Intelligente Datenanalyse zur Diagnoseunterstützung am Beispiel Schlafmedizin

Transcription

1 Intelligente Datenanalyse zur Diagnoseunterstützung am Beispiel Schlafmedizin Georg Dorffner Section for Artificial Intelligence, Center for Medical Statistics, Informatics and Intelligent Systems Medical University of Vienna The Siesta Group Schlafanalyse GmbH

2 Who s involved? WWFF

3 Collaborators Peter Anderer (Psychiatry, TSG) Georg Gruber (TSG) Gerhard Klösch (Neurology) Tatiana Miazhynskaia (OFAI) Silvia Parapatics (OFAI, TSG) Peter Rappelsberger (Brain Research) Bernd Saletu (Psychiatry) Ernst Trenker (Brain Research) Michael Wörtz (OFAI, TSG) Josef Zeitlhofer (Neurology) and many more

4 Sleep disorders 25-30% of the population suffer from disturbed sleep, e.g.: Sleep apnea Insomnia Narcolepsy Restless Leg-Syndrome Sleep medicine: Diagnosis and treatment cognitive and behavioral therapy devices (e.g. respiration support) pharmacotherapy

5 The sleep laboratory

6 Sleep signals Polysomnography EEG EOG EMG EKG Resp. etc. Quelle: DGSM

7 Quelle: DGSM 14 Hz Stage 2

8 Quelle: DGSM Stage 3/4 1 Hz

9 Quelle: DGSM Stage REM

10 The sleep profile Rechtschaffen & Kales hypnogram Patient (53 years) ICD-10: G47.0, N95.1 Healthy subject (56 years)

11 The need for automation Process time-consuming Needs trained experts Inter-rater variability Discrepancy between rules and what can be identified visually Computer can extract more information Automated processes required

12 Previous attempts Sleep classification 1968 Visual classification according to R&K Computer-assisted automatic classification 1969 Itil: Automatic classification of sleep stages; Agressologie Smith and Karacan: EEG sleep stage scoring by an automatic hybrid system; Electroencephalogr Clin Neuropysiol. 31 Computer technology Computers of the 3rd generation (based on integrated circuits) 4 kb 8 Bit core memory 1981 IBM PC: ,77MHz, 64 kb RAM, 180 kb floppy drive, PC-DOS Hoffmann et al.: Quantitative description of sleep stage electrophysiology using digital period analystic techniques. Sleep Kubicki et al.: Sleep EEG evaluation: a comparison of results obtained by visual scoring and automatic analysis with the Oxford sleep stager. Sleep Prinz et al.: C STAGE, automated sleep scoring: development and comparison with human sleep scoring for healthy older men and women. Sleep Schwaibold et al.: Automated sleep stage detection with a classical and a neural learning algorithm methodological aspects. Biomed Tech Visual classification according to R&K IBM AT:80286, 6 MHz, 256 kb RAM, HDfloppy drive with 1,2 MB, 20 MB hard disc, PC-DOS 3.0 N = 10 controls N = 45; elderly controls N = 25m & 3f; OSAS HP COMPAQ PC, Pentium 4 CPU 2.4 GHz, 1GB RAM, 400 GB hard disc, 4.7 GB DVD

13 The SIESTA database

14 The SIESTA Database TRAINING SET VALIDATION SET N = 295 N = 295

15 Somnolyzer 24x Georg Dorffner.

16 Automatic sleep spindle detection Step 1 (Step 2: LDA)

17 Automatic K-complex detection Step 1

18 Automatic K-complex detection Step 2

19 Results of validation Sleep spindles: Validation: Per experts: Per recording lab: Sensitivity: 86% Specificity: 85% Sensitivity: 77-92% Specificity : 76-90% Sensitivity: 74-94% Specificity: 83-88%

20 Results of validation K-complexes: Validation: Per experts: Per recording lab: Sensitivity: 88% Specificity: 87% Sensitivity: 78-94% Specificity : 80-93% Sensitivity: 80-92% Specificity : 73-93%

21 Expert System (1) Explicit knowledge (e.g. slow waves speak for S3 or S4) (2) Implicit knowledge (e.g. S4 usually does not follow S1 directly) (3) Smoothing rules (start and end of stage REM, 3-min rule for S2)

22 Expert System REM REM LDA-8: TD,AD,B1D, B2I,EMM EMGI&m WAKE W,REM,S1 REM,S1 REM W LDA-4: TD,AD,BD EMGI&m W,REM,S1,S2 LDA-3: TD,AD,BD EMGI&m S1 S1 S1 LDA-2: SWD,TI,AD, SPI,EMI W REM LDA-8: TD,AD,B1D, B2I,EMM EMGI&m LDA-9: TD,AD,B1D, REMI S1 S2 W LDA-1: SWD TD,AD LDA-10: DI,TD,AI, B1I,SPI, SEMI S1 W WAKE S2 LDA-9: TD,AD,B1D, REMI S3,S4 LDA-6: SWD,TD,AD, SPI,EMI, EMGI S2 REM LDA-7: SWD,TI,AD, SPI,REMI, EMGI&m W S2 S2 WAKE S3 S3 S3 S3 S2 LDA-6: SWD,TD,AD, SPI,EMI, EMGI LDA-5: SWD S3 S3 S3 S4 S4 LDA-5: SWD S4 S4

