Translating into Clinical Practice:
NASA 12-lead ECG Inventions
for
Patient-Centered Medicine & mHealth

Todd T. Schlegel,...
Summary of presentation
1. New dry electrodes, harnesses and “gloves” for 12lead ECG data acquisition (hardware);
2. New d...
Recent “12-lead ECG Harness” co-developed at NASA
Internal view

Figure:
A. Orbital’s Research Inc.’s FDA-cleared dry elec...
Why would NASA want a dryelectrode harness for 12-lead ECG?
1) Reduce lead wire “spaghetti” (like that
shown at lower left...
A disposable alternative for 12-lead ECG self-administration:
The ECG Glove from NYC-based “INeedMD, Inc” (already CE & FD...
What is meant by “properly sized”?

V5
V6

Clearly one size does not “fit all”…
(And the same thing applies to all harness...
Alternative commercialized dry-electrode embodiments for
resting 12-lead ECG (all from Israel, not NASA).

SHL’s “CardioSe...
New digital-to-analog converter
(DAC) system
Enables multiple automated interpretations of 12lead ECGs from several manufa...
Note the
automated
diagnosis
(hmm…)
What might a truly “patient-centered” automated
interpretive system for 12-lead ECG look like?
Probably
something
like thi...
START HERE
Original 12-lead ECG
collected on machine of
any “Manufacturer X”

Here’s our effort to help:
New 12-lead ECG “...
Cloud or Server-based use of the DAC for patient-centered mHealth (e.g. with “Glove” or Harness)
Other advantages of new D...
Advanced ECG Diagnostic Software (aka “A-ECG”)
A-ECG is suite of software applications that analyze, in one place, the res...
(Or Wireless mHealth 12-Lead ECG)

12-lead (8-channel) ECG data file or data stream

Large underlying database of patients...
Example Technique #1: Beat-to-beat QT interval variability (QTV)
A measurement of “temporal repolarization
heterogeneity”
...
Example Technique #2: T-wave and QRS-wave complexity via “PCA” or “SVD”

“Complexity” by
Principal
Component
Analysis
(“PC...
Example Technique #3: “3D ECG”: e.g., Spatial QRS-T angle by 12-to-Frank lead transform

Normal: Spatial QRS-T angle = 61°...
Rather than potentially bore an “informatics” audience with numerous charts and tables
outlining the details of our statis...
CASE 1: Disease or no Disease? (34-year old female)
CASE 2: Disease or no Disease? (69 year-old male)
CASE 3: Disease or no Disease? (62 year-old male)

?
CASE 4: Disease or no Disease? (57 year-old male)

Poor R-wave progression? (Clinically non-specific)

Some “flattening” (...
A-ECG results from CASE 1 (the 34 year-old female):
Lead II

(SVD)

RR (ms)

ΔQT (ms)

PCA

normal
normal

All normal

Ref...
A-ECG results from CASE 3 (the 62 year-old male) (Pd=132 ms)
Lead II

PCA

(SVD)

ΔQT (ms)

RR (ms)

borderline

abnormal
...
First the results from some simple but validated A-ECG
“logistic scores”:
(These are scores derived from results in the un...
DA for common outpatient cardiac diseases
Case:
1.
2.
3.
4.

DA Call:
Healthy
Healthy
CAD
NICM

Probabilities:
0.99959715
...
Same DA as the immediately prior slide, but now in “3D”:
Case 1Healthy; Case 2Slightly less Healthy, likely
“Healthy agi...
DA for A-ECG “age” score
32.3 years
62.3 years

34
69

-2 (A-ECG age younger than chron. age)
-7 (A-ECG age younger than c...
Bayesian A-ECG age scores

A statistically more robust way of
estimating “A-ECG age”

