- Faculty of Health Sciences, Medical College, Aga Khan University, Karachi, Pakistan
- Section of Neurosurgery, Department of Surgery, Aga Khan University Hospital, Karachi, Pakistan
- Department of Computer Science and Engineering, Qatar University, Qatar
Muhammad Shahzad Shamim
Department of Computer Science and Engineering, Qatar University, Qatar
DOI:10.4103/2152-7806.77177© 2011 Godil SS This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
How to cite this article: Godil SS, Shamim MS, Enam SA, Qidwai U. Fuzzy logic: A “simple” solution for complexities in neurosciences?. Surg Neurol Int 26-Feb-2011;2:24
How to cite this URL: Godil SS, Shamim MS, Enam SA, Qidwai U. Fuzzy logic: A “simple” solution for complexities in neurosciences?. Surg Neurol Int 26-Feb-2011;2:24. Available from: http://sni.wpengine.com/surgicalint_articles/fuzzy-logic-a-simple-solution-for-complexities-in-neurosciences/
Background:Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum.
Methods:This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology.
Results:The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures.
Conclusions:In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences.
Keywords: Fuzzy logic, neurosciences, neurology, neurosurgery, psychiatry
Man is God’s most complex creation. Clinical judgment for the diagnosis and management of mans’ diseases is an art. It can neither be acquired from textbooks alone, nor can it be taught, but has to be developed slowly through years of observation and experience. This is because unlike other professions, which thrive on calculations based on yes/no or present/absent, very little is clearly black and white in clinical medicine. Most clinical scenarios present in shades of gray. Instead of “present or absent”, patients’ symptoms are described using terms like “never, rarely, sometimes, often, most of the times, always, etc”. Moreover, each specific symptom may also be graded as “mild, moderate or severe”. This is compounded by the fact that most symptoms are experienced and described differently by patients and many symptoms may overlap in the same patient. Each individual patient may also have a multitude of characteristics other than the disease, rendering it unique in itself. Medical problems, therefore, cannot be generalized and analyzed using Aristotelian or binary logic, and an analytical program is desperately required which could integrate this complex network of problems and devise individualized solutions. Fuzzy logic is the nearest response to the call. It has the potential of combining human heuristics into computer-assisted decision making. Imagine combining the experience of five university professors with all the current literature and developing a software that can calculate probabilities based on this, tailored specifically for each individual patient. Fuzzy logic can do all that.
The concept was first introduced by Lotfi Zadeh in 1965.[
Fuzzy logic is a multi-valued logic which was introduced by Zadeh in order to deal with vague and indecisive ideas.[
Characteristics of Fuzzy Logic
There are a few basic principles of fuzzy logic which were laid down by Zadeh in 1992:[
Exact reasoning is viewed as a limiting case of approximate reasoning. Everything is a matter of degree. Knowledge is interpreted as a collection of elastic, fuzzy constraints on a collection of variables. Inference is viewed as a process of propagation of elastic constraints. Any logical system can be “fuzzified”.
Exact reasoning is viewed as a limiting case of approximate reasoning.
Everything is a matter of degree.
Knowledge is interpreted as a collection of elastic, fuzzy constraints on a collection of variables.
Inference is viewed as a process of propagation of elastic constraints.
Any logical system can be “fuzzified”.
A classical set of binary logic has “crisp” boundaries whereas fuzzy sets have fuzzy or imprecise boundaries. A fuzzy set consists of linguistic variables where values are words and not numerical.[
where A is a fuzzy set in X and μA(x) is the membership function, which can have any value between 0 and 1 inclusive.
Membership functions overlap each other as evident in
Fuzzy rule is based on “if…then” rule and connects the different input and output fuzzy variables.[
if is x A then y is B
where A is the antecedent and B is the consequent. Fuzzy rules are similar to common sense rules as they resemble human thinking and are based on human experience. For example, in order to control ICP in a patient with traumatic brain injury, sedation is often required but needs to be carefully monitored. A simple rule can be, “If the ICP is high, increase propofol infusion”, or “If the ICP is low, stop propofol infusion”. These rules are based on collective experience of specialists in the field as well as available literature. Thus, as more fuzzy rules and sets are obtained from various sources, uncertainties are potentially reduced.
