-
Notifications
You must be signed in to change notification settings - Fork 0
/
References.bib
7509 lines (6258 loc) · 432 KB
/
References.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
% This file was created with JabRef 2.10.
% Encoding: Cp1252
@article{sameni2020mathematical,
title = {Mathematical modeling of epidemic diseases; a case study of the COVID-19 coronavirus},
author = {Sameni, Reza},
journal = {arXiv preprint},
url = {https://arxiv.org/abs/2003.11371},
year = {2020}
}
@String { IEEE_J_AC = {{IEEE} Trans. Automat. Contr.} }
@String { IEEE_J_ADVP = {{IEEE} Trans. Adv. Packag.} }
@String { IEEE_J_AES = {{IEEE} Trans. Aerosp. Electron. Syst.} }
@String { IEEE_J_AIRE = {{IEEE} Trans. Airborne Electron.} }
@String { IEEE_J_ANE = {{IEEE} Trans. Aerosp. Navig. Electron.} }
@String { IEEE_J_ANNE = {{IEEE} Trans. Aeronaut. Navig. Electron.} }
@String { IEEE_J_AP = {{IEEE} Trans. Antennas Propagat.} }
@String { IEEE_J_APPIND = {{IEEE} Trans. Applicat. Ind.} }
@String { IEEE_J_AS = {{IEEE} Trans. Aerosp.} }
@String { IEEE_J_ASC = {{IEEE} Trans. Appl. Superconduct.} }
@String { IEEE_J_ASSP = {{IEEE} Trans. Acoust., Speech, Signal Processing} }
@String { IEEE_J_AU = {{IEEE} Trans. Audio} }
@String { IEEE_J_AUEA = {{IEEE} Trans. Audio Electroacoust.} }
@String { IEEE_J_AWPL = {{IEEE} Antennas Wireless Propagat. Lett.} }
@String { IEEE_J_B-ME = {{IEEE} Trans. Bio-Med. Eng.} }
@String { IEEE_J_BC = {{IEEE} Trans. Broadcast.} }
@String { IEEE_J_BME = {{IEEE} Trans. Biomed. Eng.} }
@String { IEEE_J_BMELC = {{IEEE} Trans. Bio-Med. Electron.} }
@String { IEEE_J_C = {{IEEE} Trans. Comput.} }
@String { IEEE_J_CAD = {{IEEE} Trans. Computer-Aided Design} }
@String { IEEE_J_CAPT = {{IEEE} Trans. Comp. Packag. Technol.} }
@String { IEEE_J_CAPTS = {{IEEE} Trans. Comp. Packag. Technol.} }
@String { IEEE_J_CAS = {{IEEE} Trans. Circuits Syst.} }
@String { IEEE_J_CASI = {{IEEE} Trans. Circuits Syst. {I}} }
@String { IEEE_J_CASII = {{IEEE} Trans. Circuits Syst. {II}} }
@String { IEEE_J_CASVT = {{IEEE} Trans. Circuits Syst. Video Technol.} }
@String { IEEE_J_CE = {{IEEE} Trans. Consumer Electron.} }
@String { IEEE_J_CHMT = {{IEEE} Trans. Comp., Hybrids, Manufact. Technol.} }
@String { IEEE_J_COM = {{IEEE} Trans. Commun.} }
@String { IEEE_J_COML = {{IEEE} Commun. Lett.} }
@String { IEEE_J_COMT = {{IEEE} Trans. Commun. Technol.} }
@String { IEEE_J_CPART = {{IEEE} Trans. Comp. Parts} }
@String { IEEE_J_CPMTA = {{IEEE} Trans. Comp., Packag., Manufact. Technol. {A}} }
@String { IEEE_J_CPMTB = {{IEEE} Trans. Comp., Packag., Manufact. Technol. {B}} }
@String { IEEE_J_CPMTC = {{IEEE} Trans. Comp., Packag., Manufact. Technol. {C}} }
@String { IEEE_J_CST = {{IEEE} Trans. Contr. Syst. Technol.} }
@String { IEEE_J_CT = {{IEEE} Trans. Circuit Theory} }
@String { IEEE_J_DEI = {{IEEE} Trans. Dielect. Elect. Insulation} }
@String { IEEE_J_DMR = {{IEEE} Trans. Device Mat. Rel.} }
@String { IEEE_J_EC = {{IEEE} Trans. Energy Conversion} }
@String { IEEE_J_ECOMP = {{IEEE} Trans. Electron. Comput.} }
@String { IEEE_J_ED = {{IEEE} Trans. Electron Devices} }
@String { IEEE_J_EDL = {{IEEE} Electron Device Lett.} }
@String { IEEE_J_EDU = {{IEEE} Trans. Educ.} }
@String { IEEE_J_EI = {{IEEE} Trans. Elect. Insulation} }
@String { IEEE_J_EM = {{IEEE} Trans. Eng. Manage.} }
@String { IEEE_J_EMC = {{IEEE} Trans. Electromagn. Compat.} }
@String { IEEE_J_EPM = {{IEEE} Trans. Electron. Packag. Manufact.} }
@String { IEEE_J_EVC = {{IEEE} Trans. Evol. Comput.} }
@String { IEEE_J_FUZZ = {{IEEE} Trans. Fuzzy Syst.} }
@String { IEEE_J_GE = {{IEEE} Trans. Geosci. Electron.} }
@String { IEEE_J_GRS = {{IEEE} Trans. Geosci. Remote Sensing} }
@String { IEEE_J_HFE = {{IEEE} Trans. Hum. Factors Electron.} }
@String { IEEE_J_IA = {{IEEE} Trans. Ind. Applicat.} }
@String { IEEE_J_IE = {{IEEE} Trans. Ind. Electron.} }
@String { IEEE_J_IECI = {{IEEE} Trans. Ind. Electron. Contr. Instrum.} }
@String { IEEE_J_IGA = {{IEEE} Trans. Ind. Gen. Applicat.} }
@String { IEEE_J_IM = {{IEEE} Trans. Instrum. Meas.} }
@String { IEEE_J_IP = {{IEEE} Trans. Image Processing} }
@String { IEEE_J_IT = {{IEEE} Trans. Inform. Theory} }
@String { IEEE_J_ITBM = {{IEEE} Trans. Inform. Technol. Biomed.} }
@String { IEEE_J_ITS = {{IEEE} Trans. Intell. Transport. Syst.} }
@String { IEEE_J_JLT = {J. Lightwave Technol.} }
@String { IEEE_J_JQE = {{IEEE} J. Quantum Electron.} }
@String { IEEE_J_JRA = {{IEEE} J. Robot. Automat.} }
@String { IEEE_J_JSAC = {{IEEE} J. Select. Areas Commun.} }
@String { IEEE_J_JSSC = {{IEEE} J. Solid-State Circuits} }
@String { IEEE_J_JSTQE = {{IEEE} J. Select. Topics Quantum Electron.} }
@String { IEEE_J_KDE = {{IEEE} Trans. Knowledge Data Eng.} }
@String { IEEE_J_MAG = {{IEEE} Trans. Magn.} }
@String { IEEE_J_ME = {{IEEE} Trans. Med. Electron.} }
@String { IEEE_J_MECH = {{IEEE/ASME} Trans. Mechatron.} }
@String { IEEE_J_MEMS = {J. Microelectromech. Syst.} }
@String { IEEE_J_MFT = {{IEEE} Trans. Manufact. Technol.} }
@String { IEEE_J_MGWL = {{IEEE} Microwave Guided Wave Lett.} }
@String { IEEE_J_MI = {{IEEE} Trans. Med. Imag.} }
@String { IEEE_J_MIL = {{IEEE} Trans. Mil. Electron.} }
@String { IEEE_J_MM = {{IEEE} Trans. Multimedia} }
@String { IEEE_J_MMS = {{IEEE} Trans. Man-Mach. Syst.} }
@String { IEEE_J_MTT = {{IEEE} Trans. Microwave Theory Tech.} }
@String { IEEE_J_MWCL = {{IEEE} Microwave Wireless Compon. Lett.} }
@String { IEEE_J_NANO = {{IEEE} Trans. Nanotechnol.} }
@String { IEEE_J_NB = {{IEEE} Trans. Nanobiosci.} }
@String { IEEE_J_NET = {{IEEE/ACM} Trans. Networking} }
@String { IEEE_J_NN = {{IEEE} Trans. Neural Networks} }
@String { IEEE_J_NS = {{IEEE} Trans. Nucl. Sci.} }
@String { IEEE_J_NSRE = {{IEEE} Trans. Neural Syst. Rehab. Eng.} }
@String { IEEE_J_OE = {{IEEE} J. Oceanic Eng.} }
@String { IEEE_J_PAMI = {{IEEE} Trans. Pattern Anal. Machine Intell.} }
@String { IEEE_J_PC = {{IEEE} Trans. Prof. Commun.} }
@String { IEEE_J_PDS = {{IEEE} Trans. Parallel Distrib. Syst.} }
@String { IEEE_J_PHP = {{IEEE} Trans. Parts, Hybrids, Packag.} }
@String { IEEE_J_PMP = {{IEEE} Trans. Parts, Mater., Packag.} }
@String { IEEE_J_PROC = {Proc. {IEEE}} }
@String { IEEE_J_PS = {{IEEE} Trans. Plasma Sci.} }
@String { IEEE_J_PTL = {{IEEE} Photon. Technol. Lett.} }
@String { IEEE_J_PWRAS = {{IEEE} Trans. Power App. Syst.} }
@String { IEEE_J_PWRD = {{IEEE} Trans. Power Delivery} }
@String { IEEE_J_PWRE = {{IEEE} Trans. Power Electron.} }
@String { IEEE_J_PWRS = {{IEEE} Trans. Power Syst.} }
