artificial intelligence and hypertension: new...
TRANSCRIPT
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Artificial intelligence and hypertension: new
approach in understanding of possible
mechanisms
Milovanovic Branislav,Drasko Furundzic,GligorijevićTatjana
University Clinical Center Bezanijska Kosa
Neurocardiological Laboratory,Medical Faculty , Belgrade
Mihajlo Pupin Institute, Volgina 15, 11000 Belgrade, Serbia
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Artificial intelligence will control of
cockpits
Not for nervous flyers! Boeing to test pilotless
planes next year as artificial intelligence takes
control of cockpits
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Autonomous Cars and Artificial Intelligence
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A prototype Audi A7 with self-driving technology is
seen during testing on the A9 autobahn in
Germany in May 2016.
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Applications of Neural Networks
They can perform tasks that are easy for a human but difficult for a machine −
Aerospace − Autopilot aircrafts, aircraft fault detection.
Automotive − Automobile guidance systems.
Military − Weapon orientation and steering, target tracking, object discrimination, facial recognition, signal/image
identification.
Electronics − Code sequence prediction, IC chip layout, chip failure analysis, machine vision, voice synthesis.
Financial − Real estate appraisal, loan advisor, mortgage screening, corporate bond rating, portfolio trading
program, corporate financial analysis, currency value prediction, document readers, credit application evaluators.
Industrial − Manufacturing process control, product design and analysis, quality inspection systems, welding
quality analysis, paper quality prediction, chemical product design analysis, dynamic modeling of chemical process
systems, machine maintenance analysis, project bidding, planning, and management.
Medical −Cancer cell analysis, EEG and ECG analysis,
prosthetic design, transplant time optimizer. Speech − Speech recognition, speech classification, text to speech conversion.
Telecommunications − Image and data compression, automated information services, real-time spoken language
translation.
Transportation − Truck Brake system diagnosis, vehicle scheduling, routing systems.
Software − Pattern Recognition in facial recognition, optical character recognition, etc.
Time Series Prediction − ANNs are used to make predictions on stocks and natural calamities.
Signal Processing − Neural networks can be trained to process an audio signal and filter it appropriately in the
hearing aids.
Control − ANNs are often used to make steering decisions of physical vehicles.
Anomaly Detection − As ANNs are expert at recognizing patterns, they can also be trained to generate an output
when something unusual occurs that misfits the pattern.
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Artificial Neural Networks
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Artificial Neural Networks (ANNs)?
The inventor of the first neurocomputer, Dr. Robert
Hecht-Nielsen, defines a neural network as −
"...a computing system made up of a number of
simple, highly interconnected processing elements,
which process information by their dynamic state
response to external inputs.”
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Introduction
Artificial neural networks (ANN) are data driven
learning structures based on the principles of
morphological and functional organization of
biological neurons. Basic quality of trained neural
structures, generalization, association and self-
organization, enable them reliable nonlinear multi-
variate regression, classification and clustering. The
models trained on the representative sample
generalized knowledge on the unknown test sample
with high reliability even at low level of
representativeness of the training set.
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Heart rate variability
Artificial Neural Networks
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Heart rate variability and ANN
The heart rate variability is used as the base
variable from which certain parameters are
extracted and presented to the ANN for
classification
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NEUROCARDIOLOGICAL
LABORATORY
Center for noninvasive electrocardiology
Center for autonomic nervous system
testing in clinical medicine
Syncope center
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METHODOLOGY
Data were obtained using short ECG analysis (Shiller AT-10), non-invasive beat-to-beat heart rate variability and baroreflex sensitivity (Task Force monitor) and 24 hour ambulatory ECG monitoring with long term HRV analysis