Fuzzy reasoning is also called approximate reasoning and is the process of drawing conclusions from fuzzy sets and fuzzy rules.
Fuzzy Inference System
Fuzzy inference system (FIS) is a framework which is based on fuzzy sets, fuzzy rules and fuzzy reasoning.[
Microdiskectomy is a common surgical procedure performed for low back pain and radiculopathy due to disc herniation. It provides symptom relief in most patients. However, a few patients fail to improve after this surgical procedure and fuzzy logic based FIS was used by Shamim et al, to predict this group of patients with failed microdiskectomy.[
A retrospective review of 501 patients who underwent microdiskectomy was done. A total of 16 variables from a list of 54 variables were classified as risk factors for failed microdiskectomy by an expert in the field. These variables were taken as membership function and the degrees of membership were defined. A rule base of 11 fuzzy rules was formed and each rule formed a decision bar which together made up the total decision surface. The centroid of each decision surface formed the basis of FIS decision. The output variable defined the risk of failed microdiskectomy as “very low”, “low” or “high” risk. The sensitivity and specificity of the FIS was calculated by comparing these results of FIS with the actual outcome of all patients at a six-month postoperative follow-up. The sensitivity and specificity of this FIS was found to be 88% and 86%, respectively.[
Another example highlights the application of FIS in predicting trauma-related mortality.[
One of the most important uses of fuzzy logic is in drug delivery devices. Special fuzzy logic controllers have been designed for use in anesthesia and intensive care units. One of the studies used auditory evoked potential as a measure of depth of anesthesia and the fuzzy controller administered a certain amount of drug based on it.[
All these examples clearly indicate that fuzzy logic networks and systems can easily solve various complex clinical problems.
Fuzzy logic is a solution to complex problems in all fields of life, including medicine, as it resembles human reasoning and decision making. It looks into all shades of gray and answers uncertainties and ambiguities created by human language where everything cannot be described in precise and discrete terms. Fuzzy systems help define disease extent and severity and answer questions related to individual patients taking into account their risk factors and co-morbidities.
On the other hand, it has a number of disadvantages too. It is tedious to develop fuzzy rules and membership functions and fuzzy outputs can be interpreted in a number of ways making analysis difficult. In addition, it requires lot of data and expertise to develop a fuzzy system. It does not give generalizable results and the program has to be run for each individual patient. Therefore, its clinical applicability and utilization is difficult without the availability of preprogrammed softwares for different pathologies and the basic training of clinicians to use these programs.
The application on fuzzy logic in medicine gained momentum in last two decades[
All the disciplines of medicine have used fuzzy logic in some way. The various applications include: assessing the effectiveness of drugs,[
Fuzzy logic is not only applicable to clinical medicine, but it has been a useful statistical tool in basic sciences and bioinformatics as well.[
In comparison to applicability of fuzzy logic in medicine and basic sciences, the concept is still new in the field of neurosciences. This was clearly highlighted in the review published on fuzzy logic where the contribution to the literature on fuzzy logic was much less from neurosciences as compared to other disciplines of medicine.[
Fuzzy model has been shown to be an effective tool for research related to neuroanatomy and has been used for imaging nerve fibers.[
Fuzzy logic has been used in the diagnosis, management and outcome prediction of common neurological diseases with considerable success. Stroke is a multifactorial disease and the causal relationship is complex. Jobe et al, showed that all probabilistic-statistical methods are ineffective in explaining this complex relationship except fuzzy model, which is a good tool to understand the disease causality.[
