Independent component analysis of biomedical signals: modern data analysis techniques can assist medical decision making
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin
Logos-Verl.
2001
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Zugl.: Regensburg, Univ., Diss., 2001 |
| Beschreibung: | 222 S. 21 cm |
| ISBN: | 3897227339 |
Internformat
MARC
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| 020 | |a 3897227339 |c Pb. : DM 79.00, EUR 40.50 (ab 1.1.2002), sfr 71.90, S 576.60 |9 3-89722-733-9 | ||
| 035 | |a (OCoLC)76266526 | ||
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| 100 | 1 | |a Bauer, Christoph |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Independent component analysis of biomedical signals |b modern data analysis techniques can assist medical decision making |c vorgelegt von Christoph Bauer |
| 264 | 1 | |a Berlin |b Logos-Verl. |c 2001 | |
| 300 | |a 222 S. |b 21 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Zugl.: Regensburg, Univ., Diss., 2001 | ||
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-009498762 | |
Datensatz im Suchindex
| _version_ | 1808227459458400256 |
|---|---|
| adam_text |
CONTENTS
1
PROLOGUE
11
I
INTRODUCTION
TO
BASIC
PRINCIPLES
15
2
INTRODUCTION
TO
STATISTICS
17
2.1
RANDOM
VARIABLES
.
17
2.2
MARGINAL
AND
CONDITIONAL
PROBABILITY
.
19
2.3
STATISTICAL
MOMENTS
.
20
2.4
CHARACTERISTIC
FUNCTIONS
AND
CUMULANTS
.
21
2.5
STATISTICAL
INDEPENDENCE
.
23
2.6
GAUSSIAN
PROBABILITY
DISTRIBUTIONS
.
24
2.6.1
THE
GAUSSIAN
DISTRIBUTION
.
24
2.6.2
THE
CENTRAL
LIMIT
THEOREM
(CLT)
.
25
2.7
HYPOTHESIS
TESTING
.
26
3
BASIC
CONCEPTS
OF
INFORMATION
THEORY
27
3.1
THE
CONCEPT
OF
ENTROPY
.
27
3.1.1
INFORMATION
AND
ENTROPY
.
27
3.1.2
THE
KULLBACK-LEIBLER
DIVERGENCE
.
29
3.1.3
THE
ENTROPY
OF
A
NORMAL
DISTRIBUTION
.
30
3.1.4
GIBBS
'
SECOND
THEOREM
.
31
3.1.5
NEGENTROPY
.
31
3.2
ENTROPY
OF
TRANSFORMED
RANDOM
VARIABLES
.
32
3.2.1
TRANSFORMATION
OF
A
RANDOM
VARIABLE
.
32
3.2.2
TRANSFORMATION
OF
ENTROPY
.
34
4
NEURAL
NETWORKS
35
4.1
FROM
BIOLOGICAL
TO
COMPUTATIONAL
NETWORKS
.
35
4.2
LEARNING
PROCESS
.
37
4.2.1
PRELIMINARY
REMARKS
.
37
4.2.2
HEBBIAN
LEARNING
.
38
4.2.3
COMPETITIVE
LEARNING
.
39
5
MODELLING
HIGH
DIMENSIONAL
DATA
41
5.1
KOHONEN
'
S
SELF
ORGANIZING
MAPS
.
41
5.1.1
ARCHITECTURE
AND
ALGORITHM
OF
THE
NETWORK
.
41
5.1.2
EXAMPLES
AND
APPLICATIONS
.
43
5.1.3
PROBLEMS
AND
LIMITATIONS
OF
SOM
.
44
5.2
GENERATIVE
TOPOGRAPHIC
MAPPING
.
45
5.2.1
THE
LIKELIHOOD-FUNCTION
.
45
5.2.2
BASIC
IDEA
OF
GTM
.
46
5.2.3
THE
EXPECTATION-MAXIMIZATION
ALGORITHM
.
47
5.3
PRINCIPAL
COMPONENT
ANALYSIS
.
49
5.3.1
MATHEMATICAL
BASICS
.
49
5.3.2
DIMENSION
REDUCTION
.
50
5.3.3
SPHERING
TRANSFORMATION
.
50
6
OPTIMIZATION
ALGORITHMS
51
6.1
CONJUGATE
GRADIENT
ALGORITHM
.
51
6.1.1
STEEPEST
DECENT
METHOD
.
51
6.1.2
CONJUGATE
GRADIENT
ALGORITHM
.
52
6.2
SIMULATED
ANNEALING
.
