U.S. patent application number 12/335744 was filed with the patent office on 2009-06-18 for multi-parameter based diagnosis of lung disease.
This patent application is currently assigned to SAVIC RESEARCH, LLC. Invention is credited to MICHAEL SAVIC.
Application Number | 20090156950 12/335744 |
Document ID | / |
Family ID | 39107521 |
Filed Date | 2009-06-18 |
United States Patent
Application |
20090156950 |
Kind Code |
A1 |
SAVIC; MICHAEL |
June 18, 2009 |
MULTI-PARAMETER BASED DIAGNOSIS OF LUNG DISEASE
Abstract
A method and apparatus by which deceases are identified uses
computer analysis of sound signals that are picked up from various
locations on the chest walls of a subject by a modified
stethoscope. The modification includes a small microphone in one of
the hoses of the stethoscope. Signals from the microphone are input
to a computer such as a personal computer or PC for processing. The
computer extracts from these signals features which are dominant
for particular diseases. A classifier classifies these features,
determines if the lungs are diseased, and identifies the
disease.
Inventors: |
SAVIC; MICHAEL; (Bradenton,
FL) |
Correspondence
Address: |
NOTARO & MICHALOS P.C.
100 DUTCH HILL ROAD, SUITE 110
ORANGEBURG
NY
10962-2100
US
|
Assignee: |
SAVIC RESEARCH, LLC
Bradenton
FL
|
Family ID: |
39107521 |
Appl. No.: |
12/335744 |
Filed: |
December 16, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
11467350 |
Aug 25, 2006 |
7479115 |
|
|
12335744 |
|
|
|
|
Current U.S.
Class: |
600/529 ;
381/67 |
Current CPC
Class: |
A61B 5/7267 20130101;
A61B 7/003 20130101; A61B 5/7264 20130101; A61B 5/08 20130101 |
Class at
Publication: |
600/529 ;
381/67 |
International
Class: |
A61B 5/08 20060101
A61B005/08; A61B 7/04 20060101 A61B007/04 |
Claims
1. A method of identifying a disease that produces sounds that are
characteristic of the disease, comprising: subjecting the sounds to
a plurality of multi-dimensional, multi-parameter feature space
analyses to identify a plurality of multi-dimensional features
associated with the sounds; and selecting from among the plurality
of multi-dimensional features, only dominant features that identify
the disease by using discriminant analysis.
2. A method according to claim 1, wherein the plurality fo
multi-dimensional features associated with the sounds are selected
from the group consisting of: linear predictive coding parameters,
Fourier coefficients, partial autocorrelation coefficients,
Cepstrum coefficients, and autocorrelation coefficients.
3. A method according to claim 1, wherein the discriminant analysis
is performed using neural network analysis of the dominant features
for identifying the disease.
4. A method according to claim 1, including projecting a plurality
of the multi-dimensional features for the sounds into
representations of the features, selected from among the
representations of the features, those representation that provide
relatively good correlation to the disease as compared to
representations that provide relatively poor correlation to the
disease, and identifying the disease by selection at least one pair
of the projected features that separate clusters of samples in the
representations that provide relatively good correlation to the
disease.
5. A method according to claim 1, including storing in a computer,
a data base of dominant features from pre-recorded sounds from
organs with at least one disease that produces sounds that are
characteristic of the disease, during a training phase, obtaining
sounds from an organ of a subject to be tested, conditioning the
sounds to create a signal that can be subjected to analysis in the
computer, and using the signal as the sounds to be subjected to the
plurality of multi-dimensional, multi-parameter feature space
analyses to identify the plurality of multi-dimensional features
associated with the sounds.
6. An apparatus for identifying a disease that produces sounds that
are characteristic of the disease, the apparatus comprising: a
stethoscope for acquiring the sounds from the subject; a transducer
operatively connected to the stethoscope for converting the sounds
to signals; and a computer-based analyzer for analyzing the signals
to identify the disease, a computer-based analyzer identify the
disease by subjecting the signals to multi-dimensional,
multi-parameter feature space analysis to identify a plurality of
multi-dimensional features associated with the sounds, and
selecting from among the plurality of multi-dimensional features,
only dominant multi-dimensional features that identify the disease
by using discriminant analysis.
7. An apparatus according to claim 6, wherein the stethoscope
includes at least one hose for conveying the sounds, and the
transducer comprises a microphone connected to the hose for picking
up sounds in the hose.
8. An apparatus according to claim 6, wherein the computer-based
analyzer comprises a computer that is programmed for analyzing the
signal to identify the disease using a plurality of modeling
selected from the group consisting of: linear predictive coding
parameters, Fourier coefficients, partial autocorrelation
coefficients, Cepstrum coefficients, and autocorrelation
coefficients.
9. An apparatus according to claim 6, wherein the computer-based
analyzer comprises a computer that is programmed with a program for
analyzing the signal to identify a lung disease.
10. An apparatus for identifying a disease in a subject, the
disease producing sounds that are characteristic of the disease,
the apparatus comprising: means for acquiring the sounds; and a
computer-based analyzer for analyzing the sounds to identify the
disease by subjecting the sounds to multi-dimensional,
multi-parameter feature space analysis to identify a plurality of
multi-dimensional features associated with the sounds, and
selecting from among the plurality of multi-dimensional features,
only dominant multi-dimensional features that identify the disease
by using discriminant analysis.
11. An apparatus according to claim 10, wherein the computer-based
analyzer comprises a computer that is programmed with a program for
analyzing the sounds to identify the disease, the program using at
least one of: Linear Predictive Coding Parameters, Fourier
Coefficients, Partial Autocorrelation Coefficients, Cepstrum
Coefficients, and Autocorrelation Coefficients.
12. An apparatus according to claim 10, wherein the computer-based
analyzer comprises a computer that is programmed with a program for
analyzing the signal to identify a lung disease.
13. An apparatus according to claim 10, wherein the program plots a
plurality of two-dimensional representations of coefficients of
modeling for the sounds, and selects features of the
representations that provide relatively good correlation to the
lung disease as compared to features that give relatively poor
correlation to the lung disease, and identifies the disease by
selection at least one pair of coefficients that separate clusters
of samples in the representation for the features that provide
relatively good correlation to the disease.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This is a continuation of application Ser. No. 11/467,350
filed Aug. 25, 2006 entitled COMPUTER AIDED DIAGNOSIS OF LUNG
DISEASE, and now U.S. Pat. No. ______.
FIELD AND BACKGROUND OF THE INVENTION
[0002] The present invention relates general to the field of
medical equipment for the diagnosis of ailments, and in particular
to a new and useful apparatus and method for helping with the
diagnosis of lung diseases.