FIS has also been implicated in the study and interpretation of EEG. Aarabi et al, developed a FIS, which was highly sensitive (sensitivity: 98.7%) in detecting seizures via intracranial EEG.[
A fuzzy logic based biofeedback system through EMG has been found to significantly change the activation pattern of trapezius muscle during active and passive shoulder movements (P value <0.05).[
Fuzzy logic and application of different fuzzy rules can successfully predict and model human stance and gait which is controlled by the length of limb, its orientation and trunk attitude.[
The concept of fuzzy logic has been applied in neurosurgical ICUs for precise control of different parameters like ICP and blood pressure. Fuzzy logic based controllers are found effective at maintaining stable ICP via varying propofol infusion rates.[
A fuzzy Glasgow Coma Scale (GCS) has been introduced and was used in a study conducted on traumatic brain injury patients in India.[
Samejima et al, developed a screening tool for unruptured aneurysm using fuzzy logic, based on the data from a retrospective study and opinions of experienced neurosurgeons and this tool was shown to detect 12 new cases of unruptured intracranial aneurysm.[
Surgical planning for correcting spinal deformities is another area where fuzzy logic has been used effectively. An important decision for these surgeries is the level at which surgical correction is required. It is highly dependent on opinion of experienced surgeons, and using fuzzy logic, has now been integrated into a model to aid in surgical planning.[
Morphometric measurements and analyses of gray and white matter of spine can also be done using fuzzy logic in patients with spinal cord injury.[
MRI is the most commonly used radiological investigation for diagnosis of brain tumors and stroke. A number of studies have been conducted to analyze MRI images using fuzzy models.[
Classification and segmentation of the cerebral hemispheres, cerebellum and deep structures of the brain can be performed by fuzzy systems using information from the anatomical atlas and MRI image characteristics.[
Till date, there are no specific diagnostic criteria for diagnosis of cortical malformation which is a common neurological problem leading to epilepsy, mental retardation and developmental delay in children. A recent paper has used fuzzy logic to incorporate expert opinions and formulate a system for diagnosis of cortical malformation.[
Psychiatry and Psychology
A fuzzy logic based software has been designed to keep a track of psychiatric patients. It includes all the recent diagnostic criteria for different disorders and has complete details of the patients’ histories and follow ups which help in the management of these patients as well as assisting in research.[
Qing et al, developed a fuzzy logic based functional MRI model for the evaluation of depression and its severity.[
Fuzzy systems have been successfully developed to monitor drug response in patients with drug dependence. The example of one such model is use of citalopram in alcohol drug dependence where high correlation (r = 0.99, P value <0.001) was seen between the actual and predicted response rate.[
The above discussion clearly highlights fuzzy logic as a sensitive and specific tool for various clinical problems. Although, a number of studies have been conducted on fuzzy logic, it is still largely underutilized in neurosciences. On one hand, where the concept has the potential of changing medical diagnosis and management completely, it remains to be seen how effectively it can be incorporated in routine clinical practice. If focused research is conducted, it is possible that in future neurophysiology labs will be reporting EMGs and EEGs with the help of fuzzy logic, ICUs will have fuzzy controllers for controlling blood pressure, ICP and ventilator settings, MRI scans will be analyzed by fuzzy logic softwares and neurosurgeries will be planned by FIS. However, change is always difficult to introduce. God’s most complex creation remains a creature of habit.
