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A cultured human neural network drives a robotic actuator R.M.R. Pizzi°, D. Rossetti°, G. Cino°, D. Marino* and A.L.Vescovi* °Living Networks Lab Department of Information Technologies – University of Milan Via Bramante 65, 26013 Crema (CR) Tel +39 02 503 30072- Fax +39 02 503 30010 [email protected] *Stem Cells Research Institute, DIBIT S. Raffaele Via Olgettina 58 – 20132 Milano (Italy) – tel +39 02 2643 4952 Abstract Since 2002 the Living Networks Lab (Department of Information Sciences, University of Milan) has experimented the growth of networks of human neural stem cells on a MEA (Microelectrode Array) support. The group is composed by physicists, electronic designers, computer scientists and biotechnologists, with the support of external biological labs. The group is concerned with the research in the field of computational biology, bionics and Artificial Intelligence. Many experiments have been performed developing and analyzing organized structures of biological neural networks. The neurons were stimulated by simulated perceptions in form of digital patterns and the output signals were analysed . In previous experiments, the neurons replied selectively to different patterns and showed similar reactions in front of the presentation of identical or similar patterns. Analyses performed with a novel Artificial Neural Network called ITSOM showed the possibility to decode the neural responses to different patterns. In the described experiment, the neurons are connected to a robotic actuator: simulated perceptions stimulate the neurons, that react with organized electric signals. The signals are decoded by the Artificial Neural Network that drives a minirobot. The hybrid system shows interesting performances, that are quantitatively evaluated and discussed in the paper. Keywords: MEA, neurons, stem cells, hybrid, bionics, robot, Artificial Neural Networks 1. Introduction The interest towards the development of a direct interface between electronics and nervous cells began in the early nineties with the Fromherz’s (Max Planck Institute of Biochemistry) works (Fromherz et al (1991), Fromherz et al (1993), Fromherz and Schaden (1994), Weis et al (1996), Jenkner and Fromherz (1997), Schatzhauer and Fromherz (1998)). After these pioneering experiences, researches in this field were carried out in many other laboratories with different approaches and with the aim to develop future bioelectronic prostheses, hybrid human/electronics devices and bionic robots. Advances in neurophysiology were reached by means of experiments based on the multielectrode stimulation of animal neurons (Akin et al (1994), Wilson (1994), Bove et al (1996), Canepari et al (1997), Borkholder (1997), Maher et al (1999), Jimbo and Robinson (2000), Egert (2002)). Chaos-based neural computation was accomplished by a Georgia Tech group (Schiff et al (1994), Lindner and Ditto (1996), Garcia et al (2003)). In 2005 the SISSA group (Ruaro et al (2005)) experimented the possibility to use neurons on MEAs (Micro Electrode Arrays) as “neurocomputers” able to filter digital images. While the Fromherz’s group carried on its work on more and more evolved chip-neuron junctions (Jenkner et al (2001), Zeck and Fromherz (2001), Bels and Fromherz (2002), Bonifazi and Fromherz ( 2002), Fromherz (2002)), first attempts were accomplished in developing hybrid creatures formed by animal neurons connected to robotic arms or virtual animals (Chapin et al (1999), Reger et al (2000), Taylor et al (2002)). In 2003 the Duke University’s group (Carmena et al (2003)) inserted 320 microelectrodes into a monkey brain, allowing the monkey to learn to move a robotic arm. The Potter’s group at Georgia Tech (Potter (2001), DeMarse et al (2001), Wagenaar et al (2001), DeMarse et al (2002)) created a hybrid creature made by neurons from rat cortex able to learn from the environment. Lately, this and other groups designed closed-loop studies with hybrid natural/artificial systems (hybrots-neurobots): Kositsky et al (2003), Wagenaar and Potter (2004), Martinoia et al (2004), Potter et al (2005). In Bakkum et al (2004), under the control of the neural network a Koala 6wheeled rover was commanded to approach another randomly driven robot. Nonetheless, the dynamics of a network of neurons that receives sensory inputs, stores memories and controls movement and behaviour is not fully understood yet. The current methods are based on the detection and analysis of spike trains (Rieke et al (1997), Borst and Theunissen(1999), Paninski et al (2004), Pillow et al (2005)), with interesting but not decisive results. Our group used MEAs to culture human neural stem cells and an Artificial Neural Network to interpret the neural signals in its entirety, analyzing not only the spike trains but the whole signals. As we will describe in the following, this allowed our hybrid neurons/electronics system to learn simulated perceptions and to react correctly to a following presentation of the learnt patterns. 