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NOISE and INTERFERENCE in BIOMEDICAL ENGINEERING
 

The path to our destination is not always a straight one. We go down the wrong road,

 we get lost, we turn back. Maybe it doesn't matter which road we embark on.

Maybe what matters is that we embark” (Barbara Hall)

  1. Da Zhang, Paul Solomon, Shi-Li Zhang, Zhen Zhang: An impedance model for the low-frequency noise originating from the dynamic hydrogen ion reactivity at the solid/liquid interface. Sensors and Actuators B: Chemical, 2017, DOI 10.1016/j.snb.2017.07.054

  2. Da Zhang, Paul Solomon, Shi-Li Zhang, Zhen Zhang: Correlation of Low-Frequency Noise to the Dynamic Properties of the Sensing Surface in Electrolytes. ACS Sensors, 2017. DOI 10.1021/acssensors.7b00285

  3. Da Zhang, Indrek Must, Nathan L. Netzer, Xingxing Xu, Paul Solomon, Shi-Li Zhang, Zhen Zhang: Direct assessment of solid–liquid interface noise in ion sensing using a differential method. Appl. Phys. Lett., Vol. 108, no. 15, 2016, Article # 151603. http://dx.doi.org/10.1063/1.4946857

  4. Takao Hiroyuki, Yeh Yu Chih, Arita Hiroyuki, et al.: Primary Salvage Survey of the Interference of Radiowaves Emitted by Smartphones on Medical Equipment. Health Physics, Vol. 111, no. 4, 2016, pp. 381 – 392. DOI 10.1097/HP.0000000000000535

  5. Mariappan Periyasamy M., Raghavan Dhanasekaran R., Aleem Shady H.E.A., et al.: Effects of electromagnetic interference on the functional usage of medical equipment by 2G/3G/4G cellular phones: A review. J. of Advanced Research, Vol. 7, no. 5, 2016, pp. 727 – 738. DOI 10.1016/j.jare.2016.04.004

  6. Sidhu Preena, Shankargouda Swapnil, Dicksit Daniel DevaPrakash, et al.: Evaluation of Interference of Cellular Phones on Electronic Apex Locators: An In Vitro Study. J. of Endodontics, Vol. 42, no. 4, 2016, pp. 622 – 625. DOI 10.1016/j.joen.2015.12.027

  7. Ishida Kai, Fujioka Tomomi, Endo Tetsuo, et al.: Evaluation of Electromagnetic Fields in a Hospital for Safe Use of Electronic Medical Equipment. J. of Medical Systems, Vol. 40, no. 3, 2016, Article # 46. DOI 10.1007/s10916-015-0411-3

  8. Yuan Zou, Viswam Nathan, Roozbeh Jafari: Automatic Identification of Artifact-related Independent Components for Artifact Removal in EEG Recordings. IEEE Journal of Biomedical and Health Informatics (J-BHI), Vol. 20, no. 1, 2016, pp. 73 – 81. DOI 10.1109/JBHI.2014.2370646

  9. Viswam Nathan, Roozbeh Jafari: Design Principles and Dynamic Front End Reconfiguration for Low Noise EEG Acquisition with Finger Based Dry Electrodes. IEEE Transactions on Biomedical Circuits and Systems T-BioCAS), Vol. 9, no. 5, 2015, pp. 631 – 640. DOI 10.1109/TBCAS.2015.2471080

  10. Maebatake Akira, Akamatsu Go, Miwa Kenta, et al.: Relationship between the image quality and noise-equivalent count in time-of-flight positron emission tomography. Annals of Nuclear Medicine, Vol. 30, no. 1, 2016, pp. 68 – 74. DOI 10.1007/s12149-015-1032-0

  11. Kelly J.W., Siewiorek D.P., Smailagic A., Wang W.: An Adaptive Filter for the Removal of Drifting Sinusoidal Noise Without a Reference. IEEE Journal of Biomedical and Health Informatics, Vol. 20, no. 1, 2016, pp. 213 – 221. DOI 10.1109/JBHI.2014.2375318

