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International application of a new probability algorithm for the diagnosis of coronary artery disease. (in any major vessel: attributes 59 through 68 are vessels)ħ6 name: last name of patient (I replaced this with the dummy string "name")ĭetrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., & Froelicher, V. Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteriaĢ3 dig (digitalis used furing exercise ECG: 1 = yes 0 = no)Ģ4 prop (Beta blocker used during exercise ECG: 1 = yes 0 = no)Ģ5 nitr (nitrates used during exercise ECG: 1 = yes 0 = no)Ģ6 pro (calcium channel blocker used during exercise ECG: 1 = yes 0 = no)Ģ7 diuretic (diuretic used used during exercise ECG: 1 = yes 0 = no)ħ = bike 150 kpa min/min (Not sure if "kpa min/min" is what was written!)Ģ9 thaldur: duration of exercise test in minutesģ0 thaltime: time when ST measure depression was notedģ4 tpeakbps: peak exercise blood pressure (first of 2 parts)ģ5 tpeakbpd: peak exercise blood pressure (second of 2 parts)ģ8 exang: exercise induced angina (1 = yes 0 = no)Ĥ0 oldpeak = ST depression induced by exercise relative to restĤ1 slope: the slope of the peak exercise ST segmentĤ4 ca: number of major vessels (0-3) colored by flourosopyĤ7 restef: rest raidonuclid (sp?) ejection fractionĤ8 restwm: rest wall (sp?) motion abnormalityĤ9 exeref: exercise radinalid (sp?) ejection fractionĥ1 thal: 3 = normal 6 = fixed defect 7 = reversable defectĥ5 cmo: month of cardiac cath (sp?) (perhaps "call")ĥ8 num: diagnosis of heart disease (angiographic disease status) Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) To see Test Costs (donated by Peter Turney), please see the folder "Costs"Ģ ccf: social security number (I replaced this with a dummy value of 0)ĥ painloc: chest pain location (1 = substernal 0 = otherwise)Ħ painexer (1 = provoked by exertion 0 = otherwise)ħ relrest (1 = relieved after rest 0 = otherwise)ġ0 trestbps: resting blood pressure (in mm Hg on admission to the hospital)ġ3 smoke: I believe this is 1 = yes 0 = no (is or is not a smoker)ġ6 fbs: (fasting blood sugar > 120 mg/dl) (1 = true 0 = false)ġ7 dm (1 = history of diabetes 0 = no such history)ġ8 famhist: family history of coronary artery disease (1 = yes 0 = no)ġ9 restecg: resting electrocardiographic results All four unprocessed files also exist in this directory. One file has been "processed", that one containing the Cleveland database. The names and social security numbers of the patients were recently removed from the database, replaced with dummy values. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0). It is integer valued from 0 (no presence) to 4. The "goal" field refers to the presence of heart disease in the patient. In particular, the Cleveland database is the only one that has been used by ML researchers to This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.ĭavid W. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.Ĥ. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.ģ. Click here to try out the new site.ĭownload: Data Folder, Data Set DescriptionĪbstract: 4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beachġ. 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There are several math errors in astropixel's post (multiplication and failure to take square root): More importantly it makes dark frame subtraction rather difficult if not impossible. Mike's tool mentioned above certainly didn't produce expected results for me due to these issues. So you need to be wary of using tools and techniques designed for unadulterated CDD data on very much adulterated DSLR data. This was discovered by Craig Stark and documented a few years ago, and I ran my own tests on my 500D, written up here: If you plot brightness against time or temperature it goes down, which is the opposite of what should happen. If you take dark frames and plot noise against increasing exposure time, or against fixed exposure time and increasing temperature (same difference), the noise increases as you'd expect. The accumulated dark current is measured from the optical black area and subtracted somehow (specific details of how are not available outside Canon as far as I know). You need to be aware that most Canon DSLRs (certainly of the 450D era) suppress dark current by pre-processing on camera before writing out the RAW file. Total noise ? dark frame -5?C 1800sec : -5?C 1800sec dark: 12.95*0.664 = 8.599 e-Įstimate the dark noise from the total noise of the -5?C bias and dark framesĮstimate dark noise standard deviation : -5?Cdarknoise = Sqrt(8.599^2 - 7.284^2) = 4.57 e-Įstimate dark signal due to dark current : -5?Cdarksignal = -5?Cdarknoise^2 = 20.886 e-Įstimate dark current : -5?Cdarkcurrent = -5?Cdarksignal / 1800 = 0.116 e-/sec Run the same exercise for -5?C bias and dark frames Total noise ? dark frame 20?C 1800sec - 48.07*0.664 = 31.918 e-Įstimate the dark noise from the total noise of the 20?C bias and dark framesĮstimate dark noise standard deviation : 20?CDarkNoise = Sqrt(31.918^2 ? 7.385^2) = 964.251 e-Įstimate dark signal due to dark current : 20?CDarkSignal = 20?CDarkNoise^2 = 929780.262 e-Įstimate dark current - 20?CDarkCurrent : 20?CDarkSignal/1800 = 516.545 e-/sec Over or under exposure will make estimates unreliable.Ĭalculate dark noise, dark signal and dark current Be sure your flats are well exposed, say to about 50% full well. PS: Based on other data I have seen, I think your gain estimate for ISO400 is reasonable, to within roughly +/- 10%. IMO this estimate is also suspect due to the relatively short dark exposure. I was not clear in my dark noise formula, do this instead:Ģ0CDarkNoise = Sqrt(6.84^2 - 6.72^2) = 1.27 e- (estimate of standard deviation of noise due to dark current)Ģ0CDarkSignal = 20CDarkNoise^2 = 1.63 e- (estimate of signal due to dark current)Ģ0CDarkCurrent = 20CDarkSignal / 180 = 0.009 e-/sec (dark current signal divided by time) But even with longer exposures, if offset drifts enough, this offset formula is not going to work at all. For low dark current detectors, it takes a while for a dark electron or two or more to appear in a pixel, so to be measured reliably. Dark exposures should be longer for reliable estimation, maybe 10 to 20 times longer. But offset does vary frame to frame due to detector electronics drift, and probably in this case the drift overwhelmed the increase due to dark current. This is typically the case as dark current increases offset. The offset forumla requires dark offset to be larger than bias offset. So detector 14-bit DN's are identical to PI's 16-bit DN's. This follows naturally from how PI reads and normalizes 14-bit data into the lower quarter of the normalized 0 to 1 range. Bit size is accounted for correctly by default in PI. Then we will follow up your training with our job placement assistance program that is available to all of our graduates. We provide the bartending education, the speed, the organizational skills as well as customer service and alcohol awareness at our bartending school. The Bartending School of Little Rock, Arkansas has created bartending programs to answer the demand for professionally trained bartenders. 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