2. Materials 2.1 The MEAs Our system is based on the MEA (Microelectrode Array) technology. A MEA is a glass Petri dish where small electrodes are inserted. The cultured neural cells adhere directly on these electrodes. Each electrode is connected by means of an isolated track to a pad suitable for the external connection. MEAs allow to record the activity of cells simultaneously on different channels for a long time without damaging the cultures.. Our Panasonic MEAs have 64 ITO (Indium Tin Oxide)- Platinum microelectrodes. The microelectrode size is 50µ, the interpolar distance 150µ. Their very low impedance (10kΩ) is critical to achieve a good signal-to-noise ratio . In particular, MEAs are suitable for our experiments that study the dynamical behavior of a whole neural network. 2.2 The neural cells In our experiments we used human neural stem cells isolated from the diencephal/telencephal area of human fetuses naturally aborted at the tenth week of gestation (Vescovi (1999)). The stem cells can be isolated and cultured in-vitro as they have the ability to proliferate forming at first neurospheres that, as multipotent progenitors, can differentiate if suitably stimulated and become neurons, astrocytes and oligodendrocytes (McKay (1997), Gritti et al (1999), Gritti et al (2000), Gritti et al (2001), Galli et al (2003)) It is important to settle if differentiated stem-derived cells have appropriate electrophysiological properties. It has been proved (Hiroki et al (2000), Song et al (2002)) that adult neural stem cells isolated from a rat hippocampus, after differentiating by means of BDNF (Brain Derived Neurotrophic Factor), showed to have developed functional synapses in-vitro. Besides, the existence of in-vitro synapses has been demonstrated also on the basis of the morphological appearance (see Fig. 1). The neural maturation takes 18 days and the increase of the currents amplitude in the Na channels and the appearance of action potentials suggest that the channel functionalities grow gradually during their maturation. In conclusion we can say that the existence of functional synapses in cultures of neural cells derived by neural stem cells has been experimentally proved. The use of stem cells was justified by the fact that this kind of cells shows a high adaptation ability that allow them to differentiate and adhere on the MEAs. Besides they have the capability to proliferate exponentially without requiring the use of multiple dissections. Moreover, their well-known adaptability to grow in any kind of tissue make them optimal candidates to future possible bionic implants. The stem cells in form of neurospheres have been plated at a density of 3500 cells/cm2 in a chemical medium containing EGF (Epidermal Growth Factor) and FGF-2 (Fibroblast Growth Factor 2). The MEAs must be accurately washed before the use because they are assembled using silicon, that is neurotoxic, and the electrodes must be perfectly clean in order to conduct correctly the electrical signals. The washing protocol includes 48 hours of rinsing with distilled water and 70% methylic alcohol, then an overnight exposure to UV rays and sterile cap drying. Fig. 1. Stem cells on the MEA electrodes Once the washing procedure is finished, it is possible to lay down the matrigel on the MEAs. This is a delicate step, as the matrigel (an adhesion molecule derived by a mouse sarcoma) is at a gel state at 4 °C but solidifies at 37 °C after 20 minutes. However we experimented a detachment of the matrigel after 2-3 days (see Fig. 2), that caused the cells’ death. Fig. 2. Matrigel detachment After many trials we solved the problem by leaving the matrigel overnight on the MEAs before adding the cells, and this techniques allowed a good adhesion of the matrigel during the 25 days needed to cell maturation. Cells were mechanically dissociated and plated in a number of 50000 on the MEAs with FGF2, BDNF and other components for 15 days before our learning experiment. Observing the cells after 15 days it is possible to see that in some areas the matrigel is missing and the cells adhere directly on the electrodes. The MEAs are put inside a thermically controlled box at 37 °C., whose resistors are carbonbased and are DC supplied. In order to ensure a good electromagnetical shielding, the plexy-glass box is coated by a brass thick-meshed (1 mm) net. However, the thermocontrolled box does not ensure the suitable athmosphere ( Oxygen 20%, CO2 5%) for the cells’ survival. Thus we adopted a Tyrode buffer that allows to maintain the correct ph and glucose levels ( containing NaCl, KCl, CaCl2 , MgCl2 , glucose and 7.4 ph). Adding the Tyrode allowed 2 hours of survival , enough to complete or experiments. It is worth mentioning that at the end of the experiment we performed a last recording on cells treated with TTX (tetrodotoxyn). TTX is a potent neurotoxin extracted from the Tetraodontidae fish (blowfish) which blocks selectively the sodium ion transport through the membrane, modifying excitability and inhibiting the action potentials. After adding TTX to the cell culture, the voltage values attenuated significantly up to a noiselevel signal. In this way we could show that the recorded signals contained a functional neuronal activity and that it was active up to the end of the recording activity. 