Download Signal processing and machine learning methods and algorithm

FACULDADE DE E NGENHARIA DA U NIVERSIDADE DO P ORTO
Signal processing and machine learning
methods and algorithm analysis for
classication of lung function on a
smartphone app
EEC0035 - Preparação da Dissertação
João Pedro Fonseca Teixeira
Mestrado Integrado em Engenharia Electrotécnica e de Computadores
FEUP Supervisor: Luís Teixeira
External Supervisor: Miguel Coimbra
February 26, 2014
c João Pedro Fonseca Teixeira, 2014
Signal processing and machine learning methods and
algorithm analysis for classication of lung function on a
smartphone app
EEC0035 - Preparação da Dissertação
João Pedro Fonseca Teixeira
Mestrado Integrado em Engenharia Electrotécnica e de Computadores
February 26, 2014
Contents
1
PDI report intro
1.1 Project objectives and motivations . . . . . . . . . . . . . . . . . . . . . . . . .
1
1
2
Problem statement
2.1 Defining the problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Refining the problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
3
3
3
Literature Review
3.1 Introduction . . . . . . . . . . . . . .
3.2 Spirometry Technology . . . . . . . .
3.2.1 Spirometers and Sensors . . .
3.2.2 Research and Development . .
3.2.3 Games and Applications . . .
3.3 Methods and Algorithms . . . . . . .
3.3.1 Spirometric Parameters . . . .
3.3.2 Signal Processing . . . . . . .
3.3.3 Machine Learning . . . . . .
3.4 Discussion and Decision . . . . . . .
3.4.1 Signal Processing Discussion
3.4.2 Machine Learning Discussion
4
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Work Plan
4.1 Calendarization . . . . . . . . . . . . .
4.2 Methodology . . . . . . . . . . . . . .
4.3 Technologies, tools and work platforms
4.4 Concluded tasks . . . . . . . . . . . . .
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References
23
i
ii
CONTENTS
List of Figures
3.2
3.3
Common Flow Sensing Devices (Pneumotachometers). A-Fleisch type sensor,
B-Heated wire sensor, C-Pitot tube sensor, D-Turbine sensor, E-Ultrasonic sensor
Flow vs Volume representations for different lung functions . . . . . . . . . . . .
Perceptron diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
11
15
4.1
Time distribution diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19
3.1
iii
iv
LIST OF FIGURES
List of Tables
3.1
Distance metric functions between instances (x and y). . . . . . . . . . . . . . .
16
4.1
Temporal distribution of the project’s tasks. . . . . . . . . . . . . . . . . . . . .
20
v
vi
LIST OF TABLES
Abbreviations and Symbols
Medical Related
COPD
FEF25%−75%
FEM
FEV1 /FVC
FEVt
FVC
MMV
PEF
RV
SVC
TD
VC
VTG
Chronic Obstructive Pulmonary Disease
Forced Expiratory Flow between 25% and 75%, average flow during FVC
maneuver
Forced Expiratory Maneuver
Tiffeneau-Pinelli index
Forced Expiratory Volume in the first t seconds
Forced Vital Capacity
Maximum Voluntary Ventilation
Peak Expiratory Flow
Residual Volume
Slow Vital Capacity
Tidal Volume
Vital Capacity
Thoracic Gas Volume
Engineering Related
ADC
ANN
APF
BPF
CART
DIY
DFT
FFT
HPF
kNN
LPF
ML
SL
SVM
UL
USB
Analog to Digital Converter
Artificial Neural Network
All Pass Filter
Band Pass Filter
Classification And Regression Tree
Do It Yourself
Discrete Fourier Transform
Fast Fourier Transform
High Pass Filter
k-Nearest Neighbors
Low Pass Filter
Machine Learning
Supervised Learning
Support Vector Machines
Unsupervised Learning
Universal Serial Bus
CAGR
Compound Annual Growth Rate
vii
Chapter 1
PDI report intro
This document aims to present the work developed throughout the curricular unit Preparação da
Dissertação.
Chapter 2 contains a description of the problems that will be approached during the project.
In chapter 3 the State of the Art concerning the relevant topics for the development of the project
will be presented. Also, the methods that will be researched in finer detail will be summarized.
Those include:
• Signal processing methods of forced expiration recordings.
• Feature gathering on original and processed signals.
• Machine Learning algorithms for input classification.
Finally, in chapter 4 the work plan for the semester will be presented . In addition, the platforms and technologies which are expected to be used will be described.