1. Aarabi A, Fazel-Rezai R, Aghakhani Y. Seizure detection in intracranial EEG using a fuzzy inference system. Conf Proc IEEE Eng Med Biol Soc. 2009. 2009: 1860-3
2. Aissaoui R, Desroches G. Stroke pattern classification during manual wheelchair propulsion in the elderly using fuzzy clustering. J Biomech. 2008. 41: 2438-45
3. Akinyokun CO, Obot OU, Uzoka FM, Andy JJ. A neuro-fuzzy decision support system for the diagnosis of heart failure. Stud Health Technol Inform. 2010. 156: 231-44
4. al-Holou N, Joo DS. Development of a fuzzy logic based system to monitor the electrical responses of nerve fiber. Biomed Sci Instrum. 1997. 33: 376-81
5. Alayon S, Robertson R, Warfield SK, Ruiz-Alzola J. A fuzzy system for helping medical diagnosis of malformations of cortical development. J Biomed Inform. 2007. 40: 221-35
6. Aldridge BB, Saez-Rodriguez J, Muhlich JL, Sorger PK, Lauffenburger DA. Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling. PLoS Comput Biol. 2009. 5: e1000340-
7. Algorri ME, Flores-Mangas F. Classification of anatomical structures in MR brain images using fuzzy parameters. IEEE Trans Biomed Eng. 2004. 51: 1599-608
8. Allen R, Smith D. Neuro-fuzzy closed-loop control of depth of anaesthesia. Artif Intell Med. 2001. 21: 185-91
9. Allum JH, Honegger F, Troescher M. Principles underlying real-time nystagmus analysis of horizontal and vertical eye movements recorded with electro-, infra-red-, or video-oculographic techniques. J Vestib Res. 1998. 8: 449-63
10. Amin AP, Kulkarni HR. Improvement in the information content of the Glasgow Coma Scale for the prediction of full cognitive recovery after head injury using fuzzy logic. Surgery. 2000. 127: 245-53
11. Andrews RJ, Mah RW. The NASA Smart Probe Project for real-time multiple microsensor tissue recognition. Stereotact Funct Neurosurg. 2003. 80: 114-9
12. Axer H, Jantzen J, Keyserlingk DG, Berks G. The application of fuzzy-based methods to central nerve fiber imaging. Artif Intell Med. 2003. 29: 225-39
13. Bates JH, Hatzakis GE, Olivenstein R. Fuzzy logic and mechanical ventilation. Respir Care Clin N Am. 2001. 7: 363-77
14. Bates JH, Young MP. Applying fuzzy logic to medical decision making in the intensive care unit. Am J Respir Crit Care Med. 2003. 167: 948-52
15. Bay OF, Usakli AB. Survey of fuzzy logic applications in brain-related researches. J Med Syst. 2003. 27: 215-23
16. Belhassen S, Zaidi H. A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET. Med Phys. 2010. 37: 1309-24
17. Brandt ME, Bohan TP, Kramer LA, Fletcher JM. Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images. Comput Med Imaging Graph. 1994. 18: 25-34
18. Carvalho LM, Nassar SM, Azevedo FM, Carvalho HJ, Monteiro LL, Rech CM. A neuro-fuzzy system to support in the diagnostic of epileptic events and non-epileptic events using different fuzzy arithmetical operations. Arq Neuropsiquiatr. 2008. 66: 179-83
19. Chauvet E, Fokapu O, Hogrel JY, Gamet D, Duchene J. Automatic identification of motor unit action potential trains from electromyographic signals using fuzzy techniques. Med Biol Eng Comput. 2003. 41: 646-53
20. Ciofolo C, Barillot C. Atlas-based segmentation of 3D cerebral structures with competitive level sets and fuzzy control. Med Image Anal. 2009. 13: 456-70
21. Ciofolo C, Barillot C. Brain segmentation with competitive level sets and fuzzy control. Inf Process Med Imaging. 2005. 19: 333-44
22. Cosenza-Andraus ME, Nunes-Cosenza CA, Gomes-Nunes R, Fantezia-Andraus C, Alves-Leon SV. Video-electroencephalography prolonged monitoring in patients with ambulatory diagnosis of medically refractory temporal lobe epilepsy: application of fuzzy logic’s model. Rev Neurol. 2006. 43: 7-14
23. Datta S, Sajja BR, He R, Wolinsky JS, Gupta RK, Narayana PA. Segmentation and quantification of black holes in multiple sclerosis. Neuroimage. 2006. 29: 467-74
24. Dembele D, Kastner P. Fuzzy C-means method for clustering microarray data. Bioinformatics. 2003. 19: 973-80