ECG parameters were obtained from the signals of all 12 ECG channels over the past 5 minutes using commercial software (Schiller AT-10, Austria)
The Task Force Monitor (CNSystems, Graz, Austria), was used to monitor beat-to-beat heart rate (HR) by ECG and beat-to-beat blood pressure by the vascular unloading technique [12], which was corrected automatically to the oscillometric blood pressure measured on the contralateral arm. The Task Force Monitor automatically provides beat to beat spectral analysis of heart rate, systolic and diastolic blood pressure variability, applying an autoregressive methodology
Baroreceptor reflex sensitivity (BRS) was automatically assessed using the sequence technique according to Parati
Twenty-four-hour ambulatory ECG recordings were acquired by a 12 leads electrocardiogram, sampling rate 1000 Hz per each lead (Cardioscan, D.M.S.USA) and analyzed by an experienced analyst
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TASK FORCE monitor
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TASK FORCE monitor
Finger blood pressure monitoring
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PORTAPRES
The Portapres® is the ambulatory Finapres
technology solution. The Portapres® offers
on top of standard ambulatory blood
pressure monitoring (ABPM) insight into
hemodynamic parameters such as stroke
volume and cardiac output. For almost 20
years the technology has proven itself in
clinical settings, high altitude research on
mountain heights and in space by top
scientific institutes like NASA
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Overview parameters
The following parameters are available using the Portapres® in combination with BeatScope® software:
Parameter Abreviation
1 Blood Pressure SYS SYS
2 Blood Pressure DIA DIA
3 Blood Pressure MEAN MEAN
4 Heart rate HR
5 Inter beat interval IBI
6 Cardiac output CO
7 Stroke volume SV
8 Pulse rate variability* PRV
9 Baroreflex sensitivity* BRS
10 Total peripheral resistance TPR
11 Total arterial compliance CwK
12 Max. steepness of current upstroke dp/dt
13 Ascending aortic impedance at DIA Zao
14 Left ventricular ejection time LVET
15 Rate pressure product PS*HR
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1. Autonomic Nervous System Activity
Method
ANSA SCAN METHOD
2. Autonomic Nervous System Activity
Scanning
ANSA SCAN SOFTWARE
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ANSA SCAN SOFTWARE
o 33 parameters of short and long time
HRV analysis
o 21981 in statistical analyse
o Scanning of type of autonomic balance
o Individualized approach related to drugs
> Ansa Scan Plus
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ANSA SCAN PLUS
Short time parameters
EKGSpectral
parameters BRS and BPQTc
QT
PR
QRS
P
Paxis
QRSaxis
Taxis
Mean RR
SDRR
PNN50
RMSSD
LF nu
LF ms
HF nu
HF ms
VLF ms
VLF nu
LF ms
HF ms
TP ms
LF/HF ms
LF/HF nu
BRS
BI
HR
sBP
dBP
mBP
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ANSA SCAN PLUS
Long time parameters
Time domain Spectral Blood pressure
Mean RR
Avg HR
SDNN/24
SDANN INDEX
SDNN INDEX
rMSSD
PNN50
TP ms
VLF ms
VLF nu
LF ms
LF nu
HF ms
HF nu
LF/HF ms
ULF ms
ULF nu
sBP
dBP
PULS PRESSURE
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ANSA SCAN PLUS
Groups
Groups II Groups III Groups IV
1.Parasympathetic predominance
2.Sympathetic predominance
1.
Parasympathetic
predominance
2.
Balance state
3.
Sympathetic
1.
High parasympathetic
predominance
2.
Mild parasympathetic
predominance
3.
Mild sympathetic
predominance
4.
High sympathetic
predominance
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ANSA SCAN PLUS
Combination of parameters with TP,VLF,BRS
4 groups
1.
Low parameter with low total power
2.
Low parameter with high total power
3.
High parameter with low total power
4.
High parameter with high total power
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ANSA SCAN PLUS
Combination of parameters with TP,VLF,BRS
9 groups
1.
Low parameter with low total power
2.
Low parameter with normal total power
3.
Low parameter with high total power
4.
Normal parameter with low total power
5.
Normal parameter with normal total power
6.
Normal parameter with high total power
7.
High parameter with low total power
8.
High parameter with normal total power
9.
High parameter with high total power
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ANSA SCAN PLUS
Combination of parameters with TP,VLF,BRS
8 groups
Very low parameter with low total power
2.
Very low parameter with high total power
3.
Mild low parameter with low total power
4.
Mild low parameter with high total power
5.
Mild high parameter with low total power
6.
Mild high parameter with high total power
7.
Very high parameter with low total power
8.