(many pages worth of this kind of
ma...
A-ECG Telemedicine in the 3rd World
Fiji: Oct 20-23rd 2013
(Snapshot A-ECG screening via
WiFi ECG + Android tablet/phone)
...
Thanks to NASA, Dr. Walter Kulecz (programming), and all students, visiting faculty,
clinical collaborators and patients w...
References for beat-to-beat QT interval variability:
Atiga WL, Calkins H, Lawrence JH, Tomaselli GF, Smith JM, Berger RD. ...
References for T-wave Complexity:
Priori SG, Mortara DW, Napolitano C, et al. Evaluation of the spatial aspects of T-wave ...
References for 3D ECG (especially spatial mean QRS-T Angle)
Kardys I, Kors JA, van der Meer IM, Hofman A, van der Kuip DA,...
Recent references for A-ECG that are freely available online:
Schlegel, T.T. , et al. Accuracy of advanced versus strictly...
Additional and/or
back-up slides
Upcoming SlideShare
Loading in …5
×

Translating into Clinical Practice: NASA 12-lead ECG Inventions for Patient-Centered Medicine & mHealth

1,736 views
1,521 views

Published on

Presented by Todd T. Schlegel MD
Nicollier-Schlegel SARL

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,736
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
1
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Translating into Clinical Practice: NASA 12-lead ECG Inventions for Patient-Centered Medicine & mHealth

