should be based on a previously examined medical experience
combined into a knowledge base. Relying on this knowledge, a system
will be able to make decisions about certain correlations between medical parameters
and formulate responses typical for elderly people group. A universal
tool, potentially capable of providing required functionality is fuzzy logic - a
well known technique which has been introduced by Lotfi Zadeh in 1965 year.
Originally, it received a huge amount of skepticism from the scientific society
and was nearly forgotten. However, further demand in renovating and improving
decision-making algorithms played an important role in rising fuzzy logic as
an irreplaceable tool for applications in different areas. Ever since then, scientists
have made several attempts and described several ways to use fuzzy logic
in medicine [4]. Some of these projects will be briefly presented below.
A decision making process in medical area is always connected with a certain
degree of uncertainty [22]. It can possibly be imprecise information, inaccurate
information, missing information or conflicting information. While
developing a decision making or expert system we should also account for un28
certainties in different components of such a system including uncertainty in
Knowledge Base and Patient Data. As a brief example we can suggest a blood
pressure variance for the patients with different health condition. Moreover, a
developed healthcare system should be able to output a particular degree of an
alarm in case of emergency. Assuming currently listed facts, fuzzy logic and its’
versatile approach can help in dealing with previously mentioned uncertainties.
Unlike common binary logic fuzzy approach applies a special decision mechanism
which can be compared to a human logic and does not have any certain
restrictions or boundaries. This characteristic can be successfully used in various
number of applications in medical field. The recent statistic has shown an
exponential growth in number of articles describing fuzzy implementation in
the very same area [4].
A project by Novruz Allahverdi surveys and summarize the most popular
examples of collaboration between fuzzy logic and medicine[4]. Some of the
recent applications can accomplish such sophisticated tasks as determination
of the disease risk. It leads to the fact that subsequently this type of systems
would serve as an advisor for a hospital personnel. More specifically, two fuzzy
expert systems were developed in this particular direction. The first work contained
a number of rules combined together in a fuzzy expert system (FES) in
order to use laboratory or personal data and help specialists to predict a numerical
value of the prostate cancer risk disease. In this study prostate specific
antigen (PSA) age and prostate volume (PV) were used as system input parameters,
together with prostate cancer risk serving as an output. A nearly similar
approach was used for the second project, where hierarchical fuzzy expert system
was designed to provide assistance in determining coronary heart disease
diagnosis according to the next 10 years risk of patient. Two system resemble
in definition of the input parameters. This time age, cholesterol and blood pressure
were used as an input and same risk value were set as an output.
Proceeding with other examples in medical field, we should mention following
projects as a demonstration of additional capabilities of the fuzzy approach
[4]. They do not relate directly to the current project, however show a high usability
of the chosen technique. Assuming economical and accuracy benefits of
the fuzzy control scheme, a special expert system was introduced for the maintenance
of the necessary conditions in the operating room. In this case heat,
humidity, oxygen and particle values were considered as an input meanwhile
fresh air entrance and fan circulations were used for output. An experiment
with operating room prototype was held to prove feasibility of the approach.
The analysis of the achieved results led to conclusion that while using an appropriate
linguistic expressions and a membership functions for these expressions,
fuzzy control system provides more economical, reliable and consistent solution
than commonly used systems.
Another attempt to make some of the medical processes autonomous was
successfully implemented and described in the very same article[4]. A serious
issue, connected to drug dose determination has been addressed and tested with
fuzzy logic implementation. An amount of medical treatment given to the patient
depends on various number of parameters including age, weight, sex, disease
history blood segmentation etc. It can also vary from one disease to another.
A series of clinical tests were performed in order to compare a reliability
of the fuzzy drug determination with physician recommendations. As a result,
developed system helped to shorten the treatment duration and minimize negative
effects in determination process.
All the above mentioned projects make fuzzy logic and relative approaches
to be a better choice. Some of the results achieved with those techniques showed
a high degree of reliability, in several cases rising up to 96.55 %,94.71%,94,11%
[12]. However, in our particular case (assuming continuous monitoring), correlation
between main medical parameters has a complicated behavior and still
requires meticulous investigation. It would be superfluous and premature to
rely on incomplete knowledge while developing a monitoring system. Therefore,
implementing advanced decision making algorithm using previously mentioned
fuzzy logic functionality is a subsequent step. Preliminary, we make a
step towards full-scale experimental research in order to clarify a nature of the
correlation.
Some Importantant links below with reports.just view the link below. if u want any project report just search any project on our search box
Arduino interesting projects:
Arduino 30 simple and good projects
Atmega projects lists
Android Electronics projects lists
Rf based Projects with report
engineering study notes
GSM GPS based projects with report
Bluetooth based projects with reports
if u like the post just say thank u in comment box.
