Giovanni Sparacino
Selected
Publications
JOURNALS
(List updated on April 26, 2018)
1.
Sparacino, G., C.Cobelli. A stochastic deconvolution approach to reconstruct
insulin secretion rate after a glucose stimulus. IEEE Transactions on Biomedical Engineering 42:
512‑529, 1996.
2.
Sparacino, G., C. Cobelli. Reconstruction of insulin secretion by deconvolution: Domain of validity of a
monoexponential impulse response model. Technology
and Health Care 4: 87‑95, 1996.
3.
Sparacino, G., C. Cobelli. Impulse response model in reconstruction of insulin
secretion by deconvolution. Role of input design in the identification
experiment. Annals of
Biomedical Engineering 25: 398‑416, 1997.
4.
Sparacino, G., Vicini, P., Bonadonna, R., Marraccini, P.,
Lehtovirta, M., Ferrannini, E., Cobelli, C. Removal of catheter distortion in multiple indicator
dilution studies: A deconvolution‑based method and case studies on
glucose blood‑tissue exchange. Medical & Biological Engineering & Computing 35: 337-342,
1997.
5.
Vicini, P., G. Sparacino,
A.Caumo e C.Cobelli. Estimation of hepatic glucose release after a glucose
perturbation by nonparametric stochastic deconvolution. Computer Methods and Programs in Biomedicine
52: 147‑156, 1997.
6.
De Nicolao, G., G. Sparacino
e C.Cobelli. Nonparametric input estimation in physiological
systems: problems, methods, case studies. Automatica 33: 851‑870, 1997.
7.
Sparacino, G., R.Bonadonna, H.Steinberg, A.Baron, e C.Cobelli. Estimation of organ transport function from
recirculating indicator dilution curves. Annals of Biomedical Engineering 26: 128-137, 1998.
8.
Sparacino, G. , S. Milani, V. Magnavita, E.Arslan. Electrocochleography potentials evoked from
condensation and rarefaction clicks independently derived by a new numerical filtering approach. Audiology & Neuro-Otology 5: 276-291, 2000.
9.
Sparacino, G., C.Tombolato, C.Cobelli. Maximum Likelihood vs Maximum a Posteriori Parameter
Estimation of Physiological System Models: The C-peptide Impulse Response Case
Study. IEEE Transactions on
Biomedical Engineering 47: 801-811, 2000 .
10.
Sparacino, G., F. Bardi, C.Cobelli. Approximate Entropy studies of hormone pulsatility
from plasma concentration time-series: influence of the kinetics assessed by
simulation. Annals of
Biomedical Engineering 28: 665 - 676, 2000.
11.
De Nicolao, G., G. Ferrari Trecate, G. Sparacino. Fast spline smoothing via spectral factorization concepts.
Automatica 36: 1733-1739, 2000.
12.
Magni, P, Bellazzi, R, Sparacino, G C.Cobelli. Bayesian identification of a population compartmental
model of C-peptide kinetics.
Annals of Biomedical Engineering 28: 812-823, 2000.
13.
E.Arslan, R.Santarelli, G.Sparacino and G.Sella. Compound action potential and cochlear microphonic
extracted from electrocochleographic responses to condensation or rarefaction
clicks, Acta Otolaryngol
120:192-196, 2000.
14.
Pillonetto, G., G. Sparacino,
and C. Cobelli. Reconstructing
insulin secretion rate after a glucose stimulus by an improved stochastic
deconvolution method. IEEE Transactions on Biomedical Engineering
48:1352-1354, 2001.
15.
Sparacino, G., G.Pillonetto, M.Capello, G. De Nicolao, C.Cobelli. Winstodec: a stochastic deconvolution interactive
program for physiological and pharmacokinetic systems. Computer Methods and Programs in
Biomedicine 67: 67-77, 2002.
16. Pillonetto, G., Sparacino, G.,
Magni, P., Bellazzi, R., Cobelli, C. Minimal model
S(I)=0 problem in NIDDM subjects: nonzero Bayesian estimates with credible
confidence intervals. American Journal of
Physiology: Endocrinology and Metabolism,
282(3):E564-573, 2002
17. Sparacino, G, D.M. Shames, P.Vicini, J.C. King, C.Cobelli. Domain of validity
of the double isotope tracer method for measuring fractional zinc absorption:
theoretical justification. American Journal of
Physiology: Endocrinology and Metabolism 282:
E679-687, 2002.
18.
Sparacino, G., S.Milani , E.Arslan, C.Cobelli. A Bayesian approach to estimate evoked potentials.
Computer Methods and Programs in
Biomedicine, 68: 233-2482, 2002.
19.
Pillonetto G, Sparacino
G, Cobelli C. Handling
non-negativity in deconvolution of physiological signals: a nonlinear
stochastic approach. Annals of
Biomededical Engineering, 30:1077-87, 2002.
20. Pillonetto G, Sparacino G,
Cobelli C. Numerical
non-identifiability regions of the minimal model of glucose kinetics:
superiority of Bayesian identification. Mathematical Biosciences, 184: 53-67, 2003.
21. Bertoldo A, Sparacino G, Cobelli
C. "Population" approaches improve parameter
estimation of kinetic models from dynamic PET data. IEEE
Transactions on Medical Imaging
23:297-306, 2004.
22. Magni, P. G.Sparacino,
R.Bellazzi, G.M. Toffolo, C.Cobelli. Insulin Minimal Model Indexes and Secretion: Proper
Handling of Uncertainty by a Bayesian Approach. Annals of Biomedical
Engineering Ann Biomed Eng:1027-37, 2004.
23. Sparacino, G, R.Santarelli, A.Nale, E.Arslan. Deconvolution Method for Auditory Steady-State
Responses. Medical & Biological Engineering & Computing
42(4):569-76, 2004.
24. Magni P, Sparacino
G, Bellazzi R, Cobelli C. Reduced sampling schedule for the glucose minimal
model: importance of Bayesian estimation. Am J Physiol
Endocrinol Metab. 290(1):E177-E184, 2006.
25. Pillonetto G, Caumo A,
Sparacino G, Cobelli C. A new dynamic index of insulin sensitivity.
IEEE Trans Biomed Eng. 53(3):369-79, 2006.
26. Sparacino, G. Zanderigo F., Maran A, Cobelli C. Continuous glucose Monitoring and Hypo/Hyperglycaemia
Prediction e , Diabetes Res
Clin Pract. 74
Suppl 2:S160-3. 2006.
27.
