Detection of traumatic brain injuries using fuzzy logic algorithm

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Expert Systems with Applications Expert Systems with Applications 34 (2008) 1312–1317 www.elsevier.com/locate/eswa

Detection of traumatic brain injuries using fuzzy logic algorithm Inan Gu¨ler a

a,*

, Aysße Tunca b, Eyyu¨p Gu¨lbandilar

c

Gazi University, Faculty of Technical Education, Department of Electronic and Computer Education, Besßevler, 06500 Ankara, Turkey b Dirimsel Outpatient Clinic, Department of Neurology, Balgat, 06520 Ankara, Turkey c Dumlupinar University, Faculty of Engineering, Department of Computer Engineering, Ku¨tahya, Turkey

Abstract The aim of this study was to develop a diagnostic system for detecting the severity of traumatic brain injuries using fuzzy logic. Twenty-six traumatic brain injury patients in different age and gender were taken in the study. Electroencephalography, Trauma and Glasgow coma scores were used for evaluating the system. The results were compared with the findings of neurologists. We found a significant relationship between the findings of neurologists and systems output for normal, mild and severe electroencephalography tracing data. Getting this system in routine use will facilitate to make a rapid decision for the degree of trauma with electroencephalography.  2006 Elsevier Ltd. All rights reserved. Keywords: Fuzzy logic; Traumatic brain injuries; Electroencephalogram; Trauma scores; Glasgow coma scores

1. Introduction Traumas are important health problems all over the world (Chawda, Hildebrand, Pape, & Giannoudis, 2004; Robertson & Redmond, 1993). On the other hand, half of the mortalities related to traumas are traumatic brain injuries (TBI) (Robertson & Redmond, 1993). TBI cause more permanent deficiencies and much more mortality than other trauma incidences. TBI produces symptoms of physical, sensory, emotional, and behavioural such as headache, reduced attention, reduced reaction and sexual dysfunctions (Chawda et al., 2004). Even mild TBI can cause changes in brain structures (Gaetz & Bernstein, 2001). There are several methods/systems to diagnose and detect severity of TBI. The Glasgow coma score (GCS) can be used as a first assessment of the severity and prognosis immediately following brain trauma (Thatcher, Biver et al., 2001). However, GCS itself has a practical limitation

*

Corresponding author. Tel.: +90 312 212 3976; fax: +90 312 212 0059. E-mail addresses: [email protected] (I. Gu¨ler), [email protected] (A. Tunca), [email protected] (E. Gu¨lbandilar). 0957-4174/$ - see front matter  2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2006.12.009

because it cannot be put into practice in emergency rooms or in hospitals where TBI patients are first transported. Revised trauma scores use more parameters than GCS (Robertson & Redmond, 1993). Both the duration of unconsciousness and the duration of posttraumatic amnesia are two other clinical predictors for the severity of TBI (Thatcher, Biver et al., 2001). Gaetz and Bernstein (2001) reported that computed tomography (CT) may be less sensitive than certain functional techniques. Magnetic resonance imaging (MRI) and electroencephalogram (EEG) are visually read standardized methods. Both are not sensitive enough to detect differences between mild and moderate TBI (Thatcher, Biver et al., 2001; Thatcher, North et al., 2001). However, EEG is recommended to detect mild TBI (Gaetz & Bernstein, 2001; Pointenger, Sarahrudi, Poeschl, & Munki, 2002). Also evoked potentials (EP), eventrelated potentials (ERP), and magnetoencephalography (MEG) can be used to detect mild TBI as well as (Gaetz & Bernstein, 2001). In practice more than one method can be used for diagnosis which assures reliability and accuracy. Some systems were developed to increase the reliability of methods. Quantitative EEG (qEEG) is used as an accurate method in earlier literature for detection of severity of TBI