  1. 1. Translating into Clinical Practice: NASA 12-lead ECG Inventions for Patient-Centered Medicine & mHealth Todd T. Schlegel, MD Short Bio: Born/raised in Minneapolis, Minnesota Medically trained at Mayo Clinic, Rochester, Minnesota Joined NASA’s Johnson Space Center in 1992: Medical Officer Senior Scientist Moved from Houston to Geneva, Switzerland in mid-2013 after 20+ years at NASA Remain a consultant to NASA, but for management of Ethics Committees, not R&D *Disclosures: Present: Nicollier-Schlegel SARL, Switzerland *New start-up company focused on commercializing the NASA-owned & other IP
  2. 2. Summary of presentation 1. New dry electrodes, harnesses and “gloves” for 12lead ECG data acquisition (hardware); 2. New digital-to-analog converter system for enabling multiple automated interpretations of 12-lead ECGs from several manufacturers at one time (hardware + software); 3. New advanced ECG (“A-ECG”) software for improved ECG-based diagnosis of heart diseases (software) All can be used either locally, or remotely for telemedicine
  3. 3. Recent “12-lead ECG Harness” co-developed at NASA Internal view Figure: A. Orbital’s Research Inc.’s FDA-cleared dry electrode as an individual sensor (Orbital also owns harness IP) B. NanoSonics’ dry electrode as an individual sensor (not yet FDA cleared) C. Orbital’s electrodes in our 12-lead ECG harness, with wireless ECG transmission (dotted arrow) via Bluetooth D. Receiving Android smart phone E. Printout of 12-lead ECG obtained from harness as received by phone. Abbreviations: RA, RL, LA, LL: right arm, right leg, left arm and left leg electrodes, respectively
  4. 4. Why would NASA want a dryelectrode harness for 12-lead ECG? 1) Reduce lead wire “spaghetti” (like that shown at lower left) 2) Eliminate ECG electrode disposables 3) Eliminate adhesives (irritating and uncomfortable) 4) Reduce don/doff time and overall time for 12-lead ECGs 5) Prevent the common clinical problem of reversed lead wire placements 6) Make 12-lead ECGs more “mobile” 7) Allow non-physician crewmembers to easily “self-collect” 12-lead ECGs
  5. 5. A disposable alternative for 12-lead ECG self-administration: The ECG Glove from NYC-based “INeedMD, Inc” (already CE & FDA cleared) - Uses standard adhesives (not dry electrodes) like any other single-use disposable. Mason-Likar configuration. - Expense a potential issue, but advantages are similar to the harness, plus also higher portability and sterility - If properly sized, it works very well for resting 12-lead ECG
  6. 6. What is meant by “properly sized”? V5 V6 Clearly one size does not “fit all”… (And the same thing applies to all harnesses, shirts, etc)
  7. 7. Alternative commercialized dry-electrode embodiments for resting 12-lead ECG (all from Israel, not NASA). SHL’s “CardioSenC” harness for 12-lead ECG; has an embedded earlier generation cell phone for slow transmission of snapshot ECGs only. One squeezes the RA and LA electrodes into one’ s armpits. Tapuz Medical’s rubberized “Apron” for resting 12-lead ECG; patient “stretches” arm-loop electrodes backward SHL’s newer “SmartHeart” version (left); has CE and FDA clearance now, but for SHL’s “telemedicine-center” clients only (evidently). Commwell Medical’s “Glove” for resting 12-lead ECG (Israeli designed, Illinois based)
  8. 8. New digital-to-analog converter (DAC) system Enables multiple automated interpretations of 12lead ECGs from several manufacturers at one time
  9. 9. Note the automated diagnosis (hmm…)
  10. 10. What might a truly “patient-centered” automated interpretive system for 12-lead ECG look like? Probably something like this: ECG data formatted by Manufacturer X ECG data formatted by Manufacturer Y ECG data formatted by Manufacturer Z Software (e.g., in EHR system) that recognizes all of these different formats and converts them to a common format from which the interpretive functionalities of all three manufacturers can then be accessed The unsure/querying clinician and the patient benefit by choosing the best/most likely automated 12-lead interpretations from several manufacturers. Similar to being able to access the “Poll the Audience” lifeline on “Who Wants to be a Millionaire?” Manufacturer X’s automated interpretation + Manufacturer Y’s automated interpretation + Manufacturer Z’s automated interpretation The Problem? Multiple clinician researchers with expertise in ECG have been trying to get Manufacturers X, Y and Z to cooperate in this fashion for decades, but no luck. (A clash between patient-centered medicine and manufacturer economic interest?) So how might we assist clinicians and patients, at least for now?