Facchinetti, A, Sparacino
G., Cobelli C.. Reconstruction
of glucose in plasma from interstitial fluid continuous glucose monitoring
data: role of sensor calibration. Journal Of Diabetes Science And
Technology. Vol. 1, pp. 617-623, 2007
28. Sparacino G., Zanderigo F, Corazza S, Maran A, Facchinetti A, Cobelli C.. Glucose
concentration can be predicted ahead in time from continuous glucose monitoring
sensor time series. IEEE
Transactions on Biomedical Engineering. Vol. 54, pp. 931-937, 2007
29. Zanderigo, F, Sparacino G.,
Kovatchev B.P, Cobelli C. Glucose
prediction algorithms from continuous monitoring data: assessment of accuracy
via continuous glucose error-grid analysis. Journal Of Diabetes
Science And Technology. Vol. 1, pp. 645-651, 2007
30.
Sparacino G., Facchinetti A, Maran A, Cobelli C. Continuous glucose
monitoring time series and hypo/hyperglycemia prevention: requirements,
methods, open problems. Curr Diabetes Rev. 4:181-192, 2008
31.
Amodio P, Orsato R, Marchetti P, Schiff S, Poci C, Angeli
P, Gatta A, Sparacino G, Toffolo GM.
Electroencephalographic
analysis for the assessment of hepatic encephalopathy: comparison of
non-parametric and parametric spectral estimation techniques. Neurophysiol Clin. 39:107-15,
2009.
32. D’Avanzo, C.,
V.Tarantino, P. Bisiacchi, G. Sparacino. A wavelet methodology for EEG time-frequency
analysis in a time discrimination task, International Journal of
Bioelectromagnetism, Vol. 11, No. 4, pp.185-188, 2009
33.
G.
Varotto, E.Visani, S. Franceschetti, G. Sparacino, F. Panzica. Spectral and Coherence
Analysis of EEG during Intermittent Photic Stimulation in Patients with
Photosensitive Epilepsy, International Journal of Bioelectromagnetism, Vol. 11,
No. 4, pp.189-193, 2009.
34. Cobelli, C. ; Dalla Man,
C. ; Sparacino, G. ; Magni, L. ; De Nicolao, G. ; Kovatchev, B.P.
Diabetes: Models, Signals, and Control. IEEE Reviews
in Biomedical Engineering, 2, 54-96, 2009.
35.
Facchinetti
A, Sparacino G, Cobelli C. Modeling
the Error of Continuous Glucose Monitoring Sensor Data: Critical Aspects
Discussed through Simulation Studies. J Diabetes Sci
Technol. 2010
Jan 1;4(1):4-14.
36. Pérez-Gandía C,
Facchinetti A, Sparacino G, Cobelli C, Gómez EJ, Rigla M, de Leiva A,
Hernando ME. Artificial
neural network algorithm for online glucose prediction from continuous glucose
monitoring. Diabetes Technol Ther. 2010 Jan;12(1):81-8.
37. Facchinetti A, Sparacino
G, Cobelli C. An
Online Self-Tunable Method to Denoise CGM Sensor Data. IEEE Trans
Biomed Eng. 2010 Mar;57(3):634-41.
38. Facchinetti A, Sparacino
G, Cobelli C. Enhanced
accuracy of continuous glucose monitoring by online extended kalman filtering.
Diabetes Technol Ther. 2010 May;12(5):353-63.
39. Sparacino G, Facchinetti A, Cobelli C. “Smart” Continuous Glucose Monitoring Sensors: On-Line
Signal Processing Issues. Sensors 10 (7): 6751-6772, 2010.
40. Facchinetti A, Sparacino
G. Trifoglio E. Cobelli C. A New Index to Optimally Design and Compare CGM
Glucose Prediction Algorithms, Diabetes Technol Ther. 2011
Feb;13(2):111-9.
41. F. Scarpa, S. Cutini, P.
Scatturin, R. Dell’Acqua, and G. Sparacino, "Bayesian filtering of human brain hemodynamic activity
elicited by visual short-term maintenance recorded through functional
near-infrared spectroscopy (fNIRS)," Opt. Express 18,
26550-26568 (2010)
42. C. D’Avanzo, S.Shiff, P.
Amodio, G.Sparacino A Bayesian method to estimate single-trial
event-related potentials with application to the study of the P300 variability.
J
Neurosci Methods 198(1):114-24, 2011
43.
Vigili
de Kreutzenberg S, Fadini GP, Boscari F, Rossi E, Guerra S, Sparacino G,
Cobelli C, Ceolotto G, Bottero M, Avogaro A. Impaired
hemodynamic response to meal intake in insulin-resistant subjects: an impedance
cardiography approach. Am J Clin Nutr. 93:926-33, 2011.
44. Guerra S, Sparacino
G, Facchinetti A, Schiavon M, Dalla Man C, Cobelli C. A
Dynamic Risk Measure from Continuous Glucose Monitoring Data.
Diabetes Technol Ther. 2011 Aug;13(8):843-52.
45. Guerra S, Boscari F,
Avogaro A, Di Camillo B, Sparacino G, Vigili De Kreutzenberg S. Haemodynamics
assessed via approximate entropy analysis of impedance cardiography time
series: effect of metabolic syndrome. Am J Physiol Heart Circ
Physiol. 2011
Aug;301(2):H592-8.
46. Facchinetti A, Sparacino
G, Cobelli C.Online
Denoising Method to Handle Intra-Individual Variability of Signal-to-Noise
Ratio in Continuous Glucose Monitoring. IEEE Trans Biomed Eng. 2011
Sep;58(9):2664-71.
47. Marchetti P, D'Avanzo
C, Orsato R, Montagnese S, Schiff S, Kaplan PW, Piccione F, Merkel C, Gatta A, Sparacino
G, Toffolo GM, Amodio P. Electroencephalography Alterations in Patients with
Cirrhosis. Gastroenterology.
2011 Nov;141(5):1680-1689.
48. Goljahani A, D'Avanzo
C, Schiff S, Amodio P, Bisiacchi P, Sparacino G. A
novel method for the determination of the EEG individual alpha frequency,
Neuroimage 60: 774–786, 2012.
49. Zecchin C.,
Facchinetti A, Sparacino G, De Nicolao G, Cobelli C. Neural
Network Incorporating Meal Information Improves Accuracy of Short-Time
Prediction of Glucose Concentration, IEEE Trans Biomed Eng
59(6):1550-60, 2012
50. Guerra S, Facchinetti
A, Sparacino G, De Nicolao G, Cobelli C. Enhancing
the accuracy of subcutaneous glucose sensors: a real-time deconvolution-based
approach, IEEE Trans Biomed Eng 59(6):1658-69, 2012.
51. Zanon M, Sparacino
G, Facchinetti A, Riz M, Talary MS, Suri RE, Caduff A, Cobelli C. Non-invasive
continuous glucose monitoring: improved accuracy of point and trend estimates
of the Multisensor system. Med. Biol. Eng. Comput. 2012, 50,
1047–1057.