1.1
Project objectives and motivations
This project aims to develop a mobile and easy to use platform that can help evaluate a patient’s
respiratory function. This kind of technology can also enable monitoring systems for patients
with lung problems such as asthmatics, COPD patients and people with other obstructive and/or
restrictive airway diseases. The following requirements are also desirable for the platform:
– Self contained,
– Detached from external systems (no external sensors),
– Fast enough (Timely results),
– Precise in the classification,
1
2
PDI report intro
The mobile and the self contained characteristics are important in order to reach the most
isolated places, which generally have worse medical care, if any. It not only makes sense the use
of smartphones in isolated places as it has been studied that areas in relatively severe poverty have
had a booming growth in smartphone use. Africa and Middle East is expected to increase from
the current 10% of share of smart devices and connections to 36% until 2018. The mobile data
traffic CAGR will also grow to 70% (Cisco, 2014). Currently about 1 in 5 Africans have access
to a smartphone (AfricanTelecomsNews, 2012).
The possibility of reaching such a number of people with less access to medical facilities
would certainly improve respiratory health care and awareness.
Chapter 2
Problem statement
2.1
Defining the problem
Lung function evaluation methods often revolve the forced expiratory maneuver (FEM). Since the
ultimate goal consists in the development of an Android app, the problem is to collect breathing
data in a way that the data resembles the FEM’s. Though the cooperation of the recording patients
and the correctness of the maneuver is important, only the topics concerning the development of
the application will be addressed.
The available data to work with will be an air pressure waveform which will be transformed
in a classification of the lung function.
2.2
Refining the problem
Since the problem is quite complex it urges to follow a divide and conquer strategy. In this sense,
the main problem is broken down to a few simpler and more contained problems:
– Physical Model of the Signal Propagation: Most of the spirometric relevant measures are
based on air flow and volume. The microphone is only capable of picking up data in the form
of pressure. In addition, the measurable pressure is attenuated and dispersed comparing
with the produced by the vocal tract. Therefore it might be beneficial to develop/assume a
radiation model and a conversion model from pressure to flow.
– Signal Processing: The signals, either previously processed or not, should go through a
series of processes that should have significant meaning in terms of audio. Processes may
include: FFT, Hilbert Transform, Filtering.
– Signal Feature Extraction: All the audio should be analyzed and distinct features should
be passed on to the classification stage. Features may include: FEV1 , FEV1 /FVC, PEF and
audio related measurements.
– Optimization and Classification: The features will be used to train a supervised learning
model of how to produce a characteristic label of the inputs.
3
4
Problem statement
– Method set comparison: Finally, several different method sets have to be evaluated on
which gives the best results.
Chapter 3
Literature Review
3.1
Introduction
In this chapter, a literature review deemed relevant for a better understanding of the problem
under study will be presented. A brief description of current spirometry technology is shown on
section 3.2. In section 3.3 some algorithms for Signal Processing and Machine Learning that are
currently available are presented. Finally, section 3.4 will present a brief discussion of which
methods might be best for the problem at hands.
3.2
Spirometry Technology
The precursor of the modern spirometer was introduced by Hutchinson around 1844. It was a
volume displacement water-sealed device. In recent years the sensor paradigm has shifted towards
the flow measure type thanks to the evolution and wide-spreading of electronic devices. Also, it
became possible to measure lung function parameters, using several other methods.
3.2.1
Spirometers and Sensors
During the course of lung function testing history many types of systems have been developed.
Some of them enable exams that complement each other and others are specific for providing
data from particular respiratory related diseases. A short presentation of each medically accepted
spirometry device is provided as described on Ruppel (2008):
3.2.1.1
Pneumotachometers
From Greek, pneuma, "wind" or "breath", thacos, "speed" and metron, "measure", pneumotachometer means that it measures breath speed. "The flow sensors that historically have been,
and continue to be, the mainstay of the respiratory laboratory utilize flow resistors with approximately linear pressure–flow relationships. These devices are usually referred to as pneumotachometers" (Macia, 2006).
5
6
Literature Review
Volume Displacement Spirometers As the name implies, these spirometers measure directly
the shifting fluid volumes. Since the designs of this kind of devices were made during a time prior
to the electronic massive growth they are mainly based on mechanical actuation. The movement
caused by the breathing expirations is transferred to a stylus which could write on a moving chart.
More recently, with the development of computers, potentiometers were incorporated so the signal
could be better analyzed.
Water-Seal Spirometer: This device consists of a large bell suspended in a container with
its open end submerged. Breathing into the spirometer moves upwards the bell by an amount
proportional to the volume expired. During an inspiration the bell submerges beyond the stable
level. For many years the bell had a pen attached, that moved sympathetically with the bell.
The pen recorded on a rolling chart, the kymograph, the respective volume. In recent years the
bell activates a potentiometer handle that varies a DC voltage output. This signal could later be
digitized with an ADC and processed by a computer.
Dry Rolling-Seal Spirometer:
This spirometer is cylindrically shaped holding a lightweight
piston inside. A flexible plastic seal couples the piston to the cylinder wall. The seal rolls on itself
when the piston moves slightly with the gas’ volume displacement. The piston shaft rides on a
linear bearing which actuates a rotary potentiometer. This generates an analog signal for flow and
volume that can be fed to an ADC and analyzed.