2.3 The hardware As described in previous works (Pizzi et al (2004), Pizzi et al (2007)), our technique consists of stimulating the cells by means of simulated perceptions in form of digital patterns constituted by organized bursts of multichannel electrical stimulations. These can be preset by means of a custom preamplifier circuit, that is shown in Fig. 3 . Fig. 3. The 8 separated circuits of the controller All the circuits are isolated and contained into a thick metallic box connected to the ground. In order to avoid the presence of spurious signal, input and output signals are completely isolated by means of special Texas Instruments chips that avoid any kind of coupling. Even the digital signals are uncoupled from the internal circuit by means of photocouplers. In this way the MEA electrodes are never in contact with the outside. Four LiON batteries supply a “clean” voltage. All the system is connected to the ground. The cells receive electrical stimulations thanks to eight shielded cables connected to the MEA, and the same cables collect the electrical reactions of the cells after the simulated perceptions. In Fig. 4 the controller is depicted with the eight cables connected to the MEA inside the brass box. Fig. 4 . Thermocontrolled Faraday cage containing the MEAs is connected to the preamplifier/controller . After the amplifier, the signals generated by the cells are acquired by a NI6052E National Instruments DAQ with the following specifications: 333 kS/s , 16 bit, 16 analog inputs, 2 analog output, 8 digital I/O lines. 3. Methods 3.1 The past experiments In the past experiments the cells were stimulated by electrical pulses with different frequencies and voltages (30-100 mV, suitable for neuron stimulation). On the basis of the experiments referred in the first section of this paper and of the daily experience of clinical and research laboratories, the stimulations are usually delivered by means of square wave pulses. The duration of one pulse was set from 1.25 ms to 25 ms in different experiments The pulses were delivered simultaneously on all the electrodes in the form of patterns and delivered to the network as trains of electrical pulses in such a way as to represent black (bit 1) or white (bit 0) squares of a bitmap, as in the example showed in Fig. 5. Fig. 5. Example of bitmaps submitted to the biological network In the past experiments our group verified that the cells reply selectively to this kind of patterns (Pizzi et al (2004), Pizzi et al (2007)). We implemented connection schemata on the MEAs resembling an artificial neural network (ANN) architecture. In particular, we arranged eight input channels picked from eight electrodes, on which living cells were attached (Fig.2). The cells were cultured on the connection sites of the MEAs and connected each others as in the case of the Hopfield network (Hopfield (1984), Tank and Hopfield (1989)) As in the Hopfield model, the output channels coincide with the input channels, thus after disconnecting the stimulation circuit and after a short relaxation time (around 10 ms), the output signals were collected from the same electrodes. We iterated the experiment with different stimulation lengths, ranging from 1.25 to 25 ms. The experiments showed that the network reacts with different electrical behaviors depending on the delivered pattern. We used Recurrence quantification analysis (RQA) (Takens (1981), Zbilut and Webber (1992), Kononov (1996), Zbilut et al (2002)) to evaluate the organization state of the biological networks before and after the training procedure. RQA is a non-linear statistics procedure suitable for physiological time series. The resulting plots show how the vectors of the dynamical system represented by the time series are near or distant each others. This analysis shows (Pizzi et al (2004), Pizzi et al (2007)) that introduction of organized stimuli modifies the network structure and increases and maintains the information content even after the end of stimulation, suggesting a form of learning and memorization as in the case of ANNs. Moreover, the RQA analysis shows that the network reacts in an organized fashion, and behaves differently depending on the input signal and on the different channels. We observed that signals coming from similar bitmaps gave rise to similar recurrent plots. The self-organization is maintained after the end of stimulations. The biological network show sto be able to answer selectively to different patterns. The signal behavior changes depending on the network channels, and similar patterns give rise to similar answers. 