  12. Yamada Isao: Stochastic model for quantum noise analysis in flat-panel detectors for medical imaging applications. J. of the Optical Society of America A - Optics Image Science & Vision, Vol. 33, no. 12, 2016, pp. 2443 – 2449. DOI 10.1364/JOSAA.33.002443

  13. Nakaya Ryo, Ando Hidenawo, Anzai Daisuke, et al.: Statistical Measurement of Electromagnetic Noise Characteristics of ESD in Wireless Frequency Bands and Influence Evaluation on Communication Performance. IEICE Trans on Communications, Vol. E99B, no. 11, 2016, pp. 2399 – 2405. DOI 10.1587/transcom.2015EBP3527

  14. Erickson J.C., Putney J., Hilbert D., et al.: Iterative Covariance-Based Removal of Time-Synchronous Artifacts: Application to Gastrointestinal Electrical Recordings. IEEE Trans on Biomedical Eng., Vol. 63, no. 11, 2016, pp. 2262 – 2272. DOI 10.1109/TBME.2016.2521764

  15. Lee D., Kim Y.-s., Choi S., et al.: Improvement of material decomposition and image quality in dual-energy radiography by reducing image noise. J. of Instrumentation, Vol. 11, 2016, Article # T08003. DOI 10.1088/1748-0221/11/08/T08003

  16. Bhargava Shruti, Somkuwar Ajay: Estimation of Noise Removal Techniques in Medical Imaging Data-A Review. J. of Medical Imaging and Health Informatics, Vol. 6, no. 4, 2016, pp. 875 – 884. DOI 10.1166/jmihi.2016.1797

  17. Lutman M.E., Coles R.R.A., Buffin J.T.: Guidelines for quantification of noise-induced hearing loss in a medicolegal context. Clinical Otolaryngology, Vol. 41, no. 4, 2016, pp. 347 – 357. DOI 10.1111/coa.12569

  18. Rocek A., Slavicek K., Dostal O., et al.: A new approach to fully-reversible watermarking in medical imaging with breakthrough visibility parameters. Biomedical Signal Processing and Control, Vol. 29, 2016, pp. 44 – 52. DOI 10.1016/j.bspc.2016.05.005

  19. Dumont D.M., Walsh K.M., Byram B.C.: Improving Displacement Signal-to-Noise Ratio for Low-Signal Radiation Force Elasticity Imaging Using Bayesian Techniques. Ultrasound in Medicine and Biology, Vol. 42, no. 8, 2016, pp. 1986 – 1997. DOI 10.1016/j.ultrasmedbio.2016.03.004

  20. Zhu Zack, Satizabal H.F., Blanke U., et al.: Naturalistic Recognition of Activities and Mood Using Wearable Electronics. IEEE Trans on Affective Computing, Vol. 7, no. 3, 2016, pp. 272 – 285. DOI 10.1109/TAFFC.2015.2491927

  21. Das Devarshi Mrinal, Srivastava Abhishek, Ananthapadmanabhan J., et al.: A novel low-noise fully differential CMOS instrumentation amplifier with 1.88 noise efficiency factor for biomedical and sensor applications. Microelectronics J., Vol. 53, 2016, pp. 35 – 44. DOI 10.1016/j.mejo.2016.04:008

  22. Kishore P.V.V., Sastry A.S.C.S., Rahman Md. Zia Ur: Double Technique for Improving Ultrasound Medical Images. J. of Medical Imaging and Health Informatics, Vol. 6, no. 3, 2016, pp. 667 – 675. DOI 10.1166/jmihi.2016.1743

  23. Nguyen Tuan-Anh, Nakib Amir, Nguyen Huy-Nam: Medical image denoising via optimal implementation of non-local means on hybrid parallel architecture. Computer Methods and Programs in Biomedicine, Vol. 129, 2016, pp. 29 – 39. DOI 10.1016/j.cmpb.2016.02.002