52. Sparacino G, M Zanon, A Facchinetti, C Zecchin, A
Maran, C Cobelli. Italian
Contributions to the Development of Continuous Glucose Monitoring Sensors for
Diabetes Management Sensors 2012, 12(10), 13753-13780
53. Zecchin, C.
Facchinetti, A, Sparacino, G. and Cobelli, C. Reduction
of Number and Duration of Hypoglycemic Events by Glucose Prediction Methods: A
Proof-of-Concept In Silico Study. Diabetes Technol Ther. 2013
Jan;15(1):66-77.
54.
Facchinetti, A.; Sparacino, G.; Guerra, S.; Luijf, Y.M.;
DeVries, J.H.; Mader, J.K.; Ellmerer, M.; Benesch, C.; Heinemann, L.;
Bruttomesso, D.; Avogaro, A.; Cobelli, C.; on behalf of the AP at home
Consortium. Real-time improvement of continuous glucose monitoring
accuracy: the smart sensor concept. Diabetes Care 2013 Apr;36(4):793-800. doi:
10.2337/dc12-0736.
55. Facchinetti, A, Del
Favero, S., Sparacino, G. and Cobelli, C. An
Online Failure Detection Method of the Glucose Sensor-Insulin Pump System:
Improved Overnight Safety of Type-1 Diabetic Subjects. IEEE Trans
Biomed Eng. 2013 Feb;60(2):406-16.
56.
D’Avanzo C, Goljahani A, Pillonetto G, De Nicolao G., Sparacino G.. A multi-task learning approach
for the extraction of single-trial evoked potentials. Comput Methods Programs Biomed. 2013
May;110(2):125-36. doi: 10.1016/j.cmpb.2012.11.001.
57. Scarpa F, Brigadoi S,
Cutini S, Scatturin P, Zorzi M, Dell'Acqua R, Sparacino G. A
Reference-Channel Based Methodology to Improve Estimation of Event Related
Hemodynamic Response from fNIRS Measurements. Neuroimage. 2013 May
15;72:106-19. doi: 10.1016/j.neuroimage.2013.01.021.
58. Zanon, M.; Sparacino,
G.; Facchinetti, A.; Talary, M.S.; Mueller, M.; Caduff, A.; Cobelli, C. Non-Invasive
Continuous Glucose Monitoring with Multi-Sensor Systems: A Monte Carlo-Based
Methodology for Assessing Calibration Robustness. Sensors 2013, 13,
7279-7295.
59.
Zecchin
C, Facchinetti A, Sparacino G, Dalla Man C, Manohar C, Levine JA, Basu
A, MD2, Kudva YC, Cobelli C. Physical activity measured by PAMS correlates with
glucose trends reconstructed from CGM, Diabetes Technology and Therapeutics
Diabetes Technol Ther. 2013 Oct;15(10):836-44. doi: 10.1089/dia.2013.0105. Epub 2013 Aug 14.
60.
Zanon,
M, Sparacino G, Facchinetti A, Talary MS, Caduff A, Cobelli C. Regularised Model Identification Improves Accuracy of Multisensor
Systems for Non-Invasive Continuous Glucose Monitoring in Diabetes Management,
Journal of Applied Mathematics, vol. 2013, Article ID 793869, 10 pages, 2013.
doi:10.1155/2013/793869
61. Schiavon M, Hinshaw L,
Mallad A, Dalla Man C, Sparacino G, Johnson ML, Carter RE, Basu R, Kudva
YC, Cobelli C, Basu A. Postprandial Glucose Fluxes and Insulin Sensitivity
during Exercise:A Study in Healthy Individuals. Am J Physiol
Endocrinol Metab. 2013 Aug;305(4):E557-66. doi: 10.1152/ajpendo.00182.2013.
62. Fabris C., De Colle W, Sparacino
G. Voice Disorders Assessed by (Cross-) Sample Entropy of
Electroglottogram and Microphone Signals. Biomedical Signal
Processing and Control 8 (6) , pp. 920-926 2013.
63. Facchinetti A., Sparacino
G., Cobelli C. on behalf of the AP at home Consortium. Signal
processing algorithms implementing the “smart sensor” concept to improve
continuous glucose monitoring in diabetes. J Diabetes Sci
Technol. 2013
Sep 1;7(5):1308-18.
64. Schiff S., D’Avanzo
C., Cona G., Goljahani A., Montagnese S., Volpato C., Gatta, A., Sparacino
G., Bisiacchi P., Amodio P. Insight
into the relationship between brain/behavioural speed and variability in
patients with minimal hepatic encephalopathy. Clin Neurophysiol.
2014 Feb;125(2):287-97. doi: 10.1016/j.clinph.2013.08.004. Epub 2013 Sep 10.
65. Facchinetti A, Del
Favero S, Sparacino G, Castle J, Ward W, Cobelli C. Modeling
the Glucose Sensor Error. IEEE Trans Biomed Eng. 2014
Mar;61(3):620-9. doi: 10.1109/TBME.2013.2284023. Epub 2013 Sep 30.
66. C.Zecchin, A.Facchinetti,
G. Sparacino, C. Cobelli Jump
Neural Network for Online Short-Time Prediction of BloodGlucose from Continuous
Monitoring Sensors and Meal Information. Comput Methods Programs
Biomed. 2014 Jan;113(1):144-52. doi: 10.1016/j.cmpb.2013.09.016. Epub 2013 Oct
9.
67. Garcia A, Rack-Gomer
AL, Bhavaraju NC, Hampapuram H, Kamath A, Peyser T, Facchinetti A, Zecchin C, Sparacino
G, Cobelli C. Dexcom
G4AP: an advanced continuous glucose monitor for the artificial pancreas.
J
Diabetes Sci Technol. 2013
Nov 1;7(6):1436-45.
68. Goljahani A, Bisiacchi
P, Sparacino G. An EEGLAB plugin to analyze individual EEG alpha
rhythms using the "channel reactivity-based method".
Comput Methods Programs Biomed. 2014 Mar;113(3):853-61. doi:
10.1016/j.cmpb.2013.12.010. Epub 2013 Dec 31.
69. Del Favero S,
Facchinetti A, Sparacino G, Cobelli C. Improving
accuracy and precision of glucose sensor profiles: retrospective fitting by
constrained deconvolution. IEEE Trans Biomed Eng. 2014
Apr;61(4):1044-53. doi: 10.1109/TBME.2013.2293531.
70. Fabris C, Sparacino
G, Sejling AS, Goljahani A, Duun-Henriksen J, Remvig LS, Juhl CB, Cobelli C, Hypoglycemia-Related
EEG Changes Assessed by Multiscale Entropy, Diabetes Technol Ther.
2014 Oct;16(10):688-94. doi: 10.1089/dia.2013.0331.
71. Fabris C, Facchinetti A, Sparacino
G, Zanon M, Guerra S, Maran A, Cobelli C,
Glucose Variability Indices in Type 1 Diabetes:
Parsimonious Set of Indices Revealed by Sparse Principal Component Analysis,
Diabetes Technol Ther. 2014 Oct;16(10):644-52. doi: 10.1089/dia.2013.0252.