25. Dojat M, Harf A, Touchard D, Laforest M, Lemaire F, Brochard L. Evaluation of a knowledge-based system providing ventilatory management and decision for extubation. Am J Respir Crit Care Med. 1996. 153: 997-1004
26. Ellingson BM, Ulmer JL, Prost RW, Schmit BD. Morphology and morphometry in chronic spinal cord injury assessed using diffusion tensor imaging and fuzzy logic. Conf Proc IEEE Eng Med Biol Soc. 2006. 1: 1885-8
27. Emblem KE, Nedregaard B, Hald JK, Nome T, Due-Tonnessen P, Bjornerud A. Automatic glioma characterization from dynamic susceptibility contrast imaging: brain tumor segmentation using knowledge-based fuzzy clustering. J Magn Reson Imaging. 2009. 30: 1-10
28. Grant P, Naesh O. Fuzzy logic and decision-making in anaesthetics. J R Soc Med. 2005. 98: 7-9
29. Hanson CW, Marshall BE. Artificial intelligence applications in the intensive care unit. Crit Care Med. 2001. 29: 427-35
30. Hazelzet JA. Can fuzzy logic make things more clear?. Crit Care. 2009. 13: 116-
31. Helgason CM. The application of fuzzy logic to the prescription of antithrombotic agents in the elderly. Drugs Aging. 2004. 21: 731-6
32. Helgason CM, Jobe TH. Causal interactions, fuzzy sets and cerebrovascular ‘accident’: the limits of evidence-based medicine and the advent of complexity-based medicine. Neuroepidemiology. 1999. 18: 64-74
33. Helgason CM, Jobe TH. Fuzzy logic and causal reasoning with an ‘n’ of 1 for diagnosis and treatment of the stroke patient. Expert Rev Neurother. 2004. 4: 249-54
34. Helgason CM, Jobe TH. Fuzzy logic and continuous cellular automata in warfarin dosing of stroke patients. Curr Treat Options Cardiovasc Med. 2005. 7: 211-8
35. Helgason CM, Malik DS, Cheng SC, Jobe TH, Mordeson JN. Statistical versus fuzzy measures of variable interaction in patients with stroke. Neuroepidemiology. 2001. 20: 77-84
36. Hu H, Li S, Wang Y, Qi X, Shi Z. The universal fuzzy logical framework of neural circuits and its application in modeling primary visual cortex. Sci China C Life Sci. 2008. 51: 902-12
37. Huang SJ, Shieh JS, Fu M, Kao MC. Fuzzy logic control for intracranial pressure via continuous propofol sedation in a neurosurgical intensive care unit. Med Eng Phys. 2006. 28: 639-47
38. Huang W, Zhang J, Huang D. A simple method to analyze the similarity of biological sequences based on the fuzzy theory. J Theor Biol. 2010. 265: 323-8
39. Jacobs R. Control model of human stance using fuzzy logic. Biol Cybern. 1997. 77: 63-70
40. Janda M, Bajorat J, Simanski O, Kahler R, Pohl B, Noldge-Schomburg GF. Feedback control of depth of anesthesia during propofol administration: Bispectral index as the controlled variable. Anaesthesist. 2010. 59: 621-7
41. Jo HG, Park JY, Lee CK, An SK, Yoo SK. Genetic fuzzy classifier for sleep stage identification. Comput Biol Med. 2010. 40: 629-34
42. Jobe TH, Helgason CM. The fuzzy cube and causal efficacy: Representation of concomitant mechanisms in stroke. Neural Netw. 1998. 11: 549-55
43. Jobe TH, Helgason CM, Roitberg BZ. “Show me the numbers”: The application of numbers to medical science. Surg Neurol. 2001. 56: 3-7
44. Kannan SR, Ramathilagam S, Sathya A, Pandiyarajan R. Effective fuzzy c-means based kernel function in segmenting medical images. Comput Biol Med. 2010. 40: 572-9
45. Keller T, Bitterlich N, Hilfenhaus S, Bigl H, Loser T, Leonhardt P. Tumour markers in the diagnosis of bronchial carcinoma: New options using fuzzy logic-based tumour marker profiles. J Cancer Res Clin Oncol. 1998. 124: 565-74
46. Kocer S. Classification of EMG signals using neuro-fuzzy system and diagnosis of neuromuscular diseases. J Med Syst. 2010. 34: 321-9
47. Kouame D, Biard M, Girault JM, Bleuzen A. Adaptive AR and neurofuzzy approaches: Access to cerebral particle signatures. IEEE Trans Inf Technol Biomed. 2006. 10: 559-66
48. Kovacs M, Juranovics J. “Auctoritas” psychiatric expert system shell. Medinfo. 1995. 8: 997-
49. Lan TH, Loh EW, Wu MS, Hu TM, Chou P, Lan TY. Performance of a neuro-fuzzy model in predicting weight changes of chronic schizophrenic patients exposed to antipsychotics. Mol Psychiatry. 2008. 13: 1129-37
50. Lee CS, Wang MH. A fuzzy expert system for diabetes decision support application. IEEE Trans Syst Man Cybern B Cybern 2010 in pre. p.