[0003] The Signal and Speech Research Group (SRG) of Rensselaer
Polytechnic Institute (RPI) has carried out research on algorithms
for signal processing and their application since 1981. The group
has been led by Professor Michael Savic, the inventor of the
present application. Since 1985 the SRG has focused on high-risk,
innovative research in all aspects of signal processing. Current
areas include speaker recognition, language identification,
detection of cholesterol deposits in blood vessels, speech
recognition, automatic pipeline leak detection, voice character
transformation, speaker separation (the cocktail party problem) and
others.
[0004] Patents have been awarded for some of this work to RPI and
Professor Savic, including: U.S. Pat. No. 7,024,360 for System for
reconstruction of symbols in a sequence; U.S. Pat. No. 5,675,506
for Detection of leaks in vessels; U.S. Pat. No. 5,623,421 for
Monitoring pressurized vessels for leaks, ruptures or hard hits;
U.S. Pat. No. 5,416,724 for Detection of leaks in pipelines; and
U.S. Pat. No. 5,327,893 for Detection of cholesterol deposits in
arteries. These patents are all incorporated here by reference.
[0005] Chest auscultation, that is, the act of listening for sounds
made by the lungs to aid in the diagnosis of certain disorders, is
an effective, nonintrusive and inexpensive way to assess the
condition of a patient's lungs. Many clinicians use chest
auscultation in a cursory manner because they are not skilled in
recognizing certain lung sounds.
[0006] A brief explanation of this technique can be found in an
article at URL:
http://www.leedsth.nhs.uk/sites/emibank/clinicians/nursing/documents-
/ausculta.pdf, entitled "Chest Auscultation," by Bob McMaster,
February 2001.
[0007] The training of doctors for this technique is long and
difficult. Moreover, the human ear is sensitive to a certain sound
frequency range only (e.g. 15 to 20,000 Hz) and some significant
lung sounds are not in this range and therefore will not be heard,
even by the most highly trained practitioner.
[0008] To better understand the present invention, a brief
description of breath sounds and of what is under investigation is
necessary. Many distinctive sounds are generated by a diseased
lung. These may be roughly grouped into two broad categories, the
adventitious sounds and the abnormally transmitted sounds. See, for
example, Lehrer, S., "Understanding Lung Sounds", W.B. Saunders
Company, 1993. Sounds like crackles, wheezes and pleural friction
rubs are included in the adventitious sounds group while sounds
like egophony, pectoriloque, bronchophony, bronchial breathing and
abnormally diminished breath sounds are included in the abnormally
transmitted sounds group. Recent scientific investigations, aided
by advances in acoustics and electronics, provide insights into the
mechanism of production of these sounds.
[0009] Diseases that are explored here are identifiable in the
adventitious breath sounds and especially in crackles. Crackles are
short, explosive, nonmusical sounds that can be described as to
quantity (scanty or profuse) and timing (inspiratory or expiratory,
early or late). Two commonly accepted theories suggest that
crackles can be produced by the bubbling of air through airway
secretions or by sudden opening of small airways (see: Wilkins, L.
R. at al, "Lung Sounds: A practical guide", Mosby-Yaer Book, Inc.,
1996). Crackles associated with the movement of airways secretions
in larger airways are typically coarse and may occur during both
inspiration and expiration. They may clear with suctioning of
effective coughing. Crackles associated with the sudden opening of
airways may be produced by a rapid equalization of pressure between
open and collapsed airways (see: Ploysongsang, Y. and Schondeld S.
A., "Mechanism of production of crackles after atelactasis during
low-volume breathing", Am Rev Resp Dis 126:413, 1982; and Forgacs,
P., "The functional basis of pulmonary sounds", Chest 73:399,
1978). These crackles are inspiratory sounds, which may occur when
peripheral airways pop open as atelectatic regions are
inflated.
[0010] With atelectasis due to shallow breathing, the crackles
often disappear after a few deep breaths or after changes in the
position; whereas with pulmonary fibrosis, the crackles persist. In
mild pulmonary fibrosis, a disease that is explored here, the
crackles are predominantly heard late in inspiration, but may
become pan-inspiratory with an end-inspiratory accentuation as the
disease progresses. Late-inspiratory crackles are often repetitive
with several respiratory cycles and initially identified in
dependent lung zones. Late-inspiratory crackles indicate a loss in
lung volume and are audible over the chest walls. Early-inspiratory
crackles are scanty, low-pitched and audible at the mouth as well
as over the chest.
[0011] Although the present invention to be disclosed here can be
used to identify any lung disease that produces sounds that can be
processed using the apparatus and method of the invention, two
particular diseases have been used to demonstrate the effectiveness
of the invention and, therefore are discussed in some detail
here.
[0012] Chronic Bronchitis
[0013] This condition produces excessive secretion of mucus,
resulting in chronic cough productive of sputum. Pathologically,
bronchitis is characterized by proliferation and hyperplasia of the
mucus glands in the large airways, extending abnormally into small
airways, often without evidence of inflammatory changes, although
the changes may be associated with bacterial infection. Chronic
bronchitis is commonly caused by the inhalation of cigarette smoke,
although the disease is found in a few nonsmokers as well,
particularly miners and people living in polluted urban
environments. The principal complication associated with chronic
bronchitis is the development of obstructive airway disease.
[0014] Interstitial Fibrosis
[0015] This condition, also called interstitial pneumonitis, is
associated with interstitial and alveolar infiltrates and fibrosis.
Patients complain of coughing, or difficulty in breathing and,
although rarely, of fever. Pulmonary function studies show
restriction and are sensitive indicators of the extend of the
illness. Interstitial fibrosis may be caused by cancer
chemotherapeutic agents (bleomycin, cyclophosphamide,
methotrexate), radiation therapy, the antibiotic nitrofurantoine,
high oxygen concentrations inhaled over a long period, and heavy
metals such as gold. It is most often idiopathic.
SUMMARY OF THE INVENTION
[0016] It is an object of the present invention to provide an
apparatus and a method or technique by which lung deceases can be
detected and categorized without relying on human hearing and
interpretation of what was heard. Using features of sound signals
from the chest of a patient and applying digital signal processing,
a classifier determines if the lungs are healthy or not, and, if
not, the classifier identifies the lung disease. While the
apparatus and method of the invention can be used to detect any
lung decease, the invention is demonstrated in this disclosure by
the detection of chronic bronchitis and fibrosis.
[0017] Another object of the invention is to use autoregressive
modeling for recognition and identification of lung diseases.
Autoregressive modeling provides suitable tools for recognition and
identification of particular lung diseases.
[0018] A still further object of the invention is to use preferably
LPC and/or PARCOR coefficients.