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(Thatcher, Biver et al., 2001). Thatcher, North et al. (2001) recommended an EEG severity index to diagnose accuracy of the extent of brain injury by providing an objective and independent measure of the severity. Thatcher, Biver et al. (2001) suggested that clinical sensitivity of EEG and MRI may be improved by biophysical linkages and deeper understanding of their common origins. Fuzzy logic is a computational paradigm that provides a mathematical tool for representing and manipulating information in a way that resembles human communication and reasoning processes (Yager & Zadeh, 1994). Fuzzy logic has been used in the biological and agricultural systems (Center & Verma, 1998). According to Gu¨ler, Hardalac, and Barisci (2002) fuzzy logic is a functional method to determine the type of cardiac diseases. Depth of anesthesia can be predicted using fuzzy logic (Allen & Smith, 2001; Elkfafi, Shie, Linkens, & Peacock, 1997; Muthuswamy & Roy, 1999; Zhang & Roy, 2001). Data from different tools/methods such as ultrasonography, GCS and EEG can be used to develop models using fuzzy logic (Amin & Kulkarni, 2000; Gu¨ler et al., 2002; Muthuswamy & Roy, 1999; Zouridakis, Jansen, & Boutros, 1997). EEG is still the most widely used technique in diagnosis of TBI and post-traumatic cases (Guerit, 2000; Guerit, 1999; Quinonez, 1998). However, some deficiencies of EEG in diagnosis of TBI have been reported. The aim of this study was to develop a diagnostic system for detecting the severity of TBI using fuzzy logic. 2. Materials and methods Data from 32 patients with TBI (mean age: 42.5 ± 74; 18–66 years; 13 female and 19 male) who were admitted to Neurology outpatient clinics of Fatih University and Gu¨lhane Military Medical School Hospitals were used as study group. Study group patients were asked for detailed informations, such as duration of trauma, posttraumatic amnesia and use of medications. All the patients went to computed tomography (CT). A radiologist examined CT images. The patients who had old CT lesions independent from trauma or had used medications that could affect EEG were not taken into the examination. Another five healthy subjects’ data were used to check the reliability of our developed system. Glasgow coma scores (GCS) of all the patients were calculated using physiological parameters such as eyes open, best verbal response and best motor response (Table 1) (Teasdale & Jennett, 1974; Teasdale, Murray, Parker, & Jennett, 1979). Physiological parameters; breath, death of breath, systolic blood pressure and capillary refilling were all graded using a score schema (Table 2). For calculation of main trauma score (TS) GCS was used. For an example, if GCS was a value between 3 and 4, 1 point was added on the total score of each patient that was established on Table 2 (Robertson & Redmond, 1993). In this way, main TS values were got and used as the first input of fuzzification.

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Table 1 Glasgow coma score (GCS) register (Teasdale & Jennett, 1974; Teasdale et al., 1979) Physiological parameters

Situation

Scores

Eyes open

Spontaneous To speech To pain None

4 3 2 1

Best verbal response

Orientated Confused Inappropriate words Incomprehensible sounds None

5 4 3 2 1

Best motor response

Obeys commands Localised pain Withdraws to pain Flexion to pain Extension to pain None

6 5 4 3 2 1

Table 2 Trauma score register (TS) (Robertson & Redmond, 1993) Physiological parameters

Situation

Scores

Breath

P36 25–35 10–24 1–9 No spontaneous breath

2 3 4 1 0

Depth of breath

Normal Superficial Retractif

1 0 0

Systolic blood pressure (mmHg)

P90 70–89 50–69 0–49 No pulsation

4 3 2 1 0

Capillary refilling

Normal Delayed No capillary refilling

2 1 0

The second input of fuzzification was EEG data, which were recorded using two different tools (one from Fatih University and other from Gu¨lhane Military Medical Hospital). In different hospital, Medelec Profile EEG Recording with a 256 Hz sampling rate were used. The amplifier bandwidths were nominally 0.5–70 Hz. The EEG data was recorded with 18- or 19-channel bipolar montage according to using the tools. The electrodes localized on the skull using international 10–20-electrode placement. Each record lasted about 20 min. Nine patients were examined with 18 channels tool (9 · 18 = 162) and 23 patients with 19 channels tool (23 · 19 = 437). So, total 599 EEG tracing data (EEG TD) were recorded from 32 patients. The data stored in ASCII format on the hard disk of the PC. EEG records in ASCII format have transformed into frequency information by using Discrete-Time Fourier Transform (DFT). DFT and fuzzy logic algorithms were performed by the usage of MATLAB programs. A flow chart of the algorithm is shown in Fig. 1.

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ezoids were formed using TS, which ranges from 0 to 16 (Fig. 2). Membership degrees corresponding TS were calculated by using membership functions (Eq. (1)): 8 xi a ; a 6 xi 6 b > ba > > < 1; b < x < c i ð1Þ lðxi Þ ¼ dxi > > > dc ; c 6 xi 6 d : 0; otherwise

START

Input the physiological parameters (TP)

Calculate the TP membership degree

where a, b, c and d are limits of the membership function. For instance, TP5 = 0.67 and TP6 = 0.67 can be calculated for TS = 7. Trapezoid shape was also chosen to express the second crisp input for EEG frequency. Five membership functions were formed for frequencies between 0 and 8 Hz (Fig. 3). Membership degrees were calculated using Eq. (1). For instance, T2 = 0.33 and T3 = 1.00 can be obtained for EEG = 4.5 Hz.

