Ambient Intelligence (AmI) provides extended but unobtrusive sensing and computing devices and ubiquitous networking for human/environment interaction. It is a new paradigm in information technology compliant with the international Integrating Healthcare Enterprise board (IHE) and eHealth HL7 technological standards in the functional integration of biomedical domotics and informatics in hospital and home care. AmI allows real-time automatic recording of biological/medical information and environmental data. It is extensively applicable to patient monitoring, medicine and neuroscience research, which require large biomedical data sets; for example, in the study of spontaneous or condition-dependent variability or chronobiology. In this respect, AML is equivalent to a traditional laboratory for data collection and processing, with minimal dedicated equipment, staff, and costs; it benefits from the integration of artificial intelligence technology with traditional/innovative sensors to monitor clinical or functional parameters. A prototype AmI platform (MIMERICA*) has been implemented and is operated in a semi-intensive unit for the vegetative and minimally conscious states, to investigate the spontaneous or environment-related fluctuations of physiological parameters in these conditions.
ElulR. Gaussian behavior of the electroencephalogram: changes during performance of mental task. Science. 1969;164(3877):328–331.
2.
GevinsASSchafferREDoyleJCCutilloBATannehillRSBresslerSL. Shadows of thought: shifting lateralization of human brain electrical patterns during a brief visuomotor task. Science. 1983;220(4592):97–99.
3.
BresslerSLCoppolaRNakamuraR. Episodic multiregional cortical coherence at multiple frequencies during visual task performance. Nature. 1993;366(6451):153–156.
4.
NuñezPL. Neocortical dynamics of macroscopic-scale EEG measurements. IEEE Eng Med Biol Mag. 1998;17(5):110–117.
5.
NuñezPLWingeierBMSilbersteinRB. Spatial-temporal structures of human alpha rhythms: theory, microcurrent sources, multiscale measurements, and global binding of local networks. Hum Brain Mapp. 2001;13(3):125–164.
6.
CelesiaGGKaufmanDConeS. Effects of age and sex on pattern electroretinograms and visual evoked potentials. Electroencephalogr Clin Neurophysiol. 1987;68(3):161–171.
7.
MillerRJ.Glucose-regulated potassium channels are sweet news for neurobiologists. Trends Neurosci. 1990;13(6):197–199
8.
JoelsMDe KloetER. Control of neural excitability by corticosteroid hormones. Trends Neurosci. 1992;15(1):25–30.
9.
MacalusoCOnoeSNiemeyerG. Changes in glucose level affect rod function more than cone function in the isolated perfused cat eye. Invest Ophthalmol Vis Sci. 1992;33(10):2798–2808.
10.
GronfierCChapototFWeibelLJounyCPiquardFBrandenbergerG. Pulsatile cortisol secretion and EEG delta waves are controlled by two independent but synchronized generators. Am J Physiol. 1998;275(1 pt 1):E94–E100.
11.
SannitaWGLoizzoAGarbarinoSGesinoDMassimillaSOgliastroC. Adrenocorticotropin-related modulation of the human EEG and individual variability. Neurosci Lett. 1999;262(3):147–150.
12.
ChapototFJounyCMuzetABuguetABrandenbergerG. High frequency waking EEG: reflection of a slow ultradian rhythm in daytime arousal. Neuroreport. 2000;11(10):2223–2227.
13.
BeelkeMCarozzoSDe CarliF. Factor structure and ammonia-related modulation of the human retinal oscillatory potentials. Clin Neurophysiol. 2001;112(2):344–350.
14.
DeboerT. Electroencephalogram theta frequency changes in parallel with euthermic brain temperature. Brain Res. 2002;930(1-2):212–215.
15.
JensenOHariRKailaK. Visually evoked gamma responses in the human brain are enhanced during voluntary hyperventilation. Neuroimage. 2002;15(3):575–586.
16.
LiebregtsMTMcLachlanRSLeungLS. Hyperthermia induces age-dependent changes in rat hippocampal excitability. Ann Neurol. 2002;52(3):318–326.
17.
KarlssonKABlumbergMS. Temperature-induced reciprocal activation of hippocampal field activity. J Neurophysiol. 2004;91(1):583–588.