[0019] Another object of the invention is to provide an apparatus
for identifying a lung disease in a subject, the lung disease
producing lung sounds that are characteristic of the lung disease,
the apparatus comprising: a stethoscope for acquiring the lung
sounds from the subject; a transducer operatively connected to the
stethoscope for converting the lung sounds to an electric signal;
and a computer-based analyzer for analyzing the signal, to identify
the lung disease.
[0020] The stethoscope may include at least one hose for conveying
the sounds, and the transducer may be a microphone inserted into
the hose or connected to the hose for picking up sounds in the
hose.
[0021] The computer-based analyzer may comprise a computer that is
programmed for extracting and classifying features from lung sounds
using autoregressive modeling. Based on the signal features
characteristic for a particular disease, the software classifier
identifies the lung disease.
[0022] It is noted that the data base used to train and test the
system of the present invention included recordings of the chest
sounds caused by 53 lung diseases. These recordings were made
professionally at prominent hospitals and where originally meant
for use in teaching and tutoring medial personnel in the technique
and, to some extent, art of chest auscultation.
[0023] Accordingly, another object of the invention is to provide
an apparatus and a method which mechanizes, standardizes and
improves the diagnostic results that can be achieved with chest
auscultation.
[0024] The various features of novelty which characterize the
invention are pointed out with particularity in the claims annexed
to and forming a part of this disclosure. For a better
understanding of the invention, its operating advantages and
specific objects attained by its uses, reference is made to the
accompanying drawings and descriptive matter in which preferred
embodiments of the invention are illustrated.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] In the drawings:
[0026] FIG. 1 is a schematic representation of the apparatus of the
present invention;
[0027] FIG. 2 is a two-dimensional (2-D) representation of LPC
coefficients 2 and 3 produced according to the present invention
where the dark stars are indicative of samples from patients with
bronchitis and the other stars are from other samples;
[0028] FIG. 3 is a two-dimensional (2-D) representation of PARCOR
coefficients 2 and 3 produced according to the present invention,
where the dark stars are indicative of samples from patients with
bronchitis and the other stars are from other samples;
[0029] FIG. 4 is a two-dimensional (2-D) representation of LPC
coefficients 2 and 3 produced according to the present invention
where the dark stars are indicative of samples from patients with
fibrosis and the other stars are from other samples;
[0030] FIG. 5 is a two-dimensional (2-D) representation of PARCOR
coefficients 2 and 3 produced according to the present invention
where the dark stars are indicative of samples from patients with
fibrosis and the other stars are from other samples;
[0031] FIG. 6 is a two-dimensional (2-D) representation of various
diseases using PARCOR coefficients 2 and 3; and
[0032] FIG. 7 is a two-dimensional (2-D) representation of various
diseases using PARCOR coefficients 3 and 4.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0033] Referring now to the drawings, FIG. 1 shows how lung sounds
or signals are picked up from various locations, e.g. points
{circle around (1)} to {circle around (8)} on the front or back of
the chest of a patient 10, by a modified stethoscope 12, and are
input, either directly, or through an interface 14, to a computer
16. These points can be of the known types that follow the current
conventions for chest auscultation, or may be newly discovered
points where perhaps higher or lower frequency sounds that are
outside the human hearing range have been discovered to be
associated with lung diseases by use of the present invention in
the future.
[0034] According to the present invention, a small microphone or
other sound transducer 20, that is sensitive and capable of picking
up sounds in a wide frequency range and converting them to
electrical audio signals, is installed in one of the hoses 22 of
the stethoscope 12. The transducer 20 is connected by a cable 24 to
the interface 14, or can be wirelessly connected to the interface,
for receiving the audio signals. A further cable 26 or a wireless
connection, connects the interface to the computer 16.
Alternatively the interface may be incorporated into the computer.
In any case, a computer-based analyzer is provided for analyzing
the signal to identify the lung disease.
[0035] After a series of preliminary procedures in the interface
14, these signals are converted into a suitable form for computer
processing in computer 16 which has been programmed with the signal
analyzing algorithms needed according of the present invention.
This includes analog-to-digital conversion, filtering and frequency
downsampling. Next, features are extracted using autoregressive
methods. Feature extraction means obtaining parameters which
distinguish one class from another, in this case, classes are
particular lung diseases.
[0036] A classifier is used to recognize if the lungs are diseased,
and if they are, to identify the disease. Many different
classifiers can be used for this purpose, however the system was
exemplified using two classifiers, a simple two dimensional
Graphical Classifier (GC) and a more sophisticated, Artificial
Neural Net (NN) classifier.
[0037] Other suitable and known classifiers, or classifiers that
might be later discovered by those skilled in the art, could be
used as well. It is important to mention that samples used in this
research are real lung sounds, recorded from real patients and not
artificially generated sounds.
[0038] Summary of the Approach
[0039] Computer analysis of lung sounds starts with processing of
the signal data input to the computer 16, which is created from the
signals from the stethoscope mounted transducer 20.
[0040] Processing includes, feature extraction using autoregressive
methods, dominant feature selection, initialization, and training
the neural network with sounds characteristic for particular
diseases.
[0041] Preparation of the data samples is performed using various
software packages for processing of sound. Feature extraction is
accomplished using MATLAB routines. The neural network of the
invention was constructed and trained using the Neural Network
toolbox of MATLAB. MATLAB is a high-level language and interactive
environment that enables one to perform computationally intensive
tasks.
[0042] Further Description of the Modified Stethoscope
[0043] A standard stethoscope 12, has two hoses 20 that connect to
parts 30 that end in ear-pieces 32 to be inserted into a
physician's ears. The hoses 20, either together or after they are
merged into a single hose, connect to the bell or sound pick-up 34
that is placed on the chest of the patient. The small microphone or
transducer 20 is inserted into one of the hoses of the stethoscope
as noted above. During examination, the stethoscope is placed on
the chest of the patient using known chest auscultation techniques
to pick up sounds from the lungs. The output signal from the
microphone is sent to the computer 16.
[0044] Some lung diseases can be easier identified from the sound
during the inspiration period, and some others during the
expiration. For example, in the case of bronchitis the inspiratory
period is essential. In this case the invention is used to collect
the information contained in the signal only during the period when
the patient inhales. A simple way to record the sound during the
inspiration (inhaling) period is to ask the patient to inhale and
hold his or her breath. A similar procedure is used if the sound
during the expiration (exhaling) should be investigated. The
inhaling and exhaling cycles can be separated automatically using
an algorithm like the Hidden Markov Model as in the procedure
described in U.S. Pat. No. 7,024,360 which is incorporated here by
reference.