@String { IEEE_J_R = {{IEEE} Trans. Rel.} }
@String { IEEE_J_RA = {{IEEE} Trans. Robot. Automat.} }
@String { IEEE_J_RE = {{IEEE} Trans. Rehab. Eng.} }
@String { IEEE_J_RFI = {{IEEE} Trans. Radio Freq. Interference} }
@String { IEEE_J_SAP = {{IEEE} Trans. Speech Audio Processing} }
@String { IEEE_J_SE = {{IEEE} Trans. Software Eng.} }
@String { IEEE_J_SENSOR = {{IEEE} Sensors J.} }
@String { IEEE_J_SM = {{IEEE} Trans. Semiconduct. Manufact.} }
@String { IEEE_J_SMC = {{IEEE} Trans. Syst., Man, Cybern.} }
@String { IEEE_J_SMCA = {{IEEE} Trans. Syst., Man, Cybern. {A}} }
@String { IEEE_J_SMCB = {{IEEE} Trans. Syst., Man, Cybern. {B}} }
@String { IEEE_J_SMCC = {{IEEE} Trans. Syst., Man, Cybern. {C}} }
@String { IEEE_J_SP = {{IEEE} Trans. Signal Processing} }
@String { IEEE_J_SPL = {{IEEE} Signal Processing Lett.} }
@String { IEEE_J_SSC = {{IEEE} Trans. Syst. Sci. Cybernetics} }
@String { IEEE_J_SU = {{IEEE} Trans. Sonics Ultrason.} }
@String { IEEE_J_TCAD = {{IEEE} J. Technol. Computer Aided Design} }
@String { IEEE_J_TJMJ = {{IEEE} Transl. J. Magn. Jpn.} }
@String { IEEE_J_UE = {{IEEE} Trans. Ultrason. Eng.} }
@String { IEEE_J_UFFC = {{IEEE} Trans. Ultrason., Ferroelect., Freq. Contr.} }
@String { IEEE_J_VC = {{IEEE} Trans. Veh. Commun.} }
@String { IEEE_J_VCG = {{IEEE} Trans. Visual. Comput. Graphics} }
@String { IEEE_J_VLSI = {{IEEE} Trans. {VLSI} Syst.} }
@String { IEEE_J_VT = {{IEEE} Trans. Veh. Technol.} }
@String { IEEE_J_WCOM = {{IEEE} Trans. Wireless Commun.} }
@String { IEEE_M_AES = {{IEEE} Aerosp. Electron. Syst. Mag} }
@String { IEEE_M_AP = {{IEEE} Antennas Propagat. Mag.} }
@String { IEEE_M_ASSP = {{IEEE} {ASSP} Mag.} }
@String { IEEE_M_C = {{IEEE} Computer} }
@String { IEEE_M_CAP = {{IEEE} Comput. Appl. Power} }
@String { IEEE_M_CAS = {{IEEE} Circuits Syst. Mag.} }
@String { IEEE_M_CD = {{IEEE} Circuits Devices Mag.} }
@String { IEEE_M_CGA = {{IEEE} Comput. Graph. Appl.} }
@String { IEEE_M_COM = {{IEEE} Commun. Mag.} }
@String { IEEE_M_COMSOC = {{IEEE} Commun. Soc. Mag.} }
@String { IEEE_M_CONC = {{IEEE} Concurrency} }
@String { IEEE_M_CS = {{IEEE} Control Syst. Mag.} }
@String { IEEE_M_CSE = {{IEEE} Comput. Sci. Eng.} }
@String { IEEE_M_CSEM = {{IEEE} Comput. Sci. Eng. Mag.} }
@String { IEEE_M_DTC = {{IEEE} Des. Test. Comput.} }
@String { IEEE_M_EI = {{IEEE} Electr. Insul. Mag.} }
@String { IEEE_M_EMB = {{IEEE} Eng. Med. Biol. Mag.} }
@String { IEEE_M_EMR = {{IEEE} Eng. Manag. Rev.} }
@String { IEEE_M_EXP = {{IEEE} Expert} }
@String { IEEE_M_HIST = {{IEEE} Annals Hist. Comput.} }
@String { IEEE_M_IA = {{IEEE} Ind. Appl. Mag.} }
@String { IEEE_M_IC = {{IEEE} Internet Comput.} }
@String { IEEE_M_IM = {{IEEE} Instrum. Meas. Mag.} }
@String { IEEE_M_IS = {{IEEE} Intell. Syst.} }
@String { IEEE_M_ITP = {{IEEE} {IT} Prof.} }
@String { IEEE_M_MICRO = {{IEEE} Micro} }
@String { IEEE_M_MM = {{IEEE} Multimedia} }
@String { IEEE_M_MW = {{IEEE} Microwave} }
@String { IEEE_M_NET = {{IEEE} Network} }
@String { IEEE_M_PCOM = {{IEEE} Personal Commun. Mag.} }
@String { IEEE_M_PE = {{IEEE} Power Energy Mag.} }
@String { IEEE_M_PER = {{IEEE} Power Eng. Rev.} }
@String { IEEE_M_POT = {{IEEE} Potentials} }
@String { IEEE_M_RA = {{IEEE} Robot. Automat. Mag.} }
@String { IEEE_M_S = {{IEEE} Softw.} }
@String { IEEE_M_SP = {{IEEE} Signal Processing Mag.} }
@String { IEEE_M_SPECT = {{IEEE} Spectr.} }
@String { IEEE_M_TODAY = {Today's Eng.} }
@String { IEEE_M_TS = {{IEEE} Technol. Soc. Mag.} }
@String { IEEE_M_WC = {{IEEE} Wireless Commun. Mag.} }
@InProceedings{Yan2006,
Title = {Trace Quotient Problems Revisited},
Author = {Shuicheng Yan and Xiaoou Tang},
Booktitle = {ECCV (2)},
Year = {2006},
Pages = {232-244},
Bibsource = {DBLP, http://dblp.uni-trier.de},
Crossref = {DBLP:conf/eccv/2006-2},
Ee = {http://dx.doi.org/10.1007/11744047_18},
Owner = {sameni},
Timestamp = {2012.10.22}
}
@Article{Abed-Meraim2000,
Title = {Fast orthonormal PAST algorithm},
Author = {Abed-Meraim, K. and Chkeif, A. and Hua, Y.},
Journal = {Signal Processing Letters, IEEE},
Year = {2000},
Month = {march },
Number = {3},
Pages = {60 -62},
Volume = {7},
Doi = {10.1109/97.823526},
ISSN = {1070-9908},
Keywords = {OPAST algorithm;adaptive signal processing;fast estimation;fast orthonormal PAST algorithm;fast tracking;global convergence property;iteration;linear complexity;natural power method;principal components;principal subspace components;projection approximation and subspace tracking;vector sequence;weight matrix;adaptive signal processing;approximation theory;computational complexity;convergence of numerical methods;matrix algebra;parameter estimation;tracking;},
Owner = {sameni},
Timestamp = {2012.10.22}
}
@Article{Abeysekera1991,
Title = {{Some physiologically meaningful features obtained from Fourier descriptors of vectorcardiograph}},
Author = {R.M.S.S. Abeysekera},
Journal = IEEE_M_EMB,
Year = {1991},
Pages = {58--63},
Volume = {10},
No = {3},
Owner = {sameni},
Timestamp = {2012.10.22}
}
@InCollection{Abramovich1995,
Title = {{Thresholding of wavelet coefficients as multiple hypotheses testing procedure}},
Author = {Abramovich, Felix and Benjamini, Yoav},
Booktitle = {Wavelets and Statistics},
Publisher = {Springer-Verlag},
Year = {1995},
Editor = {A., Antoniadis and G., Oppenheim},
Pages = {5--14},
Volume = {103},
Date-added = {2008-03-30 17:21:15 +0200},
Date-modified = {2008-03-30 17:27:04 +0200},
Owner = {sameni},
Timestamp = {2012.10.22}
}
@InProceedings{Agante1999,
Title = {{ECG Noise Filtering Using Wavelets with Soft-thresholding Methods}},
Author = {P. M. Agante and J. P. Marques de S\'{a}},
Booktitle = {Proc. Computers in Cardiology'99},
Year = {1999},
Pages = {535--542},
Owner = {sameni},
Timestamp = {2012.10.22},
Vol = {26}
}
@Article{Akay1996,
Title = {Examining fetal heart-rate variability using matching pursuits},
Author = {Akay, M. and Akay, M. and Mulder, E.},
Journal = IEEE_M_EMB,
Year = {1996},
Number = {5},
Pages = {64--67},
Volume = {15},
Doi = {10.1109/51.537061},
Editor = {Mulder, E.},
ISSN = {0739-5175},
Keywords = {cardiology, medical signal processing, time-frequency analysis, algorithm, burst-type structures, complex energy structures, continuous activity, fetal heart-rate variability, matching pursuits, power-spectrum analysis, time-frequency analysis approach, wavelet transforms},
Owner = {sameni},
Timestamp = {2008.04.30}
}
@Article{Allen93,
Title = {The Limit of Viability -- Neonatal Outcome of Infants Born at 22 to 25 Weeks' Gestation},