Arduino interesting projects:
Arduino 30 simple and good projects
Atmega projects lists
Android Electronics projects lists
Rf based Projects with report
engineering study notes
GSM GPS based projects with report
Bluetooth based projects with reports
Chapter 2
Background
The following chapter will provide a current state of the art in monitoringsystems development focusing on the following aspects: Health-Care systems
general overview, sensors, devices communication, data processing. A separate
part will analyze a usability and reliability of the fuzzy logic approach and show
various number of examples where this particular type of logic was successfully
implemented in medicine.
2.1 Remote Monitoring of Physiological Parameters
As a part of the Ubiquitous Health-care Information System (UHIS) RemoteMonitoring has three objectives, which are to reduce time loss due to lag, reduce
the medium cost, and reduce inaccuracy in traditional medical flow 1. In
addition, it has to fully realize that events and changes in the real environment
must be reflected in UHIS, and changes and decisions made in UHIS must be
forwarded to the real life. In other words, ubiquitous health-care consumers
will send out data from various sources, receive real-time medical information,
knowledge, and relevant expertise and search out relevant and useful information,
especially for remote monitoring of physiological parameters. All the
systems developed and currently used in this area can be categorized by several
aspects: type of sensors, type of connection between sensors, monitoring/processing
device and signal processing.
The choice of sensors in many cases is the most essential step while creating
a monitoring systems. Implementing different scenarios(indoor or outdoor
monitoring) demands deploying different types of sensors. However, there are
two major categories: wearable and non-wearable. The first includes all devices
which require contact with the patient’s body in order to provide measurements.
They can often be attached to a different body parts or integrated with
some clothes and represent, for instance, a common article of T-shirt [24]. A
main reason for close communication with person’s body is peculiarity of the
medical data, measured by wearable sensors. The most considerable parameters
are: heart rate, oxygen saturation, blood pressure and sugar rate. It is
possible in some cases to avoid using wearable devices for measuring this data,
and use , for instance, sensors embedded in pillows or blankets [34], however
this approach is highly limited in terms of usage and can only be employed for
monitoring at night.
The second category contains all the sensors that can be appropriately used
to provide information about surrounding environment. In many situations this
data can make a huge impact for the overall analyzes and play a vital role in
maintaining patient’s health condition. One of the examples could be CO2 sensor
[32] which was incorporated into a wireless platform and provide user with
information on carbon dioxide level in the room. In this particular project it is
embedded into the user’s piece of equipment (e.g. firefighter boot), however
other ways of usage are not excluded. There is also a several number of techniques
for posture recognition and activity measurements implemented with
cameras integrated in the living environment[8]. Usually non-wearable sensors
are combined into a sensor network which provides all kind of information,
appropriate for overall analysis, but at the same time can significantly reduce
patient’s mobility and monitoring area. At the same time, one of the major
aims of the project is to provide a person with a sophisticated system which
will maintain his/her activity level including outdoor activities (like shopping
or taking a promenade). The second type of sensors are not capable of working
in terms of permanently changing environment and therefore can not be used.
Another important issue is the number of measuring devices. The more sensors
we use the wider spectrum of possible scenarios and events we are able to
cover. However, overloading patient with electronics can cause inconvenience
and affect his mental condition. Moreover, a power consumption problem is a
serious consequence in this case, which was previously addressed and leads to
unnecessary complexity of the system [5] [38].
At the next stage, sensor layer of every remote monitoring system is typically
connected to the processing device. This communication can be either
wired [14] or wireless, depending on initial goal of the application. The wireless
communication is usually provided by Bluetooth or ZigBee confection [42].
However, there are some alternative approaches, and one of them was described
in IEEE journal on selected areas in communications [13]. Systems, designed in
this project are capable of integrating different types of sensors with Radio Frequency
Identification into a Radio Frequency board through a programmable
interface chip. This chip can use a comprehensive signal processing to extract
bio-signal feature parameters and only transmit them. These systems, however,
have an additional aggregation node for signal processing and data converting.
In our case it is replaced with a mobile phone device, which provides more
computational power and wider range of functionality.
The opposite to wireless is a wired sensor networks, where each sensor in
the system is connected to a processing device. Normally, it is a wearable sensornetwork which integrates sensors, electrodes and wired means of communication
in order to monitor patients health [14].
Since the overall mobility and unobtrusiveness of the system together with
patients active life are among the highest priorities of the project, we have to
consider wireless communication as the only appropriate approach.
