├── REPORT.pdf
├── Results
├── 1
│ ├── graph.png
│ └── output.png
├── 2
│ ├── graph.png
│ └── output.png
└── 3
│ ├── graph.png
│ └── output.png
├── User Manual.txt
├── LICENSE
├── README.md
├── .gitignore
├── SOURCE CODE.py
└── Dataset
├── data 3.csv
├── data 2.csv
└── data 1.csv
/REPORT.pdf:
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https://raw.githubusercontent.com/DebalekhaChakraborty/Myocardial-Ischemia-Detection-by-Analysing-ECG-Signal/HEAD/REPORT.pdf
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/Results/1/graph.png:
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https://raw.githubusercontent.com/DebalekhaChakraborty/Myocardial-Ischemia-Detection-by-Analysing-ECG-Signal/HEAD/Results/1/graph.png
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/Results/2/graph.png:
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https://raw.githubusercontent.com/DebalekhaChakraborty/Myocardial-Ischemia-Detection-by-Analysing-ECG-Signal/HEAD/Results/2/graph.png
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/Results/3/graph.png:
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https://raw.githubusercontent.com/DebalekhaChakraborty/Myocardial-Ischemia-Detection-by-Analysing-ECG-Signal/HEAD/Results/3/graph.png
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/Results/1/output.png:
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https://raw.githubusercontent.com/DebalekhaChakraborty/Myocardial-Ischemia-Detection-by-Analysing-ECG-Signal/HEAD/Results/1/output.png
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/Results/2/output.png:
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https://raw.githubusercontent.com/DebalekhaChakraborty/Myocardial-Ischemia-Detection-by-Analysing-ECG-Signal/HEAD/Results/2/output.png
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/Results/3/output.png:
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https://raw.githubusercontent.com/DebalekhaChakraborty/Myocardial-Ischemia-Detection-by-Analysing-ECG-Signal/HEAD/Results/3/output.png
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/User Manual.txt:
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1 |
2 | Guide To Run the Source Code :
3 |
4 | Step 1: Install Matplotlib, Pandas, Numpy Libraries.
5 | Step 2: Update the address of the ECG Dataset in line 5 of the source code.
6 | Step 3: Run the source code on Python 3.7 idle.
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2020 Debalekha Chakraborty
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
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/README.md:
--------------------------------------------------------------------------------
1 | # Myocardial-Ischemia-Detection-by-Analysing-ECG-Signal
2 | Processing ECG Signal, QRS and ST Segment Detection, BPM Calculation, ST Slope Measurement and Myocardial Ischemia Detection.
3 |
4 | I have done this project on my 4th year of B.Tech and submitted as Final Project. In this project I collected various ecg signals from online opensources and used them to study the behaviour of the signal in the presence of abnormal heart rate and myocardial ischemia.
5 |
6 | SYSTEM ANALYSIS & DESIGN:
7 |
8 | The QRS complex (or R peak) is detected from the ECG signal. Based on that, BPM (Beats Per Minute) is calculated. Then ST segmentation is computed and slope of the ST segment is measured. A threshold is set and ischemic episode is detected according to that threshold.
9 |
10 | • Database: ECG signals are used from the Physiobank database (Goldberger et al., 2000). The European ST-T database in Physiobank contains ECG signals with ST segment and T wave changes (Taddei, et al., 1992). The database contains ECG signals from ischemic patients where normal ECG sections and sudden ischemic episodes are annotated.
11 |
12 | • Proposed Algorithm Process Flow:
13 |
14 | 
15 |
16 |
17 | • Result:
18 |
19 | 
20 | 
21 |
22 |
23 |
24 | For Details, refer to the "REPORT" file of the Repository.