Author = {Allen, M. C. and Donohue, P. K. and Dusman, A. E.},
Journal = {Bull. Soc. Roy. Belg. Gynec. Obstet. },
Year = {1993},
Month = {Oct},
Number = {22},
Pages = {1597--1601},
Volume = {329}
}
@Article{Allen00,
Title = {{A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI}},
Author = {Philip J. Allen and Oliver Josephs and and Robert Turner},
Journal = {Neuroimage},
Year = {2000},
Month = {August},
Number = {2},
Pages = {230--239},
Volume = {12},
Abstract = {Combined EEG/fMRI recording has been used to localize the generators of EEG events and to identify subject state in cognitive studies and is of increasing interest. However, the large EEG artifacts induced during fMRI have precluded simultaneous EEG and fMRI recording, restricting study design. Removing this artifact is difficult, as it normally exceeds EEG significantly and contains components in the EEG frequency range. We have developed a recording system and an artifact reduction method that reduce this artifact effectively. The recording system has large dynamic range to capture both low-amplitude EEG and large imaging artifact without distortion (resolution 2 microV, range 33.3 mV), 5-kHz sampling, and low-pass filtering prior to the main gain stage. Imaging artifact is reduced by subtracting an averaged artifact waveform, followed by adaptive noise cancellation to reduce any residual artifact. This method was validated in recordings from five subjects using periodic and continuous fMRI sequences. Spectral analysis revealed differences of only 10 to 18\% between EEG recorded in the scanner without fMRI and the corrected EEG. Ninety-nine percent of spike waves (median 74 microV) added to the recordings were identified in the corrected EEG compared to 12\% in the uncorrected EEG. The median noise after artifact reduction was 8 microV. All these measures indicate that most of the artifact was removed, with minimal EEG distortion. Using this recording system and artifact reduction method, we have demonstrated that simultaneous EEG/fMRI studies are for the first time possible, extending the scope of EEG/fMRI studies considerably.}
}
@Article{Allen98,
Title = {{Identification of EEG Events in the MR Scanner: The Problem of Pulse Artifact and a Method for Its Subtraction}},
Author = {Philip J. Allen and Giovanni Polizzi and Karsten Krakow and David R. Fish and Louis Lemieux},
Journal = {Neuroimage},
Year = {1998},
Month = {October},
Number = {3},
Pages = {229--239},
Volume = {8},
Abstract = {Triggering functional MRI (fMRI) image acquisition immediately after an EEG event can provide information on the location of the event generator. However, EEG artifact associated with pulsatile blood flow in a subject inside the scanner may obscure EEG events. This pulse artifact (PA) has been widely recognized as a significant problem, although its characteristics are unpredictable. We have investigated the amplitude, distribution on the scalp, and frequency of occurrence of this artifact. This showed large interindividual variations in amplitude, although PA is normally largest in the frontal region. In five of six subjects, PA was greater than 50 µV in at least one of the temporal, parasagittal, and central channels analyzed. Therefore, we developed and validated a method for removing PA. This subtracts an averaged PA waveform calculated for each electrode during the previous 10 s. Particular attention has been given to reliable ECG peak detection and ensuring that the average PA waveform is free of other EEG artifacts. Comparison of frequency spectra for EEG recorded outside and inside the scanner, with and without PA subtraction, showed a clear reduction in artifact after PA subtraction for all four frequency ranges analyzed. As further validation, lateralized epileptiform spikes were added to recordings from inside and outside the scanner: PA subtraction significantly increased the proportion of these spikes that were correctly identified and decreased the number of false spike detections. We conclude that in some subjects, EEG/fMRI studies will be feasible only using PA subtraction.}
}
@Book{allen2004signal,
Title = {{Signal analysis: time, frequency, scale, and structure}},
Author = {R. L. Allen and D. W. Mills},
Publisher = {IEEE Press},
Year = {2004},
ISBN = {9780471234418},
Lccn = {2004298648}
}
@Article{Almasi2013214,
Title = {Bayesian denoising framework of phonocardiogram based on a new dynamical model},
Author = {A. Almasi and M. Bagher Shamsollahi and L. Senhadji},
Journal = {\{IRBM\} },
Year = {2013},
Number = {3},
Pages = {214 - 225},
Volume = {34},
Doi = {http://dx.doi.org/10.1016/j.irbm.2013.01.017},
ISSN = {1959-0318},
Url = {http://www.sciencedirect.com/science/article/pii/S1959031813000559}
}
@Article{Amer-Wahlin2001,
Title = {Cardiotocography only versus cardiotocography plus ST analysis of fetal electrocardiogram for intrapartum fetal monitoring: a Swedish randomised controlled trial},
Author = {Amer-Wahlin, I and Hellsten, C and Noren, H and Hagberg, H and Herbst, A and Kjellmer, I and Lilja, H and Lindoff, C and Mansson, M and Martensson, L and Olofsson, P and Sundstrom, A and Marsal, K},
Journal = {Lancet},
Year = {2001},
Pages = {534--538},
Volume = {358},
Doi = {10.1016/S0140-6736(01)05703-8},
Owner = {sameni},
Pubmedid = {11520523},
Timestamp = {2012.10.22}
}
@Article{Amer-Wahlin2005,
Title = {Implementation of new medical techniques: experience from the Swedish randomized controlled trial on fetal ECG during labor},
Author = {Amer-Wahlin, I and Kallen, K and Herbst, A and Rydhstroem, H and Sundstrom, AK and Marsal, K},
Journal = {J Matern Fetal Neonatal Med},
Year = {2005},
Pages = {93--100},
Volume = {18},
Doi = {10.1080/14767050500233191},
Owner = {sameni},
Pubmedid = {16203593},
Timestamp = {2012.10.22}
}
@InProceedings{Amini08,
Title = {{MR Artifact Reduction in the Simultaneous Acquisition of EEG and fMRI of Epileptic Patients}},
Author = {L. Amini and R. Sameni and C. Jutten and G.A. Hossein-Zadeh and H. Soltanian-Zadeh},
Booktitle = {{EUSIPCO2008 - 16th European Signal Processing Conf.}},
Year = {2008},
Address = {Lausanne, Switzerland},
Month = {August 25-29},
Owner = {sameni},
Timestamp = {2008.04.22}
}
@Article{Anastassiou2001,
Title = {Genomic signal processing},
Author = {Anastassiou, D.},
Journal = {Signal Processing Magazine, IEEE},
Year = {2001},
Month = {jul.},
Number = {4},
Pages = {8 -20},
Volume = {18},
Doi = {10.1109/79.939833},
ISSN = {1053-5888},