72. Goljahani A, D’Avanzo C,
Silvoni S, Tonin P, Piccione F, Sparacino G, Preprocessing by a Bayesian Single-Trial Event-Related
Potential Estimation Technique Allows Feasibility of an Assistive
Single-Channel P300-Based Brain-Computer Interface, Computational
and Mathematical Methods in Medicine, vol. 2014, Article ID 731046, 9 pages,
2014. doi:10.1155/2014/731046
73. Facchinetti, A., Del
Favero, S., Sparacino, G., Cobelli, C.Model
of glucose sensor error components: identification and assessment for new
Dexcom G4 generation devices Med Biol Eng Comput. 2015 Dec;53(12):1259-69.
doi: 10.1007/s11517-014-1226-y)
74. Del Favero S,
Facchinetti A, Sparacino G, Cobelli C. Retrofitting
of continuous glucose monitoring traces allows more accurate assessment of glucose
control in outpatient studies. Diabetes Technol Ther. 2015
May;17(5):355-63. doi: 10.1089/dia.2014.0230. Epub 2015 Feb 11.
75. Vettoretti M,
Facchinetti A, Del Favero S, Sparacino G, Cobelli C. On-line
calibration of glucose sensors from the measured current by a time-varying
calibration function and Bayesian priors. IEEE Trans Biomed Eng.
2015 Apr 24.
76. Frigo, G., S.
Brigadoi, G. Giorgi, G. Sparacino, C. Narduzzi. Measuring
Cerebral Activation From fNIRS Signals: An Approach Based on Compressive
Sensing and Taylor–Fourier Model IEEE Transactions on
Instrumentation and Measurement Eng. 65, Vol. 6: 1310 - 1318, 2016, DOI: 10.1109/TIM.2016.2518363
77. Rubega M, Sparacino
G, Sejling AS, Juhl CB, Cobelli C. Hypoglycemia-Induced
Decrease of EEG Coherence in Patients with Type 1 Diabetes. Diabetes
Technol Ther. 2016 Mar;18(3):178-84. doi: 10.1089/dia.2015.0347.
78. Facchinetti A, Del
Favero S, Sparacino G, Cobelli C. Modeling
Transient Disconnections and Compression Artifacts of Continuous Glucose
Sensors. Diabetes Technol Ther. 2016 Apr;18(4):264-72. doi:
10.1089/dia.2015.0250. Epub 2016 Feb 16.
79. Zecchin C, Facchinetti
A, Sparacino G, Cobelli C. How
Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding
Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept
Study. J Diabetes Sci Technol. 2016 Aug 22;10(5):1149-60. doi:
10.1177/1932296816654161.
80. G. Acciaroli, M.
Vettoretti, A. Facchinetti, G. Sparacino, C. Cobelli. From
Two to One Per Day Calibration of Dexcom G4 Platinum by a Time-Varying
Day-Specific Bayesian Prior. Diabetes Technol Ther. 2016
Volume 18, Number 8, 2016 DOI: 10.1089/dia.2016.0088
81. M. Rubega, R. Fontana,
S. Vassanelli, G. Sparacino. A
tunable local field potentials computer simulator to assess minimal
requirements for phase-amplitude cross-frequency-coupling estimation.
Network: Computation in Neural Systems, 2016;27(4):268-288.
82. Vettoretti M,
Facchinetti A, Sparacino G, Cobelli C. Predicting
Insulin Treatment Scenarios with the Net Effect Method: Domain of Validity.
Diabetes Technol Ther. 2016 Nov;18(11):694-704.
83. Fontana R, Agostini M,
Murana E, Mahmud M, Scremin E, Rubega M, Sparacino G, Vassanelli S,
Fasolato C. Early
hippocampal hyperexcitability in PS2APP mice: role of mutant PS2 and APP.
Neurobiol Aging. 2017 Feb;50:64-76. doi: 10.1016/j.neurobiolaging.2016.10.027.
84. Rubega M, Sparacino
G. Neurological Changes in Hypoglycemia.
Diabetes Technol Ther. 2017 Feb;19(2):73-75. doi: 10.1089/dia.2017.0009.
85.
Rubega
M, Cecchetto C, Vassanelli S, Sparacino G. Algorithm
and software to automatically identify latency and amplitude features of local
field potentials recorded in electrophysiological investigation. Source Code Biol Med.
2017 Feb 7;12:3. doi: 10.1186/s13029-017-0062-5.
86.
Vettoretti M, Facchinetti A, Sparacino
G, Cobelli C. A Model of Self-Monitoring Blood Glucose Measurement
Error. J Diabetes Sci Technol. 2017 Mar 1:1932296817698498. doi:
10.1177/1932296817698498.
87.
Del
Favero S, Facchinetti A, Sparacino G, Cobelli C. Retrofitting Real-Life Dexcom
G5 Data. Diabetes Technol Ther. 2017 Apr;19(4):237-245.
doi: 10.1089/dia.2016.0413.
88.
G.
Acciaroli, M. Vettoretti, A. Facchinetti, G. Sparacino, C. Cobelli. Reduction of blood glucose
measurements to calibrate subcutaneous glucose sensors: a Bayesian multi-day
framework. IEEE Transactions on Biomedical Engineering, Year: 2017 (in
press), DOI: 10.1109/TBME.2017.2706974
89.
G.Acciaroli,
G. Sparacino, L. Hakaste, A. Facchinetti, G.M, Di Nunzio, A. Palombit,
T. Tuomi, R. Gabriel, J. Aranda, S. Vega, C. Cobelli. Diabetes and Prediabetes
Classification Using Glycemic Variability Indices from Continuous Glucose
Monitoring Data. Journal of Diabetes Science and Technology, DOI:
10.1177/1932296817710478, 2017
90.
Scarpa F, Rubega M, Zanon M, Finotello F, Sejling
AS, Sparacino G. Hypoglycemia-induced
EEG complexity changes in Type 1 Diabetes assessed by fractal analysis
algorithm, Biomedical Signal Processing and Control, doi:
10.1016/j.bspc.2017.06.004
91.
E.
Longato; M. Garrido; D. Saccardo; C. Montesinos Guevara; A. Mani; M. Bolognesi;
P. Amodio; A. Facchinetti; G. Sparacino; S. Montagnese. Expected accuracy of
proximal and distal temperature estimated by wireless sensors, in relation to
their number and position on the skin, Plos ONE 2017
92.
Vettoretti, M., Facchinetti, A., Sparacino,
G., Cobelli, C., Type
1 diabetes patient decision simulator for in silico testing safety and
effectiveness of insulin treatments ,2017, IEEE Transactions on Biomedical
Engineering
93.