51. Lekkas S, Mikhailov L. Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases. Artif Intell Med. 2010. 50: 117-26
52. Li M, Ye ZQ. Monitoring the depth of anesthesia using a fuzzy neural network based on EEG. Zhongguo Yi Liao Qi Xie Za Zhi. 2006. 30: 253-5
53. Licata G. Probabilistic and fuzzy logic in clinical diagnosis. Intern Emerg Med. 2007. 2: 100-6
54. Lin GC, Wang CM, Wang WJ, Sun SY. Automated classification of multispectral MR images using unsupervised constrained energy minimization based on fuzzy logic. Magn Reson Imaging. 2010. 28: 721-38
55. Liu J, Udupa JK, Odhner D, Hackney D, Moonis G. A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. Comput Med Imaging Graph. 2005. 29: 21-34
56. MA Hatiboglu AA, Ozger M, Iplikcioglu AC, Cosar M, Turgut N. A predictive tool by fuzzy logic for outcome of patients with intracranial aneurysm. Exp Syst Appl. 2010. 37: 1043-9
57. Magy Seif El-Nasr J. FLAME–Fuzzy logic adaptive model of emotions autonomous agents and multi-agent systems. Auton Agent Multi-Agent Syst. 2004. 3: 219-57
58. Mahfouf M, Abbod MF, Linkens DA. A survey of fuzzy logic monitoring and control utilisation in medicine. Artif Intell Med. 2001. 21: 27-42
59. Martin JF. Fuzzy control in anesthesia. J Clin Monit. 1994. 10: 77-80
60. Mason DG, Ross JJ, Edwards ND, Linkens DA, Reilly CS. Self-learning fuzzy control with temporal knowledge for atracurium-induced neuromuscular block during surgery. Comput Biomed Res. 1999. 32: 187-97
61. Massaro DW, Cohen MM. Perceiving speech from inverted faces. Percept Psychophys. 1996. 58: 1047-65
62. Miller DJ, Nelson CA, Oleynikov D. Shortened OR time and decreased patient risk through use of a modular surgical instrument with artificial intelligence. Surg Endosc. 2009. 23: 1099-105
63. Mohamed SS, Li JM, Salama MM, Freeman GH, Tizhoosh HR, Fenster A. An automated neural-fuzzy approach to malignant tumor localization in 2D ultrasonic images of the prostate. J Digit Imaging 2010 in press. p.
64. Moonis G, Liu J, Udupa JK, Hackney DB. Estimation of tumor volume with fuzzy-connectedness segmentation of MR images. AJNR Am J Neuroradiol. 2002. 23: 356-63
65. Morris MK, Saez-Rodriguez J, Sorger PK, Lauffenburger DA. Logic-based models for the analysis of cell signaling networks. Biochemistry. 2010. 49: 3216-24
66. Mzenda B, Hosseini-Ashrafi M, Gegov A, Brown DJ. A fuzzy convolution model for radiobiologically optimized radiotherapy margins. Phys Med Biol. 2010. 55: 3219-35
67. Naranjo CA, Bremner KE, Bazoon M, Turksen IB. Using fuzzy logic to predict response to citalopram in alcohol dependence. Clin Pharmacol Ther. 1997. 62: 209-24
68. Nault ML, Labelle H, Aubin CE, Balazinski M. The use of fuzzy logic to select which curves need to be instrumented and fused in adolescent idiopathic scoliosis: A feasibility study. J Spinal Disord Tech. 2007. 20: 594-603
69. Nemoto T, Hatzakis GE, Thorpe CW, Olivenstein R, Dial S, Bates JH. Automatic control of pressure support mechanical ventilation using fuzzy logic. Am J Respir Crit Care Med. 1999. 160: 550-6
70. Nunes CS, Amorim P. A neuro-fuzzy approach for predicting hemodynamic responses during anesthesia. Conf Proc IEEE Eng Med Biol Soc. 2008. 2008: 5814-7
71. Nunes CS, Mahfouf M, Linkens DA, Peacock JE. Modelling and multivariable control in anaesthesia using neural-fuzzy paradigms: Part I: Classification of depth of anaesthesia and development of a patient model. Artif Intell Med. 2005. 35: 195-206