  24. Al-Frejat Mu'taz, Btoush Mohammad Hjouj: A New Approach for Enhancing the Quality of Medical Computerized Tomography Images. Int. J. of Advanced Computer Science & Applications, Vol. 7, no. 5, 2016, pp. 476 – 483. DOI 10.14569/IJACSA.2016.070564

  25. Zhou Xin, Wang Pei, Ju Yingyun, et al.: A New Active Contour Model Based on Distance-Weighted Potential Field. Circuits Systems & Signal Processing, Vol. 35, no. 5, 2016, pp. 1729 – 1750. DOI 10.1007/s00034-015-0143-0

  26. Gao Yue Ming, Ye Yan Ting, Vai Mang I., et al.: Channel modeling and power consumption analysis for galvanic coupling intra-body communication. EURASIP Journal on Wireless Communications & Networking, 2016, Article # 106. DOI 10.1186/s13638-016-0604-6

  27. Cheng Jie-Zhi, Ni Dong, Chou Yi-Hong, et al.: Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans. Scientific Reports, Vol. 6, 2016, Article # 24454. DOI 10.1038/srep24454

  28. Dey Nilanjan, Bose Soumyo, Das Achintya, et al.: Effect of Watermarking on Diagnostic Preservation of Atherosclerotic Ultrasound Video in Stroke Telemedicine. J. of Medical Systems, Vol. 40, no. 4, 2016, Article # 91. DOI 10.1007/s10916-016-0451-3

  29. Kittisuwan Pichid: Medical image denoising using simple form of MMSE estimation in Poisson-Gaussian noise model. Int. J. of Biomathematics, Vol. 9, no. 2, 2016, Article # 1650020. DOI 10.1142/S1793524516500200

  30. Rampazzi S., Danese G., Leporati F., et al.: A Localized Surface Plasmon Resonance-Based Portable Instrument for Quick On-Site Biomolecular Detection. IEEE Trans on Instr. & Meas., Vol. 65, no. 2, 2016, pp. 317 – 327. DOI 10.1109/TIM.2015.2465691

  31. Gromann L.B., Beque D., Scherer K., et al.: Low-dose, phase-contrast mammography with high signal-to-noise ratio. Biomedical Optics Express, Vol. 7, no. 2, 2016, pp. 381 – 391. DOI 10.1364/BOE.7.000381

  32. Feng Wensen, Qiao Hong, Chen Yunjin: Poisson Noise Reduction with Higher-Order Natural Image Prior Model. SIAM Journal on Imaging Sciences, Vol. 9, no. 3, 2016, pp. 1502 – 1524. DOI 10.1137/16M1072930

  33. Abhinav G. A., Saseendran S., et al.: Design and Development of an Innovative Biomedical Engineering Application Toolkit (BEAT (R)) for m-Health Applications. Advances in Signal Processing and Intelligent Recognition Systems (SIRS), Book Series: Advances in Intelligent Systems and Computing, Vol. 425, 2016, pp. 299 – 308, Springer-Verlag Berlin. DOI 10.1007/978-3-319-28658-7_26

  34. Pandey Madhulika Bhati, Madhulika Bansa, Abhay: An Anatomization of Noise removal techniques on Medical Images. 1st Int. Conf on Innovation and Challenges in Cyber Security (ICICCS), 2016, pp. 225 – 230.