Cappon, G., Acciaroli, G., Vettoretti, M.,
Facchinetti, A., Sparacino, G., Wearable
continuous glucose monitoring sensors: A revolution in diabetes treatment ,2017,
Electronics (Switzerland)
94.
Acciaroli, G., Vettoretti, M., Facchinetti,
A., Sparacino, G., Toward
Calibration-Free Continuous Glucose Monitoring Sensors: Bayesian Calibration
Approach Applied to Next-Generation Dexcom Technology ,2018, Diabetes
Technology and Therapeutics
95.
Cappon, G., Vettoretti, M., Marturano, F.,
Facchinetti, A., Sparacino, G., A Neural-Network-Based
Approach to Personalize Insulin Bolus Calculation Using Continuous Glucose
Monitoring ,2018, Journal of Diabetes Science and Technology
96.
Acciaroli, G., Vettoretti, M., Facchinetti,
A., Sparacino, G., Calibration
of minimally invasive continuous glucose monitoring sensors: State-of-the-art
and current perspectives ,2018, Biosensors
97.
Longato, E., Acciaroli, G., Facchinetti, A.,
Hakaste, L., Tuomi, T., Maran, A., Sparacino, G., Glycaemic variability-based
classification of impaired glucose tolerance vs. type 2 diabetes using
continuous glucose monitoring data ,2018, Computers in Biology and Medicine
98. Vettoretti,
M., Cappon, G., Acciaroli G., Facchinetti, A., Sparacino, G., Continuous
glucose monitoring: current use in diabetes management and possible future
applications, 2018, Journal of Diabetes Science and Technology.
99. Cappon,
G., Marturano, F., Vettoretti, M., Facchinetti, A., Sparacino, G., In
Silico Assessment of Literature Insulin Bolus Calculation Methods Accounting
for Glucose Rate of Change,2018, Journal of Diabetes Science and Technology
CHAPTERS IN BOOKS
1. Sparacino, G., G.De Nicolao, C.Cobelli. Deconvolution. In:
“E.Carson and C.Cobelli (Editors), Modeling Methodology for Physiology and
Medicine (Biomedical Engineering Series)", Academic Press, San Diego,
California, USA, pp. 45-76, 2001 (ISBN: 0121602451).
2.
Cobelli, C, Sparacino G., A.Caumo, M.P.Saccomani, E
G.Toffolo. (2006). Compartmental
Models of Physiologic Systems. In:
J.Bronzino Editor. Biomedical
Engineering Fundamentals (The Biomedical Engineering Handbook, 3rd Edition). (pp. 9.1-9.14). CRC Taylor & Francis (USA).
3. Sparacino G, G Pillonetto, G De Nicolao, C Cobelli (2011). Deconvolution for Physiological Signal Analysis. In: S. Cerutti and C. Marchesi. Advanced Methods of Biomedical Signal Processing. p.
169-198, Hoboken, NJ: John Wiley & Sons, ISBN: 9780470422144, doi:
10.1002/9781118007747.ch8
4. Cobelli, C, Sparacino
G., A.Caumo, M.P.Saccomani, E G.Toffolo. (2006). Compartmental Models of Physiologic Systems. In: J.D. Bronzino, D.R.
Peterson (Editors), “Molecular, Cellular,
and Tissue Engineering (Series: The Biomedical Engineering Handbook, Fourth
Edition), CRC Press, 2015 ISBN 9781439825303
5.
G. Sparacino,
G. De Nicolao, G.Pillonetto, C.Cobelli. Deconvolution. In “E.Carson
and C.Cobelli (Editors), Modeling Methodology for Physiology and Medicine
(Second Edition)", 2014, Pages 45–68,
doi:10.1016/B978-0-12-411557-6.00003-3, Elsevier (ISBN: 978-0-12-411557-6)
6. P.Magni, G. Sparacino.
“Chapter 5.
Parameter Estimation”. In “E.Carson
and C.Cobelli (Editors), Modeling Methodology for Physiology and Medicine
(Second Edition)", Elsevier (ISBN 9780124115576), 2014, Pages 83–110,
2014, doi:10.1016/B978-0-12-411557-6.00005-7
7.
Zecchin, C.,
Facchinetti, A., Sparacino, G., Cobelli, C. Jump neural network for real-time
prediction of glucose concentration. In: “Cartwright, H. (Editor, Artificial
Neural Networks (Series: Methods in Molecular Biology)”, Volume
1260, 2015, Pages 245-259, Springer
New York, doi: 10.1007/978-1-4939-2239-0_15
CONFERENCE PROCEEDINGS
(after 2006)
1.
Facchinetti A., Sparacino
G., Zanderigo F., Cobelli C.. “Reconstructing by Deconvolution Plasma from
Continuous Monitoring Sensor Glucose: Domain of Validity of a
Plasma-Interstitium Compartmental Model”. Proceedings of EMBS 2006, New York,
NY, USA, 31 August-3 September 2006.
2.
Facchinetti A., Sparacino
G., Zanderigo F., Cobelli C. “Prediction of Glucose Concentration from CGM Data
through AR Time-Series Models: Role of Sampling Frequency and Other Design
Variables”. Proceedings of Diabetes Technology Meeting 2006. Atlanta,
Georgia,USA, November 2006.
3. Facchinetti A., Vio E., Baruzzo T., Sparacino G., Cobelli C (2007). "On-Line Noise Removal of Continuous Glucose
Monitoring (CGM) Data: Comparison of Filtering Techniques" Book of
Abstracts, 7th Diabetes Technology Meeting, 25 - 27 October 2007, San Francisco
(California - USA), pp A35.
4. Facchinetti A., Baruzzo T., Vio E., Sparacino G., Cobelli C (2007). "On-Line Time-Series Prediction Models for
Continuous Glucose Monitoring (CGM) Data". Book of Abstracts, 7th Diabetes
Technology Meeting, 25 - 27 October 2007, San Francisco (California - USA), pp
A36.
5. Facchinetti A, Sparacino
G, Cobelli C (2008). Hypoglycaemia prevention using CGM time-series: relative performance of
different prediction methods. 27th Workshop of the AIDPIT Study Group, 2nd
European Diabetes Technology and Transplantation Meeting (EuDTT), Igls /
Austria, Jan 27-29, 2008
6. Facchinetti A, Sparacino
G, Cobelli C. (2008). An on-line bayesian filtering approach to deal with SNR variability of
CGM data, 1st International Conference on Advanced Technologies and Treatments
for Diabetes taking place in Prague, Czech Republic, February 27 – March 1,
2008.