25 |
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/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | pip-wheel-metadata/
24 | share/python-wheels/
25 | *.egg-info/
26 | .installed.cfg
27 | *.egg
28 | MANIFEST
29 |
30 | # PyInstaller
31 | # Usually these files are written by a python script from a template
32 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
33 | *.manifest
34 | *.spec
35 |
36 | # Installer logs
37 | pip-log.txt
38 | pip-delete-this-directory.txt
39 |
40 | # Unit test / coverage reports
41 | htmlcov/
42 | .tox/
43 | .nox/
44 | .coverage
45 | .coverage.*
46 | .cache
47 | nosetests.xml
48 | coverage.xml
49 | *.cover
50 | *.py,cover
51 | .hypothesis/
52 | .pytest_cache/
53 |
54 | # Translations
55 | *.mo
56 | *.pot
57 |
58 | # Django stuff:
59 | *.log
60 | local_settings.py
61 | db.sqlite3
62 | db.sqlite3-journal
63 |
64 | # Flask stuff:
65 | instance/
66 | .webassets-cache
67 |
68 | # Scrapy stuff:
69 | .scrapy
70 |
71 | # Sphinx documentation
72 | docs/_build/
73 |
74 | # PyBuilder
75 | target/
76 |
77 | # Jupyter Notebook
78 | .ipynb_checkpoints
79 |
80 | # IPython
81 | profile_default/
82 | ipython_config.py
83 |
84 | # pyenv
85 | .python-version
86 |
87 | # pipenv
88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
91 | # install all needed dependencies.
92 | #Pipfile.lock
93 |
94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
95 | __pypackages__/
96 |
97 | # Celery stuff
98 | celerybeat-schedule
99 | celerybeat.pid
100 |
101 | # SageMath parsed files
102 | *.sage.py
103 |
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
109 | ENV/
110 | env.bak/
111 | venv.bak/
112 |
113 | # Spyder project settings
114 | .spyderproject
115 | .spyproject
116 |
117 | # Rope project settings
118 | .ropeproject
119 |
120 | # mkdocs documentation
121 | /site
122 |
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 |
128 | # Pyre type checker
129 | .pyre/
130 |
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/SOURCE CODE.py:
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1 | import pandas as pd
2 | import matplotlib.pyplot as plt
3 | import numpy as np
4 | import math
5 | dataset = pd.read_csv(r"C:\Users\MAHABHARAT\Desktop\ECG\FINAL PROJECT 8 SEM\data.csv")#read data
6 | #Calculate moving average with 0.75s in both directions, then append do dataset
7 | hrw = 0.200 #One-sided window size, as proportion of the sampling frequency
8 | fs = 250 #The example dataset was recorded at 250Hz
9 | mov_avg = dataset['hart'].rolling(int(hrw*fs)).mean() #Calculate moving average #for older panda version mov_avg = pd.rolling_mean(dataset.hart, window=int(hrw*fs))
10 | #Impute where moving average function returns NaN, which is the beginning of the signal where x hrw
11 | avg_hr = (np.mean(dataset.hart))
12 | mov_avg = [avg_hr if math.isnan(x) else x for x in mov_avg]
13 | mov_avg = [x*1.2 for x in mov_avg] #For now we raise the average by 20% to prevent the secondary heart contraction from interfering