  35. Amini Zahra, Rabbani Hossein: Classification of Medical Image Modeling Methods: A Review. Current Medical Imaging Reviews, Vol. 12, no. 2, 2016, pp. 130 – 148. DOI 10.2174/1573394711666150827203543

  36. Lee D., Kim Y.-s., Choi S., et al.: A feasibility study for anatomical noise reduction in dual-energy chest digital tomosynthesis. J. of Instrumentation, Vol. 11, 2016, Article # P01016. DOI 10.1088/1748-0221/11/01/P01016

  37. Assecondi S., Lavallee C., Ferrari P., et al.: Length matters: Improved high field EEG-fMRI recordings using shorter EEG cables. J. of Neuroscience Methods, Vol. 269, 2016, pp. 74 – 87. DOI 10.1016/j.jneumeth.2016.05.014

  38. Valente W., Jr., Raizer A., Pichon L.: The Use of Equivalent Model and Numerical Simulation for EMC Analysis in Hospital Environments. IEEE Trans on EMC, Vol. 58, Part: 1, no. 4, 2016, pp. 950 – 955. DOI 10.1109/TEMC.2016.2552170

  39. Liu Thomas T.: Noise contributions to the fMRI signal: An overview. Neuroimage, Vol. 143, 2016, pp. 141 – 151. DOI 10.1016/j.neuroimage.2016.09.008

  40. Triantafyllou C., Polimeni J.R., Keil B., et al.: Coil-to-Coil Physiological Noise Correlations and Their Impact on Functional MRI Time-Series Signal-to-Noise Ratio. Magnetic Resonance in Medicine, Vol. 76, no. 6, 2016, pp. 1708 – 1719. DOI 10.1002/mrm.26041

  41. Sahib Ashish Kaul, Mathiak K., Erb M., et al.: Effect of Temporal Resolution and Serial Autocorrelations in Event-Related Functional MRI. Magnetic Resonance in Medicine, Vol. 76, no. 6, 2016, pp. 1805 – 1813. DOI 10.1002/mrm.26073

  42. Veraart J., Novikov D.S., Christiaens D., et al.: Denoising of diffusion MRI using random matrix theory. Neuroimage, Vol. 142, 2016, pp. 384 – 396. DOI 10.1016/j.neuroimage.2016.08.016

  43. Veraart J., Fieremans E., Novikov D.S.: Diffusion MRI Noise Mapping Using Random Matrix Theory. Magnetic Resonance in Medicine, Vol. 76, no. 5, 2016, pp. 1582 – 1593. DOI 10.1002/mrm.26059

  44. Gonzalez-Castillo J., Panwar Puja, Buchanan L.C., et al.: Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI. Neuroimage, Vol. 141, 2016, pp. 452 – 468. DOI 10.1016/j.neuroimage.2016.07.049

  45. Heydari M., Karami M.-R., Babakhani A.: A new adaptive coupled diffusion PDE for MRI Rician noise. Signal Image & Video Processing, Vol. 10, no. 7, 2016, pp. 1211 – 1218. DOI 10.1007/s11760-016-0878-5

  46. Hu Jinrong, Zhou Jiliu, Wu Xi: Non-local MRI denoising using random sampling. Magnetic Resonance Imaging, Vol. 34, no. 7, 2016, pp. 990 – 999. DOI 10.1016/j.mri.2016.04.008

  47. Jann Kay, Smith R.X., Piedra E.A.R., et al.: Noise Reduction in Arterial Spin Labeling Based Functional Connectivity Using Nuisance Variables. Frontiers in Neuroscience, Vol. 10, 2016, Article # 371. DOI 10.3389/fnins.2016.00371

  48. Lucano E., Liberti M., Mendoza G.G., et al.: Assessing the Electromagnetic Fields Generated By a Radiofrequency MRI Body Coil at 64 MHz: Defeaturing Versus Accuracy. IEEE Trans on Biomedical Eng., Vol. 63, no. 8, 2016, pp. 1591 – 1601. DOI 10.1109/TBME.2015.2506680

  49. Layton K.J., Kroboth S., Jia Feng, et al.: Trajectory Optimization Based on the Signal-to-Noise Ratio for Spatial Encoding with Nonlinear Encoding Fields. Magnetic Resonance in Medicine, Vol. 76, no. 1, 2016, pp. 104 – 117. DOI 10.1002/mrm.25859

  50. Alberdi Ane, Aztiria A., Basarab Adrian: On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey. Artificial Intelligence in Medicine, Vol. 71, 2016, pp. 1 – 29. DOI 10.1016/j.artmed.2016.06.003