Keywords = {DNA;DSP;Fourier transforms;agriculture;alphabet size;biomolecular sequence analysis;biomolecular sequences;character strings;color spectrograms;digital filtering;digital signal processing;genomes sequences;genomic information science;genomic information technology;genomic signal processing;living organisms;local texture;medicine;numerical sequences;phase magnitude;protein coding regions;proteins;simulations;DNA;Fourier transforms;digital filters;digital simulation;medical signal processing;molecular biophysics;proteins;sequences;}
}
@Book{AndersonMoore1979,
Title = {{Optimal Filtering}},
Author = {Brian D. O. Anderson and John B. Moore},
Publisher = {{Dover Publications, Inc.}},
Year = {1979},
Owner = {sameni},
Timestamp = {2009.02.14}
}
@Article{AndeSS98,
Title = {Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks.},
Author = {C. W. Anderson and E. A. Stolz and S. Shamsunder},
Journal = {IEEE Trans. Biomed. Eng.},
Year = {1998},
Month = {Mar},
Number = {3},
Pages = {277--286},
Volume = {45},
Abstract = {This article explores the use of scalar and multivariate autoregressive (AR) models to extract features from the human electroencephalogram ({EEG}) with which mental tasks can be discriminated. This is part of a larger project to investigate the feasibility of using {EEG} to allow paralyzed persons to control a device such as a wheelchair. {EEG} signals from four subjects were recorded while they performed two mental tasks. Quarter-second windows of six-channel {EEG} were transformed into four different representations: scalar AR model coefficients, multivariate AR coefficients, eigenvalues of a correlation matrix, and the Karhunen-Loève transform of the multivariate AR coefficients. Feature vectors defined by these representations were classified with a standard, feedforward neural network trained via the error backpropagation algorithm. The four representations produced similar results, with the multivariate AR coefficients performing slightly better and more consistently with an average classification accuracy of 91.4\% on novel, untrained, {EEG} signals.},
File = {AndeSS98.pdf:AndeSS98.pdf:PDF},
Institution = {Department of Computer Science, Colorado State University, Fort Collins 80523, USA. anderson@cs.colostate.edu},
Keywords = {Electroencephalography; Feasibility Studies; Humans; Mental Processes; Models, Statistical; Multivariate Analysis; Neural Networks (Computer); Regression Analysis},
Owner = {Cedric Gouy-Pailler},
Pmid = {9509744},
Timestamp = {2008.01.17}
}
@Article{Andreao2006,
Title = {{ECG signal analysis through hidden Markov models}},
Author = {Andreao, R.V. and Dorizzi, B. and Boudy, J.},
Journal = {Biomedical Engineering, IEEE Transactions on},
Year = {2006},
Month = {aug. },
Number = {8},
Pages = {1541 -1549},
Volume = {53},
Abstract = {This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG. The performance was evaluated on the two-channel QT database in terms of waveform segmentation precision, beat detection and classification. Our waveform segmentation results compare favorably to other systems in the literature. We also obtained high beat detection performance with sensitivity of 99.79% and a positive predictivity of 99.96%, using a test set of 59 recordings. Moreover, premature ventricular contraction beats were detected using an original classification strategy. The results obtained validate our approach for real world application},
Doi = {10.1109/TBME.2006.877103},
ISSN = {0018-9294},
Keywords = {ECG signal analysis;beat detection;electrocardiogram;hidden Markov model;multichannel beat segmentation;online beat segmentation;signal classification;two-channel QT database;waveform modeling;electrocardiography;hidden Markov models;medical signal detection;medical signal processing;signal classification;waveform analysis;}
}
@Book{Arce2004,
Title = {{Nonlinear Signal Processing: A Statistical Approach}},
Author = {Gonzalo R. Arce},
Publisher = {John Wiley \& Sons Inc.},
Year = {2004},
Address = {New York},
Owner = {sameni},
Timestamp = {2008.01.30}
}
@Book{arfken2005mathematical,
Title = {Mathematical Methods For Physicists International Student Edition},
Author = {Arfken, G.B. and Weber, H.J. and Harris, F.E.},
Publisher = {Elsevier Science},
Year = {2005},
ISBN = {9780080470696}
}
@Article{ABAC99,
Title = {{Improved Estimation of Pericardial Potentials From Body-Surface Maps Using Individualized Torso Models}},
Author = {R. M. Arthur and D. G. Beetner and H. D. Ambos and M. E. Cain},
Journal = {J. of Electrocardiology},
Year = {1999},
Pages = {106--113},
Volume = {31(supp.)}
}
@Article{Astrom00,
Title = {Vectorcardiographic loop alignment and the measurement of morphologic beat-to-beat variability in noisy signals},
Author = {Astrom, M. and Santos, E.C. and Sornmo, L. and Laguna, P. and Wohlfart, B.},
Journal = {Biomedical Engineering, IEEE Transactions on},
Year = {2000},
Month = {April },
Number = {4},
Pages = {497-506},
Volume = {47},
Abstract = {The measurement of subtle morphologic beat-to-beat variability in the electrocardiogram (ECG)/vectorcardiogram (VCG) is complicated by the presence of noise which is caused by, e.g., respiration and muscular activity. A method was recently presented which reduces the influence of such noise by performing spatial and temporal alignment of VCG loops. The alignment is performed in terms of scaling, rotation and time synchronization of the loops. Using an ECG simulation model based on propagation of action potentials in cardiac tissue, the ability of the method to separate morphologic variability of physiological origin from respiratory activity was studied. Morphologic variability was created by introducing a random variation in action potential propagation between different compartments. The results indicate that the separation of these two activities can be done accurately at low to moderate noise levels (less than 10 /spl mu/V). At high noise levels, the estimation of the rotation angles was found to break down in an abrupt manner. It was also shown that the breakdown noise level is strongly dependent on loop morphology; a planar loop corresponds to a lower breakdown noise level than does a nonplanar loop.},
Doi = {10.1109/10.828149},
ISSN = {0018-9294},
Keywords = {electrocardiography, medical signal processing, noise, physiological models10 muV, ECG signal processing, ECG simulation model, action potential propagation, cardiac tissue, electrodiagnostics, morphologic beat-to-beat variability measurement, noisy signals, random variation, respiratory activity, rotation, scaling, time synchronization, vectorcardiographic loop alignment}