53
7
BIOMEDICAL
DATA
55
7.1
THE
ELECTROENCEPHALOGRAM
.
55
7.1.1
HISTORICAL
DEVELOPMENT
OF
THE
EEG
.
55
7.1.2
RECORDING
THE
EEG
.
56
7.1.3
EEG
AS
DIAGNOSTIC
TOOL
.
57
7.1.4
ARTIFACTS
.
58
7.2
SKIN
LESIONS
.
59
7.2.1
ACTINIC
KERATOSIS
.
59
7.2.2
BASAL
CELL
CARCINOMA
.
60
7.2.3
PSORIASIS
.
61
7.2.4
FLUORESCENCE
DIAGNOSIS
AND
PHOTODYNAMIC
THERAPY
.
62
II
INDEPENDENT
COMPONENT
ANALYSIS
65
8
BASIC
IDEAS
OF
ICA
67
8.1
HISTORICAL
REMARKS
.
67
8.2
MODEL
AND
NOTATION
.
68
8.2.1
LINEAR
ICA
.
68
8.2.2
NONLINEAR
ICA
.
70
8.3
FROM
PCA
TO
ICA
.
71
9
BELL
AND
SEJNOWSKI
'
S
ICA-ALGORITHM
75
9.1
THE
INFOMAX
PRINCIPLE
.
75
9.1.1
THE
DERIVATION
OF
BELL
AND
SEJNOWSKI
'
S
LEARNING
ALGORITHM
.
75
9.1.2
INFOMAX
IMPLIES
INDEPENDENCY
.
78
9.2
NATURAL
GRADIENT
.
79
9.3
LOCAL
ICA
.
80
10
A
PROBABILISTIC
ICA-ALGORITHM
83
10.1
MIXING
MODEL
.
83
10.2
CALCULATION
OF
THE
BASE
COEFFICIENTS
.
84
10.2.1
SQUARE
CASE,
LOW
NOISE
(
TN
-
0)
.
85
10.2.2
NON-SQUARE
CASE,
LOW
NOISE
(
ZN
"
0)
.
85
10.2.3
NON-SQUARE
CASE,
WITH
NOISE
.
86
10.3
LEARNING
A
.
87
10.3.1
AN
ACCURATE
APPROXIMATION
FOR
THE
LOG-LIKELIHOOD
.
87
10.3.2
THE
DERIVATIVE
OF
THE
LOG-LIKELIHOOD
.
89
10.3.3
THE
LEARNING
ALGORITHM
.
89
6
10.4
THE
PROPER
CHOICE
OF
THE
PRIOR
.
91
10.5
STABILITY
OF
THE
ALGORITHM
.
92
11
GEOMETRIC
IC
A
95
11.1
BASIC
IDEA
AND
LIMITATIONS
.
95
11.2
A
KOHONEN-LIKE
ADAPTIVE
ICA-ALGORITHM
.
97
11.2.1
PREPROCESSION
OF
THE
DATA
.
97
11.2.2
COMPETITIVE
LEARNING
.
97
11.3
CONVERGENCE
IMPROVEMENTS
BY
SIMULATED
ANNEALING
.
99
11.4
NONLINEAR
EXTENSIONS
.
100
12
DATA
FITTING
103
12.1
GENERAL
REMARKS
.
103
12.1.1
STATIC
APPROACHES
.
104
12.1.2
SEMI-ADAPTIVE
APPROACHES
.
104
12.1.3
ADAPTIVE
APPROACHES
.
105
12.2
KERNEL
BASED
DENSITY
ESTIMATION
.
106
12.2.1
HISTOGRAMS
.
106
12.2.2
KERNEL
BASED
DENSITY
ESTIMATION
FOR
UNIVARIATE
DISTRIBUTIONS
.
106
12.2.3
KERNEL
BASED
DENSITY
ESTIMATION
FOR
MULTIVARIATE
DISTRIBUTIONS
.
110
12.2.4
ADAPTIVE
KERNEL
BASED
DENSITY
ESTIMATION
.
112
12.3
NEURAL
ADAPTATION
OF
THE
CONTRAST
FUNCTION
.
113
III
RESULTS
117
13
IDENTIFICATION
OF
INDEPENDENT
COMPONENTS
119
13.1
COMPARISON
OF
THE
BASE
SYSTEMS
.
119
13.1.1
SIMILARITY
CRITERION
.
120
13.1.2
A
SEARCH
ALGORITHM
.
121
13.2
COMPARISON
OF
THE
CORRELATIONS
IN
THE
SOURCE
SIGNALS
.