  51. Konishi Yuto, Kanazawa Yuki, Usuda Takatoshi, et al.: Simple noise reduction for diffusion weighted images. Radiological Physics and Technology, Vol. 9, no. 2, 2016, pp. 221 – 226. DOI 10.1007/s12194-016-0350-9

  52. Sudeep P.V., Palanisamy P., Rajan J., et al.: Speckle reduction in medical ultrasound images using an unbiased non-local means method. Biomedical Signal Processing and Control, Vol. 28, 2016, pp. 1 – 8. DOI 10.1016/j.bspc.2016.03.001

  53. Kobald S.O., Getzmann S., Beste C., et al.: The impact of simulated MRI scanner background noise on visual attention processes as measured by the EEG. Scientific Reports, Vol. 6, 2016, Article # 28371. DOI 10.1038/srep28371

  54. Talagala S.L., Sarlls J.E., Liu Siyuan, et al.: Improvement of Temporal Signal-to-Noise Ratio of GRAPPA Accelerated Echo Planar Imaging Using a FLASH Based Calibration Scan. Magnetic Resonance in Medicine, Vol. 75, no. 6, 2016, pp. 2362 – 2371. DOI 10.1002/mrm.25846

  55. Campbell-Washburn A.E., Atkinson D., Nagy Z., et al.: Using the Robust Principal Component Analysis Algorithm to Remove RF Spike Artifacts from MR Images. Magnetic Resonance in Medicine, Vol. 75, no. 6, 2016, pp. 2517 – 2525. DOI 10.1002/mrm.25851

  56. Marchitelli R., Minati L., Marizzoni M., et al.: Test-Retest Reliability of the Default Mode Network in a Multi-Centric fMRI Study of Healthy Elderly: Effects of Data-Driven Physiological Noise Correction Techniques. Human Brain Mapping, Vol. 37, no. 6, 2016, pp. 2114 – 2132. DOI 10.1002/hbm.23157

  57. Sotelo J., Urbina J., Valverde I., et al.: 3D Quantification of Wall Shear Stress and Oscillatory Shear Index Using a Finite-Element Method in 3D CINE PC-MRI Data of the Thoracic Aorta. IEEE Trans on Medical Imaging, Vol. 35, no. 6, 2016, pp. 1475 – 1487. DOI 10.1109/TMI.2016.2517406

  58. Fritz J., Raithel E., Thawait G.K., et al.: Six-Fold Acceleration of High-Spatial Resolution 3D SPACE MRI of the Knee Through Incoherent k-Space Undersampling and Iterative ReconstructionFirst Experience. Investigative Radiology, Vol. 51, no. 6, 2016, pp. 400 – 409. DOI 10.1097/RLI.0000000000000240

  59. Wu Xi, Yang Zhipeng, Peng Jing, et al.: Global denoising for 3D MRI. Biomedical Engineering OnLine, Vol. 15, 2016, Article # 54. DOI 10.1186/s12938-016-0168-z

  60. Scott A.D., Nielles-Vallespin S., Ferreira P.F., et al.: The effects of noise in cardiac diffusion tensor imaging and the benefits of averaging complex data. NMR in Biomedicine, Vol. 29, no. 5, 2016, pp. 588 – 599. DOI 10.1002/nbm.3500

  61. Feng Chaolu, Zhao Dazhe, Huang Min: Image segmentation using CUDA accelerated non-local means denoising and bias correction embedded fuzzy c-means (BCEFCM). Signal Processing, Vol. 122, 2016, pp. 164 – 189. DOI 10.1016/j.sigpro.2015.12.007

  62. Yan Xinqiang, Cao Zhipeng, Zhang Xiaoliang: Simulation Verification of SNR and Parallel Imaging Improvements by ICE-Decoupled Loop Array in MRI. Applied Magnetic Resonance, Vol. 47, no. 4, 2016, pp. 395 – 403. DOI 10.1007/s00723-016-0764-x