2.2 Processing Device
A processing device is an essential part for most of the health care systems. Theinitial choice can vary depending on the application purposes. Generally, medical
personnel requires collection and displaying the information measured by
sensors in order to make decisions about patient’s health conditions. This requirement
can be fulfilled with PDA (Personal Data Assistant), which can serve
as an aggregation node in these cases [13]. This approach has been successfully
implemented in the project by Fei Hu and Yang Xioa where they used a special
device to integrate all sensor data into database records and send it out.
Moreover, several attempts to perform on-line analyzes were made before
the data was sent further to the assisting group [14]. In the project by Zhengand
Zhang, system provides real-time visualization, memorizing, analysis, diagnosis
and three degrees of alarm. It was implemented through the PPU (Portable
Patient Unit) device which has a wireless connection to the wearable sensor
network.
At the same time, an approach where a mobile device is used as processing
node has not been popular in healthcare project. However, mobile service
was involved in designing of systems described in ”U-Health system with use
of smart phone and sensor network”[42] and practical case of ”MyHeart”
project[24]. In first case, a signal from each sensor is transferred to the the base
mote, which is connected to Client PC. At this stage, a body-information data
is extracted and sent further to the Server PC. In case of abnormality, Server PC
transfers a text message and patient’s condition to family and medical center. It
furthermore offers a web and mobile service to confirm patient’s condition.
The second project considers a cellular phone as a transmitter of the signal,
acquired from the sensors and processed by on-body electronics to a monitoring
center. It allows professional care-givers to consult the patient’s data and
interact with him[24].
2.3 Data Processing for Ubiquitous systems
Besides monitoring feature one of the main goals of the current project is tocreate a reliable platform for the future extensive research towards on-line
monitoring system. This implies collecting a continuous dataset and perform
a certain level of analysis, based on received measurements, which can lead to
serious restrictions in terms of algorithm complexity. In other words, we are not
interested in computationally expensive methods which might slow down the
process. Thus, for processing purposes, we employ particular methods: change
point and anomaly detection. Both procedures are commonly used in data mining
area and have been studied before [21][17][19][15][20][43].
2.3.1 Change point detection
Generally, change points are abrupt variations in the generative parameters ofa data sequence[2]. In other words every rapid drop/jump in time series is represented
by a certain point, which plays an important role in processing of this
data. Therefore, on-line detection of change points is useful in modeling and
prediction of time series in application areas such as finance, biometrics, and
robotics. In our case, this procedure indicates a situation, which can potentially
be dangerous for a patient, and, hence should be an integral part of data processing.
Popular approach, which incorporates several relative studies[20][43] is
based on Bayesian on-line change point detection and focused on the retrospective
segmentation problem[2][10]. In project by Ryan Prescott Adams and
David J.C.MacKay a particular case is examined: the model parameters before
and after the change point are independent. Later, an on-line algorithm for exact
inference of the most recent change point is derived. Authors compute the
probability distribution of the length of the current “run,” or time since the
last change point, using a simple message-passing algorithm. This implementation
is highly modular so that the algorithm may be applied to a variety of
types of data[2]. The whole procedure is based on different calculations and
can be combined into the following steps:
(1) Initialize,
(2) Observe New Datum,
(3) Evaluate Predictive Probability,
(4) Calculate Growth Probabilities,
(5) Calculate Changepoint Probabilities,
(6) Calculate Evidence,
(7) Determine Run Length Distribution,
(8) Update Sufficient Statistics,
(9) Perform Prediction,
(10) Return to step 2.
The concern is to estimate the posterior distribution over the current ”run
length” or time since the last change point, given the data so far observed [2].
It is implemented in step seven:
P(rtjx1:t) = P(rt, x1:t)=P(x1:t)
With the last step a marginal predictive distribution is calculated using the following
equation.
P(xt+1jx1:t) =
X
i=1
P(xt+1jxt, rt)P(rtjx1:t)
in both cases x refers to observations and r is a length of the current run. The
result after applying described algorithm are expected to look as it shown on
figure 2.1 bellow.
Figure 2.1: Bayesian on-line change point detection results
Another method was proposed by Allen B. Downey in his work ”Changepoint
detection in network measurements” [10]. The main idea behind this
work is also based on Bayesian theorem and implies an algorithm for simultaneous
detection and location of the change points in a time series, and a
framework for predicting the distribution of the next point in the series. The
kernel of the algorithm is a system of equations that computes, for each index
i, the probability that the last (most recent) change point occurred at i [10].
This algorithm is later evaluated by applying it to the change point detection
problem and comparing it to the generalized likelihood ratio (GLR) algorithm.