3.2 The new experiment On the basis of the past experiments we tried a step forward, aiming to decode the signals emitted by the neurons and to use the ability of the biological neural network to retain information in order to respond to commands and move an actuator. For this purpose we developed a hybrid biological-electronic system that we schematized in Fig. 6. Fig. 6. Block diagram of the hybrid system We arranged on the MEA eight input channels picked from eight electrodes, on which living cells were attached. The cells were cultured on the connection sites of the MEAs and connected each others as in the case of a Hopfield (Hopfield, 1980) artificial neural network. The stimulation occurs with a 100 mV positive voltage followed by a brief -100 mV depolarization pulse to avid electrolysis. The stimulation frequency is 433 Hz, the sampling rate is 10 kHz. Each pattern is constituted by a matrix of 8 x 8 bits. Every bit lasts 300 ms. The cells are stimulated 2.4 sec for each pattern. Each stimulation is followed by a 1 sec pause and is repeated 10 times for each pattern, in order to allow the neurons to learn. The first phase of the experiment consisted of stimulating the neurons with a set of simulated perceptions in form of four digital patterns, that are depicted in Fig. 7. Rows represent the 8 simultaneously activated channels, columns represent time. Each square represents one bit: if it contains a dot, this means that a stimulation has been activated, otherwise there is no stimulation. At the end of the tenth stimulation, the reactions of the cells, collected after disconnecting the stimulation circuit and after a 10 ms relaxation time, have been recorded and sent to an Artificial Neural Network to classifies them. The Artificial Neural Networks supplies, in form of an integer number, the direction that will drive the robot. This number is converted into a TTL signal that is directed to the parallel port of the PC. A suitable circuit converts it into an infrared signal that drives the robot. It must be stressed that the ANN works just on the signals emitted by the biological neurons 10 ms after the end of the stimulation, thus the ANN’s task is not to classify the digital patterns, but to decode the biological reactions to different patterns. Fig. 7. The four patterns : Forward, Backwards , Left, Right We developed a Labview graphical interface that allows to choose the commands both on an orderly basis (for training) and on a random basis (for testing). 3.3 The ITSOM Artificial Neural Network The model of ANN, a novel architecture called ITSOM (Inductive Tracing Self Organizing Map), was selected considering that a self-organizing architecture was necessary, as we had not a set of examples to train it. The ITSOM network model is useful to highlight structures in the temporal series of a signal. The ITSOM was tested in the past with electrophysiological signals (Pizzi et al (2002)), correctly showing their organized structures. The Self-Organizing Map (SOM) (Saarinen and Kohonen (1985), Kohonen (1990)) features are well-known, as well as its limits in classifying topologically entangled input structures. In order to overcome the SOM’s limits we developed a novel architecture based on the evidence that, even if the SOM’s winning weights may vary at any presentation epoch, their temporal sequence tends to repeat itself. The dynamical properties of the SOM are well known (Ritter and Schulten (1986), Ritter and Schulten (1988), Ermentrout(1992)) and show periodic oscillations and limit cycles. In particular we observed that the sequence of winning weights constitutes chaotic attractors that characterize univocally the input element that has determined them . In this way, due to the countless variety of possible combinations among winning neurons, these sequences allow to finely classify the corresponding input value. A detailed description of the ITSOM’s architecture is reported in (Pizzi (1997), Pizzi et al (2007)). After interrupting the network processing cycle, an algorithm is needed that codifies the obtained chaotic configurations of winning weights into a small set of outputs. To this purpose the cumulative scores related to each input have been normalized following the distribution of the standardized variable z given by: z = (x − μ) σ where μ is the average of the scores on all the competitive layer weights and σ is the root mean squared deviation. Once fixed a threshold 0 ≤ τ ≤1, we have put z=1 z=0 for z > τ, for z ≤ τ . In this way every winning configuration is represented by a binary number with as many 1’s and 0’s as many the competitive layer weights. Due to the existence of a threshold, these binary numbers coincide every time the series of winning neurons are approximately similar. Then the task of comparing these binary numbers is straightforward and allows to identify similar or identical inputs. It should be stressed that the ITSOM's crucial feature is that the network does not need to be brought to convergence, as the cyclic configurations stabilize their structure within a small number of epochs. The extremely low processing time makes this model very effective in case of real-time applications. In our experiment the ITSOM must memorize the four states representing the movement of the robot, acquiring the signals generated by the cells during the training. The network acquires such information by means of a matrix of floating point values (10000 x 8 for each directional pattern). After a series of off-line trials the ITSOM was optimized with the following parameters: 500 input neurons 12 competitive layer neurons Learning rate = 0.003 Forgetting rate = 0.001 τ=0 After finishing the training phase, the ITSOM starts generating the z-scores to compare them with the z-scores that will