  63. Tariyal Snigdha, Aggarwal H.K., Majumdar Angshul: Removing sparse noise from hyperspectral images with sparse and low-rank penalties. J. of Electronic Imaging, Vol. 25, no. 2, 2016, Article # 020501. DOI 10.1117/1.JEI.25.2.020501

  64. Kwon Kinam, Kim Dongchan, Park HyunWook: Multi-contrast MR image denoising for parallel imaging using multilayer perceptron. Int. J. of Imaging Systems & Technology, Vol. 26, no. 1, 2016, pp. 65 – 75. DOI 10.1002/ima.22158

  65. Dai Weiying, Varma Gopal, Scheidegger R., et al.: Quantifying fluctuations of resting state networks using arterial spin labeling perfusion MRI. J. of Cerebral Blood Flow and Metabolism, Vol. 36, no. 3, 2016, pp. 463 – 473. DOI 10.1177/0271678X15615339

  66. Broadbent D.A., Biglands J.D., Ripley D.P., et al.: Sensitivity of quantitative myocardial dynamic contrast-enhanced MRI to saturation pulse efficiency, noise and t(1) measurement error: Comparison of nonlinearity correction methods. Magnetic Resonance in Medicine, Vol. 75, no. 3, 2016, pp. 1290 – 1300. DOI 10.1002/mrm.25726

  67. Stemkens B., Sbrizzi A., Andreychenko A.A., et al.: An optimization framework to maximize signal-to-noise ratio in simultaneous multi-slice body imaging. NMR in Biomedicine, Vol. 29, no. 3, 2016, pp. 275 – 283. DOI 10.1002/nbm.3457

  68. Hartwig V., Vivoli G., Tassano S., et al.: Decoupling and shielding numerical optimization of MRI phased-array coils. Measurement, Vol. 82, 2016, pp. 450 – 460. DOI 10.1016/j.measurement.2016.01.021

  69. Taylor M.A., Bowen W.P.: Quantum metrology and its application in biology. Physics Reports - Review Section of Physics Lett., Vol. 615, 2016, pp. 1 – 59. DOI 10.1016/j.physrep.2015.12.002

  70. Chen Geng, Zhang Pei, Wu Yafeng, et al.: Denoising magnetic resonance images using collaborative non-local means. Neurocomputing, Vol. 177, 2016, pp. 215 – 227. DOI 10.1016/j.neucom.2015.11.031

  71. Hagiwara Akifumi, Hori Masaaki, Suzuki Michimasa, et al.: Contrast-enhanced synthetic MRI for the detection of brain metastases. Acta Radiologica Open, Vol. 5, 2016, no. 2. DOI 10.1177/2058460115626757

  72. De Santiago L., Ortiz del Castillo M., Blanco R., et al.: A signal-to-noise-ratio-based analysis of multifocal visual-evoked potentials in multiple sclerosis risk assessment. Clinical Neurophysiology, Vol. 127, no. 2, 2016, pp. 1574 – 1580. DOI 10.1016/j.clinph.2015.09.129

  73. Liu Jia, Gasbarra D., Railavo J.: Fast estimation of diffusion tensors under Rician noise by the EM algorithm. J. of Neuroscience Methods, Vol. 257, 2016, pp. 147 – 158. DOI 10.1016/j.jneumeth.2015.09.029

  74. Kousi E., Borri M., Dean J., et al.: Quality assurance in MRI breast screening: comparing signal-to-noise ratio in dynamic contrast-enhanced imaging protocols. Physics in Medicine and Biology, Vol. 61, no. 1, 2016, pp. 37 – 49. DOI 10.1088/0031-9155/61/1/37

  75. Seetha J., Raja S. Selvakumar: Denoising of MRI Images using Filtering Methods. Proc. of IEEE Int. Conf on Wireless Comm., Signal Processing & Networking (WISPNET), 2016, pp. 765 – 769.