23 Normal healthy control, 25 years, female W S1/REM S2 S3 S4 1st scorer W S1/REM S2 S3 S4 2nd scorer W S1/REM S2 S3 S4 Consensus scorer W S1/REM S2 S3 S4 Somnolyzer 24x7

24 W S1/REM S2 S3 S4 W S1/REM S2 S3 S4 W S1/REM S2 S3 S4 W S1/REM S2 S3 S4 Normal healthy control, 81 years, female 1st scorer 2nd scorer Consensus scorer Somnolyzer 24x7

25 Apnea patient (AHI: 82), 38 years, male W S1/REM S2 S3 S4 1st scorer W S1/REM S2 S3 S4 2nd scorer W S1/REM S2 S3 S4 Consensus scorer W S1/REM S2 S3 S4 Somnolyzer 24x7

26 Overall performace (validation set, N=285, 270,100 epochs) Scoring 1 vs. Scoring 2 Somnolyzer Somnolyzer 24x7 vs. Scoring24x7 vs. Scoring 1 2 Somnolyzer 24x7 vs. Consensus Scoring 87.6 Somnolyzer 24x7 vs. Scoring 1 or 2 or Consensus 99.4 Somnolyzer 24x7: QM1 vs. QM2 Anderer et al., Neuropsychobiology 2005;51(3):115-33

27 A more detailed comparison Visual Scorer 1 vs. Visual Scorer 2 W REM W REM SOMNOLYZER 24x7 vs. Visual Consensus Scorer W REM W REM

28 Performance on sleep parameters Somnolyzer 24x7 vs. Consensus Scoring: Scorer 1 vs. Scorer 2:

29 Somnolyzer 24x7 + Respiratory analysis PLM analysis Arousal analysis Spindle and K-complex analysis Artefact detection FFT analysis (incl. Pharmaco-EEG) All: Reliable, validated, according to published standards (incl. latest AASM standards) Norm data

30 Svetnik et al Zolpidem vs. Placebo N=164

31 AASM / Penzel et al. 2007, review Overall, the literature concerning sleep stage validation appears to provide evidence that human and computer agreement with some systems, circumstances of training, and human editing intervention has reached the level of results of human scoring agreement between different laboratories.

32 E-Health solution Manual processing user- Interface Data Analysis Data Analysis Module Module (TSG) (R&K) Sleep Server Sleep Data Core Data Archive Billing, Data Analysis Reporting & Module Archiving (Normative DB) Client (downloadable) Web + FTP Server Client for Viewer to communication display and with the server check the at the sleep processed processing results center Sleep data raw (EDF) Client software Internet Internet Computer EEG, EKG... Sleep Lab Sleep data - processed Subject TSG Server

33 Siesta Spot Report Page 1: Sleep initiation, maintenance and architecture Page 2: Page 3: Sleep continuity Sleep maintenance, and continuity and architecture sleep cycles per quarter

34 Business areas Scoring services for sleep labs/centers Akin to laboratory medicine Efficient, reliable, scalable Scoring services for clinical trials Reduced variance Close to standard Quality control

35 Everything s perfect for the market? All this seems a perfect business model Wrong A lot of additional factors need to be taken into account

36 Are published guidelines a standard? Rechtschaffen & Kales (1968) Iber et al. (2007), AASM All aspects of PSG Evidence-based medicine approach Conservative but unambiguous But Still many variations Everbody believes they have the only truth Examples: arousals, leg movements Customization needed Scientifically not founded But unavoidable for acceptance

37 The importance of visual scoring (?) Pathological events can be detected there are many different types of events but: is there clinical evidence? Visual scoring defining an entire profession RPSGT (although only 1/3 of their profile) Hard to accept that someone else (including Somnolyzer) could be equally correct

38 Workflow PSG recording device comes with Viewer Software (e.g. Alice, Sandman,...) No new system wanted Interface with PSG software needed but: edf is a one-way street Most manufacturers want to keep their format proprietary

39 Training how to use it correctly Like correctly driving an energysaving car Recognize Where overreading might be necessary Where Somnolyzer can be trusted Future: semi-automated

40 The complex sales process

41 Unexpected competition Major argument (still): cost-efficiency Alternative for some: Low-cost labor in Asia ($ 300/month, 10 hours/day, 6 days/week) Remaining competitive: High volumes Added benefits: quality, scalability, flexibility

42 How competition sometimes reacts Area: Scoring for clinical trials Letter to editor of major journal: there is no evidence for validity of autoscoring Despite involvement in one of the studies scientific argument used to beat competition

43 Europe vs. USA USA: Health care as business 2/3 private insurance Large centers, centralised processing Europe: Mostly public and hospital-related No cost transparency No budget for services (outsourcing) Personnel as personal asset for clinic chair Automatic scoring widely used, but no focus on quality

44 More hurdles Technical Internet bandwidth Firewalls, etc. Psychological Data leaving their premises One s own weaknesses hard to admit Researchers wanting to get rich

45 Still success If one (re)acts properly... Integrated workflow Creative pricing Efficient sales/implementation process Publications and confidence-creating measures Lobbying Etc.... successful commercialisation of medical IT is possible

46 Summary Sleep analysis is a good example of how biomedical IT can help health care However, good software is only 50% of guarantee that system will be used Do not understimate seemingly trivial factors Always take the physician s view into account Be alert, even if it s about sleep