Artificial intelligence and hypertension: new approach in understanding of possible mechanisms

Transcription

1 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, Belgrade, Serbia

2 Artificial intelligence will control of cockpits Not for nervous flyers! Boeing to test pilotless planes next year as artificial intelligence takes control of cockpits

3 Autonomous Cars and Artificial Intelligence

4 A prototype Audi A7 with self-driving technology is seen during testing on the A9 autobahn in Germany in May 2016.

5 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.

6 Artificial Neural Networks

7 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.

8 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 selforganization, enable them reliable nonlinear multivariate 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.

9 Heart rate variability Artificial Neural Networks

10 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

11 NEUROCARDIOLOGICAL LABORATORY Center for noninvasive electrocardiology Center for autonomic nervous system testing in clinical medicine Syncope center

12 METHODOLOGY Data were obtained using short ECG analysis (Shiller AT-10), noninvasive 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

13 TASK FORCE monitor

14 TASK FORCE monitor Finger blood pressure monitoring

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17 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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20 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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23 1. Autonomic Nervous System Activity Method ANSA SCAN METHOD 2. Autonomic Nervous System Activity Scanning ANSA SCAN SOFTWARE

24 ANSA SCAN SOFTWARE o 33 parameters of short and long time HRV analysis o in statistical analyse o Scanning of type of autonomic balance o Individualized approach related to drugs > Ansa Scan Plus

25 ANSA SCAN PLUS Short time parameters EKG QTc QT PR QRS P Paxis QRSaxis Taxis Mean RR SDRR PNN50 RMSSD Spectral parameters 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 and BP BRS BI HR sbp dbp mbp

26 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

27 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

28 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

29 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

30 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

31 Heart rate variability intervals,hrvi

32 NEUROCARD 2017

33 Hypertension-Healthy Clasterisation of groups FP TP TN FN

34 Healthy-Hypertension

35 Healthy-Hypertension Groups Hits

36 ECG (5) (identical 6 parameters!! )

37 Impact of ECG parameters on clusters Parameters Hipertension Controls 'QTc' 'QT 'PR 'QRS' 'P' 'mrr' 'Paxis' 'QRSaxis' 'Taxis' 'SDRR' 'PNN50' 'RMSSD'

38 Healthy-Hypertension Impact of ECG parameters (12) Hiperten. 1 Kontrol. Gr

39 Impact of ECG parameter Mean RR mrr

40 Syncope Class (1) and Control Class (0) Impact of ECG parameter PNN50% Hypertension Syncope PNN PNN50

41 Hypertension-Healthy ECG and Holter ECG parameters (53)

42 Hypertension-Healthy ECG and Holter ECG parameters (53) Hits

43 Hypertension-Healthy Impact of ECG and Holter ECG parameters (53) 6 5 Dejstvo parametara na Klase Hipertenz I Kontrolnu Klasu Hipert. 1 Kontrol Serial number of examples

44 Holter ECG parameters Impact on hypertension and controls Parameters Hypertension Controls TP VLF Mean RR

45 Impact of Holter ECG parameter Total Power (TP) TP

46 Impact of Holter ECG parameter Hypertension Very Low Frequency (VLF) VLF Syncope

47 Impact of Holter ECG parameter Mean RR interval mrr

48 Artificial network,ann Hypertension

49 ANN Low Frequency,(LF) and hypertension Sympathetic activity LFms

50 ANN Duration of P wave and hypertension Hypertension P

51 ANN Duration of QRS axis and hypertension QRSaxis

52 ANN Systolic blood pressure and hypertension sbp

53 Lyme disease Blood pressure changes Very low baroreflex sensitivity

54 Blood pressure variability Syncope and Epilepsia

55 Dysfunction of baroreflex activity Syncope

56 Patient M.V. 6 years old Syncope Epilepsia Hypertension!! Low value of vitamin D Acute infection with COXSACKIE VIRUS

57 Lyme disease Syncope Baroreflex sensitivity Blood pressure

58 Panic atack with Syncope before the head up tilt testing

59 Patient K.V.20 years old Hypertension Acute infection with Borelia burgdorferi,adeno virus,influenza A,Influenza B,Echo virus

60 0 - Kontrolni pacijenti 1- Pacijenti deca sa Sinkopama Syncope (children) and Control Group (III) 1 Klasifikacija test uzorka Sinkope Deca redni broj primera

61 LYME DISEASE AND SYNCOPE Artificialis neural network-ann

62 Lyme disease and syncope ANN

63 Lyme disease and control group

64 Values in percentages(%) Partial impact of variables 7 Partial impact of variables in the outcome Serial number of variables

65 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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