  76. Kaur Hardeep, Rani Jyoti: MRI brain image enhancement using Histogram equalization Techniques. IEEE Int. Conf on Wireless Communications, Signal Processing & Networking (WiSPNET), 2016, pp. 770 – 773.

  77. Sowjanya V., Rao G. Sasibhushana, Sarvani A.: Investigation of Optimal Wavelet Techniques for De-noising of MRI Brain Abnormal Image. Int. Conf on Computational Modelling & Security (CMS), 2016, pp. 93 – 96. DOI 10.1016/j.procs.2016.05.252

  78. Pieciak T., Vegas-Sanchez-Ferrero G., Aja-Fernandez S.: Variance Stabilization of Noncentral-CHI Data: Application to Noise Estimation in MRI. IEEE Int. Symp on Biomedical Imaging (ISBI), 2016, pp. 1376 – 1379.

  79. Saini Ashwini Kumar, Bhadauria H.S., Singh A.: A Survey of noise removal methodologies for lung cancer diagnosis. Int. Conf on Computational Intelligence & Communication Technology (CICT), 2016, pp. 673 – 678. DOI 10.1109/CICT.2016.139

  80. Gao Dongrui, Li Mingzhe, Li Jianfu, et al.: Effects of various typical electrodes and electrode gels combinations on MRI signal-to-noise ratio and safety issues in EEG-fMRI recording. Biocybernetics & Biomedical Eng. (BBE), Vol. 36, no. 1, 2016, pp. 9 – 18. DOI 10.1016/j.bbe.2015.11.007

  81. Marquardt D., Hohmann V., Doclo S.: Interaural Coherence Preservation in Multi-Channel Wiener Filtering-Based Noise Reduction for Binaural Hearing Aids. IEEE/ACM Trans on Audio, Speech & Language Processing, Vol. 23, no. 12, 2015, pp. 2162 – 2176. DOI 10.1109/TASLP.2015.2471096

  82. Zele A.J., Feigl B., Kambhampati P.K., et al.: A method for estimating intrinsic noise in electroretinographic (ERG) signals. Documenta Ophthalmologica, Vol. 131, no. 2, 2015, pp. 85 – 94. DOI 10.1007/s10633-015-9510-1

  83. Akwei-Sekyere Samuel: Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis. PeerJ 3:e1086, 2015, 15 pages. https://doi.org/10.7717/peerj.1086

  84. Tan Yang, Holleman J.: An Ultralow-Power Low-Noise CMOS Biopotential Amplifier for Neural Recording. IEEE Trans on CAS II : Express Briefs, Vol. 62, no. 10, 2015, pp. 927 – 931. DOI 10.1109/TCSII.2015.2457811

  85. Chandrakumar H., Markovic D.: A Simple Area-Efficient Ripple-Rejection Technique for Chopped Biosignal Amplifiers. IEEE Trans on CAS II : Express Briefs, Vol. 62, no. 2, 2015, pp. 189-193. DOI 10.1109/TCSII.2014.2387686

  86. Shi Feng, Rui Qiang, Kainz W., Ji Chen: A Technique to Evaluate MRI-Induced Electric Fields at the Ends of Practical Implanted Lead. IEEE Trans on MTT, Vol. 63, no. 1, 2015, pp. 305 – 313. DOI 10.1109/TMTT.2014.2376523

  87. Dawei Li, Jianfeng Zheng, Yan Liu, Changwang Pan, Kainz, W., Fan Yang, Wen Wu, Ji Chen: An Efficient Approach to Estimate MRI RF Field Induced In Vivo Heating for Small Medical Implants. IEEE Trans on EMC, Vol. 57, no. 4, 2015, pp. 643 – 650. DOI 10.1109/TEMC.2015.2435519

  88. Abdel-Hafez M., Husseini G.A.: Predicting the Release of Chemotherapeutics From the Core of Polymeric Micelles Using Ultrasound. IEEE Trans on NanoBioscience, Vol. 14, no. 4, 2015, pp. 378 – 384. DOI 10.1109/TNB.2015.2399100