[0045] After preliminary processing, the signal from the microphone
is converted into suitable form for computer processing.
Preliminary processing includes analog-to-digital conversion,
filtering, and frequency downsampling. Next, multiple features from
signals are extracted. These features can be LPC (Linear Predictive
Coding) Parameters, Fourier Coefficients, PARCOR (Partial
Autocorrelation) Coefficients, Cepstrum Coefficients,
Autocorrelation Coefficients, or others.
[0046] Equations for extraction of these features are well known
and are given in the literature, such as in the articles:
[0047] Oppenheim V. A., Schafer, W. R., "Discrete Signal
Processing", Prentice Hall, 1999;
[0048] Iyer, V. K, Ramamoorthy, P. A., and Ploysongsang, Y.,
"Autoregressive modeling of lung sounds: characterization of source
and transmission", IEEE Trans. Biomed. Eng. BME-36(11), 1133-1137,
1989;
[0049] Makhoul, J., "Linear prediction: a tutorial review", Proc.
IEEE 63:561-580, 1975;
[0050] Kay, S. M., "Recursive maximum likelihood estimation of
autoregressive processes", IEEE Trans. Accoust. Speech Signal
Processing ASSP-28:292-303, 1980; and
[0051] Orfanidis, S., "Optimum Signal Processing", MacMillan, New
York, 1985.
[0052] Some features are more suitable to identify particular
diseases than other features. Autoregressive Coefficients have been
found by the inventor to be very good features, because they
discriminate particular diseases better than other features. "Good
features" or "dominant features" for a particular disease are
features that require the least amount of computation to accurately
identify a particular disease. For best results "good features"
should be used. Features must be selected very carefully from a
variety of available features using some kind of "Discriminant
Analysis." Dominant Feature Selection means the selection of those
features which best distinguish one disease from another.
[0053] Data Processing
[0054] The procedure for detection and categorization of lung
diseases according to this invention is performed in two phases;
Training and Recognition.
[0055] Training (Feature Extraction and Selection)
[0056] A number of features are extracted from a data base of lung
sounds (signals) characteristic for different lung diseases. These
sounds are here called "Training Signals" and are used to train the
computer to recognize particular lung diseases. "Training Signals"
are available on tapes or CDs, and recordings of these lung sounds
were made professionally at prominent hospitals, and are used for
teaching and tutoring medical personnel as noted.
[0057] The method of the invention is exemplified using LPC and
PARCOR coefficients as features. Other features can be used as
well, such as the Fourier Coefficients, Cepstrum Coefficients,
Autocorrelation Coefficients, or others, however LPC and PARCOR
Coefficients turned out to be suitable for signals from the lungs,
and they provided good results.
[0058] The best features from among the extracted features are then
selected. The best features or the dominant features for a
particular disease are features that best distinguish one disease
from another, using minimum computer power. Dominant features for
particular diseases are extracted from the Training Signals, and
are stored in the memory of the computer to create a data base of
dominant features for particular diseases.
[0059] Recognition
[0060] During this phase, sound signals are picked up from the
chest of the patient 10 using the modified stethoscope 12, and then
brought into the computer 16 after preliminary processing in
interface 14, or inside the computer if the preliminary processing
functions are included in the computer itself.
[0061] In either case, the audio signals from the microphone 20 are
converted into suitable form for computer processing. Preliminary
processing includes analog-to-digital conversion, filtering, and
frequency downsampling. Next, multiple features from the signals
are extracted. These features can be LPC coefficients, Fourier
Coefficients, PARCOR Coefficients, Cepstrum Coefficients,
Autocorrelation Coefficients, or others as with the training
phase.
[0062] The method of the invention is here exemplified using LPC
and PARCOR coefficients as features. These features produced good
results, however other suitable features can be used as well. Next,
the extracted features are forwarded to the classifier.
[0063] The classifier is a device or program running in the
computer that makes the decision based on the extracted features.
The "classifier" determines if the lungs are healthy or diseased,
and identifies the disease. Many classifiers can be used to make
these decisions, however in a preferred embodiment of the invention
two classifiers have been selected and implemented. These two
classifiers are the two dimensional Graphical Approach (GA), and
the Neural Net (NN) classifier.
[0064] These classifiers were used to identify if the lungs are
healthy or diseased, and if the lungs are unhealthy, to identify
the decease.
[0065] Reasons for Implementing Autoregressive Modeling
[0066] Sound signals from the lungs have some specific properties:
Parameters of these signals are degraded due to background noise.
The length of these signals is usually short, for instance the
length of the inspiration cycle is about 0.8 seconds. If the
signal-to-noise ratio is small, traditional analysis methods like
the Fourier transform, fail to provide an accurate spectrum. In
this situation FFT (Fast Fourier Transform) provides poor frequency
resolution, and it may produce even false spectral responses. To
avoid these problems, the method that is used in this invention is
the autoregressive parametric model analysis. Autoregressive
modeling has been found to exhibit outstanding performance when the
signal has sharp peaks. Parametric model analysis involves the
selection of a suitable model, and the estimation of parameters of
that model.
[0067] The assumption is made that lung sounds picked up from the
chest of the patient are produced by an all-pole filter. This is
justified because sounds from the lungs have indeed such properties
that they can be represented by an Autoregressive Parametric Model.
Consequently the autoregressive model was used in this
invention.
[0068] Pattern Recognition--General
[0069] People recognize faces of friends in a crowd, voices of
acquaintances, favorite musical compositions, and the like. In
order to do this, light or sound waves emitted from objects are
recognized by our senses and we use our capable of identifying and
discriminating such objects. This process is called pattern
recognition. In more scientific terms, the determination that an
object from a general population P belongs to a known subpopulation
S is called pattern recognition. In our case P are all sound
signals from the chest of the patient, and S is the sound signal
for a particular lung disease. The subpopulation S is called the
class, and it is defined by particular features that accurately
describe this subpopulation, these features are, for the present
invention the Autoregressive Coefficients, e.g. the LPC and PARCOR
coefficients.
[0070] The input to a pattern recognition system is a set of N
attributes, in the present case features from sounds from the
chest, and the output are the classified features.
[0071] Usually, the input is represented like an N-dimensional
vector x=[x.sub.1, x.sub.2, x.sub.3, . . . , x.sub.n], the pattern
vector and is introduced to the system after it is transformed to a
reduced set of features, the feature vector f=[f.sub.1, f.sub.2,
f.sub.3, . . . , f.sub.n], where m<n. The pattern vectors are
obtained after measurements. In the present case, the measurement
of the amplitude of the breathing sounds versus time. The total of
the pattern vectors form the pattern space while the total of the
feature vectors form the vector space. The system can recognize an
object S comparing its feature vector with the feature vector of a
known class c=[c.sub.1, c.sub.2, c.sub.3, . . . , c.sub.m]
according to a threshold value which is the difference between the
feature vector and the class.
