THE COMPLEXITY OF AUTONOMIC CONTROLLING SYSTEMS WITH NOVEL - - PowerPoint PPT Presentation

▶
the complexity of autonomic
SMART_READER_LITE
LIVE PREVIEW

THE COMPLEXITY OF AUTONOMIC CONTROLLING SYSTEMS WITH NOVEL - - PowerPoint PPT Presentation

Aug 29, 2022 418 likes •980 views

CARDIOVASCULAR VARIABILITY SIGNALS : TOWARDS A QUANTITATIVE ASSESSMENT OF THE COMPLEXITY OF AUTONOMIC CONTROLLING SYSTEMS WITH NOVEL APPLICATION TOOLS Sergio Cerutti Dipartimento di Bioingegneria Politecnico di Milano Italia BIOSTEC 2011

slide-1
SLIDE 1

CARDIOVASCULAR VARIABILITY SIGNALS : TOWARDS A QUANTITATIVE ASSESSMENT OF THE COMPLEXITY OF AUTONOMIC CONTROLLING SYSTEMS WITH NOVEL APPLICATION TOOLS

Sergio Cerutti Dipartimento di Bioingegneria Politecnico di Milano Italia BIOSTEC 2011 Rome, 26-29th January, 2011

slide-2
SLIDE 2

Biological signals carry information about the physiological systems under studying. The processing

  • f signals allow: i) to quantify and ii) to qualify such

information (for validating physiological modelling) mainly through mathematical tools

slide-3
SLIDE 3

from Koepchen HP, 1984

slide-4
SLIDE 4

spinal circuits total peripheral conductance

LF LF HF HF HF LF LF LF LF LF LF LF LF

peripheral vascular districts supraspinal circuits respiration arterial pressure baroreceptive mechanisms

  • cardiopulm. refl.

heart rate stroke volume

vessels heart lungs CNS

SN LV AB WK

phrenic n.

slide-5
SLIDE 5

Modeling of cardiovascular signal interactions

from Baselli G et al, IEEE Trans BME, 1988

slide-6
SLIDE 6

Sympathetic (red lines) and parasympathetic (blue line) nervous systems

slide-7
SLIDE 7

Cardiovascular Signals

Variability Signals

A PROBE TO ANS ASSESSMENT

slide-8
SLIDE 8

Pathology of

  • rgans

vs.

Pathology of controlling systems

slide-9
SLIDE 9

What are these HRV signals telling us ?

Healthy Congestive heart failure Atrial fibrillation

slide-10
SLIDE 10
slide-11
SLIDE 11

HEART RATE VARIABILITY DISSEMINATION

  • About 12000 papers in MedLine
  • The 3rd most-frequently-cited paper in Circulation: Task Force of the

European Society of Cardiology the North American Society of Pacing Electrophysiology:” Heart Rate Variability : Standards

  • f

Measurement, Physiological Interpretation, and Clinical Use, Circulation, 1996 93: 1043-1065

  • The 6th most-frequently cited paper in Circulation Research: M Pagani,

F Lombardi, S Guzzetti, O Rimoldi, R Furlan, P Pizzinelli, G Sandrone, G Malfatto, S Dell''Orto, E Piccaluga, G. Baselli, S.Cerutti, A.Malliani, Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog, Circ Res, 1986 59: 178-193

slide-12
SLIDE 12
  • INTEGRATION BETWEEN PHYSIOLOGICAL

MODELS AND DATA & SIGNALS TREATMENT

  • INTEGRATION OF INFORMATION FROM SIGNALS

AND IMAGES FROM  SYSTEMS, WITH  MODALITIES, ON  SCALES…. (COMPLEMENTARY) TO IMPROVE: i) PHYSIOLOGICAL KNOWLEDGE OF SYSTEM/S ii) CLINICAL PROCEDURES (diagnosis, therapy and rehabilitation)

INTEGRATION

slide-13
SLIDE 13

Next step: Transesophageal RT3DE

slide-14
SLIDE 14

3D Point- tracking technique

slide-15
SLIDE 15

3D Papillary muscles Analysis

3D manual navigation of the dataset, with recognition of papillary muscle tips and computation of distances and angles

slide-16
SLIDE 16

COMPLETE GEOMETRICAL MODEL

Characteristics

  • Leaflet inclinations:

anterior=8°,posterior=7°

  • 82

insertions

  • n

free margin, 42 insertions behind leaflet borders, 13 insertions of 3° order.