7. C. D’Avanzo, S. Schiff, P. Amodio, G. Sparacino (2008). Implementation of a wavelet-based procedure for eeg
quantification during cognitive task, 13th International Symposium of the
International Society on Hepatic Encephalopathy and Nitrogen Metabolism
(ISHEN), 2008
8. S.Schiff, E. Veronese E, G. Sparacino, G.M. Toffolo, P.Bisiacchi, A. Gatta, P. Amodio (2008). Mechanisms of P300 amplitude reduction in cirrhotic
patients, 13th International Symposium of the International Society on Hepatic
Encephalopathy and Nitrogen Metabolism (ISHEN), 2008
9. G. Sparacino,
C. D’Avanzo, E. Pasqualotto, E. Veronese, S.Schiff, P. Amodio. "Event Related Potentials Mesurement: A Bayesian
Approach to Perform Improved Averaging and Single-Trial Estimation". In:
Workshop Proceedings of SIMPAR 2008 Intl. Conf. on SIMULATION, MODELING and
PROGRAMMING for AUTONOMOUS ROBOTS, Venice(Italy) 2008 November,3-4, ISBN 978-88-95872-01-8,
pp. 389-394
10. G. Sparacino,
W. De Colle, D. De Luca, E. Arslan, Electroglottography and Microphone Signals
Assessed by Approximate Entropy in Normal and Dysphonic Subjects, In
Proceedings of the 6th International Workshop on Models and Analysis of Vocal
Emissions for Biomedical Applications, Florence (Italy) 15-19 Decembre 2009,
ISBN: 978-88-6453-094-9, pp.77-80
11. Facchinetti A., Sparacino G., Vianello C., Cobelli C.. “Toward a Smart CGM Sensor: On-Line Algorithms for
Calibration and Filtering”. Book of Abstracts, p. 3, 28th Workshop of the
AIDPIT Study Group, 3rd European Diabetes Technology and Transplantation
Meeting (EuDTT), Igls (Austria), January 25–27, 2009.
12. Perez-Gandia C., Hernando E., Facchinetti A., Sparacino G.,
Cobelli C., Gomez E.. “A new Methodology to
Compare Prediction Algorithms from Continuous Glucose Monitoring Data”. Book of
abstracts, 2st International Conference on Advanced Technologies and Treatments
for Diabetes (ATTD), Atene (Grecia), February 25–28, 2009.
13. Facchinetti A., Sparacino G., Cappellotto P., Cobelli C.. “A new Extended Kalman Filtering Approach for the
Calibration of Continuous Glucose Monitoring Sensors”. Book of abstracts, p.
146, 2st International Conference on Advanced Technologies and Treatments for
Diabetes (ATTD), Atene (Grecia), February 25–28, 2009.
14. Guerra S., Dalla Man C., Sparacino G., Renard
E., and Cobelli C. The oral glucose minimal model in type 1 diabetes: an
ingredient of DIAdvisorTM , , World Congress 2009 – Medical Physics and
Biomedical Engineering, Munich, 7-12 Settembre 2009.
15. Facchinetti A., Sparacino G., Kovatchev B., Cobelli C.. “Accuracy of CGM Sensors Improved in Real-Time by
Exploiting Short-Time Prediction”. Book of Abstracts, p. A33, 9th Diabetes
Technology Meeting (DTM), San Francisco (CA, USA), November 5–7, 2009.
16. Facchinetti A., Sparacino G., Kovatchev B., Cobelli C.. “Real-Time Detection of CGM Sensor Failure”. Book of
Abstracts, p. A34, 9th Diabetes Technology Meeting (DTM), San Francisco (CA,
USA), November 5–7, 2009.
17. Guerra S., Facchinetti A., Sparacino G., De Nicolao G., Cobelli
C.. “Comparison of Four Methods
for On-Line Calibration of CGM Data”. Book of Abstracts, p. A51, 9th Diabetes
Technology Meeting (DTM), San Francisco (CA, USA),November 5–7, 2009.
18. C. D’Avanzo, S. Schiff, E. Pasqualotto, P. Amodio, G. Sparacino. A Bayesian methodology to estimate single-trial ERPs
with application to the study of the P300 variability in cirrhosis. In
Proceedings of the World Congress on Medical Physics and Biomedical
Engineering, September 7 - 12, 2009, Munich, Germany 2009
19. A. Goljahani, C.D'Avanzo, V. Tarantino, P. Bisiacchi, G. Sparacino.
Event-related EEG
desynchronization and synchronization assessed during a time discrimination
task. In Proceedings of the World Congress on Medical Physics and Biomedical
Engineering, September 7 - 12, 2009, Munich, Germany 2009.
20. Schiff S, D'Avanzo C, Cona G, Sparacino G,
Bisiacchi P, Amodio P, Single-trial analysis explains reduction of P300
amplitude in cirrhotic patients. Psychophysiology: 46, S92, 2009.
21. Tarantino V, Goljahani A, D'Avanzo C, Sparacino
G, Bisiacchi P. Electrophysiological Correlates of Reference Memory in a Time
Discrimination Task: An ERP and ERD/ERS Study. Psychophysiology: 46, S47, 2009
22. Dalla Man C., Guerra S., Sparacino G., Renard
E. and Cobelli C. "A Reduced type 1 Diabetes Model For Model Predictive
Control", Book of abstracts, 3rd International Conference on Advanced
Technologies and Treatments for Diabetes (ATTD), Basel (Switzerland), February
10–13, 2010
23. Guerra S., Facchinetti A., Prendin A., Sparacino
G., Cobelli C. “New Method for Recalibration of CGM Time-Series: Performance
and Robustness Assessed by Simulation”, Book of abstracts, 3rd International
Conference on Advanced Technologies and Treatments for Diabetes (ATTD), Basel
(Switzerland), February 10–13, 2010.
24. Guerra S., Facchinetti A., Schiavon M., Dalla Man
C., Sparacino G. “A New Dynamic Risk Measure for Continuous Glucose
Monitoring Time Series”. Book of abstracts, 3rd International Conference on
Advanced Technologies and Treatments for Diabetes (ATTD), Basel (Switzerland),
February 10–13, 2010.
25. Facchinetti A., Trifoglio E., Sparacino G.,
Cobelli C. “Real-Time Self-Adaptive Prediction Algorithm for CGM Data Evaluated
by Combining Different Indexes”, Book of abstracts, 3rd International
Conference on Advanced Technologies and Treatments for Diabetes (ATTD), Basel
(Switzerland), February 10–13, 2010.
26. Facchinetti A., Zecchin C., Sparacino G.,
Cobelli C. “Comparison of Different Neural Networks Structures for the
Real-Time Prediction of Glucose Level”, Book of Abstract at 10th Diabetes
Technology Meeting (DTM), Bethesda (MD, USA), November 11-13, 2010.
27. Facchinetti A., Trifoglio E., Sparacino G.,
Cobelli C. “New Index to Optimally Design a CGM Glucose Prediction Algorithm”, Book
of Abstract at 10th Diabetes Technology Meeting (DTM), Bethesda (MD, USA),
November 11-13, 2010.