[0072] Pattern recognition is performed in two steps, feature
extraction and classification, i.e. input signal->feature
extraction->classification.
[0073] Features
[0074] An object in a population is characterized by different
attributes, many of them can be used to separate or distinguish
this object among similar or different ones. There are attributes
that can distinguish objects and there are attributes that cannot
distinguish objects. For instance, color is a good attribute to
distinguish oranges from apples but it cannot distinguish lemons
from grapefruits. The attributes are called features in pattern
recognition terminology and they are extracted by the use of linear
or non-linear transformations.
[0075] More particularly, let s be an N-dimensional pattern vector,
s=[s.sub.1, s.sub.2, s.sub.3, . . . , s.sub.n]. The transformation
f=L{s} is a linear or a non-linear transformation that maps the s
vector into an f vector, the feature vector, where m<n. It
should be noticed that since m<n, this mapping is not
one-to-one, and the inverse transformation f=L.sup.-1{s} is not
unique. Feature extraction is a process that essentially reduces
the dimensionality of the pattern vector N to the dimensionality of
the feature vector M. This process is useful for the following
reasons. The feature space is often physically more meaningful than
the pattern space. For example, in speech analysis, the frequency
spectrum is more meaningful than the speech waveform. If a Fourier
transform of a signal is taken the frequency components could be
better features for classification than the features in the time
domain. During the transformation from the time domain into the
feature space it is important to retain as much information from
the pattern vector as possible, in a more efficient way. This is
achieved by an appropriate selection of the set of features that
contain all the information of the pattern vector required for
classification, but in lower dimensional vectors. In addition,
there may be some prior knowledge that the measurements are
redundant, and that the data are highly correlated. If this is the
case, the dimensionality can be reduced with very little loss of
information.
[0076] Furthermore, the redundant information can introduce noise
and degrade system performance. Any set of extracted features that
contains all necessary information but is not redundant, is called
an optimal feature set. In practice, near-optimal sets are
desirable. Consequently, an important step in the classification
process is the selection of a suitable set of features. As
mentioned before, there are features that distinguish one class
(disease) but cannot distinguish another class (disease). The
selection of the features is therefore, highly dependent on the
nature of the objects. It is proven that highly correlated features
increase the classification error, while uncorrelated (independent)
features provide better discrimination. Features can be
characterized as correlated when features of one object depend
strongly on the features of another object. A measure of dependency
is, therefore, desired. The most common measure of dependency is
the correlation coefficient.
[0077] Let the feature vectors X have the dimension N and let the
components of the feature vector x=[x.sub.1, x.sub.2, x.sub.3, . .
. , x.sub.n], be ordered according to the ranked feature
importance. This importance may be assigned by the total magnitude
of its range (the greatest values minus the least values) or its
variance. If {x.sup.1, x.sup.2, . . . x.sup.t} is a sample of
feature vectors that are numerous and different, so as to be from
all of the various classes, then the sample correlation between the
features is defined according to the article: Iyer, V. K,
Ramamoorthy, P. A., and Ploysongsang, Y., "Autoregressive modeling
of lung sounds: characterization of source and transmission", IEEE
Trans. Biomed. Eng. BME-36(11), 1133-1137, 1989; as a function of
the expected value of the i-th component over the sample population
T and the expected value operator. A standardized dimensionless
correlation coefficient can also be found and expression
correlation coefficients can be compared for different
situations.
[0078] Correlation coefficient of any feature X with itself is 1.
Two features are independent if the correlation coefficient
approaches zero. Uncorrelated features would produce a graph of
multiple clusters of point that are spaced far from each other
while a case where features are highly correlated would look more
like FIGS. 2 to 5, which will be discussed later in this
disclosure.
[0079] Except in a very few special cases, the optimal selection
can only be done by testing all possible sets of M features chosen
from the N data objects (Trasos Axiotis, RPI, Master's Project.
2000). Of course, the number of different combinations may be a
very large number. Thorough search is the only way to find a true
optimal feature set, but this is not practical in most situations
(Iyer, V. K, Ramamoorthy, P. A., and Ploysongsang, Y.,
"Autoregressive modeling of lung sounds: characterization of source
and transmission", IEEE Trans. Biomed. Eng. BME-36(11), 1133-1137,
1989).
[0080] It should be mentioned that the measurement selection is a
very important process and the success or failure of pattern
recognition strongly depends on the selection of which measurements
to make and how these measurements are performed. For instance, a
low quality measurement can introduce a large amount of noise
causing the extracted feature vector to diverge significantly from
what it really is.
[0081] The GA (Graphical Approach) Classifier
[0082] In order to have some idea about the quality and usefulness
of the extracted features, and to visualize same properties of the
extracted features, it is practical to use the first two
coefficients of these features and to represent them in a two
dimensional representation, i.e. in a plane.
[0083] For example the first two LPC coefficients LPC.sub.1 and
LPC.sub.2 can be extracted for each sample, and plotted in two
dimensions as shown in FIGS. 2 and 4.
[0084] This classifier classifies features in two dimensions, it is
simple and it gives good results if the features and the two
dimensions are selected so that clusters do not overlap. If the
Neural Net (NN) classifier to be explained later, is used, the
selection of features and dimensions is not as critical as in the
GA classifier, because the Neural Net performs multidimensional
classification.
[0085] The mathematical function that estimates the LPC
coefficients normalizes the output by dividing every coefficient by
the first one. The first coefficient is therefore always 1 (one)
and is ignored because it does not carry any information.
Therefore, only the higher coefficients are used in this approach.
Consequently, in the presented examples only the second and third
coefficients are used. Since the PARCOR (Partial Correlation
Coefficients) are dependent on the LPC coefficients, only the
second and the third PARCOR coefficient are used in the shown
examples. Mathematical algorithms can be used to extract various
features from the signal that is picked up from the chest of the
patient, however a simple and practical way to extract these
features is to use the prepackaged software routines from the
MATLAB "Signal Processing Package". As mentioned, in order to
provide some visualization, the location of a number of sampling
data-points for various diseases is presented in two dimensions.
The two dimensional diagrams are generated and plotted using the
MATLAB routines: cbr2d and cfb2d.
[0086] FIGS. 2, 3, 4 and 5 show the location of data points for
particular diseases, when the second and third LPC and PARCOR
coefficients are used as X-Y coordinates in a two dimensional
plane. FIG. 6 shows the location of data points for particular
diseases when PARCOR coefficients 2 and 3 are used, and FIG. 7
shows the location of data points for the same diseases when the
third and fourth PARCOR coefficients are used as X-Y coordinates in
2-D. These data points are recorded for the inhalation
(inspiration) cycle.