72. Obot OU, Uzoka FM. Experimental study of fuzzy-rule based management of tropical diseases: Case of malaria diagnosis. Stud Health Technol Inform. 2008. 137: 328-39
73. Ohayon MM. Improving decisionmaking processes with the fuzzy logic approach in the epidemiology of sleep disorders. J Psychosom Res. 1999. 47: 297-311
74. Pagava K, Kiseleva T. New approach to estimate different drugs and/or other medical interventions effectiveness based on fuzzy logic principles. Georgian Med News. 2008. 5: 65-8
75. Pandey B, Mishra RB. Knowledge and intelligent computing system in medicine. Comput Biol Med. 2009. 39: 215-30
76. Patriarche J, Erickson B. A review of the automated detection of change in serial imaging studies of the brain. J Digit Imaging. 2004. 17: 158-74
77. Pena-Reyes CA. Evolutionary fuzzy modeling human diagnostic decisions. Ann N Y Acad Sci. 2004. 1020: 190-211
78. Phillips WE, Velthuizen RP, Phuphanich S, Hall LO, Clarke LP, Silbiger ML. Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme. Magn Reson Imaging. 1995. 13: 277-90
79. Prochazka A. The fuzzy logic of visuomotor control. Can J Physiol Pharmacol. 1996. 74: 456-62
80. Qing L, Haiteng J, Haiyan L, Gang L, Gaojun T, Zhijian Y. Depression severity evaluation for female patients based on a functional MRI model. J Magn Reson Imaging. 2010. 31: 1067-74
81. Ressom H, Reynolds R, Varghese RS. Increasing the efficiency of fuzzy logic-based gene expression data analysis. Physiol Genomics. 2003. 13: 107-17
82. Roitberg B. Fuzzy logic in the neurosurgical intensive care unit. Surg Neurol. 2006. 65: 217-
83. Roitberg B. Research news and notes. Surg Neurol. 2008. 69: 439-40
84. Ross JJ, Mason DG, Linkens DA, Edwards ND. Self-learning fuzzy logic control of neuromuscular block. Br J Anaesth. 1997. 78: 412-5
85. Russell Andrews RM, Papasin R, Guerrero M, DaSilva L. The NASA smart probe for real-time tissue identification: Potential applications in neurosurgery minimally invasive neurosurgery and multidisciplinary Neurotraumatology. 2007. 1: 3-7
86. Samani A, Holtermann A, Sogaard K, Madeleine P. Active biofeedback changes the spatial distribution of upper trapezius muscle activity during computer work. Eur J Appl Physiol. 2010. 110: 415-23
87. Samani A, Holtermann A, Sogaard K, Madeleine P. Advanced biofeedback from surface electromyography signals using fuzzy system. Med Biol Eng Comput. 2010. 48: 865-73
88. Samejima H, Ushikubo Y, Mizokami T, Aoki K, Iwabuchi S, Kasai K. New screening system for unruptured cerebral aneurysms-combination of an expert system and DSA examination. Neurol Med Chir (Tokyo). 1990. 30: 575-81
89. Saraoglu HM, Sanli S. A fuzzy logic-based decision support system on anesthetic depth control for helping anesthetists in surgeries. J Med Syst. 2007. 31: 511-9
90. Schmidt B, Bocklisch SF, Passler M, Czosnyka M, Schwarze JJ, Klingelhofer J. Fuzzy pattern classification of hemodynamic data can be used to determine noninvasive intracranial pressure. Acta Neurochir Suppl. 2005. 95: 345-9
91. Schneider J, Bitterlich N, Velcovsky HG, Morr H, Katz N, Eigenbrodt E. Fuzzy logic-based tumor-marker profiles improved sensitivity in the diagnosis of lung cancer. Int J Clin Oncol. 2002. 7: 145-51
92. Schneider J, Peltri G, Bitterlich N, Neu K, Velcovsky HG, Morr H. Fuzzy logic-based tumor marker profiles including a new marker tumor M2-PK improved sensitivity to the detection of progression in lung cancer patients. Anticancer Res Am. 2003. 23: 899-906