28. Guerra S., Dalla Man C., Sparacino G., Renard
E., Avogaro A., Maran A., Cobelli C. “Glucose Rate of Appearance and Plasma
Insulin Concentration Models for Use in Prediction Algorithms” Book of Abstract
at 10th Diabetes Technology Meeting (DTM), Bethesda (MD, USA), November 11-13,
2010.
29. Mezzalana E., Bizzotto R., Sparacino G.,
Zamuner S. "Multinomial logistic functions in Markov-chain models for
modeling sleep architecture: internal validation based on VPCs", PAGE.
Abstracts of the Annual Meeting of the Population Approach Group in Europe,
ISBN/ISSN: 1871-6032, PAGE 19, Abstr 1893
[www.page-meeting.org/?abstract=1893], 2010.
30. Schiff S, Goljahani A, D'Avanzo C, Parpaiola F,
Amodio P, Sparacino G, Bisiacchi P, "Induced theta activity during
event-based prospective memory task", in Proc. of the 3rd International
Conference on Prospective Memory , Vancouver, Canada, July 2010.
31. Facchinetti A, Del Favero S, Sparacino G,
Cobelli C. Detecting failures of the glucose sensor-insulin pump system:
Improved overnight safety monitoring for Type-1 diabetes. Conf Proc IEEE Eng
Med Biol Soc. 2011 Aug;2011:4947-50.
32. Zanon M, Riz M, Sparacino G, Facchinetti A,
Suri RE, Talary MS, Cobelli C. Assessment of linear regression techniques for
modeling multisensor data for non-invasive continuous glucose monitoring. Conf
Proc IEEE Eng Med Biol Soc. 2011 Aug;2011:2538-41.
33. Scarpa F, Brigadoi S, Cutini S, Scatturin P, Zorzi
M, Dellracqua R, Sparacino G. A methodology to improve estimation of
stimulus-evoked hemodynamic response from fNIRS measurements. Conf Proc IEEE
Eng Med Biol Soc. 2011 Aug;2011:785-8.
34. Zecchin C., Facchinetti A., Sparacino G., De
Nicolao G., Cobelli C. “A new neural network approach for short-term glucose
prediction using continuous glucose monitoring time-series and meal
information”, Conf Proc IEEE Eng Med Biol Soc. 2011 Aug;2011:5653-6.
35. Goljahani A., D'Avanzo C., Genna C., Silvoni S.,
Piccione F., Sparacino G. “Performance of a P300-based BCI system
improved by a Bayesian single-trial ERP estimation technique”. Proceedings of
the 5th International Brain-Computer Interface Conference 2011, Graz University
of Technology, Austria, September 22-24, 2011, pp52-55, ISBN 978-3-85125-140-1.
36. S. Brigadoi, F. Scarpa, S. Cutini, P. Scatturin, R.
Dell’Acqua, G. Sparacino (2011) “Development of a new method to reduce
global physiological trends in fNIRS measures of brain activation” . Brain'11
& BrainPET'11, Barcelona, Spain, May 25 28.
37. Facchinetti A., Sparacino G., Calore F.,
Cobelli C. “On-Line CGM Denoising Improves Hypo/Hyperglycemic Alert
Generation”. Book of abstracts, 4th International Conference on Advanced
Technologies and Treatments for Diabetes (ATTD), London (UK), Feb 16–19, 2011.
38. Facchinetti A., Zecchin C., Sparacino G., De
Nicolao G., Cobelli C. “A New Neural Network Approach to Improve Effectiveness
of Short-Term Glucose Prediction”. Book of abstracts, 4th International
Conference on Advanced Technologies and Treatments for Diabetes (ATTD), London
(UK), Feb 16–19, 2011.
39. Guerra S., Sparacino G., Facchinetti A.,
Maran A., Cobelli C. “Dynamic Risk Space of CGM Time-Series: Assessment of
Quality of Glucose Control”. Book of abstracts, 4th International Conference on
Advanced Technologies and Treatments for Diabetes (ATTD), London (UK), Feb
16–19, 2011.
40. Zanon M., Riz M., Facchinetti A., Sparacino
G., Cobelli C., Suri R., Mueller M., De Feo O., Caduff A., Talary M.
“Assessment of Linear Techniques to Model Multisensor Data for Non-Invasive
Continuous Glucose Monitoring”. Book of abstracts, 4th International Conference
on Advanced Technologies and Treatments for Diabetes (ATTD), London (UK), Feb
16–19, 2011.
41. Facchinetti A., Del Favero S., Sparacino G.,
Cobelli C. “Improving Overnight Safety Monitoring in Type-1 Diabetic Subjects:
a Method to Detect Failures of the Glucose Sensor-Insulin Pump System”. Book of
abstracts, 11th Diabetes Technology Meeting (DTM), San Francisco (CA, USA),
October 25-27, 2011.
42. Guerra S. Zanon M., Maran A., Sparacino G.,
Cobelli C. "Parsimonious description of Glucose Variability investigated
by a Sparse PCA Approach". Book of abstracts, 11th Diabetes Technology
Meeting (DTM), San Francisco (CA, USA), October 25-27, 2011.