}
@Article{audoly2001global,
Title = {Global identifiability of nonlinear models of biological systems},
Author = {Audoly, Stefania and Bellu, Giuseppina and D'Angio, Leontina and Saccomani, Maria Pia and Cobelli, Claudio},
Journal = {Biomedical Engineering, IEEE Transactions on},
Year = {2001},
Number = {1},
Pages = {55--65},
Volume = {48},
Publisher = {IEEE}
}
@Article{Avendano-Valencia2007b,
Title = {Reduction of power line interference on ECG signals using Kalman filtering and Delta operator},
Author = {Avenda{\~{n}}o-Valencia, Luis David and Avenda{\~{n}}o, Luis Enrique and Castellanos-Dom{\'{\i}}nguez, C{\'{e}}sar Germ{\'{a}}n and Villegas-Jaramillo, Eduardo Jos{\'{e}}},
Year = {2007},
Booktitle = {23rd ISPE International Conference on CAD/CAM robotics and factories of the future 2007},
Owner = {DavidAVN},
Timestamp = {2010.08.23}
}
@InProceedings{Avendano-Valencia-2007,
Title = {Improvement of an extended Kalman filter power line interference suppressor for {ECG} signals},
Author = {Avenda{\~{n}}o-Valencia, Luis David and Avenda{\~{n}}o, Luis Enrique and Ferrero, J.M. and Castellanos-Dom{\'{\i}}nguez, C{\'{e}}sar Germ{\'{a}}n},
Booktitle = {Computers in Cardiology, 2007},
Year = {2007},
Month = {30 2007-oct. 3},
Pages = {553 -556},
Abstract = {The powerline interference reduction in ECG records is a challenging problem which is still open for research. The powerline signal, measured directly from the transmission line may have amplitude, phase and frequency variations. These reasons make the classical filtering methods sub-optimal in the powerline interference reduction. We propose a tracking method based on Kalman filtering which uses an state space model for the noisy signal and allows adequate discrimination between the ECG signal and the perturbation, even during non-stationarities. The parameters of this algorithm are optimized via genetic algorithms, obtaining a set of values that give it a mean correlation index on the QT database over 0,99.},
Doi = {10.1109/CIC.2007.4745545},
ISSN = {0276-6547},
Keywords = {ECG signals;QT database;extended Kalman filter;filtering methods;genetic algorithms;power line interference suppressor;transmission line;Kalman filters;electrocardiography;genetic algorithms;medical signal processing;}
}
@InProceedings{Azzerboni2005,
Title = {A new approach based on wavelet-ICA algorithms for fetal electrocardiogram extraction.},
Author = {Bruno Azzerboni and Fabio {La Foresta} and Nadia Mammone and Francesco Carlo Morabito},
Booktitle = {Proceedings of the 13th European Symposium on Artificial Neural Networks (ESANN 2005)},
Year = {2005},
Pages = {193--198},
Bibsource = {DBLP, http://dblp.uni-trier.de},
Ee = {http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2005-63.pdf},
Owner = {sameni},
Timestamp = {2012.10.22}
}
@Manual{B.DeMoor,
Title = {{Database for the Identification of Systems (DaISy)}},
Author = {{B. De Moor}},
Owner = {sameni},
Timestamp = {2012.10.22},
Url = {http://homes.esat.kuleuven.be/~smc/daisy/}
}
@Article{bach03beyond,
Title = {Beyond independent components: trees and clusters},
Author = {F. Bach and M. Jordan},
Journal = {Journal of Machine Learning Research},
Year = {2003},
Pages = {1205--1233},
Volume = {4},
Url = {http://cmm.ensmp.fr/~bach/bach03a.pdf}
}
@Article{Bailey1990,
Title = {{Recommendations for standardization and specifications in automated electrocardiography{:} bandwidth and digital signal processing.}},
Author = {J. J. Bailey and A. S. Berson and A. J. Garson},
Journal = {Circulation},
Year = {1990},
Pages = {730--739},
Volume = {81},
Owner = {sameni},
Timestamp = {2012.10.22}
}
@Article{Baker1995,
Title = {Measurement of fetal liver, brain and placental volumes with echo-planar magnetic resonance imaging.},
Author = {P. N. Baker and I. R. Johnson and P. A. Gowland and J. Hykin and V. Adams and P. Mansfield and B. S. Worthington},
Journal = {Br J Obstet Gynaecol},
Year = {1995},
Month = {Jan},
Number = {1},
Pages = {35--39},
Volume = {102},
Abstract = {OBJECTIVE: To quantify accurately in utero fetal liver, brain and placental volumes using echo planar imaging, and to assess whether the technique has the potential to enhance intrauterine fetal assessment. DESIGN: Thirty-two singleton, complicated pregnancies were scanned using echo planar imaging, a form of magnetic resonance imaging. Pregnancies were subdivided on the basis of whether the fetus was found subsequently to have an individualised birthweight ratio above (n = 21) or below (n = 11) the 10th centile. Comparisons of the organ volumes of these two groups were made. RESULTS: The first quantitative in utero measurement of fetal liver volume showed a linear relation between liver volume and gestational age in fetuses where the individualised birthweight ratio was above the 10th centile (the normal growth group). Ten of the 11 liver volume measurements of fetuses subsequently found to have an individualised birthweight ratio below the 10th centile fell on or outside the 95\% confidence limits established for the normal growth group. In contrast, no such differences were demonstrated when the brain and placental volumes were considered, with 10 of the 11 brain measurements and all of the 11 placental measurements falling within the 95\% confidence limits of the normal growth group. CONCLUSIONS: A single measurement of fetal liver volume using echo planar imaging enabled accurate identification of fetuses subsequently found to have individualised birthweight ratios below the 10th centile. If these findings are repeated in larger, more representative studies, this suggests that the technique has the potential to contribute to intrauterine fetal assessment.},
Institution = {Department of Obstetrics and Gynaecology, University of Nottingham, UK.},
Keywords = {Adult; Brain; Echo-Planar Imaging; Female; Fetus; Gestational Age; Humans; Liver; Placenta; Pregnancy},
Owner = {sameni},
Pmid = {7833308},
Timestamp = {2008.05.09}
}
@Article{Barbati2004,
Title = {Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals},
Author = {G. Barbati and C. Porcaro and F. Zappasodi and P.M. Rossini and F. Tecchio},
Journal = {Clin Neurophysiol},
Year = {2004},
Pages = {1220-1232},
Volume = {115},
Owner = {sameni},
Timestamp = {2012.10.22}
}
@InCollection{Barr89,