Bellows-Type Spirometer:
This kind of device uses a collapsible bellow that folds or un-
folds depending on the fluid’s flow direction. The common bellow design resembles a flexible
accordion. One end is fixed and the other moves freely with the expirations. This movement actuates either a pen that writes on a moving chart or a potentiometer similar to the other volume
displacement spirometers.
Flow-Sensing Spirometers The following spirometers measure air flow based on several different principles. All of them include the respective sensor and a tube to contain the expiration.
Figure 3.1 illustrates the following sensor types.
Turbine: The turbine, or respirometer, is an instrument that "consists of a vane connected to
precision gears". The vane’s rotation is triggered by the gas flowing through it. This sensor is more
suited to measure SVC. The sensor’s configuration can also use a light source and a photoreceptor
to retrieve the number of rotations per second, just like an odometer for motor controllers.
Pressure Differential Flow Sensors: This types of sensors make a pressure differential
measurement. The sampling openings are placed one before and another after a resistive element, in the middle of the sensor tube. The element accelerates the gas by narrowing the gas flow
3.2 Spirometry Technology
7
Figure 3.1: Common Flow Sensing Devices (Pneumotachometers). A-Fleisch type sensor, BHeated wire sensor, C-Pitot tube sensor, D-Turbine sensor, E-Ultrasonic sensor1 .
opening, while reducing the pressure. This is called the Venturi effect. While changing the gas’
velocity and pressure, the element also ensures laminar flow.
The resistive element can either be a bundle of capillary tubes, the Fleisch type, or a mesh
screen or membrane, the Silverman or Lilly type. The Fleisch type is more reliable than the Lilly
type. Though the Lilly type is better suited for measuring widely varying flows. The disadvantages of these kinds of measurement devices are that they are very sensitive to local atmospheric
pressure, temperature and humidity. As a consequence, they need to be calibrated daily.
Heated-Wire Flow Sensors:
The heated wire method is based on the cooling effect of a
flowing gas. The wire is preheated to a fixed amount and, during the expiration, the gas’ flow
reduces the wire’s temperature. The instantaneous energy consumption to reheat the wire gives a
flow value at that time.
Pitot Tube Flow Sensors: The Pitot tube sensors are based on the principle that states that
the pressure of a fluid is related to it’s density and velocity. In general, a spirometry Pitot tube
sensor has, at least, two sets of small tubes, one facing each flow direction for measuring both
expiration and inspiration. The tubes are connected to pressure transducers.
Ultrasonic Flow Sensors:
A sound wave is a compression wave that is influenced by the
means through it travels. The flowing of gas can increase or decrease a sound’s speed depending of
1 Image
taken from Ruppel (2008).
8
Literature Review
the relative direction it travels. The sensor consists of two oppositely faced ultrasound transducers,
angled with the tube. By measuring the sound’s traveling time the flow measurement can be
integrated into volume. There is also a different type of gas flow measurement that can be obtained
by quantifying the ultrasound’s frequency shift, based on the Doppler effect.
The design includes a disposable tube which is introduced between the sensors. This allows
a great advantage over several other spirometer types since it needs not calibration and solves the
problem of patient cross-contamination.
3.2.1.2
Peak Flow Meters
This instrument was designed to measure Peak Expiratory Flow (PEF). It provides a simple measurement that aids asthma monitoring with an inexpensive device. The meters themselves tend to
be made disposable. These meters work by forcing air through a a resistor or a flow tube with a
movable indicator.
3.2.1.3
Plethysmographs
From Greek, plethysmos, "increase" and graphein, "to write". The plethysmograph is an instrument that measures the change in volume within an organ or body.
There are two types of body plethysmographs: the constant-volume variable-pressure and the
flow or variable volume plethysmographs, Pressure plethysmograph and flow plethysmograph,
respectively. Both are devices that include a sealed box which the patient has to be in. They
are used to measure the thoracic gas volume (VTG ), the specific airway resistance (sRaw) and its
derivatives.
Pressure Plethysmograph:
This spirometer is based on an adaptation of Boyle’s law. The
change in volume inside the chamber is inversely proportional to the pressure variation. The
pressure change is caused by compressing and decompressing the atmosphere inside the box, including the patients chest. If the temperature remains constant, the pressure change corresponds
to a specific volume change. Since the chamber has rigid walls and is sealed, its free volume
experiences the same, mirror-image shift volume as the lungs (Criée et al., 2011).
Flow Plethysmograph:
This kind of plethysmograph uses a flow transducer in the chamber’s
wall. This device measures the volume changes inside the box. As gas is compressed and decompressed, flow passes through the chamber’s opening. That flow is integrated and volume change is
recorded as the sum of the volume compressed and the one that left through the opening. With this
device there are also some other tests that can be conduced using a pneumotachometer connected
to the outside room.
