PowerPoint-presentatieEUSIPCO 2017: 25th European Signal Processing Conference Kos Island, Greece
August 28-September 2, 2017
in Smart Patient Monitoring
Prof. Sabine Van Huffel
•Examples in ECG based Cardiac Monitoring •Examples in Magnetic Resonance (Spectroscopic) Imaging •Conclusions and new directions
2
stratification
Smart Patient
Blind source separation Signal analysis difficult because of artefacts REMOVE Matrix based Blind Source Separation (BSS) Non-unique Constraints are needed!
o sources orthogonal (PCA), o sources statistically independent (ICA) o sources uncorrelated and of different autocorrelation (CCA)
EEG A ?
ST ?=4
5
Uniqueness means Interpretable
Contributors (nonexhaustive list): L. De Lathauwer, P. Comon, T. Kolda, B. Bader, L-H Lim, C. Van Loan, E. Acar, A. Cichocki, O. Alter, R. Bro, M. Morup, N. Sidiropoulos, I. Domanov, M. Sorensen, L. Sorber, M. Ishteva, L. Albera, M. Haardt, …. and collaborators
kA + kB + kC
7
8
βα
βα
βα
βα
βα
βα
CORE tensor S is in general full (not pseudodiagonal), but
0...
forSS βαβα
*special case of LMLRA, also called Higher-order SVD (HOSVD) or Tucker Decomposition
U,
U
1
b
a
b
a
b
a
b
a
b
a
b
a
¹
Computation of MLSVD : matrix unfolding
• From 3D tensor to regular matrix by slicing in different directions and juxtaposition (reformatting)
• The ranks of the different unfoldings are in general different !!
• Left singular vectors (U- matrix of the SVD) of each of the unfoldings delivers U-matrix of MLSVD
horizontal
frontal
vertical
Block Tensor Decomposition
De Lathauwer et al., SIMAX, 2008; Sorber et al., SIOPT, 2013
10
www.tensorlab.net
11
Sorber L, Van Barel M, De Lathauwer L, IEEE J. of Selected Topics in Signal Proc., 2015 12
Contents Overview
•T wave alternans detection •Irregular heartbeat detection •Multiscale approaches
•Examples in Magnetic Resonance (Spectroscopic) Imaging •Conclusions and new directions
13
addresses spatio-temporal heart dynamics exploits multi-mode correlations
15
Tensor-based detection of T wave alternans in multilead ECG signals
T wave alternans detection Goal: Automatic detection of T wave alternans
Abnormal pattern ABABAB in T wave amplitude
T wave segmentation
CPD : tensor decomposition TWA detection
(1) Goovaerts, G., Vandenberk, B., Willems, R., Van Huffel, S. (2015). Tensor-based detection of T wave alternans using ECG. Proc. of the 37th international conference of the IEEE Engineering in Medicine and Biology Society. EMBC 2015. Milan, Italy, 25-29 Aug. 2015(pp. 6991-6994).
(2) Goovaerts, G., Vandenberk, B., Willems, R., Van Huffel, S. (2017). Tensor-based Detection of T Wave Alternans in Multilead ECG Signals. Automatic Detection of T wave Alternans Using Tensor Decompositions in Multilead ECG Signals. Physiological Measurement 38:1513-1528
• Correlated with risk of arrhythmia • Predictor of Sudden Cardiac Death
after Myocardial Infarction (MI) • Linked with repolarization of heart • T-wave Differences ~ microVolts
17
3D: 1. Channels 2. Time 3. T waves
18
A1
B1
C1
A1
B1
C1
AR
BR
CR
A
B
C
Here: 1 component, with 3 factors (~ 3 dimensions) • B1 = Time: ‘Average T wave’ • A1 = Channels • C1 = T waves : T wave changes
19
From CPD to PARAFAC2
• PROBLEM: T waves not very well aligned o Differences in heart rate o (Movement) artefacts o Natural variation
No rank-1 decomposition possible
20
Examples
21
22
23
Clinical data: patients with multiple TWA tests TWA (4 subjects, 13 signals) versus Control (5 subjects, 20 signals) Proposed clinical threshold 13 microV
Good results for both methods in ideal circumstances Real-life circumstances: PARAFAC2
Allows heart rate differences Good results under moderate noise
24
Irregular heartbeat classification
Aim: Assess risk on sudden Cardiac death and other cardiac disorders
Similar approach:
2. Construct heartbeat tensor
3. Tensor decomposition CPD
Other approach: Kronecker Product Equations Goovaerts, G., De Wel, O., Vandenberk, B., Willems, R., Van Huffel, S. (2015). Detection of Irregular Heartbeats using tensors. Proc. of the 42nd Annual Computing in Cardiology. CinC 2015. Nice, France, Sep. 2015 (pp. 573-576).