Title = {Genesis of the Electrocardiogram},
Author = {R.C. Barr},
Booktitle = {Comprehensive Electrocardiology},
Publisher = {Pergamon Press},
Year = {1989},
Address = {Oxford},
Chapter = {5},
Editor = {Macfarlane, P. W. and Lawrie, T. T. V.},
Pages = {129--151},
Vol = {{I}}
}
@Article{Barros1998,
Title = {Removing Artifacts From {ECG} Signals Using Independent Components Analysis},
Author = {Barros, AK and Mansour, A. and Ohnishi, N.},
Journal = {Neurocomputing},
Year = {1998},
Pages = {173--186},
Volume = {22},
Owner = {sameni},
Timestamp = {2012.10.22},
Vol = {22}
}
@Article{Barros2001,
Title = {Extraction of specific signals with temporal structure.},
Author = {A. K. Barros and A. Cichocki},
Journal = {Neural Comput},
Year = {2001},
Month = {Sep},
Number = {9},
Pages = {1995--2003},
Volume = {13},
Abstract = {In this work we develop a very simple batch learning algorithm for semiblind extraction of a desired source signal with temporal structure from linear mixtures. Although we use the concept of sequential blind extraction of sources and independent component analysis, we do not carry out the extraction in a completely blind manner; neither do we assume that sources are statistically independent. In fact, we show that the a priori information about the autocorrelation function of primary sources can be used to extract the desired signals (sources of interest) from their linear mixtures. Extensive computer simulations and real data application experiments confirm the validity and high performance of the proposed algorithm.},
Doi = {10.1162/089976601750399272},
Institution = {Bio-mimetic Control Research Center, RIKEN, Moriyama-ku, Shimoshidami, Nagoya 463-0003, Japan.},
Keywords = {Algorithms; Computer Simulation; Electrocardiography; Female; Fetal Heart; Heart; Humans; Models, Biological; Normal Distribution; Pregnancy; Reproducibility of Results},
Owner = {sameni},
Pmid = {11516354},
Timestamp = {2008.04.30},
Url = {http://dx.doi.org/10.1162/089976601750399272}
}
@Article{Bartelmaos2008,
Title = {Fast Principal Component Extraction Using Givens Rotations},
Author = {Bartelmaos, S. and Abed-Meraim, K.},
Journal = {Signal Processing Letters, IEEE},
Year = {2008},
Month = { },
Pages = {369 -372},
Volume = {15},
Doi = {10.1109/LSP.2008.920006},
ISSN = {1070-9908},
Keywords = {Givens rotation;PCA;adaptive estimation;eigenvector;iterative method;orthogonal projection approximation;positive Hermitian covariance matrix;principal component extraction;singular value decomposition;subspace tracking;weight matrix;Hermitian matrices;adaptive estimation;adaptive signal processing;approximation theory;covariance matrices;eigenvalues and eigenfunctions;iterative methods;principal component analysis;tracking;},
Owner = {sameni},
Timestamp = {2012.10.22}
}
@Book{Batzel2007,
Title = {Cardiovascular and Respiratory Systems Modeling, Analysis, and Control},
Author = {Jerry J. Batzel and Franz Kappel and Daniel Schneditz and Hien T. Iran},
Publisher = {SIAM},
Year = {2007},
ISBN = {9780898716177}
}
@Article{Bednar84,
Title = {Alpha-trimmed means and their relationship to median filters},
Author = {Bednar, J. and Watt, T.},
Journal = {Acoustics, Speech, and Signal Processing, IEEE Transactions on},
Year = {1984},
Month = {February},
Number = {1},
Pages = {145--153},
Volume = {32},
ISSN = {0096-3518 }
}
@Conference{BeharAJOG2013,
Title = {{Evaluation of the fetal QT interval using non-invasive fetal ECG technology}},
Author = {Joachim Behar and Adam Wolfberg and Tingting Zhu and Julian Oster and Alisa Niksch and Douglas Mah and Terrence Chun and James Greenberg and Cassandre Tanner and Jessica Harrop and Alexander Van Esbroeck and Amy Alexander and Michele McCarroll and Timothy Drake and Angela Silber and Reza Sameni and Jay Ward and Gari Clifford},
Booktitle = {{American Journal of Obstetrics and Gynecology}},
Year = {2014},
Address = {New Orleans, LA},
Month = {February },
Number = {1},
Organization = {{Society for Maternal-Fetal Medicine}},
Pages = {S283--S284},
Volume = {210}
}
@Article{BehrensScharf1994,
Title = {Signal processing applications of oblique projection operators},
Author = {R. T. Behrens and L. L. Scharf},
Journal = IEEE_J_SP,
Year = {1994},
Pages = {1413--1424},
Volume = {42},
Owner = {sameni},
Timestamp = {2009.02.14}
}
@Book{Bellman57,
Title = {Dynamic Programming},
Author = {Bellman, R.E.},
Publisher = {Princeton University Press, Princeton, NJ.},
Year = {1957},
Note = {Republished 2003: Dover},
Owner = {sameni},
Timestamp = {2008.04.11}
}
@Article{Belouchrani1997,
Title = {{A Blind Source Separation Technique Using Second-Order Statistics}},
Author = {A. Belouchrani and K. Abed-Meraim and J-F. Cardoso and Eric Moulines},
Journal = IEEE_J_SP,
Year = {1997},
Month = {Feb.},
Pages = {434--444},
Volume = {45},
No = {2}
}
@Article{bb16291,
Title = {Nonorthogonal Representation of Signals by {G}aussians and {G}abor Functions},
Author = {Ben-Arie, J. and Rao, K.R.},
Journal = IEEE_J_CASII,
Year = {1995},
Month = {June},
Number = {6},
Pages = {402-413},
Volume = {42}
}
@Article{BV03,
Title = {{On the Kernel Widths in Radial-Basis Function Networks}},
Author = {N. Benoudjit and M. Verleysen},
Journal = {Neural Processing Letters},
Year = {2003},
Month = {Oct.},
Pages = {139--154},
Volume = {18},
No = {2}
}
@Article{Bergveld1981,
Title = {{A New Technique for the Suppression of the MECG}},
Author = {Bergveld, Piet and Meijer, Wietze J. H.},
Journal = {Biomedical Engineering, IEEE Transactions on},
Year = {1981},
Month = {April},
Number = {4},
Pages = {348--354},
Volume = {BME-28},
Abstract = {After a review of the different techniques in use up to now for the detection of an interference-free abdominal fetal electrocardiogram (FECG), with the limitations of these techniques indicated, a new technique is described which does not suffer from these limitations. This technique is based on an optimization procedure applied to the multiplication coefficients of six independent abdominal signals which are added together. The theoretical background of this method is given, as well as the required operational conditions and electrode positions, leading to an FECG reading guaranteed free of maternal electrocardiogram (MECG).},
Doi = {10.1109/TBME.1981.324803},
ISSN = {0018-9294},
Keywords = {Electrocardiography;Female;Fetal Heart;Heart Rate;Humans;Pregnancy;},
Owner = {sameni},
Timestamp = {2012.10.22}
}
@InProceedings{Bienati2001,