14 | dataset['hart_rollingmean'] = mov_avg #Append the moving average to the dataframe
15 |
16 | #Mark regions of interest
17 | window = []
18 | peaklist = []
19 | listpos = 0 #We use a counter to move over the different data columns
20 | for datapoint in dataset.hart:
21 | rollingmean = dataset.hart_rollingmean[listpos] #Get local mean
22 | if (datapoint < rollingmean) and (len(window) < 1): #If no detectable R-complex activity -> do nothing
23 | listpos += 1
24 | elif (datapoint > rollingmean): #If signal comes above local mean, mark ROI
25 | window.append(datapoint)
26 | listpos += 1
27 | else: #If signal drops below local mean -> determine highest point
28 | maximum = max(window)
29 | beatposition = listpos - len(window) + (window.index(max(window))) #Notate the position of the point on the X-axis
30 | peaklist.append(beatposition) #Add detected peak to list
31 | window = [] #Clear marked ROI
32 | listpos += 1
33 | ybeat = [dataset.hart[x] for x in peaklist] #Get the y-value of all peaks for plotting purposes
34 | print (peaklist)
35 |
36 | RR_list = []
37 | cnt = 0
38 | while (cnt < (len(peaklist)-1)):
39 | RR_interval = (peaklist[cnt+1] - peaklist[cnt]) #Calculate distance between beats in # of samples
40 | ms_dist = ((RR_interval / fs) * 1000.0) #Convert sample distances to ms distances
41 | RR_list.append(ms_dist) #Append to list
42 | cnt += 1
43 | bpm = 60000 / np.mean(RR_list) #60000 ms (1 minute) / average R-R interval of signal
44 | print ("Average Heart Beat is: %.01f" %bpm) #Round off to 1 decimal and print
45 | if (bpm>100):
46 | print (" Heart Condition : TACHYCARDIA ")
47 | elif(bpm<60):
48 | print(" Heart Condition : BRADYCARDIA ")
49 | else:
50 | print (" Heart Condition : NORMAL ")
51 |
52 |
53 |
54 | CycleTime = 60000/bpm # Time duration of a single cycle (60*1000)/bpm in ms
55 | STStartTime = CycleTime*0.055 # CycleTime*(110/1000)/2 = 0.055
56 | STStartTimeSample = round (STStartTime*0.250) # converting ms to sample value
57 | STDuration = CycleTime*0.120 # Duration of ST
58 | STDurationSample = round (STDuration*0.250) # converting ms to sample value
59 |
60 | S_Point=[]
61 | count=0
62 | while (count <= (len(peaklist)-1)):
63 | S = (peaklist[count] + STStartTimeSample)
64 | S_Point.append(S)
65 | count +=1
66 | print (S_Point)
67 | y1beat = [dataset.hart[x1] for x1 in S_Point] #Get the y-value of S points for plotting purposes
68 |
69 |
70 | T_Point=[]
71 | count=0
72 | while (count <= (len(S_Point)-1)):
73 | T = (S_Point[count] + STDurationSample)
74 | T_Point.append(T)
75 | count +=1
76 | print (T_Point)
77 | y2beat = [dataset.hart[x2] for x2 in T_Point] #Get the y-value of T points for plotting purposes
78 |
79 |
80 | ST = []
81 | ST_interval = np.subtract(y2beat,y1beat)
82 | ST.append(ST_interval)
83 | print(ST)
84 |
85 | slope=[]
86 | slope_is =np.divide(ST,STDurationSample)
87 | slope.append(slope_is)
88 | print(slope)
89 |
90 | avg_slope =np.mean(slope)
91 | print ("Average Slope is: %.01f" %avg_slope)