3.2 Spirometry Technology
3.2.2
9
Research and Development
There is a significant effort to develop spirometric devices focused on the availability of materials
or cost efficiency. This aims to reach areas with less access to health care and in isolation as
described on 1.1.
A team from Penn State University developed a Lilly-Pneumotach type spirometer from easily
accessible materials (der Mauer et al., 2013). This Mashavu project prototype was designed as a
DIY concept enabling the wide-spreading of the design. It used a pressure differential electronic
sensor to measure the airflow and was made with PVC tubing, coffee stirrers, protoboard as a
mesh along with cotton cloth for filtering.
Another relevant project came from a multidisciplinary team of students from the University of
Washington in St.Louis. They worked towards a low cost spirometer (Brimer et al., 2012). Their
solution consisted of using a microphone as a pressure transducer and coupling it to a custom
designed breathing piece. The piece was relatively cheap to produce, could be easily sterilized
and was of robust manufacturing.
One project that motivated this dissertation was the SpiroSmart application developed by Larson et al. (2012) addressing the Ubiquitous Computing field. The application consists of a somewhat complex software that records the patient’s breath while giving incentive and showing the
performance. It employs signal processing and machine learning methods to measure lung function.
A later project developed by van Stein (2013) for his Master’s thesis was based on SpiroSmart
but primarily focused on robustness to noise. Additionally, by using offline methods it could
enable better results since the methods were more powerful and compute-intensive than real-time
methods such as those used by SpiroSmart.
3.2.3
Games and Applications
Throughout spirometry history there have been several technological advances. As described
on 3.2.1, sensory methods have shifted from purely mechanical to partially electronic. As a consequence, spirometry apparatuses have become computerized and became able to include secondary
functions or add-ons. Computers have maintained the tendency of getting smaller, following a
corollary of Moore’s law2 . This means that there has been a reduction of the size of the platforms where spirometry related software run. In recent years, significant effort has been focused
around smartphones since they are continuously carried around and include a significant amount
of electronic devices.
Games for compliance:
The forced expiration maneuver is somewhat difficult to execute cor-
rectly. This case is specially true for children that need to make a lung function evaluation.
2 Moore’s law is an empirical prediction which states that within roughly 18 months a chip performance will double,
due to the miniaturization of transistor technology.
10
Literature Review
Therefore pulmonary function laboratories commonly have computer programs that guide children through the process, most of the time without them even noticing. Usual tests comprise
computer games that are played using breathing apparatus, such as breathing forcefully through a
tube. This enables a better cooperation from the patient since kids rarely wish to lose a game.
Games for entertainment:
With the growth in smartphone use and a rising concern about respi-
ratory diseases, several independent application developers have created sets of games that relate
to spirometry. Many measure the loudness recorded by the smartphone’s microphone to actuate something. Some of them actually advertise spirometry, though they appear under the game
application category.
Mobile health applications:
There is a significant amount of developers that create smartphone
applications that measure spirometric parameters. Those applications usually have an external
sensor that is connected by Bluetooth, Wi-Fi or USB to the smartphone. The application becomes
a mean to relay data, observe it and update a patient’s profile. Some solutions are also compatible
with multiple sensors for different health problems such as heart rate monitors, pulse glucose and
cholesterol meters, oximeters and so on.
3.3
3.3.1
Methods and Algorithms
Spirometric Parameters
Methods are only successful if interesting features can be extracted from data. Standard spirometers measure flow of air that goes through the sensor to generate Flow vs Time plots. The flow
can then be integrated to achieve Volume vs Time plots that provide medical doctors another way
to visualize the information. Also, by mapping air volume to time, Flow vs Volume plots can be
made. From this last plot several quantities are measured:
• Forced Vital Capacity (FVC): total expelled volume during the expiratory maneuver.
• Forced Expiratory Volume in one second (FEV1 ): lung volume displaced in the first
second of the maneuver.
• FEV1 /FVC: the ratio of the aforementioned values, also called the Tiffeneau-Pinelli index.
• Peak Expiratory Flow: the maximum flow velocity present in the maneuver.
"The most common clinically-reported measures are FEV1 , FVC, and FEV1 /FVC as they
are used to quantify the degree of airflow limitation in chronic lung diseases such as asthma,
COPD, and cystic fibrosis" according to Larson et al. (2012). These features try to summarize the
expiration curve’s characteristics, though a physician can retrieve a great deal of information from
analyzing the curve itself.
3.3 Methods and Algorithms
11
Figure 3.2: Flow vs Volume representations for different lung functions3 .
Though the curve also provides relevant data, the study will focus on extracting these features based on curves approximations. A completely accurate curve shape regression will not be
developed.
3.3.2
Signal Processing
Signal Processing (SP) is a field of engineering and applied mathematics that focuses on manipulating signals to extract features or to turn them into a more desirable form for other applications.