be generated during the testing phase. During the testing phase we send to the neurons several stimulations corresponding to one of the four patterns, in a random order. The ITSOM generates new z-scores and compares them to those stored after the training phase. We carried out the experiment both with non differentiated cells and with mature cells. The behaviour of the non differentiated cells showed to be random, thus the following considerations refer to the response of the differentiated cells. From different off-line tests on portions of the signals we observed that in the first portion of 400 ms the information content is sufficient to a correct classification. The rest of the signal contains a more debased information and it is advisable to eliminate it. 4. Results In the described experiment (differentiated cells) we tested the hybrid system with 25 random patterns. In a previous experiment with a non-tuned ITSOM we collected the following results (Fig. 8): Correct answers: 8 out of 25 Wrong answers: 13 out of 25 Non classified patterns: 4 Once we tuned the ITSOM with the above mentioned parameters, we obtained the following results (Fig. 9): Correct answers: 15 out of 25 Wrong answers: 7 out of 25 Non classified patterns: 3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Fig. 8. Histogram of the classification performed by the non-tuned ITSOM: the long bars describe the correctly classified patterns, the short bars the non classified patterns, the absence of the bar indicates wrongly classified patterns The low number of samples is due to the poor lifetime of the human neural stem cells in non optimal atmosphere and put through electrical stimulations. In fact before the 25 random stimulations, the cells were subjected to 40 previous stimulations (10 for each command) necessary for the training. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Fig. 9. Histogram of the classification performed by the ITSOM after tuning We can immediately observe that the classification of the last samples was less effective. The 85.7 % of the wrong answers happened in the last 9 samples, after more than 60 electrical stimulations that could have damaged the cells. In Tab. 1 and 2 other results of our experiment are displayed. Observing the tables we can draw the following considerations. The classification percentage of the “F” and “B” patterns reach high values (80% and 83.33%). The B pattern is recognized with a 100% percentage. All the values are very far from the random value (25%), if we calculate it already on the classifiable z-scores: but it must be stresses that the network operates a choice among countless non classifiable z-scores. This is due both to the ITSOM’s effectiveness and to a correct information content of the biological signals. In order to estimate the quality of this classification we elaborated two-classes confusion matrixes. For each confusion matrix we can define four important parameters: False Positive (FP), False Negative (FN), True Positive (TP), True Negative (TN). Once we define sensitivity and specificity of a test by means of the following formulas, where TP are true positive classifications, TN true negative, FN false negative and FP false positive classifications: Sensitivity = (TP / ( TP + FN))*100 Specificity = (TN / (TN + FP))*100 we obtain for the four patterns (Tab. 3): In summary (Tab. 4), the evaluation of the proposed model presents an accuracy of 80.11% and a precision of 90.50%. These results allow us to consider the effectiveness of our hybrid classifier quite satisfactory. 5. Discussion and conclusions Aim of our research is on one side to improve the knowledge of the neurophysiological learning and memory functionalities; on the other side to evaluate the feasibility of biological computation, or of non-invasive neurological prostheses, able to improve or substitute damaged nervous functionalities . We started from the assumption that the neural signal could have an information content richer than that formed by the spike trains, therefore it could be important to analyze it in its wholeness, considering not only the frequencies of spikes, but the wide-ranging frequencies and the amplitudes of the whole signal. To reach our purpose we developed a hybrid (biological-electronic) system composed by a network of human neurons connected to an Artificial Neural Network and to a minirobot *. A training sequence of simulated perceptions in form of electrical stimulations was delivered to the biological network. The output of the biological neurons constitute the input of the ANN, that classifies the electrical signals coming from the neurons, self-organizing on the basis of the temporal series of the whole neural electrical signals. It is known that an artificial neural network acts as a black box able to extract the information content of the input irrespective of the level of comprehension of the input structure. We believe that this could be the reason why, avoiding to consider just the spike trains of the signals but letting the ANN process the signal in its wholeness, we reached interesting results. Despite the lack of a detailed physiological interpretation of the