  11. 11. START HERE Original 12-lead ECG collected on machine of any “Manufacturer X” Here’s our effort to help: New 12-lead ECG “DAC” Device: END HERE Same 12-lead ECG tracing but interpreted by machine(s) of See: Kothadia et al, New system for digital to Manufacturer Y, Z, etc. instead) analog transformation and reconstruction of 12- ”second opinion” automated dxs lead ECGs. PLoS One. 2013 Apr 11;8(4):e61076. A hardware/software device that nonengineers might regard as a “black box” of sorts (prototype below) Raw digital 12-lead ECG data of known format from manufacturer X’s XML or similar file (or live data stream) Software + Board that receives the digital 12-lead ECG data (wirelessly if desired), then reformats those data into an optimized binary format, then streams them out at an optimal sampling rate to an optimally configured digital-to-analog converter (DAC). To any other ECG manufacturer’s machine(s), for “redigitization” and “reanalysis” there. . 1 2 Level shifter chips that just allowed our prototype Board (left) to communicate with our prototype DAC (right), given that they used different logic voltage levels. 3 Optimally configured DAC that converts the digital data streamed from the Board into an optimized set of analog signals that can be “re-fed” into any manufacturer’s ECG machine(s) for re-digitization and re-analysis there.
  12. 12. Cloud or Server-based use of the DAC for patient-centered mHealth (e.g. with “Glove” or Harness) Other advantages of new DAC: - Fully patient centered (patient can initiate too) - Savings on ECG capital equipment purchases - Smaller, more mobile (if space constrainted) - Improve all manufacturers’ diagnostic algorithms Disadvantage: - A pure software solution coming from cooperating major manufacturers would be less cumbersome (more practical), albeit not “universal” like the DAC Inexpensive Android Tablet or Smart Phone digital ECG data (in optimized format) Remote bot Reports Multiple Reports (conv. ECG + A-ECG) Remote physician (if needed) Secure Server/Cloud New 12-lead ECG “DAC” device Or… To any manufacturers’ ECG device(s)!
  13. 13. Advanced ECG Diagnostic Software (aka “A-ECG”) A-ECG is suite of software applications that analyze, in one place, the results from nearly every advanced & conventional 12-lead ECG parameter known to have diagnostic or prognostic utility. R&D philosophy: “Collect once, analyze everything of potential value” A-ECG utilizes: • A large and ever-growing database of 12-lead ECG information collected from populations of both healthy and diseased individuals as defined by accompanying, gold-standard cardiac imaging results, and • Both basic (e.g., logistic regression) and advanced (e.g., “pattern recognition”) statistics, accompanied by branch-and-bound, jackknifing, bootstrapping, etc procedures, to determine which of the 400+ advanced and conventional ECG parameters studied are truly the most essential, especially in combination, for detecting any given cardiac disease process; To produce: • Automated “A-ECG scores” (=“fingerprints” for each disease) that: 1) are diagnostically more powerful than information gleaned from strictly conventional ECGs; and that 2) can be applied forward to “new” patients to allow for improved diagnosis plus continuous iteration and improvement
  14. 14. (Or Wireless mHealth 12-Lead ECG) 12-lead (8-channel) ECG data file or data stream Large underlying database of patients with A-ECG plus known “gold standard” clinical imaging results
  15. 15. Example Technique #1: Beat-to-beat QT interval variability (QTV) A measurement of “temporal repolarization heterogeneity” e.g., In multiple studies performed by multiple different investigator teams, increased QTV compared to underlying RRV (i.e., increased “temporal repolarization heterogeneity” or “lability” at rest) strongly predicts the presence of cardiac disease and the propensity for life-threatening cardiac events. We employ several unique QTV parameters in A-ECG From Starc and Schlegel, J. Electrocardiol 39: 358-367, 2006.