121
13.2.1
SIMILARITY
CRITERION
.
122
13.2.2
A
NEW
SEARCH
ALGORITHM
.
123
13.3
ENTROPY
BASED
IDENTIFICATION
OF
INDEPENDENT
SIGNALS
.
125
14
EXPERIMENTAL
COMPARISON
OF
THE
ICA-ALGORITHMS
127
14.1
TWO
TYPES
OF
MIXTURES
.
127
14.1.1
A
PARAMETERIZED
PROBABILITY
DENSITY
P
7
(X)
.
127
14.1.2
AN
ARTIFICIAL
MIXTURE
.
128
14.2
EXPERIMENTAL
RESULTS
USING
P
7
(Z)
.
130
14.2.1
INFOMAX
ALGORITHM
.
130
14.2.2
PROBABILISTIC
ICA
.
132
14.2.3
GEOMETRIC
ICA
.
133
14.3
EXPERIMENTAL
RESULTS
USING
AN
ARTIFICIAL
MIXTURE
.
133
14.3.1
INFOMAX
ALGORITHM
.
134
14.3.2
PROBABILISTIC
ICA
.
137
14.3.3
GEOMETRIC
ICA
.
139
14.4
DISCUSSION
.
140
7
14.4.1
INFOMAX
ALGORITHM
.
140
14.4.2
PROBABILISTIC
ICA
.
141
14.4.3
GEOMETRIC
ICA
.
141
15
ICA
OF
ELECTROENCEPHALOGRAPHIC
DATA
143
15.1
MOTIVATION
.
143
15.2
PATIENT
WITHOUT
BRAIN
TUMOR
.
144
15.2.1
IDENTIFICATION
USING
CORRELATION
COEFFICIENTS
.
146
15.2.2
IDENTIFICATION
USING
THE
ENTROPY
.
148
15.2.3
CORRECTION
OF
THE
EEG
RECORDINGS
.
149
15.3
PATIENT
WITH
BRAIN
TUMOR
.
150
15.3.1
ARTIFACTUAL
AND
TUMOR-RELATED
INDEPENDENT
COMPONENTS
.
151
15.3.2
TOPOGRAPHIC
ALLOCATION
OF
THE
INDEPENDENT
COMPONENTS
.
154
15.3.3
COMPARISON
OF
THE
ALGORITHMS
.
155
15.3.4
OVERCOMPLETE
IC-ANALYSIS
.
158
15.4
DISCUSSION
.
159
16
SKIN
LESION
CLASSIFICATION
USING
PCA
AND
ICA
163
16.1
IMAGE
PREPROCESSING
.
163
16.1.1
CONTRAST
MANIPULATIONS
.
163
16.1.2
HISTOGRAM
EQUALIZATION
.
165
16.1.3
PRESENTATION
OF
THE
SAMPLES
.
166
16.2
CLASSIFICATION
BASED
ON
EIGENIMAGES
.
166
16.2.1
CALCULATION
OF
THE
EIGENIMAGES
.
166
16.2.2
EUCLIDIAN
DISTANCE
BASED
CLASSIFICATION
.
167
16.2.3
RECONSTRUCTION
ERROR
BASED
CLASSIFICATION
.
170
16.3
CLASSIFICATION
USING
INDEPENDENT
BASE
IMAGES
.
172
16.3.1
ARCHITECTURE
1:
STATISTICALLY
INDEPENDENT
BASE
IMAGES
.
172
16.3.2
ARCHITECTURE
2:
FACTORIAL
CODE
.
175
16.4
CLASSIFICATION
USING
LOCAL
FEATURES
EXTRACTED
BY
ICA
.
178
16.4.1
LOCAL
ICA
USING
SOM
.
178
16.4.2
LOCAL
ICA
USING
GTM
.
182
16.5
PERFORMANCE
COMPARISON
.
184
17
SUMMARY
AND
CONCLUSIONS
189
A
SOME
MATHS.
197
A.L
CALCULATION
OF
LOG
(P(X|
A))
.
197
A.1.1
CALCULATION
OF
DV/DA
.
198
A.L.
2
CALCULATION
OF
DV/DS
.
198
A.L.
3
CALCULATION
OF
DS/DAKI
.
199
A.2
THE
COFACTOR
EXPANSION
OF
A
MATRIX
.
200
A.2.1
MATRIX
INVERSION
BY
COFACTOR
EXPANSION
.
200
A.
3
PROOF
OF
THE
IDENTITY
=
(A
-1
)
T
DET
A
.
201
A.4
PROOF
OF
THE
IDENTITY
.