However, there is one serious drawback, considered by author in the conclusion
section of the paper: the proposed algorithm requires time proportional
to n2 at each time step, where n is the number of steps since the second-to-last
change point. If the time between change points is more than a few thousand
steps, it may be necessary to desample the data to control run time [10]. Assuming
the specific requirement, announced in the beginning of this section, the
proposed algorithm is computationally expensive for the current project.
In spite of the fact that, change point detection algorithm plays an essential
role in data processing for our particular case, it wont be able to satisfy all the
requirements. When it comes to personal health care, a large number of different
scenarios can not to be covered by a single processing technique. Therefore,
these scenarios should be considered in conjunction and processed by multiple
data mining methods.
2.3.2 Anomaly detection
Unlike change point detection, which only registers abrupt variation of time series,anomaly detection provides us with a wider spectrum of information about
the signal. Normally this procedure refers to detecting patterns in a given data
set that do not conform to an established normal behavior. We assume, that
if any particular section differs from the rest of the signal, it could potentially
represent a dangerous situation and should be analyzed by a medical specialist.
At the same time, anomaly detection itself would not be able to register rapid
changes in a processed data if they repeat several times. It basically leads to
a fact, that both methods have their own benefits and compliment each other
during data analysis.
A numerous number of works provide a variety of algorithms and methods
for anomaly detection [7] [29] [31] [37]. A common procedure in many
related works presumes implementing several basic steps. As a rule, anomaly
detection algorithms learn models of normalities by fitting models to training
sets deemed as normal. With a next step, previously unseen instances are tested
by measuring their distances to the learned models, using threshold for determining
anomalies[37]. Using the previously mentioned sequence we eliminate
a clustering algorithm, commonly used in data mining, as it was claimed to be
meaningless in our case [18].
Moreover, before initiating a detection algorithm, it is considerable to determine
data representation. In many cases, this pre-step is simply eliminated
and data is processed directly [21]. However, assuming a specific nature of the
measured records (medical sensors, specific format), a choice of data representation
might play an essential role for future analysis. A possible approach is
to convert time series into a number of symbolic strings representing particular
sections of the entire data sequence [17] [19]. In the paper from ”ASME
Transaction on Mechatronics”, authors propose a novel method for anomaly
detection in mechanical systems, which make use of hidden Markov model, derived
from the time-series data of pertinent measurements[17]. In this particular
project a concept of Symbolic dynamics is used for converting the data. More
precisely, they are performing a partitioning of a compact region of the phase
space and a mapping from partitioned space into the symbol alphabet, which
becomes a representation of the system dynamics defined by the trajectories.
A rather alternative way of performing anomaly detection involves fuzzy
rules automatic extraction[30]. An idea behind this method is the following:
we split previously collected data into equal parts and extract primitive rules
about the signal behavior, based on the first part. For example,
IF pulse is High THEN oximetry is Low
We then examine the second part of the data and register every situation which
is not covered by previously formulated rules. The entire procedure is therefore
reduced to implementing a sufficient automatic fuzzy rules extraction algorithm.
This problem is common in data mining, and has been studied from
many perspectives[30][6][9][39][23].
Among the popular approaches is a rule extraction from support vector
machines [6]. Shuwei Chen, Jie Wand and Dongshu Wang are considering a
general fuzzy model with m fuzzy rules of the form
IF x1 is A1
j and x2 is A2
j ... xl is Al
j THEN y is Bj,
where xi are the input variables, y is the output variable of the fuzzy system;
and Al
j and Bj are linguistic forms characterized by fuzzy membership functions.
They later use a special fuzzy basis function as a kernel function for SVM
and perform a step by step SVM learning procedure:
(1) Assign parameters, (2)
Generate fuzzy rules from SVMs
(3) Create a combined fuzzy rule base. The
three step procedure creates a combined fuzzy rule base whose rules are from
either those generated from numerical data or linguistic rules [6].
In a similar paper [9] authors are using 3 steps procedure again. Firstly, they
obtain membership degrees by projecting previously calculated SVM into the
coordinate axis. With the second step fuzzy sets associated with each input attribute
are constructed. And, finally, each support vector generates a fuzzy rule.
The algorithm is evaluated using a benchmark classification database (Bupa
Liver Disorders) [9].
Alternative methods for automatic rule extraction are based on various approaches
including: ID3-based method (generating a fuzzy decision tree for a
specified class)[39], separation of the input space into activation rectangles,
corresponding to different output intervals [23], genetic algorithm and constrained
nonlinear optimization of membership functions [28], extraction from
typicality and membership partitions [35], extraction based on practical swarm
optimization [25] and from a trained multi layered neural network[26].