92 |
93 | if (avg_slope>0.35):
94 | print (" Heart Condition : MYOCARDIAL ISCHEMIA ")
95 | else:
96 | print (" Heart Condition : NORMAL ")
97 |
98 |
99 |
100 | plt.title("Detected R peaks in signal")
101 | plt.xlim(0,1000)
102 | plt.xlabel('Time')
103 | plt.ylabel('Amplitude of Signal')
104 | plt.plot(dataset.hart, alpha=0.5, color='black', label="raw signal") #Plot semi-transparent HR
105 | #plt.plot(mov_avg, color ='red', label="moving average") #Plot moving average
106 | plt.scatter(peaklist, ybeat, color='purple', label="average: %.1f BPM" %bpm) #Plot detected peaks
107 | plt.scatter(S_Point, y1beat, color='red',label="ST Segment Range")
108 | plt.scatter(T_Point, y2beat, color='red')
109 | plt.legend(loc=4, framealpha=0.6)
110 | plt.show()
111 |
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/Dataset/data 3.csv:
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1 | hart
2 | 530
3 | 518
4 | 506
5 | 494
6 | 483
7 | 472
8 | 462
9 | 454
10 | 446
11 | 440
12 | 434
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68 | 559
69 | 593
70 | 630
71 | 667
72 | 703
73 | 735
74 | 759
75 | 774
76 | 782
77 | 781
78 | 771
79 | 753
80 | 728
81 | 597
82 | 562
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84 | 497
85 | 411
86 | 399
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481 | 771
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/Dataset/data 1.csv:
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219 |
220 | 501
221 | 502
222 |
223 | 505
224 | 508
225 |
226 | 510
227 | 512
228 | 515
229 | 520
230 | 535
231 |
232 | 540
233 |
234 | 546
235 |
236 | 553
237 |
238 | 558
239 | 558
240 | 560
241 | 564
242 | 565
243 | 565
244 | 561
245 | 556
246 | 548
247 | 540
248 | 531
249 | 519
250 | 508
251 | 497
252 | 486
253 | 475
254 | 465
255 | 457
256 | 450
257 | 444
258 | 438
259 | 435
260 | 432
261 | 433
262 | 435
263 | 437
264 | 441
265 | 444
266 | 450
267 | 454
268 | 459
269 | 465
270 | 470
271 | 475
272 | 481
273 | 487
274 | 490
275 | 494
276 | 497
277 | 496
278 | 500
279 | 503
280 | 505
281 | 507
282 | 509
283 | 504
284 | 503
285 | 502
286 | 501
287 | 499
288 | 498
289 | 496
290 | 495
291 | 492
292 | 492
293 | 490
294 | 490
295 | 493
296 | 495
297 | 497
298 | 495
299 | 494
300 | 496
301 | 496
302 | 495
303 | 496
304 | 496
305 | 495
306 | 495
307 | 496
308 | 497
309 | 497
310 | 500
311 | 510
312 | 530
313 | 559
314 | 593
315 | 630
316 | 670
317 | 705
318 | 740
319 | 763
320 | 777
321 | 790
322 |
323 |
324 | 760
325 |
326 |
327 | 562
328 |
329 |
330 | 411
331 |
332 | 389
333 |
334 | 433
335 |
336 |
337 |
338 | 463
339 |
340 |
341 | 471
342 |
343 | 480
344 |
345 |
346 |
347 | 490
348 |
349 | 495
350 | 493
351 |
352 | 495
353 | 498
354 | 501
355 |
356 | 504
357 | 505
358 |
359 | 508
360 | 510
361 |
362 | 515
363 | 520
364 | 535
365 |
366 | 540
367 | 544
368 | 546
369 | 550
370 | 553
371 | 556
372 | 558
373 | 558
374 | 560
375 | 564
376 | 570
377 | 570
378 | 565
379 | 560
380 | 553
381 | 540
382 | 531