The forced expirations recorded by a smartphone’s microphone will have certain characteristics that prevent a meaningful extraction of parameters of interest. As a consequence, those signals
have to be transformed in order to get relevant data. Some pertinent SP methods are summarized
below.
• Fast Fourier Transform
• Up and Down sampling
• Signal Filtering
• Hilbert Transform
• Linear Regression
3.3.2.1
Fast Fourier Transform
The Fast Fourier Transform (FFT) is an algorithm commonly used in sound processing and analysis. It is an efficient way to compute the Discrete Fourier Transform (DFT) and its inverse. This
process can be viewed as the transformation of an amplitude and time signal into its frequency
power spectrum contents.
3 Image
taken from http://www.frca.co.uk/images/flow_volume.gif.
12
Literature Review
Let x0 , ..., xN−1 be complex numbers:
N−1
DFT : Xk =
∑ xn e−i2πk
n
N
k = 0, . . . , N − 1.
n=0
Here n stands for a descretized instant of time and k is a descretized frequency. In many applications the signal is "windowed" in overlapping chunks in order to analyze how the frequencies
vary with time. This enables frequency tracking which is useful for characterizing both the signal
and its source.
3.3.2.2
Up and Down Sampling
Downsampling, or decimation, is a process which reduces a signal’s sampling rate. The decimation rate usually is an integer or a rational fraction greater than one. When the rational is used,
first the signal is upsampled by the fraction’s numerator and then downsampled by its denominator. Upsampling is the opposite process, that adds samples between the original signal ones, also
known as interpolation. Generally, the downsampling process is used to change a signal to a fixed
lower sampling rate that a signal processor will use. The upsampling is used to change back a
processed signal, making it more adequate for the processor’s external environment.
3.3.2.3
Signal Filtering
Filters are systems that change both a signal as its frequency power spectrum with respect to the
filter’s frequency response. They remove unwanted features and components from a signal and
can be described as to their frequency response as:
• Low Pass Filters: remove higher frequencies maintaining lower frequencies almost untouched. Can be perceived as a shifting mean.
• High Pass Filters: remove signal’s lower frequencies keeping the higher frequencies almost
intact.
• Band Pass Filters: remove all frequencies outside a specific frequency band. It is a minimum second order filter since it can be viewed as a combination of a LPF and a HPF.
• All Pass Filters: are a different kind of filter that maintain equally the frequency gains.
They change the phase of the different frequency components modifying the time series and
not the frequency response.
The expression removal of frequencies might not be completely accurate since most filters
tend to attenuate deeply the frequencies cut, thus not completely removing them. If the intended
response of the filter is complex it could be implemented as a series of simpler filters.
3.3 Methods and Algorithms
3.3.2.4
13
Hilbert Transform
The so called Hilbert Transform is a linear operator that results in the convolution of a real valued
signal with the signal −1/(πt). The process is a basic tool in Fourier analysis and can calculate
the harmonic conjugate of a given function or Fourier series. If this conjugate is added back to the
original signal and processed using a LPF it is possible to extract the original signal’s envelope.
3.3.2.5
Function approximation
In order to extract breath parameters robustly, one could approximate the expiratory sound envelope by a high degree function (van Stein, 2013). By using the signal’s approximation much of
undesired noise is removed and the general form of the flow-volume curve is greatly preserved.
There are several methods of approximation such as:
• Linear regression: First order approximation. The data becomes represented by a linear
function.
• Polynomial regression: A Nth order polynomial function approximates a noisy signal.
• Smoothing Spline: The spline methods consist on segmenting a signal into several smooth
polynomial functions. If the functions degree is greater than one then each segment connects
to the next polynomial such as that the functions derivatives have the same value on the
connection. For higher degrees, this gives better results than simple polynomial regression
since it can avoid Runge’s phenomenon4 .
• Orthogonal polynomials: are families of polynomials with consecutive pairs of functions
that are orthogonal to each other under some inner product. A signal can be represented by
a linear combination of those functions.
3.3.3
Machine Learning
Machine Learning (ML) is a branch of Artificial Intelligence which develops models and studies
systems that can learn from data. These methods are widely applicable since they are abstract
concerning data representation and utility.
They can be roughly categorized in two sets based on the desired outputs or the inputs available
on the training set. The Supervised Learning (SL) methods are algorithms that use both inputs
and expected outputs, provided on the data set, to replicate the intended model. The Unsupervised
Learning (UL) methods are used when the desired output is unknown. They operate on unlabeled
data and have to devise the system’s behavior.
The field of ML is vast and growing. Therefore the scope of the methods to be researched
and developed needs to be narrowed. One important aspect of the project is to be considered.
To aid the learning process a series of recordings of real patients will be made. The smartphone
4 Using equidistant points, with the increase of the polynomial function degree the interpolation fitting error might
not tend to 0.
14
Literature Review
recordings will be accompanied by a commercial spirometer from which a medical diagnosis will
be made. This way, the ML method choice problem is reduced to Supervised Learning processes.