[0087] Each dot or star on these diagrams represents data collected
from a patient with a particular disease. It appears in FIG. 6 that
there is some overlap of the clusters in these two dimensional
representations. Actually this overlap does not occur in reality,
because in reality multiple features are used which are represented
in multiple dimensions, and not just in two. If two clusters
overlap for instance like in FIG. 6 (set PARCOR 2 and 3) for
Wheezes and Fibrosis, one can represent the same samples in other
two dimensions like in FIG. 7 (set PARCOR 3 and 4) where the same
data sample points for Wheezes and Fibrosis are well separated and
do not overlap. Similarly, to separate clusters one could use (the
set PARCOR 4 and 5), (the set PARCOR 5 and 6), (the set PARCOR 3
and LPC 3) etc.
[0088] Referencing again to FIGS. 6 and 7, and to further explain
this use of the LPC and PARCOR coefficients according to the
present invention for recognition of diseases, MATLAB extracted
from the precessed lung sound recordings, 20 LPC (LPC1, LPC2, etc.)
and 20 PARCOR coefficients (PARCOR1, PARCOR2, etc.). To represent
these 20 coefficients graphically for data points associated with
various diseases, a 20 dimensional space is needed. When
representing data on paper on an X-Y diagram we use only two
dimensions. It is possible that in the 20 dimensional space
clusters of some diseases, overlap in some two dimensional planes
appear and these diseases cannot be recognized and separated. See,
for example, FIG. 6 where the clusters for Wheezes and Fibrosis are
not well separated when PARCOR 2 and PARCOR 3 are used. However if
we look at this 20 dimensional cluster from another projection, for
example, using PARCOR 3 and PARCOR 4 as in FIG. 7, the clusters for
Wheezes and Fibrosis are well separated.
[0089] Explaining further, one notices some overlap of the clusters
in these two dimensional representations. Actually this overlap
does not occur in reality, because in reality we use multiple
features which are represented in multiple dimensions, and not just
in two. If two clusters overlap for instance like in FIG. 6 (set
PARCOR 2 and 3) for Wheezes and Fibrosis, we can represent the same
samples in other two dimensions like in FIG. 7 (set PARCOR 3 and 4)
where the same data sample points for Wheezes and Fibrosis do not
overlap. Similarly, we could use (the set PARCOR 4 and 5), (the set
PARCOR 5 and 6), (the set PARCOR 3 and LPC 3) etc. as noted
above.
[0090] Since each of the features is multidimensional, the number
of combinations is extremely large, and if we combine the
components of multidimensional vectors in two dimensional sets,
there is an extremely high probability that there are projections
in which clusters will not overlap. In other words, the possibility
of mis-diagnosis when two clusters overlap can be eliminated if
additional features and/or dimensions are used. Consequently,
adding additional features helps clusters to separate better,
without overlapping.
[0091] It is noted that if the Neural Net of the present invention
is used for classification as will be explained later in this
disclosure, the overlap of clusters in some dimensions is not
important, because the neural net takes simultaneously all
dimensions into consideration.
[0092] As noted, the features are extracted from the lung signal
using the MATLAB "Signal Processing Package," and the two
dimensional diagrams are plotted using the MATLAB functions cbr2d
and cfb2d.
[0093] Thus the simple Graphical Classifier (GC) can be used to
select the best features to identify a particular lung disease.
These best features are the dominant features for that disease. The
best features and the best dimensions are the ones that separate
the clusters best. However, if the clusters do not overlap, it is
not necessary to project the data points into other dimensions. The
simple classifier is therefore adequate for the diagnosis of lung
diseases, or it can be used for dominant feature selection as
well.
[0094] Summarizing the Graphical Approach
[0095] 1. Bring the samples of lung sounds, e.g. stored on a CD,
into the computer.
[0096] 2. Extract the LPC and PARCOR coefficients from these
signals using MATLAB and the prepackaged software routines from the
MATLAB Signal Processing Package.
[0097] 3. Generate and plot two dimensional diagrams for LPC and
PARCOR coefficients using the MATLAB cbr2d and cfb2d routines,
which should look similar to FIG. 2 to 7 of this disclosure.
[0098] 4. Plot these diagrams for the second and third LPC and
PARCOR coefficients.
[0099] 5. If clusters for some diseases overlap, use the third and
fourth coefficients instead, or other two coefficients so that
there is no overlap.
[0100] Artificial Neural Networks
[0101] Artificial neural networks (ANN) are very useful classifiers
and also computational tools because of their ability to model and
solve complex problems. ANNs emulate functions of the human brain.
ANNs can be trained to perform a particular functions which is a
valuable characteristic because an ANN can be trained and not
programmed.
[0102] ANNs have become a very popular since they can be used in
many scientific applications, like in computer science, information
theory, and signal-image processing. Due to the various
applications, many different ANN models and implementations have
come into view. Most of the ANNs are direct descendants of
Rosenblant's perception circuits (1958) (Looney, G. C., "Pattern
recognition using neural networks", Oxford University Press,
1997).
[0103] The n-dimensional input vector applied to the input of a
perceptron generates a weighted sum of the input vectors, next a
threshold value .theta. is subtracted from this sum. The generated
output passes through a non-linear function x, and the result is
obtained at the output of the perceptron.
[0104] The Neural Network (NN) Approach Classifier
[0105] The advantage of the NN classifier is that it makes
decisions based on all selected dominant features simultaneously.
Many classifiers can be used for this application, however the NN
classifier proved to be suitable for this application.
[0106] It will be understood, however, that other suitable
classifiers can be used as well. It is important to note that
samples used in training and testing the Neural Net are real sounds
from lungs recorded from real patients, and not artificially
generated sounds.
[0107] The application of the Neural Net will be illustrated on
data collected from patients who have Fibrosis and Bronchitis.
[0108] After extracting features (LPC and PARCOR coefficients) for
each of these diseases, the Neural Network is used as a classifier.
The implemented Neural Network is the network specified by
Mathworks and documented in the MATLAB manual. This neural network
was chosen because it is easy to set up, because it is relatively
fast and because it can solve non-linear classification
problems.
[0109] The preliminary step is training of the Neural Network, so
that the trained network can be used for the classification of
signals. The function used for training is the MATLAB function
"nn_xxx_yy", where `xxx` denotes the used feature, and `yy` denotes
the disease. The use of combined features is not necessary, because
obtained results are good even if just one set of features is used
at a time. If only one set of features is used at a time, the
training time is minimized. In addition, this leads to a simpler
neural network.