93. Seker H, Odetayo MO, Petrovic D, Naguib RN. A fuzzy logic based-method for prognostic decision making in breast and prostate cancers. IEEE Trans Inf Technol Biomed. 2003. 7: 114-22
94. Shamim MS, Enam SA, Qidwai U. Fuzzy Logic in neurosurgery: Predicting poor outcomes after lumbar disk surgery in 501 consecutive patients. Surg Neurol. 2009. 72: 565-72
95. Shen S, Szameitat AJ, Sterr A. An improved lesion detection approach based on similarity measurement between fuzzy intensity segmentation and spatial probability maps. Magn Reson Imaging. 2010. 28: 245-54
96. Shieh JS, Fu M, Huang SJ, Kao MC. Comparison of the applicability of rule-based and self-organizing fuzzy logic controllers for sedation control of intracranial pressure pattern in a neurosurgical intensive care unit. IEEE Trans Biomed Eng. 2006. 53: 1700-5
97. Simone Hemm FC, Coste J, Vassal F, Nuti C, Derost P, Ouchchane L. Postoperative control in deep brain stimulation of the subthalamic region: the contact membership concept. Int J Comput Assist Radiol Surg. 2008. 3: 69-77
98. Torres A, Nieto JJ. Fuzzy logic in medicine and bioinformatics. J Biomed Biotechnol. 2006. 2006: 91908-
99. Torres A, Nieto JJ. The fuzzy polynucleotide space: Basic properties. Bioinformatics. 2003. 19: 587-92
100. Ubeyli ED. Fuzzy similarity index for discrimination of EEG signals. Conf Proc IEEE Eng Med Biol Soc. 2006. 1: 5346-9
101. Uncu U. Evaluation of pulmonary function tests by using fuzzy logic theory. J Med Syst. 2010. 34: 241-50
102. Vaidyanathan M, Clarke LP, Hall LO, Heidtman C, Velthuizen R, Gosche K. Monitoring brain tumor response to therapy using MRI segmentation. Magn Reson Imaging. 1997. 15: 323-34
103. Velthuizen RP, Clarke LP, Phuphanich S, Hall LO, Bensaid AM, Arrington JA. Unsupervised measurement of brain tumor volume on MR images. J Magn Reson Imaging. 1995. 5: 594-605
104. Villeger A, Ouchchane L, Lemaire JJ, Boire JY. Assistance to planning in deep brain stimulation: data fusion method for locating anatomical targets in MRI. Conf Proc IEEE Eng Med Biol Soc. 2006. 1: 144-7
105. Vitez TS, Wada R, Macario A. Fuzzy logic: Theory and medical applications. J Cardiothorac Vasc Anesth. 1996. 10: 800-8
106. Ye CZ, Yang J, Geng DY, Zhou Y, Chen NY. Fuzzy rules to predict degree of malignancy in brain glioma. Med Biol Eng Comput. 2002. 40: 145-52
107. Yoder L. Explicit logic circuits predict local properties of the neocortex’s physiology and anatomy. PLoS One. 2010. 5: e9227-
108. Yusuf Alper Kilic AK, Yorganci K, Sayek I. A novel fuzzy-logic inference system for predicting trauma-related mortality: Emphasis on the impact of response to resuscitation. Eur J Trauma Emerg Surg 2010 in press. p.
109. Zadeh L. The calculus of fuzzy if/then rules. AI Exp. 1992. 7: 22-7
110. Zadeh L. Fuzzy sets. Inform Control. 1965. 8: 338-53
111. Zahlmann G, Kochner B, Ugi I, Schuhmann D, Liesenfeld B, Wegner A. Hybrid fuzzy image processing for situation assessment. IEEE Eng Med Biol Mag. 2000. 19: 76-83
112. Zhang D, Zhu K. Modeling biological motor control for human locomotion with functional electrical stimulation. Biol Cybern. 2007. 96: 79-97
113. Zhuge Y, Udupa JK. Intensity standardization simplifies brain MR image segmentation. Comput Vis Image Underst. 2009. 113: 1095-103
114. Zouridakis G, Boutros NN, Jansen BH. A fuzzy clustering approach to study the auditory P50 component in schizophrenia. Psychiatry Res. 1997. 69: 169-81