Read the EEG

Calculate the EEG membership degree (T)

2.2. Fuzzy inference

Read the T and TP

Calculate the inference (Q)

Y Q<15

Normal

N Y 15
Mild Trauma

The second step in fuzzy logic processing is fuzzy inference. A rule base was formed. Rule base was the range of rules, which consists of outputs of fuzzification corresponding T and TP linguistic inputs. Neurologists investigated the rule base. Five linguistic outputs were used in the rule base (O1, O2, O3, O4, and O5) (Table 3). Relations obtained from the rule base were interpreted using minimum operator, ‘‘and’’. The outputs obtained from rule base were interpreted using maximum operator, ‘‘or’’. The fuzzy rules used in the current work are as follows: if T ¼ T1

N Y 40
Moderate Trauma

and

TP ¼ TP1

then

O ¼ O5

where nine outputs of TP and five outputs of T were available.

N Y 55
2.3. Defuzzification Severe Trauma

N Y 85
Coma (Brain died)

N

END

Fig. 1. A flow chart of the fuzzy logic algorithm.

2.1. Fuzzification process Multiple measured crisp inputs first have to be mapped into fuzzy membership functions. This process is called fuzzification (Gu¨ler et al., 2002). A trapezoid shape was preferred to define fuzzy membership functions. Nine trap-

The outputs of the inference mechanism are fuzzy output variables. The fuzzy logic controller must convert its internal fuzzy output variables into crisp values so that the actual system can use these variables. This conversion is called defuzzification (Gu¨ler et al., 2002). One may perform this operation in several ways. One of the most common ways is the use of height method. In this method, the centroid of each membership function for each rule is first evaluated. The final output COG, is then calculated as the average of the individual centroid, weighted by their heights as follows: Pb x¼a lA ðxÞ  x COG ¼ P ð2Þ b x¼a lA ðxÞ where COG is defuzzification output and lA(x) is minimum/maximum value of membership degree of input values. The output membership functions, O1, O2, O3, O4,

I. Gu¨ler et al. / Expert Systems with Applications 34 (2008) 1312–1317

Degree of membership

1

TP9

TP8

TP7

TP6

TP5

TP4

TP3

TP2

1315

TP1

0.5

0 -0.5

4

8.5

13

17.5

Trauma Score

T5

T4

T3

T2

T1

1

0.5

0 -0.5

1.5

3.5 5.5 EEG Frequency (Hz)

7.5

Fig. 3. Fuzzy membership functions and membership degree of EEG.

and O5 were converted to trauma severity degrees, which were between 0 and 100 (Fig. 4). 2.4. Statistical analysis Findings of neurologists and outputs of system were compared using statistical software, SPSS 11.5. We used the chi-square test for categorical comparisons of data. A p value of <0.05 was considered to indicate statistical significance; all tests were two-tailed and in the 95% confidence interval (CI). 3. Results The CGS of patients were summarized in Table 1, TS were summarized in Table 2. TBI severity of the patients, which were calculated, by neurologists and system were all summarized in Table 4. According to the neurologists’ comments 281 (46.9%) of 599 EEG TD were normal, 247 (41.2%) were mild, 20

(3.3%) were moderate and 51 (8.5%) were severe. According to the system’s comments; 281 of 599 (46.9%) EEG TD was normal, 260 (43.4%) were mild, 2 (0.3%) were moderate and 56 (9.3%) were severe. In the phase of explanation of the results, we saw that to 51 severe trauma EEG TD comments of the neurologists, the system interpreted the data as 51 severe, 3 moderate and 2 mild; to 20 moderate trauma EEG TD comments of the neurologists, the system found 2 moderate comment but interpreted the data as 3 severe and 15 moderates. To 247 mild trauma EEG TD comments of the neurologists, the system interpreted the data as 223 mild and 22 normal and 2 severe. To 281 normal trauma EEG TD comments of the neurologists, the system interpreted the data as 259 normal and 22 mild (Table 4). So, the quantity of total similar comments of neurologists and system were 533 of 599 EEG TD (88.98%).

Degree of membership

Degree of membership

Fig. 2. Fuzzy membership functions and membership degree of TS.

O5

1

O4

O3

O2

O1

0.5

0 0

10

20

30

40 50 60 70 Trauma Degree (%)

80

90 100

Fig. 4. Membership functions of linguistic output and trauma degree crisp.