neural signals , the ANN worked as a black box and managed to decode the information hidden in the neural response using the whole signal samples. In fact similar digital patterns gave rise to output signals containing similar chaotic attractors corresponding to the same ITSOM code, whereas different patterns lead to attractors corresponding to different codes. In the described experiment the hybrid system showed to have learnt and memorized the patterns in a satisfactory way. In fact the hybrid system, tested with 25 random patterns has obtained a correct classification of the four patterns with percentages respectively of 80%, 83,33%, 42,86%, 42,86%. The evaluation of the proposed model presents an accuracy of 80.11% and a precision of 90.50%. It is worth remarking that a trial with undifferentiated cells gave completely random results. Analogous random results were obtained in a testing procedure carried out before the training phase. We can infer that a correct classification depends heavily on the degree of vitality and maturity of the neural cells, and consequently on their ability to self-organize and memorize patterns. We have also seen that a well-tuned Artificial Neural Network reaches better performances than a nontuned one, but the ANN alone cannot classify the neural signals unless they originate from trained biological cells. The wrong classifications of some patterns can be due to several factors: an attenuated vitality of the cells a suboptimal ITSOM tuning an intrinsic limit of its classification algorithm suboptimal electrical stimulation parameters. The stimulation parameters could be improved evaluating different waveforms and polarization/depolarization times, amplitudes, frequencies and length of the stimulations. It should also be considered the possibility to avoid the use for stem cells and prefer neural cells with a better resistance outside the incubator. A better tuning of the Artificial Neural Network is underway. A new algorithm that substitutes the z-score procedure is also under study. The new algorithm searches for a set of maximally of winning neurons inside a restricted set of epochs. In fact the series of winning neurons shows a global preference of the ITSOM towards groups of neurons specific to each pattern. During an off-line experiment the new procedure has been able to reach better performances in the classification of the proposed patterns. The satisfactory results obtained up to now are encouraging us to improve the complexity of our hybrid system. A new hardware controller is being developed that will allow to handle much more complex patterns and a sharp increase of the number of electrode connections. Once the controller will be completely tested, we will develop a system able to receive real perceptions from suitable sensors and to react autonomously to environmental stimulations, exhibiting closed-loop performances. We will start adopting a controlled environment and low-resolution sensors. We hope that new progresses could approach us step by step to a better understanding of the neural code and of the learning process, that in the future could be applied to technological devices such as brain implants and bionic systems. Acknowledgements We are strongly indebted to Prof. G. Degli Antoni (Department of Information Technologies, University of Milan) for his valuable suggestions and encouragement , to Dr. D. Carne, Dr. A. Redolfi and Dr. R. Rossoni (Department of Biomolecular Sciences and Biotechnologies, University of Milan) for their substantial contribution. _____________________________________________________ * In honor of our town, Crema, we called our hybrid creature “Cremino”. We consider “Cremino” the first hybrid creature endowed with a small human “brain”. An excerpt of its movements during the described experiment can be seen in (Pizzi (2006)) References Akin, T., Najafi, K., Smoke, R.H., Bradley, R.M., 1994. A micromachined silicon electrode for nerve regeneration applications. IEEE Trans. Biomed. Eng. 41, 305–313. Bakkum, D.J., Shkolnik, A.C., Ben-Ary ,G., Gamblen, P., DeMarse, T.B., Potter, S.M.(2004). Removing some ‘A’ from AI: Embodied Cultured Networks. 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Robot Performances Directions Input Total Pattern F Pattern B Pattern L Pattern R Correct classification 4 5 3 3 15 Wrong classification 1 0 3 3 7 No classification 0 1 1 1 3 6 7 7 25 Yielded patterns 5 % Classified 100% 83.33% 85.71% 85.71% 88% % Correctly classified 80% 83.33% 42.86% 42.86% 60% Tab. 2. Classification Percentage 100% 80% 60% No classification 40% Wrong classification 20% Correct classification 0% Pattern F Pattern B Pattern L Pattern R Directions Totale Tab. 3. Confusion Matrixes of the model Confusion matrix of a pattern « P » P Non-P P TP FN Non-P FP TN Confusion matrix pattern B Confusion matrix pattern F F Non-F F 4 0 Non-F 1 17 Confusion matrix pattern L L Non-L L 3 1 Non-L 3 15 B Non-B B 5 6 Non-B 0 11 Confusion matrix pattern R R Non-R R 3 0 Non-R 3 16 Tab. 4. Total Sensitivity and Specificity Pattern Pattern Pattern Pattern F B L R Total Sensitivity 100% 45.45% 75% 100% 80.11% Specificity 94.44% 100% 83.33% 84.21% 90.50%