383 | 519
384 | 508
385 | 497
386 | 486
387 | 475
388 | 465
389 | 457
390 | 450
391 | 444
392 | 438
393 | 435
394 | 432
395 | 431
396 | 432
397 | 434
398 | 439
399 | 444
400 | 450
401 | 454
402 | 459
403 | 465
404 | 470
405 | 475
406 | 481
407 | 487
408 | 490
409 | 494
410 | 497
411 | 496
412 | 500
413 | 501
414 | 502
415 | 504
416 | 505
417 | 504
418 | 503
419 | 502
420 | 501
421 | 499
422 | 498
423 | 496
424 | 495
425 | 492
426 | 492
427 | 490
428 | 490
429 | 490
430 | 491
431 | 492
432 | 493
433 | 493
434 | 494
435 | 494
436 | 495
437 | 496
438 | 496
439 | 495
440 | 495
441 | 496
442 | 497
443 | 497
444 | 500
445 | 510
446 | 530
447 | 559
448 | 593
449 | 630
450 | 667
451 | 703
452 | 735
453 | 759
454 | 774
455 | 782
456 |
457 |
458 | 753
459 |
460 |
461 | 562
462 |
463 |
464 | 411
465 |
466 | 389
467 |
468 |
469 | 450
470 |
471 |
472 | 463
473 |
474 | 469
475 | 471
476 |
477 |
478 | 478
479 | 480
480 |
481 | 485
482 | 488
483 |
484 | 492
485 |
486 | 495
487 | 498
488 | 501
489 |
490 | 504
491 | 505
492 |
493 | 508
494 | 510
495 |
496 | 515
497 | 520
498 |
499 | 536
500 | 540
501 |
502 | 546
503 | 550
504 |
505 | 556
506 | 558
507 | 558
508 | 560
509 | 564
510 | 565
511 | 565
512 | 561
513 | 556
514 | 548
515 | 540
516 | 531
517 | 519
518 | 508
519 | 497
520 | 486
521 | 475
522 | 465
523 | 457
524 | 450
525 | 444
526 | 438
527 | 435
528 | 432
529 | 433
530 | 435
531 | 437
532 | 441
533 | 444
534 | 450
535 | 454
536 | 459
537 | 465
538 | 470
539 | 475
540 | 481
541 | 487
542 | 490
543 | 494
544 | 497
545 | 496
546 | 500
547 | 503
548 | 505
549 | 510
550 | 511
551 | 507
552 | 505
553 | 499
554 | 497
555 | 498
556 | 496
557 | 495
558 | 492
559 | 492
560 | 490
561 | 490
562 | 493
563 | 495
564 | 497
565 | 495
566 | 494
567 | 496
568 | 496
569 | 495
570 | 496
571 | 496
572 | 495
573 | 495
574 | 496
575 | 497
576 | 497
577 | 500
578 | 503
579 | 515
580 | 535
581 | 559
582 | 593
583 | 630
584 | 670
585 | 705
586 | 740
587 | 763
588 | 777
589 |
590 |
591 | 773
592 |
593 | 731
594 |
595 | 562
596 |
597 | 497
598 |
599 |
600 |
601 | 385
602 |
603 |
604 | 458
605 |
606 |
607 | 467
608 |
609 | 471
610 |
611 | 475
612 |
613 | 480
614 | 483
615 |
616 | 488
617 | 491
618 |
619 | 493
620 |
621 | 498
622 | 501
623 |
624 | 504
625 |
626 | 508
627 |
628 | 510
629 | 512
630 |
631 | 520
632 |
633 | 536
634 |
635 | 544
636 |
637 | 550
638 |
639 | 556
640 | 558
641 |
642 | 560
643 | 564
644 | 565
645 | 565
646 | 561
647 | 556
648 | 548
649 | 540
650 | 531
651 | 519
652 | 508
653 | 497
654 | 486
655 | 475
656 | 465
657 | 457
658 | 450
659 | 444
660 | 438
661 | 435
662 | 432
663 | 431
664 | 432
665 | 434
666 | 439
667 | 444
668 | 450
669 | 454
670 | 459
671 | 465
672 | 470
673 | 475
674 | 481
675 | 487
676 | 490
677 | 494
678 | 497
679 | 496
680 | 500
681 | 501
682 | 502
683 | 504
684 | 505
685 | 504
686 | 503
687 | 502
688 | 501
689 | 499
690 | 498
691 | 496
692 | 495
693 | 492
694 | 492
695 | 490
696 | 490
697 | 490
698 | 491
699 | 492
700 | 493