Kronecker Product (KP) Equations: What? multilinear systems
KP structure on the solution
= vec(v uT) generalizes outer productKP definition: 27
KP Equations solver: Nonlinear least squares algorithms
28
↓
Solve KPE and match:
Every heartbeat of a particular channel can then be expressed as KP equat ion
29
Classification of new heartbeat: formulate as computation of KP equation
30
heartbeat
31
Ongoing work: CinC challenge 2017 AF classification from short single lead ECG recordings Classify: normal sinus rhythm, Atrial fibrillation, Alternative rhythm, too noisy
Bousse M., Vervliet N., Domanov I., Debals O., De Lathauwer L., ``Linear Systems with a Canonical Polyadic Decomposition Constrained Solution: Algorithms and Applications'‘, Numerical Linear Algebra with Applications, 2017, submitted. Bousse M., Goovaerts G., Vervliet N., Debals O., Van Huffel S., De Lathauwer L., ``Irregular Heartbeat Classification Using Kronecker Product Equations'', Proc. 39th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC'17, JeJu Island, South Korea).
generalizes to multichannel heartbeat classification
IIT Guwahati 32
Multiscale decomposition of multilead ECG (1)
IIT Guwahati 34
IIT Guwahati 35
Multiscale MLSVDhighest compression rate (CR)
Multiscale MLSVD exploits all 3 types of correlations achieves highest CR of 45:1 Acceptable distortion levels
36
Third-order tensor fuses and analyses information from multiple cardiac cycles and multiple leads
37
•Introduction •Examples in ECG based cardiac monitoring •Examples in Magnetic Resonance (Spectroscopic) Imaging •Conclusions and new directions
43
Overview
spectroscopic imaging (MRSI)
APPLICATION 2: MR-based unsupervised brain tumor recognition
Method 1 Non-negative (N) CPD applied to MRSI
Method 2 NCPD applied to multi-parametric (MP) MRI
44
MR Scanner
46
. .
.
.
Individual metabolite profiles
Metabolite maps
APPLICATION 1- MRSI and water suppression
= =1
=
50Aim: Suppress the large water peak from all voxels
ROI
FFT
Hankel based water suppression- MLSVD* • Each individual source component in time domain can be well
approximated by complex-damped exponentials. • For each voxel in the MRSI signal construct a Hankel matrix
from the free induction decay (FID) signal and form tensor.
• Parameters of the complex-damped exponentials can be obtained using MLSVD and shift-invariance property of (1)
• Reconstruct water signal from sources outside ROI 0.25-4.2 ppm • Subtract from measured signal
H = A ×1 (1) × () ×3 (3)
55
Simulation: • Metabolite spectra generated using in-vitro signals. • Residual water generated using scaled water reference measured in-vivo
57
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Box-plot of error on simulated MRSI data
Tensor based water suppression- Results
Box-plot of difference in variance on in-vivo MRSI data
58
• Water suppression methods tested on 28 in-vivo measured MRSI signals.
APPLICATION 2: MR-based unsupervised brain tumor recognition
Grade IV Glioblastoma patient: Edema
Active tumor
Necrosis
Gliomas: 30% of all primary brain tumors and 80% of the malignant brain tumors. WHO grade of malignancy: grade I-IV. 5-year survival rates:
Anaplastic astrocytoma (grade III): 26% Glioblastoma multiforme (grade IV): 5%
Aim: To identify active tumor and tumor core pathological region
59
normal tissue active tumor necrosis
Y = matrix of spectra, Y ≈ W H
min || Y - W H || such that W ≥ 0, H ≥ 0
W = tissue-specific spectral patterns:
60
Hierarchical NMF (hNMF) Improved results on MRSI data only (Li et al., NMR in BioMed. 2013)
Y = Coregistered MP-MRI data
Wsource1, Hsource1 (tumour, necrosis,...)
Wtumor, Htumor Wnecrosis, Hnecrosis WWM, HWM WGM, HGM ...
Mask needed!
Validation Based on manual segmentation by radiologist (only pathological tissue types)
1) Dice-scores (based on H)
2) Correlation coefficients (based on W)
A B ( )2
b
Brain tumor recognition using NCPD*
• It reduces the length of spectra without losing vital information required for tumor tissue type differentiation.
• It gives more weight to the peaks and makes the signal smoother.
68 *H. N. Bharath, D. M. Sima, N. Sauwen, U. Himmelreich, L. De. Lathauwer, and S. Van. Huffel, “Non-negative canonical polyadic decomposition for tissue type differentiation in gliomas,” IEEE Journal of Biomedical and Health Informatics, vol. PP, no. 99, pp. 1–1, 2016
Brain tumor recognition using NCPD- XXT tensor
• Construct a 3-D tensor by stacking XXT from each voxel. • MRSI tensor couples the peaks in the spectra because of
the XXT in the frontal slices.