202
8
B
SAMPLES
205
B.L
SAMPLES
OF
EEG
RECORDINGS
.
206
B.L.L
PATIENT
WITHOUT
BRAIN
TUMOR
.
206
B.L.
2
PATIENT
WITH
BRAIN
TUMOR
.
207
B.2
SAMPLES
OF
SKIN
LESIONS
.
208
B.2.1
ACTINIC
KERATOSIS
.
208
B.2.2
BASAL
CELL
CARCINOMA
.
209
B.2.3
PSORIASIS
.
210
BIBLIOGRAPHY
211
ACKNOWLEDGEMENTS
223
9 |
| any_adam_object | 1 |
| author | Bauer, Christoph |
| author_facet | Bauer, Christoph |
| author_role | aut |
| author_sort | Bauer, Christoph |
| author_variant | c b cb |
| building | Verbundindex |
| bvnumber | BV013884021 |
| classification_rvk | WC 7000 |
| ctrlnum | (OCoLC)76266526 (DE-599)BVBBV013884021 |
| discipline | Biologie |
| format | Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV013884021 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-24T00:41:25Z |
| institution | BVB |
| isbn | 3897227339 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-009498762 |
| oclc_num | 76266526 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-83 |
| owner_facet | DE-355 DE-BY-UBR DE-83 |
| physical | 222 S. 21 cm |
| publishDate | 2001 |
| publishDateSearch | 2001 |
| publishDateSort | 2001 |
| publisher | Logos-Verl. |
| record_format | marc |
| spelling | Bauer, Christoph Verfasser aut Independent component analysis of biomedical signals modern data analysis techniques can assist medical decision making vorgelegt von Christoph Bauer Berlin Logos-Verl. 2001 222 S. 21 cm txt rdacontent n rdamedia nc rdacarrier Zugl.: Regensburg, Univ., Diss., 2001 Signalquelle (DE-588)4181273-6 gnd rswk-swf Faktorenanalyse (DE-588)4016338-6 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Signaltrennung (DE-588)4181278-5 gnd rswk-swf Biosignalverarbeitung (DE-588)4006899-7 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Biosignalverarbeitung (DE-588)4006899-7 s Signalquelle (DE-588)4181273-6 s Signaltrennung (DE-588)4181278-5 s Faktorenanalyse (DE-588)4016338-6 s Neuronales Netz (DE-588)4226127-2 s DE-604 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009498762&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bauer, Christoph Independent component analysis of biomedical signals modern data analysis techniques can assist medical decision making Signalquelle (DE-588)4181273-6 gnd Faktorenanalyse (DE-588)4016338-6 gnd Neuronales Netz (DE-588)4226127-2 gnd Signaltrennung (DE-588)4181278-5 gnd Biosignalverarbeitung (DE-588)4006899-7 gnd |
| subject_GND | (DE-588)4181273-6 (DE-588)4016338-6 (DE-588)4226127-2 (DE-588)4181278-5 (DE-588)4006899-7 (DE-588)4113937-9 |
| title | Independent component analysis of biomedical signals modern data analysis techniques can assist medical decision making |
| title_auth | Independent component analysis of biomedical signals modern data analysis techniques can assist medical decision making |
| title_exact_search | Independent component analysis of biomedical signals modern data analysis techniques can assist medical decision making |
| title_full | Independent component analysis of biomedical signals modern data analysis techniques can assist medical decision making vorgelegt von Christoph Bauer |
| title_fullStr | Independent component analysis of biomedical signals modern data analysis techniques can assist medical decision making vorgelegt von Christoph Bauer |
| title_full_unstemmed | Independent component analysis of biomedical signals modern data analysis techniques can assist medical decision making vorgelegt von Christoph Bauer |
| title_short | Independent component analysis of biomedical signals |
| title_sort | independent component analysis of biomedical signals modern data analysis techniques can assist medical decision making |
| title_sub | modern data analysis techniques can assist medical decision making |
| topic | Signalquelle (DE-588)4181273-6 gnd Faktorenanalyse (DE-588)4016338-6 gnd Neuronales Netz (DE-588)4226127-2 gnd Signaltrennung (DE-588)4181278-5 gnd Biosignalverarbeitung (DE-588)4006899-7 gnd |
| topic_facet | Signalquelle Faktorenanalyse Neuronales Netz Signaltrennung Biosignalverarbeitung Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009498762&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT bauerchristoph independentcomponentanalysisofbiomedicalsignalsmoderndataanalysistechniquescanassistmedicaldecisionmaking |