701 | 493
702 | 494
703 | 494
704 | 495
705 | 496
706 | 496
707 | 495
708 | 495
709 | 496
710 | 497
711 | 497
712 | 500
713 | 510
714 | 530
715 | 559
716 | 593
717 | 630
718 | 667
719 | 703
720 | 735
721 | 759
722 | 774
723 | 782
724 |
725 |
726 |
727 | 728
728 |
729 | 562
730 |
731 | 497
732 | 411
733 |
734 |
735 | 385
736 |
737 |
738 |
739 | 459
740 |
741 | 467
742 |
743 |
744 | 474
745 | 475
746 |
747 | 480
748 |
749 | 485
750 |
751 | 491
752 |
753 |
754 | 495
755 | 498
756 | 501
757 |
758 | 504
759 |
760 |
761 | 508
762 | 510
763 |
764 | 515
765 |
766 | 535
767 | 536
768 | 540
769 | 544
770 | 546
771 | 550
772 | 553
773 | 556
774 | 558
775 | 558
776 | 560
777 | 564
778 | 565
779 | 565
780 | 561
781 | 556
782 | 548
783 | 540
784 | 531
785 | 519
786 | 508
787 | 497
788 | 486
789 | 475
790 | 465
791 | 457
792 | 450
793 | 444
794 | 438
795 | 435
796 | 432
797 | 434
798 | 430
799 | 428
800 | 431
801 | 431
802 | 432
803 | 434
804 | 439
805 | 444
806 | 450
807 | 454
808 | 459
809 | 465
810 | 470
811 | 475
812 | 481
813 | 487
814 | 490
815 | 494
816 | 497
817 | 496
818 | 500
819 | 501
820 | 502
821 | 504
822 | 505
823 | 504
824 | 503
825 | 502
826 | 501
827 | 499
828 | 498
829 | 496
830 | 495
831 | 492
832 | 492
833 | 490
834 | 490
835 | 490
836 | 491
837 | 492
838 | 493
839 | 493
840 | 494
841 | 494
842 | 495
843 | 496
844 | 496
845 | 495
846 | 495
847 | 496
848 | 497
849 | 497
850 | 500
851 | 510
852 | 530
853 | 559
854 | 593
855 | 630
856 | 667
857 | 703
858 | 735
859 | 759
860 | 774
861 | 782
862 | 728
863 | 597
864 | 497
865 | 433
866 | 459
867 | 463
868 | 467
869 | 471
870 | 474
871 | 474
872 | 475
873 |
874 | 480
875 | 483
876 |
877 | 488
878 |
879 | 492
880 |
881 | 495
882 |
883 | 501
884 | 502
885 |
886 | 505
887 | 508
888 |
889 | 512
890 | 515
891 | 520
892 | 535
893 | 536
894 | 540
895 | 544
896 | 546
897 | 550
898 | 553
899 | 556
900 | 558
901 | 558
902 | 560
903 | 564
904 | 565
905 | 565
906 | 561
907 | 556
908 | 548
909 | 540
910 | 531
911 | 519
912 | 508
913 | 497
914 | 486
915 | 475
916 | 465
917 | 457
918 | 450
919 | 444
920 | 438
921 | 435
922 | 432
923 | 431
924 | 431
925 | 432
926 | 434
927 | 439
928 | 444
929 | 450
930 | 454
931 | 459
932 | 465
933 | 470
934 | 475
935 | 481
936 | 487
937 | 490
938 | 494
939 | 497
940 | 496
941 | 500
942 | 501
943 | 502
944 | 504
945 | 505
946 | 504
947 | 503
948 | 502
949 | 501
950 | 499
951 | 498
952 | 496
953 | 495
954 | 492
955 | 492
956 | 490
957 | 490
958 | 490
959 | 491
960 | 492
961 | 493
962 | 493
963 | 494
964 | 494
965 | 495
966 | 496
967 | 496
968 | 495
969 | 495
970 | 496
971 | 497
972 | 497
973 | 500
974 | 510
975 | 530
976 | 559
977 | 593
978 | 630
979 | 667
980 | 703
981 | 735
982 | 759
983 | 774
984 | 782
985 | 781
986 | 771
987 | 562
988 | 411
989 | 433
990 | 459
991 | 467
992 | 469
993 | 474
994 | 475
995 |
996 | 480
997 | 483
998 |
999 | 488
1000 |
1001 | 492
1002 |
1003 | 495
1004 |
1005 | 501
1006 | 502
1007 |
1008 | 505
1009 | 508
1010 |
1011 | 510