  16. 16. Example Technique #2: T-wave and QRS-wave complexity via “PCA” or “SVD” “Complexity” by Principal Component Analysis (“PCA”) and/or “Singular Value Decomposition” (“SVD”) PC1, PC2 and PC3 are the first, second and third “principal components” (aka “eigenvectors”) respectively, derived from “PCA” of the 8 independent ECG channels that constitute the standard 12-lead ECG. (Thus one actually obtains PC1-PC8). The vast majority of the “energy” will be in PC1 With “disease” compared to “health”, relatively more energy “shifts downstream” PC1PC2…PC8 e.g., “PCA ratio” = PC2/PC1 x 100% (normal value is <25-30%, gender specific) We employ several unique complexity-related parameters in A-ECG
  17. 17. Example Technique #3: “3D ECG”: e.g., Spatial QRS-T angle by 12-to-Frank lead transform Normal: Spatial QRS-T angle = 61° Abnormal: Spatial QRS-T angle = 111° T loop T loop QRS loop QRS loop Figures from Cortez and Schlegel, J. Electrocardiol. 2010 or Sakowski et al, Aviat. Space Environ. Med. 2011. Borderline: Spatial QRS-T angle = 88° “Do you know what your spatial QRS-T angle is?” is clinically a much more important question than: “Do you know what your cholesterol level is?” T loop QRS loop Increased spatial QRS-T angle has more prognostic value for fatal endpoints than any classic CV risk factor, including diabetes (Kardys et al, Spatial QRS-T angle predicts cardiac death in a general population Eur. Heart J. 2003;24:1357-1364). It’s also the single best known predictor of lifesaving appropriate ICD discharges in patients with ICDs (Borleffs et al, Circ. EP 2009; 2:548-554, 2009).
  18. 18. Rather than potentially bore an “informatics” audience with numerous charts and tables outlining the details of our statistical procedures & results (for which our published literature – appended to the presentation – can be referenced), here are some: Case studies that demonstrate the “informatics utility” of A-ECG compared to strictly conventional ECG All four case studies will have normal or “non-specifically abnormal” conventional 12-lead ECGs - Which of the four cases have heart disease, and which do not? - And of those with heart disease, is LVSD (LVEF <50%) also present?
  19. 19. CASE 1: Disease or no Disease? (34-year old female)
  20. 20. CASE 2: Disease or no Disease? (69 year-old male)
  21. 21. CASE 3: Disease or no Disease? (62 year-old male) ?
  22. 22. CASE 4: Disease or no Disease? (57 year-old male) Poor R-wave progression? (Clinically non-specific) Some “flattening” (maybe?) in the lateral T-waves?
  23. 23. A-ECG results from CASE 1 (the 34 year-old female): Lead II (SVD) RR (ms) ΔQT (ms) PCA normal normal All normal References for QTV technique: - Starc V. and Schlegel TT. J Electrocardiol 2006;39:358-367. - Berger, R. et al. Circulation. 1997;96:1557-1565. A-ECG results from CASE 2 (the 69 year-old male): Lead II (SVD) RR (ms) ΔQT (ms) PCA also normal borderline (but still “normal”) All normal
  24. 24. A-ECG results from CASE 3 (the 62 year-old male) (Pd=132 ms) Lead II PCA (SVD) ΔQT (ms) RR (ms) borderline abnormal All normal A-ECG results from CASE 4 (57 year-old male, poor R-wave progression) Lead II (SVD) RR (ms) ΔQT (ms) PCA All abnormal
  25. 25. First the results from some simple but validated A-ECG “logistic scores”: (These are scores derived from results in the underlying database and take the general form of “aX + bY – cZ … +/- some constant”. They provide simple, binary “yes vs. no” diagnostic calls for new patients. For statistical & other details, see Schlegel, T.T., et al. BMC Cardiovascular Disorders 2010, 10:28 here: http://www.biomedcentral.com/1471-2261/10/28 ) Case# Score 1: Probability of “Disease” 1. (34 F) 2. (69 M) 3. (62 M) 4. (57 M) 0.00068615 0.00121214 0.97871634 0.9996925 Score 1 call Score 2: Probability of LVSD “LVSD vs. no LVSD” Call Healthy Healthy Disease Disease ----0.15690945 0.78327565 No LVSD No LVSD No LVSD LVSD Thus according to our A-ECG logistic score results above: Cases #1 and #2 are not diseased; Case #3 is diseased, but does not have LVSD; and Case #4 is most (severely) diseased, and also has LVSD Next, let’s see what a follow-on Discriminant Analysis (DA) shows:
  26. 26. DA for common outpatient cardiac diseases Case: 1. 2. 3. 4. DA Call: Healthy Healthy CAD NICM Probabilities: 0.99959715 0.87930201 CAD 0.12 0.98210161 0.99999984 CAD = coronary artery disease LVH = left ventricular hypertrophy HCM = hypertrophic cardiomyopathy NICM = non-ischemic cardiomyopathy ICM = ischemic cardiomyopathy -- Thus the discriminant analysis results are very consistent with the A-ECG logistic score results. -- And most importantly, both the A-ECG logistic score and discriminant results were also very consistent with the gold-standard imaging results, which showed: Cases 1 and 2: Normal imaging with no demonstrable disease; Case 3: 2-vessel CAD, but with normal LVEF = 60%; Case 4: Severe non-ischemic cardiomyopathy with LVEF = 25%
  27. 27. Same DA as the immediately prior slide, but now in “3D”: Case 1Healthy; Case 2Slightly less Healthy, likely “Healthy aging”; Case 3CAD; Case 4NICM Canonical Plot 3D Same style of DA as above, but when one allows visualization of some of the data points that constitute the underlying background “spheres” (“circles” in 2D), which themselves represent areas of ~95% certainty for the presence of the given condition.
  28. 28. DA for A-ECG “age” score 32.3 years 62.3 years 34 69 -2 (A-ECG age younger than chron. age) -7 (A-ECG age younger than chron. age) DA’s calls re: “ECG age”: Case # 1. (34 F) 2. (69 M) Predicted decade 30s 60s Probabilty 0.75 0.50 Remainder predicted decade(s) & probability 20s 0.19 50s 0.14 70s 0.35
  29. 29. Bayesian A-ECG age scores A statistically more robust way of estimating “A-ECG age” (many pages worth of this kind of math in the background) So one needs bright statisticians to do this…. So for our two “healthy” cases: Case Case 1 Case 2 Bayesian predicted A-ECG age 32.3 years 62.3 years True chronological age 34 years 69 years Delta -2 years -7 years Conclusion A-ECG age younger than chronological age A-ECG age younger than chronological age
  30. 30. A-ECG Telemedicine in the 3rd World Fiji: Oct 20-23rd 2013 (Snapshot A-ECG screening via WiFi ECG + Android tablet/phone) Conventional and A-ECG (Data Collection) (Secure Server/ Clinical HQ in NZ) A-ECG plus additional conventional ECG results Raw conventional ECG data (Analysis) Conventional/advanced echo (validation)
  31. 31. Thanks to NASA, Dr. Walter Kulecz (programming), and all students, visiting faculty, clinical collaborators and patients who continue to share the vision of A-ECG. Special thanks to Dr. Patrick Gladding for NZ-related collaboration and to HINZ. toddschlegel@rocketmail.com
  32. 32. References for beat-to-beat QT interval variability: Atiga WL, Calkins H, Lawrence JH, Tomaselli GF, Smith JM, Berger RD. Beat-to-beat repolarization lability identifies patients at risk for sudden cardiac death. J Cardiovasc Electrophysiol. 1998;9:899-908. Berger RD, Kasper EK, Baughman KL, Marban E, Calkins H, Tomaselli GF. Beat-to-beat QT interval variability: novel evidence for repolarization lability in ischemic and nonischemic dilated cardiomyopathy. Circulation. 1997;96:1557-1565. Murabayashi T, Fetics B, Kass D, Nevo E, Gramatikov B, Berger RD. Beat-to-beat QT interval variability associated with acute myocardial ischemia. J Electrocardiol. 2002;35:19-25. Haigney MC, Zareba W, Gentlesk PJ, et al. QT interval variability and spontaneous ventricular tachycardia or fibrillation in the Multicenter Automatic Defibrillator Implantation Trial (MADIT) II patients. J Am Coll Cardiol. 2004;44:1481-1487. Starc V, Schlegel TT. Real-time multichannel system for beat-to-beat QT interval variability. J Electrocardiol. 2006;39:358-367. Piccirillo G, Magri D, Matera S, et al. QT variability strongly predicts sudden cardiac death in asymptomatic subjects with mild or moderate left ventricular systolic dysfunction: a prospective study. Eur Heart J. 2007;28:1344-1350. Vrtovec B, Okrajsek R, Golicnik A, et al. Atorvastatin therapy may reduce the incidence of sudden cardiac death in patients with advanced chronic heart failure. J Card Fail. 2008;14:140-144. Baumert et al. Conventional QT Variability Measurement vs. Template Matching Techniques: Comparison of Performance Using Simulated and Real ECG. PLoS One. 2012; 7:e41920. Freely available online here: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0041920