  89. Katsigiannis S., Zacharia E., Maroulis D.: Grow-Cut Based Automatic cDNA Microarray Image Segmentation. IEEE Trans on NanoBioscience, Vol. 14, no. 1, 2015, pp. 138 – 145. DOI 10.1109/TNB.2014.2369961

  90. Botter A., Vieira T.M.: Filtered Virtual Reference: A New Method for the Reduction of Power Line Interference With Minimal Distortion of Monopolar Surface EMG. IEEE Trans on Biomedical Eng., Vol. 62, no. 11, 2015, pp. 2638 – 2647. DOI 10.1109/TBME.2015.2438335

  91. Amparo Callejon M., Reina-Tosina J., Naranjo-Hernandez D., Roa L.M.: Measurement Issues in Galvanic Intrabody Communication: Influence of Experimental Setup. IEEE Trans on Biomedical Eng., Vol. 62, no. 11, 2015, pp. 2724 – 2732. DOI 10.1109/TBME.2015.2444916

  92. Rebersek M., Marjanovic I., Begus S., Pillet F., Rols M.-P., Miklavcic D., Kotnik T.: Generator and Setup for Emulating Exposures of Biological Samples to Lightning Strokes. IEEE Trans on Biomedical Eng., Vol. 62, no. 10, 2015, pp. 2535 – 2543. DOI 10.1109/TBME.2015.2437359

  93. Emmanouilidou D., McCollum E.D., Park D.E., Elhilali M.: Adaptive Noise Suppression of Pediatric Lung Auscultations With Real Applications to Noisy Clinical Settings in Developing Countries. IEEE Trans on Biomedical Eng., Vol. 62, no. 9, 2015, pp. 2279 – 2288. DOI 10.1109/TBME.2015.2422698

  94. Bar-Zion A.D., Tremblay-Darveau C., Yin M., Adam D., Foster F.S.: Denoising of Contrast-Enhanced Ultrasound Cine Sequences Based on a Multiplicative Model. IEEE Trans on Biomedical Eng., Vol. 62, no. 8, 2015, pp. 1969 – 1980. DOI 10.1109/TBME.2015.2407835

  95. Craven D., McGinley B., Kilmartin L., Glavin M., Jones E.: Compressed Sensing for Bioelectric Signals: A Review. IEEE Journal of Biomedical and Health Informatics, Vol. 19, no. 2, 2015, pp. 529 – 540. DOI 10.1109/JBHI.2014.2327194

  96. Jinwei Xu, Jiankun Hu, Xiuping Jia: A Multistaged Automatic Restoration of Noisy Microscopy Cell Images. IEEE Journal of Biomedical and Health Informatics, Vol. 19, no. 1, 2015, pp. 367 – 376. DOI 10.1109/JBHI.2014.2305445

  97. Peyghambari A., Dastfan A., Ahmadyfard A.: Strategy for switching period selection in random pulse width modulation to shape the noise spectrum. IET Power Electronics, Vol. 8, no. 4, 2015, pp. 517 – 523. DOI 10.1049/iet-pel.2014.0118

  98. Takahashi Y., Ahagon A., Fujiwara K., Iwashita T., Nakashima H.: Analysis of induced electric field in human body by utility power frequency magnetic field using parallel fast multipole-accelerated boundary element method. IET Science, Measurement & Technology, Vol. 9, no. 2, 2015, pp. 178 – 183. DOI 10.1049/iet-smt.2014.0195

  99. Xin Huang, Jianfeng Zheng, Xin Wu, Kono M., Hozono H., Kainz W., Fan Yang, Ji Chen: MRI Heating Reduction for External Fixation Devices Using Absorption Material. IEEE Trans on EMC, Vol. 57, no. 4, 2015, pp. 635 – 642. DOI 10.1109/TEMC.2015.2407318

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