[0110] A detailed description of the nn_xxx_yy function can be
found In the MATLAB manual.
[0111] The Implemented Neural Network--Example
[0112] In the presented example samples of signals for two
particular diseases have been divided into a training set, and a
testing set. Two sets of features, the LPC coefficients and the
PARCOR coefficients have been extracted. The two sets are
approximately of the same size, with the testing set a little
larger. The Neural Net is trained with data from the training set
and then tested with data from the testing set. The training set
contains objects of two classes, diseased and healthy, with a ratio
of approximately 1 to 4. This decision was made assuming that the
probability that a sample belongs to a particular disease is
smaller than the probability that it does not. The number of
training and testing data samples for the two diseases presented in
this example are listed in Tables 1 and 2.
TABLE-US-00001 TABLE 1 Number of data samples used for training and
testing NN for Bronchitis Samples Training set Testing Set
Bronchitis 12 10 Non-Bronchitis 40 72
TABLE-US-00002 TABLE 2 Number of data samples for training and
testing NN for Fibrosis Samples Training set Testing Set Fibrosis
12 10 Non-Fibrosis 40 72
[0113] The ratio of 4 to 1 is also reflected in the distribution of
the objects in each class in the training procedure. The number of
neurons in the network is selected by trial and error. The number
of iterations is another parameter that was defined. The learning
process should run until no further improvements can be detected,
otherwise overtraining may occur. Overtraining worsens the
performance of the network and its ability for prediction. In the
described example the trial and error method was used in order to
determine the optimal number of iterations. A series of experiments
had been performed to determine the optimal number of neurons and
iterations. The performance of each set (neurons, iterations) was
determined by two factors. The most important one is the percentage
of correctly classified samples that belong to a particular
disease. The second factor is the percentage of correctly
classified samples that do not belong to the particular disease. A
common way to present results of this type is the confusion
matrix.
TABLE-US-00003 TABLE 3 Example of the implemented Confusion Matrix
Samples from ill persons A B Samples from healthy persons C D
[0114] A: The number of samples from ill persons that are
classified as ill.
[0115] B: The number of samples from ill persons that are
classified as healthy.
[0116] C: The number of samples from healthy persons that are
classified as ill
[0117] D: The number of samples from healthy persons that are
classified as healthy.
[0118] Table 4 shows the final number for neurons and iterations
that were used in the test.
TABLE-US-00004 TABLE 4 Number for neurons and iterations that were
used in the test Neurons Iterations Bronchitis LPC coefficients 50
3000 Fibrosis LPC coefficients 80 3000 Bronchitis PARCOR
coefficients 50 2500 Fibrosis PARCOR coefficients 50 2500
[0119] Results
[0120] To verify the accuracy of the method, after the parameters
and the configuration of the neural network were determined, the
analysis with the Neural Network was performed several times for
each disease. The following tables summarize the classification
results.
TABLE-US-00005 TABLE 5 Confusion Matrix - Bronchitis using LPC
coefficients Bronchitis 100% 0% Non Bronchitis 2% 98%
TABLE-US-00006 TABLE 6 Confusion Matrix - Bronchitis using PARCOR
coefficients Bronchitis 100% 0% Non Bronchitis 0% 100%
TABLE-US-00007 TABLE 7 Confusion Matrix - Fibrosis using LPC
coefficients Fibrosis 100% 0% Non Fibrosis 15% 85%
TABLE-US-00008 TABLE 8 Confusion Matrix - Fibrosis using PARCOR
coefficients Fibrosis 100% 0% Non Fibrosis 6% 94%
[0121] Summarizing the Neural Network Approach
[0122] 1. Bring the samples of Lung Sounds from the chest into the
computer.
[0123] 2. Extract the LPC and PARCOR Coefficients from these
signals using MATLAB, and the prepackaged software routines from
the MATLAB Signal Processing Package.
[0124] 3. Use the GA classifier of the Graphical Approach to select
the best features (the GA classifier).
[0125] 4. Construct the Neural Net using the prepackaged MATLAB
routines--follow guidelines from the MATLAB manual.
[0126] 5. Train the Neural Net to recognize particular diseases
using the extracted LPC and PARCOR coefficients for these diseases.
Use the best features.
[0127] 6. The output of the Neural Net will indicate if the lungs
are healthy, and identify the disease if the lungs are
diseased.
[0128] 7. The NN method will give good results even if step 3 above
is left out, however if step 3 is implemented the accuracy will be
higher.
[0129] The inventor has found that better classification results
were accomplished when PARCOR, partial correlation coefficients
were used, although classification results are very good with LPC
coefficients as well. The implementation of both sets of features,
LPC and PARCOR provide good results, however other sets of features
can be used to practice the present invention.
[0130] Data Acquisition and Processing Details
[0131] Breathing sounds that were used for the present invention
were obtained from the lung sound database issued by a reputable
clinic. These sounds are professionally recorded with large
signal-to-noise ratios. Since the inspiratory segment of the
respiratory cycle is most important, the expiratory phase was
ignored. 134 inspiratory phases with durations between 0.5 and 0.8
seconds were used. Among these samples were 22 samples associated
with chronic bronchitis and 22 samples associated with interstitial
fibrosis. 54 samples were associated with several diseases like
lobar pneumonia. The remaining 36 samples were inspiratory phases
obtained from healthy persons.
[0132] Breath sounds are in the form of sequential respiratory
cycles, therefore it is necessary to isolate only one cycle.
Moreover if characteristic sounds for diseases under investigation
appear in the inspiratory cycle, the inspiratory cycle should be
separated from the respiratory cycle. For example this is the case
for chronic bronchitis, and interstitial fibrosis. If signals are
obtained from a data base these two phases can be separated using a
wave editor, and if signals are obtained from a live patient, the
patient can be asked to inhale and hold.
[0133] After the acquisition of the needed signals in the
inspiratory phase, the signals were converted into a suitable form
for processing. At this point MATLAB undertakes most of the work.
MATLAB calls the function sig_process. This function is used for
preparation of the signals for further processing. Next MATLAB
calls three other functions, downsample, convrt, obtain_c. The
syntax of the function is sig_process(`xx`,n), where `xx` is the
first two letters of the investigating disease and n is the number
of the signals.
[0134] The downsample function does what its name implies;
downsample the signals from 44.1 kHz to 16 kHz. It reads the wave
files under the directory/research/signals, performs the
downsampling operation and stores the resulting signals under the
directory/research/16k_signals. The original frequency of the data
is 44.1 kHz, the data is contained in a CD-ROM in digital form.