Title = {An adaptive blind signal separation based on the joint optimization of Givens rotations},
Author = {Bienati, N. and Spagnolini, U. and Zecca, M.},
Booktitle = {Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on},
Year = {2001},
Pages = {2809 -2812 vol.5},
Volume = {5},
Abstract = {Blind signal separation (BSS) is a recurrent problem in many multi-sensors applications where observations can be modelled as mixtures of N statistical independent source signals. We propose the estimation of the orthonormal transformation matrix Q in the case of whitened observations and a cost function based on the fourth-order moments. Q is described as combination of elementary Givens rotations and the optimization is carried out jointly for all the rotations. When sub-sets of angles are optimized separately the method reduces to the deflation approach which has been proved to be globally convergent. The joint estimation of Givens rotation matrices has a computational complexity O(7N2) and, compared to other adaptive BSS, simulations demonstrate that it converges faster and achieves a better crosstalk attenuation},
Doi = {10.1109/ICASSP.2001.940230},
ISSN = {1520-6149},
Keywords = {Givens rotation matrices;adaptive blind signal separation;adaptive estimation;angle quantization;blind signal separation;computational complexity;cost function;crosstalk attenuation;deflation approach;fourth-order moments;globally convergent method;joint optimization of Givens rotations;multisensors applications;orthonormal transformation matrix;statistical independent source signals;whitened observations;adaptive estimation;adaptive signal processing;computational complexity;convergence of numerical methods;crosstalk;matrix algebra;optimisation;},
Owner = {sameni},
Timestamp = {2012.10.22}
}
@Book{bishop95,
Title = {Neural Networks for Pattern Recognition},
Author = {Bishop, C.},
Publisher = {Oxford University Press},
Year = {1995},
Address = {New York}
}
@Article{Bjorck1973,
Title = {Numerical Methods for Computing Angles between Linear Subspaces},
Author = {{\AA}. Bj{\"o}rck and G. H. Golub},
Journal = {Math. Comp.},
Year = {1973},
Pages = {579--594},
Volume = {27},
Kwds = {csd},
Owner = {sameni},
Timestamp = {2012.10.22}
}
@Article{Blankertz2008,
Title = {{Optimizing Spatial filters for Robust EEG Single-Trial Analysis}},
Author = {Blankertz, B. and Tomioka, R. and Lemm, S. and Kawanabe, M. and Muller, K.-R.},
Journal = {Signal Processing Magazine, IEEE},
Year = {2008},
Month = { },
Number = {1},
Pages = {41-56},
Volume = {25},
Doi = {10.1109/MSP.2008.4408441},
ISSN = {1053-5888},
Keywords = {electroencephalography, learning (artificial intelligence), medical signal processing, neurophysiology, spatial filters, user interfacesBerlin BCI project, brain activity, brain-computer interface, common spatial pattern algorithm, electroencephalogram, machine learning, robust EEG analysis, signal processing, signal-to-noise ratio, single-trial analysis, spatial filters, spatiotemporal filters, volume conduction multichannel EEG}
}
@Misc{Blomhoj2014,
Title = {{Compartment models}},
Author = {M. Blomh{\o}j and T.H. Kjeldsen and J. Ottesen},
Month = {January},
Year = {2014},
Url = {http://www4.ncsu.edu/$~$msolufse/Compartmentmodels.pdf}
}
@Article{Bloom06,
Title = {Fetal Pulse Oximetry and Cesarean Delivery},
Author = {Bloom, Steven L. and Spong, Catherine Y. and Thom, Elizabeth and Varner, Michael W. and Rouse, Dwight J. and Weininger, Sandy and Ramin, Susan M. and Caritis, Steve N. and Peaceman, Alan and Sorokin, Yoram and Sciscione, Anthony and Carpenter, Marshall and Mercer, Brian and Thorp, John and Malone, Fergal and Harper, Margaret and Iams, Jay and Anderson, Garland},
Journal = {N Engl J Med},
Year = {2006},
Note = {For the National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network},
Number = {21},
Pages = {2195-2202},
Volume = {355},
Abstract = {Background Knowledge of fetal oxygen saturation, as an adjunct to electronic fetal monitoring, may be associated with a significant change in the rate of cesarean deliveries or the infant's condition at birth. Methods We randomly assigned 5341 nulliparous women who were at term and in early labor to either "open" or "masked" fetal pulse oximetry. In the open group, fetal oxygen saturation values were displayed to the clinician. In the masked group, the fetal oxygen sensor was inserted and the values were recorded by computer, but the data were hidden. Labor complicated by a nonreassuring fetal heart rate before randomization was documented for subsequent analysis. Results There was no significant difference in the overall rates of cesarean delivery between the open and masked groups (26.3% and 27.5%, respectively; P=0.31). The rates of cesarean delivery associated with the separate indications of a nonreassuring fetal heart rate (7.1% and 7.9%, respectively; P=0.30) and dystocia (18.6% and 19.2%, respectively; P=0.59) were similar between the two groups. Similar findings were observed in the subgroup of 2168 women in whom a nonreassuring fetal heart rate was detected before randomization. The condition of the infants at birth did not differ significantly between the two groups. Conclusions Knowledge of the fetal oxygen saturation is not associated with a reduction in the rate of cesarean delivery or with improvement in the condition of the newborn. (ClinicalTrials.gov number, NCT00098709 .)},
Doi = {10.1056/NEJMoa061170},
Eprint = {http://content.nejm.org/cgi/reprint/355/21/2195.pdf},
Url = {http://content.nejm.org/cgi/content/abstract/355/21/2195}
}
@Article{Blum1985,
Title = {First magnetoencephalographic recordings of the brain activity of a human fetus},
Author = {T. Blum and E. Saling and R. Bauer},
Journal = {Br J Obstet Gynaecol.},
Year = {1985},
Pages = {1224--1229},
Volume = {92},
No = {12},
Owner = {sameni},
Timestamp = {2012.10.22}
}
@Article{Bogaerts2009,
Title = {Automated threshold detection for auditory brainstem responses: comparison with visual estimation in a stem cell transplantation study},
Author = {S. Bogaerts and J. D. Clements and J. M. Sullivan and S. Oleskevich},
Journal = {BMC Neuroscience},
Year = {2009},
Number = {104},
Pages = {1--7},
Volume = {10},
Owner = {sameni},
Timestamp = {2012.10.22}
}
@Article{Bonmassar02,
Title = {Motion and ballistocardiogram artifact removal for interleaved recording of {EEG} and {EP}s during {MRI}},
Author = {Bonmassar, G. and Purdon, P. L. and Jaaskelainen, I. P. and Chiappa, K. and Solo, V. and Brown, E. N. and Belliveau, J. W.},
