Fuzzy Logic in Medicine

LaBrunda, Michelle; LaBrunda, Andrew

doi:10.4018/jitr.2008010103

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…Natural evolution of various diseases, complicated causal relationships, the difficulty of understanding medical data, and abstruseness of medical problems require a consistent framework that will be able to overcome the uncertainty and facilitate multiple class memberships that would provide for conjectural/approximate reasoning. FL examines very valuable classical logic in its generalized form and, rather than a definite conclusion, it is an artificial intelligence rule-based form, which uses approximate means of solution and membership functions (25). In this study, a trapezoidal membership function was selected.…”

Section: Fuzzy Logic Classificationmentioning

confidence: 99%

Using fuzzy logic for diagnosis and classification of spasticity

Alcan¹,

Canal²,

Zinnuroğlu³

2017

Turk J Med Sci

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IntroductionSpasticity is a common sensory-motor control disorder characterized by increased velocity-dependent stretch reflex responses resulting from upper motor neuron (UMN) lesions (1). Spasticity is frequently observed in cases such as spinal cord injury, multiple sclerosis, traumatic brain damage, cerebral palsy, and stroke, which can be accompanied by cerebral and spinal pathology that is dispersed or regional (2). The pathophysiology of spasticity is complicated and it is rather difficult to understand the underlying mechanism since it is necessary to know the pathophysiological mechanism that would differentiate the UMN syndrome from other symptoms (3,4). Therefore, spasticity is a disorder that is both insufficiently defined and measured (5). There are three major approaches, clinical, neurophysiological, and biomechanical, for assessing spasticity.The Ashworth Scale (AS) and the modified Ashworth Scale (MAS) are the most commonly used clinical measures of spasticity. The validity, reliability, and sensitivity of the clinical scales, which are subject to interpretation and generally intensified upon passive movement resistance, are debatable (6-8). As a consequence, many studies have been conducted using biomechanical and electrophysiological measurements with the objective of evaluating spasticity, and the obtained results have been used to make correlations with the clinical scales (8-14). Surface electromyography (EMG) has been commonly used in electrophysiological measurements of spasticity (14-16). However, parameters extracted from surface EMG have been generally obtained from analysis by using only the time domain in previous studies, and results were generally based on correlations with the clinical scales rather than using stand-alone surface EMG as well as considering causality. Due to the fact that surface EMG signals are nonstationary and random signals, some pathological data might not be discriminated in the time axis. Therefore, it is possible to utilize the short-time Fourier transform (STFT) and the wavelet transform (WT) methods from the time and the frequency components of the signals found in the pathological symptoms (17,18).Background/aim: Spasticity is generally defined as a sensory-motor control disorder. However, there is no pathophysiological mechanism or appropriate measurement and evaluation standards that can explain all aspects of a possible spasticity occurrence. The objective of this study is to develop a fuzzy logic classifier (FLC) diagnosis system, in which a quantitative evaluation is performed by surface electromyography (EMG), and investigate underlying pathophysiological mechanisms of spasticity.Materials and methods: Surface EMG signals recorded from the tibialis anterior and medial gastrocnemius muscles of hemiplegic patients with spasticity and a healthy control group were analyzed in standing, resting, dorsal flexion, and plantar flexion positions. The signals were processed with different methods: by using their amplitudes in the time domain, by applying short...

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Section: Fuzzy Logic Classificationmentioning

confidence: 99%

Using fuzzy logic for diagnosis and classification of spasticity

Alcan¹,

Canal²,

Zinnuroğlu³

2017

Turk J Med Sci

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Fuzzy-based framework for diagnosis of acid–base disorders

Amer

2011

Computers in Biology and Medicine

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A Novel Fuzzy Expert System for the Identification of Severity of Carpal Tunnel Syndrome

Kunhimangalam

Ovallath

Joseph

2013

BioMed Research International

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The diagnosis of carpal tunnel syndrome, a peripheral nerve disorder, at the earliest possible stage is very crucial because if left untreated it may cause permanent nerve damage reducing the chances of successful treatment. Here a novel Fuzzy Expert System designed using MATLAB is proposed for identification of severity of CTS. The data used were the nerve conduction study data obtained from Kannur Medical College, India. It consists of thirteen input fields, which include the clinical values of the diagnostic test and the clinical symptoms, and the output field gives the disease severity. The results obtained match with the expert's opinion with 98.4% accuracy and high degrees of sensitivity and specificity. Since quantification of the intensity of CTS is a crucial step in the electrodiagnostic procedure and is important for defining prognosis and therapeutic measures, such an expert system can be of immense use in those regions where the service of such specialists may not be readily available. It may also prove useful in combination with other systems in providing diagnostic and predictive medical opinions and can add value if introduced into the routine clinical consultations to arrive at the most accurate medical diagnosis in a timely manner.

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Fuzzy Logic in Medicine

Cited by 4 publications

References 8 publications

Using fuzzy logic for diagnosis and classification of spasticity

Using fuzzy logic for diagnosis and classification of spasticity

Fuzzy-based framework for diagnosis of acid–base disorders

A Novel Fuzzy Expert System for the Identification of Severity of Carpal Tunnel Syndrome

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