69
Brain tumor recognition using NCPD
• Non-negative (N) constraint is applied on all 3-modes. • To maintain symmetry in frontal slices common factor (S) is
used in both mode 1 and mode 2.
≈ [, ,] = =1

: , : , (: , )
• NCPD is performed in Tensorlab* using structured data fusion with l1 regularization on abundances H(sparse): [∗,∗] = min
, − ∑=1 : , : , (: , )

+ 1
70 * Vervliet N., Debals O., Sorber L., Van Barel M. and De Lathauwer L. Tensorlab 3.0, Available online, Mar. 2016. URL: http://www.tensorlab.net/
Brain tumor recognition using NCPD- Results
71
72
DWI MRSI
73 *H. N. Bharath, N. Sauwen, D. M. Sima, U. Himmelreich, L. De Lathauwer and S. Van Huffel, "Canonical polyadic decomposition for tissue type differentiation using multi-parametric MRI in high-grade gliomas," 2016 24th European Signal Processing Conference (EUSIPCO), Budapest, 2016, pp. 547-551.
Edema
74
Similarly, NCPD used with symmetry in frontal slices and l1 regularization on H(sparse)
Brain tumor recognition using MP-MRI:- results
NCPD-l1 hNMF Dice
Tumor Dice Core
Tumor source Correlation
Tumor source Correlation
Mean 0.83 0.87 0.95 0.78 0.85 0.81 Standard deviation 0.07 0.1 0.05 0.09 0.13 0.19
76
•Introduction •Examples in ECG based Cardiac monitoring •Examples in Magnetic Resonance (Spectroscopic) Imaging •Conclusions and new directions
81
Conclusions o Many BSS problems in Smart Patient Monitoring of low rank solve via matrix or tensor factorization plus constraints
o Successful examples shown, e.g., in ECG based cardiac monitoring and brain tumor recognition using MR(S)I
o Extensions to biomedical data fusion emerge,e.g. EEG-fMRI solve via coupled matrix/tensor decompositions (E. Acar, S.
Van Eyndhoven, H. Beckers, M. Morup, B. Hunyadi,…)
o Other BSS applications: bioinformatics (O. Alter, E. Acar), fMRI (C. Chatzichristos), BCI (Cichocki, Mørup, Martinez-Montes), mobile EEG (R. Zink), multichannel ECG (Padhy) and MI classification, fetal ECG extraction (L. De Lathauwer), neonatal monitoring (W. Deburchgraeve, V. Matic), multiple sclerosis progression (C. Stamile), nonconvulsive epileptic seizures (Y. R. Aldana), etc.
82
o Multiscale multimodal approaches o New emerging applications, e.g. in C(hr)onnectome
analysis modelling dynamic brain connectivity networks exploit full potential of existing Tensor(lab) toolboxes
83
Acknowledgment University Hospitals Leuven Gasthuisberg ZNA Middelheim, Queen Paola Children’s hospital EMC Rotterdam KU Leuven, Dept. Electrical Engineering-ESAT, division STADIUS & MICAS Ghent University, Dept. Telecommunication and Information Processing, TELIN-IPI Eindhoven University of Technology
ERC advanced grant 339804 BIOTENSORS in collaboration with L. De Lathauwer and group
The power of low rankTENSOR Approximations in Smart Patient Monitoring
Contents Overview
Tensor Decompositions
Slide Number 10
Slide Number 11
Slide Number 12
Tensor-based detection of T wave alternans in multilead ECG signals
T wave alternans detection
Physionet database: TWA challenge
Irregular heartbeat classification
Kronecker Product (KP) Equations:What? multilinear systems KP structure on the solution
KP Equations solver:Nonlinear least squares algorithms
Application to ECG
Classification of new heartbeat: formulate as computation of KP equation
Slide Number 31
Slide Number 32
Slide Number 34
Slide Number 35
Contents Overview
INTRODUCTION:Metabolite quantification for MRS Imaging (MRSI)
APPLICATION 1- MRSI and water suppression
Hankel based water suppression- MLSVD*
Tensor based water suppression- Results
Tensor based water suppression- Results
Slide Number 59
Slide Number 60
Hierarchical NMF (hNMF)
Brain tumor recognition using NCPD- XXT tensor
Brain tumor recognition using NCPD
Brain tumor recognition using NCPD- Results
Brain tumor recognition using NCPD- Results
Brain tumor recognition using MP-MRI*
Brain tumor recognition using MP-MRI:-XXT tensor
Brain tumor recognition using MP-MRI:- results
Contents Overview