Journal = {NeuroImage},
Year = {2002},
Number = {4},
Pages = {1127--1141},
Volume = {16}
}
@Article{PTB1,
Title = {{Nutzung der EKG-Signaldatenbank CARDIODAT der PTB uber das Internet}},
Author = {R. Bousseljot and D. Kreiseler and A. Schnabel},
Journal = {Biomedizinische Technik},
Year = {1995},
Number = {1},
Pages = {S317-S318},
Volume = {40}
}
@Article{Brace1989,
Title = {Normal amniotic fluid volume changes throughout pregnancy.},
Author = {R. A. Brace and E. J. Wolf},
Journal = {Am J Obstet Gynecol},
Year = {1989},
Month = {Aug},
Number = {2},
Pages = {382--388},
Volume = {161},
Abstract = {The purpose of this study was to provide a quantitative characterization of the changes in amniotic fluid volume that occur throughout gestation. From 705 published amniotic volumes for normal pregnancies, we found that after log transformation, amniotic fluid volume had a uniform variability over 8 to 43 weeks' gestation. Thus the 95\% confidence interval covered the range of 1/2.57 to 2.57 times the mean volume at any given gestational age. Contrary to expected trends, mean amniotic fluid volume did not change significantly between 22 and 39 weeks and averaged 777 ml, with the 95\% confidence interval ranging from 302 to 1997 ml. The data are summarized in nomograms covering 8 to 43 weeks' gestation.},
Institution = {Department of Reproductive Medicine, University of California, San Diego, La Jolla 92093.},
Keywords = {Amniotic Fluid; Female; Gestational Age; Humans; Pregnancy; Reference Values; Regression Analysis},
Owner = {sameni},
Pii = {0002-9378(89)90527-9},
Pmid = {2764058},
Timestamp = {2008.05.09}
}
@Article{Broomhead86,
Title = {Extracting qualitative dynamics from experimental data},
Author = {D S Broomhead and G P King},
Journal = {Physica D},
Year = {1986},
Number = {2-3},
Pages = {217--236},
Volume = {20},
Address = {Amsterdam, The Netherlands, The Netherlands},
Doi = {http://dx.doi.org/10.1016/0167-2789(86)90031-X},
ISSN = {0167-2789},
Publisher = {Elsevier Science Publishers B. V.}
}
@Misc{BrunLMSP08,
Title = {{BCI} Competion 2008 -- {Graz} Dataset {A}},
Author = {Clemens Brunner and Robert Leeb and Gernot R. M\"uller-Putz and Alois Schl\"ogl and Gert Pfurtscheller},
Month = {Jul},
Year = {2008},
Owner = {Cedric Gouy-Pailler},
Timestamp = {2008.11.03},
Url = {http://ida.first.fraunhofer.de/projects/bci/competition\_iv/desc\_2a.pdf}
}
@Article{KHO02,
Title = {{The principles of software QRS detection. Review and comparing algorithms for detecting this important ECG waveform}},
Author = {{B-U K\"{o}hler} and C Hennig and R. Orglmeister},
Journal = IEEE_M_EMB,
Year = {2002},
Month = {Jan/Feb},
Pages = {42-57},
Volume = {21}
}
@Article{Buxton2004S220,
Title = {Modeling the hemodynamic response to brain activation},
Author = {Richard B. Buxton and Kâmil Uludag and David J. Dubowitz and Thomas T. Liu},
Journal = {NeuroImage},
Year = {2004},
Note = {Mathematics in Brain Imaging},
Number = {Supplement 1},
Pages = {S220 - S233},
Volume = {23},
Abstract = {Neural activity in the brain is accompanied by changes in cerebral blood flow (CBF) and blood oxygenation that are detectable with functional magnetic resonance imaging (fMRI) techniques. In this paper, recent mathematical models of this hemodynamic response are reviewed and integrated. Models are described for: (1) the blood oxygenation level dependent (BOLD) signal as a function of changes in cerebral oxygen extraction fraction (E) and cerebral blood volume (CBV); (2) the balloon model, proposed to describe the transient dynamics of CBV and deoxy-hemoglobin (Hb) and how they affect the BOLD signal; (3) neurovascular coupling, relating the responses in CBF and cerebral metabolic rate of oxygen (CMRO2) to the neural activity response; and (4) a simple model for the temporal nonlinearity of the neural response itself. These models are integrated into a mathematical framework describing the steps linking a stimulus to the measured BOLD and CBF responses. Experimental results examining transient features of the BOLD response (post-stimulus undershoot and initial dip), nonlinearities of the hemodynamic response, and the role of the physiologic baseline state in altering the BOLD signal are discussed in the context of the proposed models. Quantitative modeling of the hemodynamic response, when combined with experimental data measuring both the BOLD and CBF responses, makes possible a more specific and quantitative assessment of brain physiology than is possible with standard BOLD imaging alone. This approach has the potential to enhance numerous studies of brain function in development, health, and disease.},
Doi = {DOI: 10.1016/j.neuroimage.2004.07.013},
ISSN = {1053-8119},
Keywords = {Hemodynamic response}
}
@PhdThesis{Callaerts89,
Title = {Signal Separation Methods based on Singular Value Decomposition and their Application to the Real-Time Extraction of the Fetal Electrocardiogram from Cutaneous Recordings},
Author = {Dirk Callaerts},
School = {K.U.Leuven - E.E. Dept.},
Year = {Dec. 1989},
Owner = {sameni},
Timestamp = {2010.03.07}
}
@Article{0143-0815-10-4B-001,
Title = {Description of a real-time system to extract the fetal electrocardiogram},
Author = {D Callaerts and W Sansen and J Vandewalle and G Vantrappen and J Janssens},
Journal = {Clinical Physics and Physiological Measurement},
Year = {1989},
Number = {4B},
Pages = {7-10},
Volume = {10},
Abstract = {An overview is given of the FEMME-project (Fetal Electrocardiogram Measuring Method and Equipment). The project started in 1981 and is close to producing a prototype personal computer-based system. This records simultaneously a number of cutaneous potential signals and derives from this set one or more maternal electrocardiogram-free fetal heart signals, by combining linearly the recorded signals. An on-line adaptive algorithm based on the Singular Value Decomposition (SVD) has been designed to compute the coefficients in these linear combinations. This algorithm will be implemented on a DSP board that can be plugged into the real-time recording system. The system will be very useful in studies of the fetal electrocardiogram during pregnancy but also in all other studies such as fetal heart rate variability, fetal movements, etc., where a precise trigger of the electrical signal from the fetal heart is required. },
Url = {http://stacks.iop.org/0143-0815/10/7}
}
@Article{Call86,