18.
SannitaWG. Individual variability, end-point effects and possible biases in electrophysiological research. Clin Neurophysiol. 2006;117(12):2569–2583.
19.
BullockTH. The reliability of neurons. J Gen Physiol. 1970;55(5):565–584.
Multi-Society Task Force on persistent vegetative state. N Engl J Med. 1994;330(22):1400–1579.
22.
GiacinoJTKalmarK & Whytet J. The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil. 2004;85(12):2020–2029.
23.
GiacinoJT. The minimally conscious state: defining the borders of consciousness. Prog Brain Res. 2005;150:381–395.
24.
DolceGQuintieriMSerraSLaganiVPignoloL. Clinical signs and early prognosis in vegetative state: a decisional tree, data-mining study. Brain Inj. 2008;22(7-8):617–623.
25.
DolceGLuccaLFCandelieriARoganoSPignoloLSannitaWG. Visual pursuit in the severe disorder of consciousness. J Neurotrauma. 2011;28(7):1149–1154.
26.
CorteseMDCandelieriARiganelloFDolceGSannitaWG. Within-day variability of visual pursuit in the severe disorder of consciousness. J Neurotrauma. 2011;28(10):2013–2017.
27.
GiacinoJTKalmarK. The vegetative and minimally conscious states: a comparison of clinical features and functional outcome. J Head Trauma Rehabil. 1997;12(4):36–51.
28.
LuautéJMaucort-BoulchDTellL. Long-term outcomes of chronic minimally conscious and vegetative states. Neurology. 2010;75(3):246–252.
29.
MontiMMLaureysSOwenAM. The vegetative state. BMJ. 2010;341:c3765.
30.
AartsEHarwingHSchuurmansM.Ambient Intelligence: The Invisible Future. New York, NY: MacGraw-Hill; 2001; 250.
31.
AbowdGMynattE. Charting past, present, and future research in ubiquitous computing. ACM Trans Comput Hum Interact. 2000;7(1):29–58.
32.
DurhamM.A commentary on technology and the future of health care. Perm J. 2001;5(1).
33.
RemagninoPForestiGEllisT.Ambient Intelligence, A Novel Paradigm: Springer; 2005:63–87.
34.
RivaG. Ambient Intelligence in healthcare. Cyberpsychol & Behav. 2003;6(3):295–300.
35.
LaureysSOwenAMSchiffND. Brain function in coma, vegetative state, and related disorders. Lancet Neurol. 2004;3(9):537–546.
36.
BohnJCoroamaVLangheinrichMMatternFRohsM.Social, economic, and ethical implications of ambient intelligence and ubiquitous computing. In: WeberWRabaeyJAartsE, eds. Ambient Intelligence. 2005.
37.
FinkenzellerK.RFID-Handbook. 2nd ed. John Wiley & Sons, W. Sussex, England, 2003; 456.
38.
Privacy Rights Clearinghouse. RFID position statement of consumer privacy and civil liberties organizations, 2003.
39.
FayyadUPiatetsky-ShapiroGSmythPUthurusamyR.Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press; 1997.
40.
Van BemmelJHMunsenMA.Handbook of Medical Informatics. Berlin, Germany: Springer-Verlag; 1997; 607.
41.
HanJKamberM.Data mining: concepts and techniques. In: The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann; 2001; 551.
42.
WittenIHEibeF.Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. San Francisco, CA: Morgan Kaufmann; 1999; 525.
43.
BreimanLFriedmanJHOlshenR.Classification and Regression Trees. Belmont, CA: Wadsworth International; 1984; 359.
44.
PignoloLRiganelloFCandelieriALaganiV.Vegetative state: early prediction of clinical outcome by artificial neural network. In: Proceedings of the 5th International workshop on Artificial Neural Network and Intelligent Information Processing – ANNIP 2009. Milan, Italy; 2009:91–96.
45.
MoserFRongGEMartinE.Joint cluster analysis of attribute and relationship data withouta-priori speci- fication of the number of clusters. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose, CA; 2007; 510–519.
46.
HolteRC.Very Simple Classification Rules Perform Well on Most Commonly Used Datasets. Machine Learning; 1993:63–90.