  • Transversal

section

  • f

cordae from literature

  • Commissural zone with

intermediate width in repect to the two leaflets.

  • Every papillary muscle is indicated by one single point and the leaflet

amplitude may be changed according to the clinical observations

slide-17
SLIDE 17

Results: map of main strains

slide-18
SLIDE 18

Cordae insertion of 3°order Cordae insertion of 2° order Structural cordae insertion

Results: map of main strains

slide-19
SLIDE 19

PRE-Surgery 3 months POST

Example (Memo3D – Sorin)

slide-20
SLIDE 20

Analysis of dynamic matching between aorthic and mitral annuli

[Veronesi et al.: Circulation, Cardiovasc. Imaging 2008

slide-21
SLIDE 21

SLEEP INTENDED AS AN EXAMPLE OF MULTIORGAN INVOLVEMENT

  • Sleep is classically segmented in stages on the basis of

the EEG signal and also of EMG and EOG (macrostructure of sleep)

  • REM sleep (rapid eye movement)
  • NREM sleep (stages 1-4 according to the sleep depth)

Rechschaffen A and Kales A. A manual of standardized terminology, techniques and scoring system of sleep stages of human subjects. US Government Printing Office: Washington Public Health Service, 1968.

  • Also the autonomic control on heart rate was found to

be correlated to the sleep stages

slide-22
SLIDE 22

22

  • N-REM

I II III-IV

  • REM

polysomnography hypnogram Sleep Stages

[Rechtschaffen and Kales, 1968]

Background - sleep stages

slide-23
SLIDE 23

23

  • Obstructive Sleep Apnoea
  • Insonnia

SLEEP FRAGMENTATION Hypertension/Ischemia/Heart Failure

  • nasal flow
  • Abdominal efforts
  • Heart rate
  • oxymetry
  • restorative effects
  • Physiologic changes
  • Sympathetic hyperactivity

Cardiovascular Pathologies

  • overwork of the heart
  • low adaptability to

endogenous stimulus Diurnal Consequences

  • hypnogram
  • Central Arousal

Background - sleep pathologies

slide-24
SLIDE 24

EMG EEG RR R

STAGE 2 CAP MC More complex physiological phenomena still to be completely explained

slide-25
SLIDE 25

Sleep Apnoea

Somers 1995

slide-26
SLIDE 26

OSA: example

slide-27
SLIDE 27

An important challenge:

  • Is it possible to properly detect Sleep

Parameters and classify Sleep Properties (important for the neurophysiological and clinical aspects) on the basis of ECG & derived signals only (mainly RR intervals) ?

slide-28
SLIDE 28

Cyclic recurrence of peak sympathetic activation during REM sleep, increasing from the second non-REM/REM cycle towards the early morning hours. This may be related to the incidence peak of complicances of myocardial ischemia during the early morning hours.

slide-29
SLIDE 29

Classification stage

Power VLF power HF power.. Classificator Other Parameters HF pole mod.. WA, EMD feat.

ANN HMM KNN

Feature # 1 Feature # 2

Sleep staging Apnoeas

Parameter extraction

slide-30
SLIDE 30

Risultati

24 subjects HSE (San Raffaele Hospital Sleep Centre, Milano): Accuracy 80%, SP 85%, SE 71%