2.4 Decision Support System in Remote Monitoring
Applications
Every sophisticated monitoring system tends to provide a certain level of decisionmaking assistance in situations when it is highly required. In our case,
reasoning platform should be based on a previously examined medical experience
combined into a knowledge base. Relying on this knowledge, a system
will be able to make decisions about certain correlations between medical parameters
and formulate responses typical for elderly people group. A universal
tool, potentially capable of providing required functionality is fuzzy logic - a
well known technique which has been introduced by Lotfi Zadeh in 1965 year.
Originally, it received a huge amount of skepticism from the scientific society
and was nearly forgotten. However, further demand in renovating and improving
decision-making algorithms played an important role in rising fuzzy logic as
an irreplaceable tool for applications in different areas. Ever since then, scientists
have made several attempts and described several ways to use fuzzy logic
in medicine [4]. Some of these projects will be briefly presented below.
A decision making process in medical area is always connected with a certain
degree of uncertainty [22]. It can possibly be imprecise information, inaccurate
information, missing information or conflicting information. While
developing a decision making or expert system we should also account for un28
certainties in different components of such a system including uncertainty in
Knowledge Base and Patient Data. As a brief example we can suggest a blood
pressure variance for the patients with different health condition. Moreover, a
developed healthcare system should be able to output a particular degree of an
alarm in case of emergency. Assuming currently listed facts, fuzzy logic and its’
versatile approach can help in dealing with previously mentioned uncertainties.
Unlike common binary logic fuzzy approach applies a special decision mechanism
which can be compared to a human logic and does not have any certain
restrictions or boundaries. This characteristic can be successfully used in various
number of applications in medical field. The recent statistic has shown an
exponential growth in number of articles describing fuzzy implementation in
the very same area [4].
A project by Novruz Allahverdi surveys and summarize the most popular
examples of collaboration between fuzzy logic and medicine[4]. Some of the
recent applications can accomplish such sophisticated tasks as determination
of the disease risk. It leads to the fact that subsequently this type of systems
would serve as an advisor for a hospital personnel. More specifically, two fuzzy
expert systems were developed in this particular direction. The first work contained
a number of rules combined together in a fuzzy expert system (FES) in
order to use laboratory or personal data and help specialists to predict a numerical
value of the prostate cancer risk disease. In this study prostate specific
antigen (PSA) age and prostate volume (PV) were used as system input parameters,
together with prostate cancer risk serving as an output. A nearly similar
approach was used for the second project, where hierarchical fuzzy expert system
was designed to provide assistance in determining coronary heart disease
diagnosis according to the next 10 years risk of patient. Two system resemble
in definition of the input parameters. This time age, cholesterol and blood pressure
were used as an input and same risk value were set as an output.
Proceeding with other examples in medical field, we should mention following
projects as a demonstration of additional capabilities of the fuzzy approach
[4]. They do not relate directly to the current project, however show a high usability
of the chosen technique. Assuming economical and accuracy benefits of
the fuzzy control scheme, a special expert system was introduced for the maintenance
of the necessary conditions in the operating room. In this case heat,
humidity, oxygen and particle values were considered as an input meanwhile
fresh air entrance and fan circulations were used for output. An experiment
with operating room prototype was held to prove feasibility of the approach.
The analysis of the achieved results led to conclusion that while using an appropriate
linguistic expressions and a membership functions for these expressions,
fuzzy control system provides more economical, reliable and consistent solution
than commonly used systems.
Another attempt to make some of the medical processes autonomous was
successfully implemented and described in the very same article[4]. A serious
issue, connected to drug dose determination has been addressed and tested with
fuzzy logic implementation. An amount of medical treatment given to the patient
depends on various number of parameters including age, weight, sex, disease
history blood segmentation etc. It can also vary from one disease to another.
A series of clinical tests were performed in order to compare a reliability
of the fuzzy drug determination with physician recommendations. As a result,
developed system helped to shorten the treatment duration and minimize negative
effects in determination process.
All the above mentioned projects make fuzzy logic and relative approaches
to be a better choice. Some of the results achieved with those techniques showed
a high degree of reliability, in several cases rising up to 96.55 %,94.71%,94,11%
[12]. However, in our particular case (assuming continuous monitoring), correlation
between main medical parameters has a complicated behavior and still
requires meticulous investigation. It would be superfluous and premature to
rely on incomplete knowledge while developing a monitoring system. Therefore,
implementing advanced decision making algorithm using previously mentioned
fuzzy logic functionality is a subsequent step. Preliminary, we make a
step towards full-scale experimental research in order to clarify a nature of the
correlation.
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