1012 | 512
1013 | 515
1014 | 520
1015 | 535
1016 |
1017 | 540
1018 |
1019 | 546
1020 |
1021 | 553
1022 |
1023 | 558
1024 | 558
1025 | 560
1026 | 564
1027 | 565
1028 | 565
1029 | 561
1030 | 556
1031 | 548
1032 | 540
1033 | 531
1034 | 519
1035 | 508
1036 | 497
1037 | 486
1038 | 475
1039 | 465
1040 | 457
1041 | 450
1042 | 444
1043 | 438
1044 | 435
1045 | 432
1046 | 433
1047 | 435
1048 | 437
1049 | 441
1050 | 444
1051 | 450
1052 | 454
1053 | 459
1054 | 465
1055 | 470
1056 | 475
1057 | 481
1058 | 487
1059 | 490
1060 | 494
1061 | 497
1062 | 496
1063 | 500
1064 | 503
1065 | 505
1066 | 507
1067 | 509
1068 | 504
1069 | 503
1070 | 502
1071 | 501
1072 | 499
1073 | 498
1074 | 496
1075 | 495
1076 | 492
1077 | 492
1078 | 490
1079 | 490
1080 | 493
1081 | 495
1082 | 497
1083 | 495
1084 | 494
1085 | 496
1086 | 496
1087 | 495
1088 | 496
1089 | 496
1090 | 495
1091 | 495
1092 | 496
1093 | 497
1094 | 497
1095 | 500
1096 | 510
1097 | 530
1098 | 559
1099 | 593
1100 | 630
1101 | 670
1102 | 705
1103 | 740
1104 | 763
1105 | 777
1106 | 790
1107 |
1108 |
1109 | 760
1110 |
1111 |
1112 | 562
1113 |
1114 |
1115 | 411
1116 |
1117 | 389
1118 |
1119 | 433
1120 |
1121 |
1122 |
1123 | 463
1124 |
1125 |
1126 | 471
1127 |
1128 | 480
1129 |
1130 |
1131 |
1132 | 490
1133 |
1134 | 495
1135 | 493
1136 |
1137 | 495
1138 | 498
1139 | 501
1140 |
1141 | 504
1142 | 505
1143 |
1144 | 508
1145 | 510
1146 |
1147 | 515
1148 | 520
1149 | 535
1150 |
1151 | 540
1152 | 544
1153 | 546
1154 | 550
1155 | 553
1156 | 556
1157 | 558
1158 | 558
1159 | 560
1160 | 564
1161 | 570
1162 | 570
1163 | 565
1164 | 560
1165 | 553
1166 | 540
1167 | 531
1168 | 519
1169 | 508
1170 | 497
1171 | 486
1172 | 475
1173 | 465
1174 | 457
1175 | 450
1176 | 444
1177 | 438
1178 | 435
1179 | 432
1180 | 431
1181 | 432
1182 | 434
1183 | 439
1184 | 444
1185 | 450
1186 | 454
1187 | 459
1188 | 465
1189 | 470
1190 | 475
1191 | 481
1192 | 487
1193 | 490
1194 | 494
1195 | 497
1196 | 496
1197 | 500
1198 | 501
1199 | 502
1200 | 504
1201 | 505
1202 | 504
1203 | 503
1204 | 502
1205 | 501
1206 | 499
1207 | 498
1208 | 496
1209 | 495
1210 | 492
1211 | 492
1212 | 490
1213 | 490
1214 | 490
1215 | 491
1216 | 492
1217 | 493
1218 | 493
1219 | 494
1220 | 494
1221 | 495
1222 | 496
1223 | 496
1224 | 495
1225 | 495
1226 | 496
1227 | 497
1228 | 497
1229 | 500
1230 | 510
1231 | 530
1232 | 559
1233 | 593
1234 | 630
1235 | 667
1236 | 703
1237 | 735
1238 | 759
1239 | 774
1240 | 782
1241 |
1242 |
1243 | 753
1244 |
1245 |
1246 | 562
1247 |
1248 |
1249 | 411
1250 |
1251 | 389
1252 |
1253 |
1254 | 450
1255 |
1256 |
1257 | 463
1258 |
1259 | 469
1260 | 471
1261 |
1262 |
1263 | 478
1264 | 480
1265 |
1266 | 485
1267 | 488
1268 |
1269 | 492
1270 |
1271 | 495
1272 | 498
1273 | 501
1274 |
1275 | 504
1276 | 505
1277 |
1278 | 508
1279 | 510
1280 |
1281 | 515
1282 | 520
1283 |
1284 | 536
1285 | 540
1286 |
1287 | 546
1288 | 550
1289 |
1290 | 556
1291 | 558
1292 | 558
1293 | 560
1294 | 564
1295 | 565
1296 | 565
1297 | 561
1298 | 556
1299 | 548
1300 | 540