Some of these methods are shown below:
• Decision Trees
• Perceptron-based techniques
• Instance Based-Learning
• Support Vector Machines
3.3.3.1
Decision Trees
A tree is a structure that consists of a root node and several others attached to it in some kind of
hierarchical fashion. The decision tree’s nodes represent decisions, separating data according to
the node’s function. The terminal leaf nodes are the data labels. This kind of structure is transparent to the user since it progressively sorts the data based on some feature until it is completely
defined.
The tree, or in fact the node’s functions, have to be generated based on the training data. There
is significant effort to find the best and fastest ways to divide the training data. The termination of
the dividing algorithm generally isolates many data entries to their own leaf. This causes problems
concerning the overfitting of the method. Many variations were proposed to reduce this problem
such as terminating the algorithm before the tree grows to full depth or pruning some of the outlier
branches.
There are also ensembles of methods that provide a better accuracy. A known technique of
variance reduction that diminishes overfitting problems is Bagged Decision Trees. By using n
random subdatasets, with replacement, to generate trees and enabling a voting process of new data
it can enhance local adaptivity of the boundary (Rao and Potts, 1997).
3.3.3.2
Perceptron-Based techniques
A single layer perceptron is a system that has N inputs and one output. First, the inputs are linearly
combined according to each input’s weight (typically real values between [-1, 1]). This output is
then fed to an adjustable threshold. Above it the output is 1; else it’s 0. Figure 3.3 shows a system
representation of a perceptron.
The learning algorithm changes the branches’ weights until the desired input/output behavior
is reached.
If the inputs are not linearly separable with respect to the correct categories then a perceptron
will not find a solution that fits all available data. In order to solve this problem multilayered
perceptron (ANNs) methods were developed (Rumelhart et al., 1985).
ANNs consist of several perceptrons, otherwise called nodes or units, arranged such as to
produce weighted links between each others’ inputs and outputs. They can be separated in three
3.3 Methods and Algorithms
15
Figure 3.3: Perceptron diagram5
categories according to their interface: the input layer nodes receive and process the system’s
inputs; the output layer nodes are the last stage of data processing and generate the system’s
outputs; the hidden layer nodes are perceptrons that connect input and output layer nodes by
means of activation values. As with a single layer method, a succession of training instances are
exposed to the net, and comparing the input/output response the weights are slightly adjusted in
the direction of error reduction.
One of the main problems of ANNs resides on the size/number of layers of the hidden set of
nodes. According to the desired application, too few nodes can result in a poor approximation
and generalization capabilities. If too much nodes are considered then overfitting can occur and
finding a global optimum can be proven difficult.
3.3.3.3
Instance Based-Learning
Instance based learning algorithms are what can be called as lazy algorithms. The majority of the
computation is made on the classification and not at the training phase. One of the most known
methods is k-Nearest Neighbours (kNN).
The idea behind this algorithm is based on the instances’ proximity. Instances with the similar
proprieties tend to be closer to each other than to other instances on the dataset. A new, unclassified, instance is assigned the most frequent label of its neighbors. This relative distance between
instances, this neighborhood, can be defined by a distance metric. Some of the most popular
distance metrics are presented on table 3.1.
3.3.3.4
Support Vector Machines
SVMs are focused on separating labeled data using hyperplanes. Their aim is to maximize a "margin" between the boundary of different classifications and the instances of either side. Minimizing
the Lagrangian in 3.1 with respect to the weight vector w and bias b would get the best hyperplane
cut.
5 Image
taken from http://homepages.gold.ac.uk/nikolaev/311perc.htm.
16
Literature Review
Table 3.1: Distance metric functions between instances (x and y).
r 1/r
Minkowski: D(x, y) = (∑m
i=1 kxi − yi k )
Manhattan: D(x, y) = ∑m
i=1 kxi − yi k
Chebyshev: D(x, y) = maxm
i=1 kxi − yi k
2 1/2
Euclidean: D(x, y) = ∑m
i=1 kxi − yi k
kxi −yi k
Camberra: D(x, y) = ∑m
i=1 kxi +yi k
Kendall’s Rank Correlation:
i−1
2
D(x, y) = 1 − m(m−1)
∑m
i= j ∑ j=1 sign(xi − x j )sign(yi − y j )
There are some variations of the method for solving problems such as data containing misclassifications and non-separable cases. There are even methods that remap non-linearly-separable
data in order to enable a hyperplane boundary and consequently to use SVMs. The SVMs training
algorithms necessarily reach the optimal separation, avoiding local minimums unlike ANNs for
example.