This high frequency of 44.1 kHz produces a large number of samples.
In addition, the number of required coefficients to represent these
sounds accurately is proportional to the sampling frequency. A
large number of samples and a large number of coefficients increase
the computation cost. Therefore a lower sampling frequency is a
very good step to reduce the time and required memory for
computations. The reason for choosing 16 kHz as the downsampling
frequency is because the human ear is not sensitive to signals
whose frequencies are higher than 16 kHz so that the downsampling
frequency does not interfere with auscultation.
[0135] The next function is called by sig_process is the convrt
function. This function converts the wave (*.wav) files into MATLAB
binary files (*.mat). The advantages of the specific procedure is
the minimization of the disk space required for storage and the
reduced time needed for MATLAB to load binary files, instead of
wave files.
[0136] This function loads the signals under the
directory/research/16k_signals and stores the binary files under
the directory/research/16k_signals_bin. The generated files are
temporary and are altered after processing with the consequently
function.
[0137] The next used function is the obtain_c function. The purpose
of this function is the extraction of the feature coefficients and
of the and storage of these coefficients in binary files. The
obtain_c function loads the binary files under the
directory/research/16k_signals_bin, evaluates the features and then
saves them under the directory/research/16k_coeff, with the same
file name. At the present time, each of the new generated file
contains two matrices that include feature coefficients, the first
matrix contains the linear prediction coefficients, while the
second matrix contains partial correlation coefficients. The
calculation of the features executed by the functions Ipcauto and
atorc which were developed by Jeffrey Sorensen
(http://www.campbellsorensen.com/sorenj/) under the terms of GNU
General Public License.
[0138] The default parameter for the number of coefficients that is
evaluated for each feature is twenty. This is not an arbitrary
number, but it is based on the theory of specific features that
were used. The theory states that the complete representation of a
signal using the LPC coefficients is strongly dependent by the
sampling frequency. We use one coefficient for every kHz and four
supplementary coefficients for the zeros. For that reason a 16 kHz
signal results in the evaluation of twenty coefficients.
[0139] This preliminary procedure is needed before feature
extraction. At this point the features are stored in MATLAB binary
files and they are ready for use by the neural network.
[0140] More on the Linear Prediction and Autoregressive Method
[0141] If a biomedical process is an autoregressive method, the
autoregressive parameters can be estimated on the basis of the
linear prediction method. The prediction power error e.sup.2[n] is
the excitation input power and the prediction coefficients are the
autoregressive parameters. The transfer function of the
autoregressive process is given by A(z)=1-z.sup.-1H(z) or is given
by A(z)=1-a.sub.1z.sup.-1-a.sub.2z.sup.-2+ . . . +a.sub.nz.sup.-n,
where H[x] represents the Wiener filter and M represents the
autoregressive filter order. In the z-domain we can writen
E(z)=A(z)Y(z).
[0142] The autoregressive model is also referred to as the
"all-pole model" because it is represented by a function with poles
and no zeros.
[0143] The autocorrelation method:
[0144] One way of calculating the prediction coefficients of the
autoregressive process is the autocorrelation method. The method
exploits the estimated autocorrelation lags in the Yule-Walker and
is explained in Makhoul, J., "Linear prediction: a tutorial
review", Proc. IEEE 63:561-580, 1975. The autocorrelation lags are
found as a function of an autoregressive model order and a higher
order autocorrelation function (ACF). The ACF parameters can be
calculated as a function of the data sequence and the variance of
the noise power can be estimated using the Yule-Walker equation.
Using the ACF an autocorrelation matrix can be created containing
the Toeplitz autocorrelation matrix with dimensions
(M+1).times.(M+1), a prediction vector and a noise power vector.
The prediction coefficients are obtained using the Levinson
recursion and the prediction coefficients at stage M can be
obtained recursively from those previously calculated at stage M-1
(see: Kay, S. M., "Recursive maximum likelihood estimation of
autoregressive processes", IEEE Trans. Accoust. Speech Signal
Processing ASSP-28:292-303, 1980). The relationship between the new
prediction-error filter and the old one can be calculated
(Orfanidis, S., "Optimum Signal Processing", MacMillan, New York,
1985) as
A.sub.m+1(z)=A.sub.m(z)-g.sub.m+1z.sup.-m-1A.sub.m(z.sup.-1), where
g are the reflection (PARCOR) coefficients at stage m. The
reflection coefficients at m-th stage can also be calculated.
[0145] A summary of the autocorrelation method using the Levinson
recursion can be found at Akay, M., "Biomedical Signal Processing",
Academic Press Limited, 1994.
[0146] Order Selection of the Autoregression Model
[0147] There are several approaches proposed for selecting the
order of the filter. The number of the coefficients of the filter
is equal with its order and the goal is the minimization of the
prediction error. The order can be calculated using the Akaine
final prediction criterion discussed in: Akaine, H., "Statistical
predictor identification", Ann. Inst. Statist. Math. 22:203-217,
1970 where E.sub.M is the estimation of the mean-squared error, M
is the order of the filter and N is the number of the samples of
the input data.
[0148] The number of the coefficients can also be estimated with
the Akaine information criterion, which minimizes the information
entropy of the signal (Akaine, H., "A new look at the statistical
model identification", IEEE Trans. Autom. Control AC19:726-723,
1974).
[0149] Rissanen (Rissasen, J., "Modeling by shortest data
description", Automatica 14:465-471, 1978) proposed another method,
in which the filter order can be estimated.
[0150] Resolution of the Autoregressive Analysis
[0151] The resolution of the autoregressive analysis can also be
calculated according to Akay, M., "Biomedical Signal Processing",
Academic Press Limited, 1994, as a function of sampling interval,
the order of the filter and the signal-to-noise ratio.
Qualitatively, the resolution is defined as the extent to which the
frequencies corresponding to two closely located peaks can be
distinguished.
[0152] General Considerations
[0153] The present invention is not concerned with providing a
diagnosis or for treating a patient, but rather concerns technical
solutions to technical problems that may assist a physician in
reaching a diagnosis for or treatment of a patient.
[0154] The invention is not limited to the disclosed embodiments.
Also, the word "comprising" does not here exclude other elements or
steps, and use of the words "a" or "an" does not exclude a
plurality. Further, a single processor or other unit may fulfill
the functions of several elements recited in the claims, and
features recited in separate dependent claims may be advantageously
combined. Reference signs in the claims or elsewhere in this
disclosure should not be construed as limiting the scope of the
claims.
[0155] While specific embodiments of the invention have been shown
and described in detail to illustrate the application of the
principles of the invention, it will be understood that the
invention may be embodied otherwise without departing from such
principles.
* * * * *
References