1301 | 531
1302 | 519
1303 | 508
1304 | 497
1305 | 486
1306 | 475
1307 | 465
1308 | 457
1309 | 450
1310 | 444
1311 | 438
1312 | 435
1313 | 432
1314 | 433
1315 | 435
1316 | 437
1317 | 441
1318 | 444
1319 | 450
1320 | 454
1321 | 459
1322 | 465
1323 | 470
1324 | 475
1325 | 481
1326 | 487
1327 | 490
1328 | 494
1329 | 497
1330 | 496
1331 | 500
1332 | 503
1333 | 505
1334 | 510
1335 | 511
1336 | 507
1337 | 505
1338 | 499
1339 | 497
1340 | 498
1341 | 496
1342 | 495
1343 | 492
1344 | 492
1345 | 490
1346 | 490
1347 | 493
1348 | 495
1349 | 497
1350 | 495
1351 | 494
1352 | 496
1353 | 496
1354 | 495
1355 | 496
1356 | 496
1357 | 495
1358 | 495
1359 | 496
1360 | 497
1361 | 497
1362 | 500
1363 | 503
1364 | 515
1365 | 535
1366 | 559
1367 | 593
1368 | 630
1369 | 670
1370 | 705
1371 | 740
1372 | 763
1373 | 777
1374 |
1375 |
1376 | 773
1377 |
1378 | 731
1379 |
1380 | 562
1381 |
1382 | 497
1383 |
1384 |
1385 |
1386 | 385
1387 |
1388 |
1389 | 458
1390 |
1391 |
1392 | 467
1393 |
1394 | 471
1395 |
1396 | 475
1397 |
1398 | 480
1399 | 483
1400 |
1401 | 488
1402 | 491
1403 |
1404 | 493
1405 |
1406 | 498
1407 | 501
1408 |
1409 | 504
1410 |
1411 | 508
1412 |
1413 | 510
1414 | 512
1415 |
1416 | 520
1417 |
1418 | 536
1419 |
1420 | 544
1421 |
1422 | 550
1423 |
1424 | 556
1425 | 558
1426 |
1427 | 560
1428 | 564
1429 | 565
1430 | 565
1431 | 561
1432 | 556
1433 | 548
1434 | 540
1435 | 531
1436 | 519
1437 | 508
1438 | 497
1439 | 486
1440 | 475
1441 | 465
1442 | 457
1443 | 450
1444 | 444
1445 | 438
1446 | 435
1447 | 432
1448 | 431
1449 | 432
1450 | 434
1451 | 439
1452 | 444
1453 | 450
1454 | 454
1455 | 459
1456 | 465
1457 | 470
1458 | 475
1459 | 481
1460 | 487
1461 | 490
1462 | 494
1463 | 497
1464 | 496
1465 | 500
1466 | 501
1467 | 502
1468 | 504
1469 | 505
1470 | 504
1471 | 503
1472 | 502
1473 | 501
1474 | 499
1475 | 498
1476 | 496
1477 | 495
1478 | 492
1479 | 492
1480 | 490
1481 | 490
1482 | 490
1483 | 491
1484 | 492
1485 | 493
1486 | 493
1487 | 494
1488 | 494
1489 | 495
1490 | 496
1491 | 496
1492 | 495
1493 | 495
1494 | 496
1495 | 497
1496 | 497
1497 | 500
1498 | 510
1499 | 530
1500 | 559
1501 | 593
1502 | 630
1503 | 667
1504 | 703
1505 | 735
1506 | 759
1507 | 774
1508 | 782
1509 |
1510 |
1511 |
1512 | 728
1513 |
1514 | 562
1515 |
1516 | 497
1517 | 411
1518 |
1519 |
1520 | 385
1521 |
1522 |
1523 |
1524 | 459
1525 |
1526 | 467
1527 |
1528 |
1529 | 474
1530 | 475
1531 |
1532 | 480
1533 |
1534 | 485
1535 |
1536 | 491
1537 |
1538 |
1539 | 495
1540 | 498
1541 | 501
1542 |
1543 | 504
1544 |
1545 |
1546 | 508
1547 | 510
1548 |
1549 | 515
1550 |
1551 | 535
1552 | 536
1553 | 540
1554 | 544
1555 | 546
1556 | 550
1557 | 553
1558 | 556
1559 | 558
1560 | 558
1561 | 560
1562 | 564
1563 | 565
1564 | 565
1565 | 561
1566 | 556
1567 | 548
1568 | 540
1569 | 531
1570 | 519
1571 | 508
1572 | 497
1573 | 486
1574 | 475
1575 | 465
1576 | 457
1577 | 450
1578 | 444
1579 | 438
1580 | 435
1581 | 432
1582 |
--------------------------------------------------------------------------------