LP ≡
N
N
1
kwk − ∑ αi yi (xi · w − b) + ∑ αi
2
i=1
i=1
3.4
Discussion and Decision
3.4.1
Signal Processing Discussion
(3.1)
The Hilbert Transform seems to be a crucial process since it provides a simple way to compute
the envelope of the breath signal. Another way to compute an envelope is tracking the major
frequencies of the signal, with a FFT, and regenerating a time series. Envelope signals might
need to be filtered of noise in order to extract correctly the features. Therefore, it could prove
beneficial to use LPF. On the other hand, the envelope can be made immune to noise if a function
approximation is computed.
Of those methods listed on subsection 3.3.2.5, some might work better then others for the specific case. The Linear Regression probably would not have good results since the FEM does not
follow a linear function. A Polynomial Regression seems to a good candidate though it has the
instability problem for higher orders. On the other hand, it could have good results for an order
between 3 and 5, which seems to be enough to represent the FEM accurately (van Stein, 2013).
Splines have a track record of lower order requirements and have similar results to Polynomial Regression. Though, since the polynomial order seems to be relatively low it might not be justifiable.
Orthogonal Polynomials have a significant number of different families and need to be researched
in more detail to assess their relevance for the project.
It was defined that one of the requirements of the application is to have a processing time
relatively small so not all the methods mentioned should be implemented. Additionally, the results
of some methods might be similar to the degree that feature extraction does not monitor significant
change. The methods will be subject to a deeper study.
3.4 Discussion and Decision
3.4.2
17
Machine Learning Discussion
An instance based algorithm such as kNN does not seem suited to the projects requirements. First,
since a great deal of computation is made during the classification phase, the algorithm takes a
significant amount of time to calculate the output, specially if the training data has a considerable
size. Furthermore, this also means that the method has a large storage requirements. Another
significant problem concerns the sensitivity of the kNN to the distance metric function. Finally,
the number of neighbors, k, is a difficult variable to optimize.
Perceptron based techniques such as Neural Networks have a great classification speed and
can tolerate relatively well highly interdependent attributes. Though they have a poor tolerance to
missing values and do not deal well with overfitting issues. Therefore, ANN methods do not seem
very adequate for the problem at hand.
Decision trees have generally good characteristics such as transparency, speed of classification and reduced model parameter set. Their main problem concerns noisy data and the risk of
overfitting. Though, as discussed on subsection 3.3.3.1 there are simple methods that diminish the
consequences of such situation. CART and Bootstrap aggregation methods (Bagging) seem good
candidates for tackling the problem.
Finally, SVMs have generally a very good accuracy, are quick to classify data and tolerant to
irrelevant attributes. They also have a fairly good success rate tolerating redundant and highly interdependent attributes. Their shortcomings concern the transparency of the method and handling
model parameters. Though, given the problem at hand, they are also strong candidates.
An overview of several methods can be found on (Kotsiantis, 2007).
18
Literature Review
Chapter 4
Work Plan
Planning is one of the most important stages of any project and contributes significantly to its
success. It enables the best allocation of resources while keeping in focus small and medium
objectives. It also states a rate at which those objectives need to be completed.
In section 4.1 the tasks and small objectives are presented along with the respective timeline
and description. The methodology which will be used is described in section 4.2. In section 4.3
the tools and technologies that will enable to develop the project are stated. Finally, in section 4.4
will be shown the tasks already completed.
Since every project is subject to fluctuation when it comes to task conclusion the calendarization presented below is a mere proposal and is expected to be somewhat flexible. In fact, allocated
time could be shifted between tasks in order to ameliorate the time compromise.
4.1
Calendarization
Figure 4.1: Time distribution diagram
In table 4.1 the main steps of the project are shown along with the respective expected start
and end dates plus their duration. Below, a short summary of those tasks is provided.
19
20
Work Plan
Table 4.1: Temporal distribution of the project’s tasks.
Task
Personal Web page development
Data gathering
Methods analysis and Development
Methods Simulation
Application architecture and specs
Writing the Dissertation
Preparing the Presentation
Initial date
10-2-14
14-2-14
10-2-14
18-3-14
22-4-14
12-5-14
25-6-14
Duration (weeks)
18
6
7
5
2
5
1
Ending date
30-6-14
28-3-14
28-3-14
21-4-14
2-5-14
24-6-14
1-7-14
1 Personal Web page development: During the projects development it is expected to present
several documents namely minutes from meetings with the advisors and weekly reports.
Therefore, it is needed to develop a personal website to make those documents available to
the interested parties.
2 Data gathering: In order to teach the optimization models real life data is needed. During
the course of several weeks, recordings of forced expiratory maneuvers are going to be
made.
3 Methods analysis and Development: The various methods of signal processing and machine learning currently available will be analyzed in depth. It will culminate on the selection of the set which will be developed and evaluated.
4 Methods Simulation: The methods set will be implemented in Matlab and compared with
each other to verify which combination of algorithms works best. They will be tested using
a data set not previously used.
5 Application architecture and specs: Once the best conjugation of methods is chosen, the
specification of the smartphone application will be stipulated. In addition, a concept of how
the program should be implemented will be developed.