43. F. Scarpa, C. Fabris, S. Brigadoi, S. Cutini, P. Scatturin, R.
Dell'Acqua, G. Sparacino (2012). A GLM-Based Approach to Estimate Stimulus-Evoked
Hemodynamic Response from fNIRS Measurements. Proc. 6th International
Conference on Bioinformatics and Biomedical Engineering (iCBBE), Shanghai, China,
May 17 – 20, vol. 3, pp. 736-739, ISBN 978-1-61284-099-4
44. S. Brigadoi, F. Scarpa, S. Cutini, P. Scatturin, R. Dell'Acqua, M. Zorzi,
G. Sparacino (2012). Hemodynamic response
estimation from fNIRS signal through a modeling approach exploiting the reference
channel. Proc. 6th International Conference on Bioinformatics and Biomedical
Engineering (iCBBE), Shanghai, China, May 17 – 20, vol. 3, pp. 661-664, ISBN 978-1-61284-099-4
45. Zecchin C., Facchinetti A., Sparacino G.,
Cobelli C. Hypoglycemic alerts generated
by short-time glucose prediction reduce time spent in hypo: in silico study
Book of Abstracts International Conference on Advanced Technologies and
Treatments for Diabetes 5th International Conference on Advanced Technologies
and Treatments for Diabetes Feb 2012 Barcelona (Spain) 2012
46. Zecchin C., Cherubin L., Facchinetti A., Sparacino
G., Cobelli C. Jump neural network for short time prediction of glucose
concentration using meal information in type 1 diabetes . Book of Abstracts, 34th Annual International Conference of
the IEEE-EMBS IEEE Engineering in Medicine and Biology Conference San Diego (CA, USA) 2012
47. Zecchin C., Facchinetti A., Sparacino G.,
Cobelli C. Prediction-based alerting
methods could reduce number and duration of hypoglycemic events: an in silico
quantification Book of Abstracts of Diabetes Technology Meeting Diabetes
Technology Meeting Bethesda (MD, USA) 2012
48. Bhavaraju N.C.,
Cobelli C., Facchinetti A., Garcia A., Hampapuram H., Kamath A., Peyser T.,
Rack-Gomer A.L., Sparacino G.,
Zecchin C. Dexcom G4-AP: Advanced CGM for
Artificial Pancreas Development. DIABETES TECHNOLOGY & THERAPEUTICS 15,
A65, 2013
49. Zecchin C., Facchinetti A., Manohar C., Kudva Y.,
Levine J., Basu A.,Sparacino G., Dalla Man C., Cobelli C. PHYSICAL
ACTIVITY MEASURED BY PAMS VS. CGM TRENDS: CORRELATION ANALYSIS. DIABETES
TECHNOLOGY & THERAPEUTICS 15 sup 1, A77, 2013
50. Zanon M, Sparacino G, Facchinetti A, Talary
MS, Caduff A, Cobelli C. EFFECTS OF SWEAT EVENTS ON THE CALIBRATION OF A
MULTISENSOR DEVICE FOR NON-INVASIVE CONTINUOUS GLUCOSE MONITORING. DIABETES
TECHNOLOGY & THERAPEUTICS 15, A77, 2013
51. Kamath A, Bhavaraju NC, Cobelli C, Facchinetti A,
Garcia A, Hampapuram H, Peyser T, Rack-Gomer AL, Sparacino G, Zecchin C DEXCOM G4-AP: ADVANCED CGM FOR ARTIFICIAL
PANCREAS DEVELOPMENT. DIABETES TECHNOLOGY & THERAPEUTICS 15 sup 1, A 65,
2013
52. Facchinetti A, Del Favero S, Castle J. R., Ward W.
K., Sparacino G., Cobelli C. MODEL OF CGM ERROR FROM MULTIPLE SENSOR
DATA: DISSECTION INTO PHYSIOLOGICAL AND TECHNOLOGICAL COMPONENTS. DIABETES TECHNOLOGY
& THERAPEUTICS 15 Sup 1, A61, 2013
53. Del Favero S, Facchinetti A, Sparacino G,
Cobelli C IMPROVING PATIENT OVERNIGHT
SAFETY: GLUCOSE-SENSOR AND INSULIN-PUMPS FAILURES DETECTED EXPLOITING AN
AVERAGE MODEL. DIABETES TECHNOLOGY & THERAPEUTICS 15 sup 1 A93, 2013
54. Del Favero S, Facchinetti A, Sparacino G,
Cobelli C RETROFITTING ALGORITHM FOR A
POSTERIORI ENHANCEMENT OF CGM TRACES BY BLOOD GLUCOSE REFERENCES. DIABETES
TECHNOLOGY & THERAPEUTICS 15 Sup 1, A58, 2013
55. Zecchin, C. Facchinetti, A. Sparacino, G. Cobelli,
C. Neural network for prediction of glucose concentration in type 1 diabetic
patients. Frontiers in Artificial Intelligence and Applications, 257: 303-306,
2013
56. G. Sparacino, A. Facchinetti, C. Zecchin, C. Cobelli (2013). “Algorithmically Smart” Continuous Glucose Sensor
Concept for Diabetes Monitoring. In: Proceedings of the XIII Mediterranean
Conference on Medical and Biological Engineering and Computing 2013. IFMBE
PROCEEDINGS, vol. 41, p. 1543-1546, Springer International Publishing, ISBN:
9783319008462, ISSN: 1680-0737, Seville, Spain, September 25-28, 2013, doi:
10.1007/978-3-319-00846-2_381
57. C. Fabris, A. S. Sejling, G. Sparacino, A.
Goljahani, J. Duun-Henriksen, L. S. Remvig, C. Cobelli, C. B. Juhl (2013).
Hypoglycaemia-Related EEG Changes Assessed by Approximate Entropy . In: IFMBE
ProceedingsXIII Mediterranean Conference on Medical and Biological Engineering
and Computing 2013. vol. 41, p. 686-689, ISBN: 9783319008455, Seville, Spain,
September 25-29, 2013, doi: 10.1007/978-3-319-00846-2_170
58. A. Goljahani, A. S. Sejling, G. Sparacino, C.
Fabris, J. Duun-Henriksen, L. S. Remvig, C. Cobelli, C. B. Juhl (2013).
Variability of EEG Theta Power Modulation in Type 1 Diabetics Increases during
Hypo-glycaemia. In: IFMBE ProceedingsXIII Mediterranean Conference on Medical
and Biological Engineering and Computing 2013. vol. 41, p. 539-542, ISBN:
9783319008455, Seville, Spain, September 25-29, 2013, doi:
10.1007/978-3-319-00846-2_133
59. Fabris C, Facchinetti A, Sparacino G, Cobelli
C. Sparse Principal Component Analysis for the parsimonious description of
glucose variability in diabetes. Conf Proc IEEE Eng Med Biol Soc.
2014;2014:6643-6. doi: 10.1109/EMBC.2014.6945151.
60. Rubega M, Sparacino G, Sejling AS, Juhl CB,
Cobelli C. Decrease of EEG Coherence during hypoglycemia in type 1 diabetic
subjects. Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:2375-8. doi:
10.1109/EMBC.2015.7318871.
61. Vettoretti M, Facchinetti A, Sparacino G,
Cobelli C.Patient decision-making of CGM sensor driven insulin therapies in
type 1 diabetes: In silico assessment. Conf Proc IEEE Eng Med Biol Soc. 2015
Aug;2015:2363-6. doi: 10.1109/EMBC.2015.7318868.
62. Vettoretti M, Facchinetti A, Sparacino G,
Cobelli C. Accuracy of devices for self-monitoring of blood glucose: A
stochastic error model. Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:2359-62.
doi: 10.1109/EMBC.2015.7318867.
63. Rubega M, Cecchetto C, Vassanelli S, Sparacino
G.Automated analysis of local field potentials evoked by mechanical whisker
stimulation in rat barrel cortex. Conf Proc IEEE Eng Med Biol Soc. 2015
Aug;2015:1520-3. doi: 10.1109/EMBC.2015.7318660.
64. Frigo, G., Brigadoi, S., Giorgi, G., Sparacino,
G., Narduzzi, C. A compressive sensing spectral model for fNIRS haemodynamic
response de-noising. Proceedings IEEE International Symposium on Medical
Measurements and Applications, MeMeA 2015, pp. 244-249, 2015
65. Frigo, G., Rubega, M., Lezziero, G., (...), Sparacino,
G., Bertocco, M. A software-based platform for multichannel
electrophysiological data acquisition. Proceedings IEEE International Symposium
on Medical Measurements and Applications, MeMeA 2015, pp. 353-358, 2015.
INTERNATIONAL PATENTS