Table 3 Database of trapezoid membership function

T5 T4 T3 T2 T1

TP9

TP8

TP7

TP6

TP5

TP4

TP3

TP2

TP1

O1 O1 O1 O1 O1

O1 O1 O1 O1 O1

O1 O1 O1 O1 O2

O1 O1 O2 O2 O2

O1 O2 O2 O2 O3

O1 O3 O3 O3 O4

O1 O3 O3 O3 O4

O1 O3 O3 O4 O4

O1 O4 O4 O4 O5

O1: Coma (brain died), O2: severe trauma, O3: moderate trauma, O4: mild trauma, O5: normal.

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Table 4 Relationship between neurologists and system findings Neurologists findings

System findings

Total

Severe EEGTD Moderate EEGTD Mild EEGTD Normal EEGTD

Total

Severe EEGTD

Moderate EEGTD

Mild EEGTD

Normal EEGTD

51 0 0 0

3 2 15 0

2 0 223 22

0 0 22 259

56 2 260 281

51

20

247

281

599

We found statistically relationship between neurologists and system findings with x2 test; for normal–mild–severe EEG TD, p < 0.001 and j = 0.82. The comments of neurologists and system for five healthy people were in agreement. 4. Discussion This is a new developed system that will help to make a rapid decision for the severity of TBI with using EEG TD and TS. There are several methods/systems to diagnose and detect the severity and prognosis of TBI. One of them is GCS and can be used as a first assessment immediately after brain trauma. However, GCS itself has a practical limitation (Thatcher, Biver et al., 2001). TS could be used easily for a healthy evaluation (Robertson & Redmond, 1993). One of the visually read standardized methods is CT and may not have enough sensitivity to detect mild TBI (Gaetz & Bernstein, 2001). Other methods are magnetic resonance imaging (MRI) and EEG but both are not sensitive enough to detect differences between mild and moderate TBI (Pointenger et al., 2002; Thatcher, Biver et al., 2001; Thatcher, North et al., 2001). In the previous literatures, quantitative EEG (qEEG) was used as an accurate method for detection of the severity of TBI (Thatcher, Biver et al., 2001). Fuzzy logic was using in different parts of medicine and EEG (Abrahams, Saha, Hurst, LeRoux, & Udupa, 2002; James, Jones, Bones, & Carroll, 1998; Wellman, Lehto, Sorock, & Smith, 2004). Kittel, Epstein, and Hayes (1992), studied on a new topic, fuzzy classification of spike events in the EEG. Huupponen et al. (2002), developed fuzzy reasoning based method for the detection of alpha ¨ beyli’s (2005) activity in sleep EEG analysis. Gu¨ler and U study described a model for classification of EEG signals using wavelet transform and adaptive neuro-fuzzy inference system (ANFIS). Subasi (2006) presented to one model using neural network and fuzzy logic for detection of epileptic seizure to allow for the incorporation of both heuristics and deep knowledge to exploit the best characteristics of each. Lopez, Jobe, and Helgason (2006) applied fuzzy logic to a theory of memory representation and computation in the human cerebral cortex.

Amin and Kulkarni (2000) were used the Fuzzy logic in GCS. According to the authors, classical GCS uses only observed variables, while the proposed fuzzy GCS uses latent constructs together with the observations. So classical GCS predicts the outcome, while fuzzy GCS predicts the possibility of the outcome. In this study, we developed another automated classification system with combination of TS and EEG TD. Our study suffers from several limitations. There was a fair agreement between the neurologists and the system especially in normal and mild cases but not in moderate cases. According to us, the cause of this difference could be explained that TS is a stable score and EEG is a tool getting multi-channel records from different areas of the brain. So we found only one TS point for a patient but according to using tools got 18 or 19 different EEG TD for each patient. Comparing these 18 or 19 different data with one stable TS might got a restriction to the effectiveness of the system. 5. Summary This is a new developed system that will help to make a rapid decision for the severity of TBI with using EEG TD and TS. We found a fair agreement between the findings of neurologists and systems outputs for normal, mild and severe EEG TD. So, getting this system in routine might be facilitating to make a rapid and successful decision to determine the severity of trauma in these categories. Acknowledgements We acknowledge support by the Neurology department of Gu¨lhane Military Medical School. We also wish to acknowledge the help of the trainers of electrophysiology laboratories of Fatih University and Gu¨lhane Military Medical School Hospital. References Abrahams, J. M., Saha, P. K., Hurst, R. W., LeRoux, P. D., & Udupa, J. K. (2002). Three-dimensional bone-free rendering of the cerebral circulation by use of computed tomographic angiography and fuzzy connectedness. Neurosurgery, 51, 264–269. Allen, R., & Smith, D. (2001). Neuro-fuzzy closed-loop control of depth of anaesthesia. Artificial Intellingence in Medicine, 21, 185–191.

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