6 Writing the Dissertation: The last stage of the project will consist in writing a report
compiling all relevant data, work developed and findings.
7 Preparing the Presentation: The dissertation will be accompanied by a presentation that
demonstrates the work done.
4.2
Methodology
During the course of the project it is intended to follow this methodology:
– Gather forced expiratory recordings from real patients with different types of lung function
characteristics/diagnosis.
4.3 Technologies, tools and work platforms
21
– Research the Signal Processing methods currently used and relevant for the data type.
– Research the Machine Learning and Optimization techniques suitable for Supervised Learning methods.
– Evaluate which methods are interesting enough to analyze in grater detail.
– Implement the more promising methods in Matlab and simulate in various configurations.
– Compare results and chose the best set of methods and configuration.
– Specify and design an architecture for the android app.
– Write the final report and presentation.
4.3
Technologies, tools and work platforms
The implementation and simulation of the methods is expected to be done in Matlab.
Has a requirement of Preparação da Dissertação, the periodically generated documents will be
uploaded to a personal website, which is to be developed.
The final report, presentation and weekly reports will be done in LATEXsince it is an important
document editing tool that, once configured, doesn’t require much maintenance concerning text
formatting.
4.4
Concluded tasks
In Preparação para a Dissertação several initial milestones were achieved:
• Initial research of the topic.
• Understanding of commercial grade spirometry sensors.
• Market survey of commercial spirometers.
• Development of a smartphone application for enhanced maneuver recording.
• Development of some methods (such as Hilbert Transform and classification tree generation)
• Familiarization with LATEX.
22
Work Plan
References
AfricanTelecomsNews. Africa Mobile Factbook 2012. Technical report, Africa & Middle East Telecom-Week, 2012. URL http://www.mikekujawski.ca/2012/05/30/
finally-some-2012-statistics-for-the-african-mobile-phone-market/.
Andrew Brimer, Abigail Cohen, Olga Neyman, Braden Eliason, and Charles Wu. Low Cost
Spirometer - DEBUT. Technical report, Washington University in St.Louis, St.Louis, 2012.
URL
http://s3.amazonaws.com/challengepost/zip_files/production/
3622/zip_files/Low-CostSpirometerReport-DEBUT.pdf?1338659900.
Cisco. Cisco Visual Networking Index : Global Mobile Data Traffic Forecast Update , 2013 –
2018. Technical report, Cisco, 2014.
C P Criée, S Sorichter, H J Smith, P Kardos, R Merget, D Heise, D Berdel, D Köhler, H Magnussen, W Marek, H Mitfessel, K Rasche, M Rolke, H Worth, and R A Jörres. Body
plethysmography–its principles and clinical use. Respiratory medicine, 105(7):959–71, July
2011. ISSN 1532-3064. doi: 10.1016/j.rmed.2011.02.006. URL http://www.ncbi.nlm.
nih.gov/pubmed/21356587.
Stefanie Auf der Mauer, Samantha Chan, Peter Chhour, Josh Homa, Vy Pham, and Karthik Pisupati. Mashavu Spirometer Project, 2013. URL https://decibel.ni.com/content/
docs/DOC-5837.
S. B. Kotsiantis. Supervised machine learning: A review of classification techniques. informatica
31:249–268, 2007.
Eric C Larson, Mayank Goel, Gaetano Boriello, Sonya Heltshe, Margaret Rosenfeld, Shwetak N
Patel, and Computer Science. SpiroSmart: Using a Microphone to Measure Lung Function on
a Mobile Phone. In 14th ACM International Conference on Ubiquitous Computing, Pittsburgh,
Pennsylvania, USA, 2012. ISBN 9781450312240.
N. F. Macia. Pneumotachometers. In J. G. Webster, editor, Encyclopedia of Medical Devices and
Instrumentation, Vol. 5, chapter Pneumotachometers, pages 367–379. Wiley, New York, 2nd
edition, 2006.
J. Sunil Rao and William J.E. Potts. Visualizing Bagged Decision Trees. In KDD, pages 243–
246, Providence, Rhode Island, USA, 1997. URL http://www.aaai.org/Papers/KDD/
1997/KDD97-050.pdf.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error propagation. In D. E. Rumelhart, James L. McClelland, and CORPORATE PDP Research Group, editors, Parallel Distributed Processing Explorations in the Microstructure of Cognition, volume 1, chapter 8, pages 318–362. MIT Press, Cambridge, MA,
23
24
REFERENCES
1985. URL http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=
html&identifier=ADA164453.
Gregg L. Ruppel. Manual of Pulmonary Function Testing. Mosby, 9th edition, 2008. ISBN
9780323052122.
Bas van Stein. A Mobile Smart Care platform Home spirometry by using the smartphone microphone. Master’s thesis, Leiden University, Leiden, The Netherlands, 2013.