MO Mendez et al.,. Int J Biom Engin & Tech, 2010

Automatic classification (R&K) Mean accuracy (70 - 90% in normal subjects, 65 - 87% in sleep disturbances Mean agreement among different examiners (87,5%) 7 Beat-to-beat features Mean over 30 sec Classificator HMM (REM/nonREM/awake)

slide-31
SLIDE 31

The Society of Information

slide-32
SLIDE 32

Wearable system

  • It detects and monitors data and signals with an improved

comfort degree for the user in respect to portable monitoring systems

  • Goal: to realize non-intrusive systems which do not

interfere with the daily activities of monitored subject

Dispositivi Dispositivi portabli Dispositivi indossabili

Comfort/Daily Activity Burden/ Weight

slide-33
SLIDE 33

Wearable vs portable

slide-34
SLIDE 34
slide-35
SLIDE 35

Textile fibers with electroconductive properties

  • Conductive fibers mixed

to natural or synthetic yarns

  • Electroconductive yarns

Examples of textile electrodes

slide-36
SLIDE 36

ECG monitoring

Contemporaneous acquisition of 5 ECG leads:

  • Pseudo Einthoven Leads: I, II, III
  • Precordial leads: V2, V5
slide-37
SLIDE 37

4 thorax electrodes: injection of 50kHz current into the external electrodes. The ratio between the potential difference from the internal electrodes and the current provides the modulus of impedance.

Respiration through impedenzometric sensors

slide-38
SLIDE 38

Measurement of respiration through piezoresistive electrodes

Thoracic and abdominal respiration signals

slide-39
SLIDE 39

Electrode-skin contact

External layer to reduce the evaporation rate Electrode Filling layer to increase the pressure

Electrode-skin contact improvement

slide-40
SLIDE 40

My-Heart EU VI Framework Programme Advanced ICT tools

  • User interfaces
  • Textiles
  • Electronics
  • Algorithms
  • System integration
  • Testing
slide-41
SLIDE 41

41

TakeCare: Risk Management modules

  • Focus area @ home

Sleep quality improvement Stress management Daily activity management Weight management

slide-42
SLIDE 42

Wearable / textile sensors for vital signals acquisition

Smartex CSEM

Resp (25 Hz, 1/beat) BA BR ECG (250 Hz) HR, HRV

slide-43
SLIDE 43

Sleep Signal Acquisition

  • Bed Foil (VTT)

Bed Sensor with 8 channel piezo foils

slide-44
SLIDE 44

SLEEP STUDIES

slide-45
SLIDE 45

Bodily accellerometer

slide-46
SLIDE 46

Sleep fragmentation index

Sleep Fragmentation Index (SFI)

SFI = 3* (No. Arousals in TST 1/3) + (No. Arousals in TST 2/3) + 0.33* (No. Arousals in TST 3/3). SFI < 70 70 < SFI < 100 SFI > 100

GOOD MODERATE BAD Sensitivity = 81% Specificity = 99% Accuracy = 98.5%

slide-47
SLIDE 47
slide-48
SLIDE 48

Application: behavioural therapy for insomnia

HRV/Res Activity Caffein Stress h sleep h wake-up . . . Sleep quality

slide-49
SLIDE 49

Energy expenditure (black-box O2 consumption)

  • Signal magnitude and Pressure Gradient are the model inputs.
  • Oxygen Uptake (mlO2/(Kg*min)) is the model output.
  • The classification information is used to improve the model performance.

Energy expenditure Using Triaxial Accelerometers and Barometric Measurements, Voleno M. et al., IEEE- EMBS Conference, Buenos Aires, 2010

With classification information Without classification information

slide-50
SLIDE 50

PSYCHOPHYSICAL STRESS

slide-51
SLIDE 51

Relaxation

%RSA

slide-52
SLIDE 52

HeartCycle (HF and CAD) (2008-2011)

slide-53
SLIDE 53

Psyche (Bipolar Disorder) (2010-2013)

Measurement & Detection Daily feedback Long term feedback Processing

clinician Interface

Professional care Electronic agenda Activity & movement monitoring Voice analysis Biochemical screening Sleep monitoring Biofeedback & stress management Data analysis Daily feedback

slide-54
SLIDE 54

BIOSIP Lab, Bioengineering Deptm, Milano None of us is as good as all of us !!

Baselli Caiani Cerutti Bianchi Mainardi Signorini