  33. 33. References for T-wave Complexity: Priori SG, Mortara DW, Napolitano C, et al. Evaluation of the spatial aspects of T-wave complexity in the long-QT syndrome. Circulation. 1997;96:3006-3012. Malik M, Acar B, Gang Y, Yap YG, Hnatkova K, Camm AJ. QT dispersion does not represent electrocardiographic interlead heterogeneity of ventricular repolarization. J Cardiovasc Electrophysiol. 2000;11:835-843. Zabel M, Acar B, Klingenheben T, Franz MR, Hohnloser SH, Malik M. Analysis of 12-lead T-wave morphology for risk stratification after myocardial infarction. Circulation. 2000;102:1252-1257. Zabel M, Malik M, Hnatkova K, et al. Analysis of T-wave morphology from the 12-lead electrocardiogram for prediction of long-term prognosis in male US veterans. Circulation. 2002;105:1066-1070. Okin PM, Devereux RB, Fabsitz RR, Lee ET, Galloway JM, Howard BV. Principal component analysis of the T wave and prediction of cardiovascular mortality in American Indians: the Strong Heart Study. Circulation. 2002;105:714-719. Okin PM, Malik M, Hnatkova K, et al. Repolarization abnormality for prediction of all-cause and cardiovascular mortality in American Indians: the Strong Heart Study. J Cardiovasc Electrophysiol. 2005;16:1-7. Rautaharju PM, Kooperberg C, Larson JC, LaCroix A. Electrocardiographic abnormalities that predict coronary heart disease events and mortality in postmenopausal women: the Women's Health Initiative. Circulation. 2006;113:473-480. Batdorf BH, Feiveson AH, Schlegel TT. The effect of signal averaging on the reproducibility and reliability of measures of T-wave morphology. J Electrocardiol. 2006;39:266-270.
  34. 34. References for 3D ECG (especially spatial mean QRS-T Angle) Kardys I, Kors JA, van der Meer IM, Hofman A, van der Kuip DA, Witteman JC. Spatial QRS-T angle predicts cardiac death in a general population. Eur Heart J. 2003;24:1357-1364. de Torbal A, Kors JA, van Herpen G, et al. The electrical T-axis and the spatial QRS-T angle are independent predictors of long-term mortality in patients admitted with acute ischemic chest pain. Cardiology. 2004;101:199-207. Yamazaki T, Froelicher VF, Myers J, Chun S, Wang P. Spatial QRS-T angle predicts cardiac death in a clinical population. Heart Rhythm. 2005;2:73-78. Fayn J, Rubel P, Pahlm O, Wagner GS. Improvement of the detection of myocardial ischemia thanks to information technologies. Int J Cardiol. 2006. Rautaharju PM, Kooperberg C, Larson JC, LaCroix A. Electrocardiographic abnormalities that predict coronary heart disease events and mortality in postmenopausal women: the Women's Health Initiative. Circulation. 2006;113:473-480. Restier-Miron L, Fayn J, Millat G, et al. Spatiotemporal electrocardiographic characterization of ventricular depolarization and repolarization abnormalities in long QT syndrome. J Electrocardiol. 2007. Borleffs et al. Predicting Ventricular Arrhythmias in Patients With Ischemic Heart Disease: Clinical Application of the ECG-Derived QRS-T Angle. Circ Arrhythmia Electrophysiol. 2009;2:548-554. Cortez, D.L., and Schlegel, T.T. When deriving the spatial QRS-T angle from the conventional 12lead ECG, which transform is more Frank: regression or inverse Dower? J. Electrocardiol. 2010; 43: 302–309.
  35. 35. Recent references for A-ECG that are freely available online: Schlegel, T.T. , et al. Accuracy of advanced versus strictly conventional 12-lead ECG for detection and screening of coronary artery disease, left ventricular hypertrophy and left ventricular systolic dysfunction. BMC Cardiovascular Disorders 2010, 10:28; doi:10.1186/1471-2261-10-28. Freely available online here: http://www.biomedcentral.com/1471-2261/10/28 Or here: http://www.medscape.com/viewarticle/725244 Starc, V. et al. Can functional cardiac age be predicted from the ECG in a normal healthy population? Computing in Cardiology 2012; 39:101-104. Freely available online here: http://cinc.org/archives/2012/pdf/0101.pdf Gladding et al. Open access integrated therapeutic and diagnostic platforms for personalized cardiovascular medicine. J. Pers. Med. 2013, 3(3), 203-237; doi:10.3390/jpm3030203 Freely available online here: http://www.mdpi.com/2075-4426/3/3/203 (A-ECG sections are on pages 209-211, 220-221 and 223-226)
  36. 36. Additional and/or back-up slides

×