Very high parameter with high total power
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Heart rate variability intervals,HRVI
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NEUROCARD 2017
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Hypertension-Healthy
Clasterisation of groups
0 50 100 150 200 250 300 3500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
FP
FNTN
TP
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Healthy-Hypertension
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Healthy-Hypertension
Groups
-1 -0.5 0 0.5 1 1.5 2 2.5-1
-0.5
0
0.5
382 116
Hits
83 274274 83116 382
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ECG (5)(identical 6 parameters !! )
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Impact of ECG parameters on clusters
Parameters Hipertension Controls
1.0000 'QTc' 1.0084 0.96122.0000 'QT‘ 0.8163 0.79103.0000 'PR‘ -0.0829 -0.12454.0000 'QRS' -0.3432 -0.36965.0000 'P' -0.2843 -0.31216.0000 'mRR' 2.5784 -0.63827.0000 'Paxis' -0.5547 -0.68878.0000 'QRSaxis' -0.6086 -0.58589.0000 'Taxis' -0.5693 -0.551010.0000 'SDRR' -0.6489 -0.533311.0000 'PNN50' -0.6939 -0.574212.0000 'RMSSD' -0.6173 2.6253
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Healthy-Hypertension
Impact of ECG parameters
(12)
1 2 3 4 5 6 7 8 9 10 11 12-1
-0.5
0
0.5
1
1.5
2
2.5
3
Hiperten. 1
Kontrol. Gr.2
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Impact of ECG parameter
Mean RR
400 500 600 700 800 900 1000 1100 1200 1300 14000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
mRR
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Impact of ECG parameter
PNN50%
Hypertension Syncope
0 5 10 15 20 25 30 35 40 450
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
PNN50 0 5 10 15 20 25 30 35 40 450
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
PNN50
Syncope C
lass (
1)
and C
ontr
ol C
lass (
0)
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Hypertension-Healthy
ECG and Holter ECG parameters
(53)
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Hypertension-Healthy
ECG and Holter ECG parameters
(53)
-1 -0.5 0 0.5 1 1.5 2 2.5-1
-0.5
0
0.5
382 116
Hits
83 274274 83116 382274 83382 11682 275274 83
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Hypertension-Healthy
Impact of ECG and Holter ECG parameters
(53)
0 10 20 30 40 50 60-1
0
1
2
3
4
5
6
Serial number of examples
Dejstvo parametara na Klase Hipertenz I Kontrolnu Klasu
Hipert. 1
Kontrol 2
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Holter ECG parameters
Impact on hypertension and controls
Parameters Hypertension Controls
19.0000 TP 5.1540 3.6695
20.0000 VLF 3.3694 2.3626
23.0000 Mean RR 1.2626 0.6988
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Impact of Holter ECG parameter
Total Power (TP)
0 2000 4000 6000 8000 10000 12000 140000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
TP
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Impact of Holter ECG parameter
Very Low Frequency (VLF)
0 2000 4000 6000 8000 10000 120000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
VLF
Hypertension Syncope
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Impact of Holter ECG parameter
Mean RR interval
550 600 650 700 750 800 850 900 950 1000 10500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
mRR
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Artificial network,ANN
Hypertension
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ANN
Low Frequency,(LF) and hypertension
Sympathetic activity
0 1000 2000 3000 4000 5000 6000 70000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
LFms
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ANN
Duration of P wave and hypertension
50 100 150 2000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
P
Hypertension
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ANN
Duration of QRS axis and
hypertension
-200 -150 -100 -50 0 50 100 150 2000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
QRSaxis
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ANN
Systolic blood pressure and
hypertension
80 100 120 140 160 180 200 2200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
sBP
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Lyme disease
Blood pressure changes
Very low baroreflex sensitivity
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Blood pressure variability
Syncope and Epilepsia
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Dysfunction of baroreflex activity
Syncope
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Patient M.V. 6 years old
Syncope
Epilepsia
Hypertension!!
Low value of vitamin D
Acute infection with
COXSACKIE VIRUS
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Lyme disease
Syncope
Baroreflex sensitivity Blood pressure
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Panic atack with Syncope before the
head up tilt testing
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Patient K.V.20 years old
Hypertension
Acute infection with Borelia burgdorferi,Adeno
virus,Influenza A,Influenza B,Echo virus
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Syncope (children) and Control Group
(III)
0 10 20 30 40 50 60 70 80 90 100-0.2
0
0.2
0.4
0.6
0.8
1Klasifikacija test uzorka Sinkope Deca
redni broj primera
0 -
Kontr
oln
i pacije
nti
1-
Pacije
nti d
eca s
a S
inkopam
a
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Artificialis neural network-ANN
LYME DISEASE AND SYNCOPE
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Lyme disease and syncope
ANN
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Lyme disease and control group
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Partial impact of variables
0 5 10 15 20 25 30 35 40 450
1
2
3
4
5
6
7
Valu
es in p
erc
enta
ges(%
)
Serial number of variables
Partial impact of variables in the outcome
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Conclusion
ANN models can be used in modeling different
types of pathology and diagnostics. In this
particular case the ANN structure enabled us a
highly reliable discrimination of patients with
hyperetnsion,syncope and patients without risk,
based on standard cardiologic examination
procedure
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