Alzheimer s disease patients classification through EEG signals processing

Transcription

1 Introduction Methods Experiments Conclusions Alzheimer s disease patients classification through EEG signals processing G.Fiscon, E.Weitschek, P.Bertolazzi, G.Felici, S.De Salvo, P.Bramanti, MC.De Cola Giulia Fiscon fiscon@dis.uniroma1.it Institute for Systems Analysis and Computer Science (IASI-CNR) The IEEE SSCI 2014, Orlando, Florida 9 12 December, / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

2 Introduction Methods Experiments Conclusions Outline Introduction: Electroencephalography (EEG) signals and Alzheimer s Disease (AD) Methods: EEG signals processing 1 EEG recording (Data collection) 2 Pre-processing 3 Features extraction 4 Classification Results: Classification Results seconds EEG signals 2 30-seconds EEG epochs Conclusions and Future Works 2 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

3 Introduction Methods Experiments Conclusions Electroencephalography (EEG) signals Electroencephalography (EEG): non-invasive recording of the electrical spontaneous activity of the brain measured at different sites of the scalp. EEG measures the voltage fluctuations resulting from ionic current flows over the scalp during synaptic excitation of the dendrites of many neurons on the cerebral cortex. Brain rhythms: brain oscillatory activity exhibited in specific frequency bands γ (> 30 Hz, highest): hyper brain activity β (13 30 Hz, high): thinking activities α (8 13 Hz, moderate): relaxed, meditative θ (4 8 Hz, slow): drowsy, dreaming δ (0.5 4 Hz, slowest): deep dreamless, sleep 3 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

4 Introduction Methods Experiments Conclusions Alzheimer s disease and EEG Alzheimer s Disease (AD) and its preliminary stage - Mild Cognitive Impairment (MCI) - are the most widespread neurodegenerative disorders EEG signals of AD patients can differ from those of healthy people in: 1 Reduced complexity EEG are more regular because of the loss of neurons. 2 Changes in rhythm AD patients: lower frequencies of α rhythm, increasing of δ activity. MCI patients: decreasing of β and increasing of θ activity. 3 Perturbation in synchrony abnormal synchronization of neural oscillation EEG-signals processing may support the diagnosis of AD and MCI pathologies and help to discriminate the healthy control subjects 4 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

5 Introduction Methods Experiments Conclusions Alzheimer s disease and EEG Alzheimer s Disease (AD) and its preliminary stage - Mild Cognitive Impairment (MCI) - are the most widespread neurodegenerative disorders EEG signals of AD patients can differ from those of healthy people in: 1 Reduced complexity EEG are more regular because of the loss of neurons. 2 Changes in rhythm AD patients: lower frequencies of α rhythm, increasing of δ activity. MCI patients: decreasing of β and increasing of θ activity. 3 Perturbation in synchrony abnormal synchronization of neural oscillation EEG-signals processing may support the diagnosis of AD and MCI pathologies and help to discriminate the healthy control subjects 4 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

6 Introduction Methods Experiments Conclusions Recording Pre-processing Features Extract Classification EEG signals processing Design of an automatic procedure that analyzes EEG signals 1 EEG recording: EEG signals are acquired 2 EEG pre-processing: EEG signals are pre-elaborated in order to improve the noise-signal rate 3 Features extraction: relevant features are extracted, in order to detect specific waveforms (Time-Frequency analysis) 4 EEG classification: subjects EEG signals are identified as belonging to appropriate classes 5 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

7 Introduction Methods Experiments Conclusions Recording Pre-processing Features Extract Classification Step 1. EEG recording Multichannel EEG signals are acquired at IRCCS Centro Neurolesi Bonino-Pulejo of Italy 19 electrodes (according to the International System for electrode placement) 100 subjects that belong to AD, MCI, and control healthy classes sampling frequency of 256 and 1024 samples per second 300 seconds signal duration Clinical N. of subjects [%] Age (mean ± std.dev. [years]) Condition Female Male Tot Female Male Tot CT 5 (36%) 9 (64%) ± ± ± 9.4 AD 29 (59%) 20 (41%) ± ± ± 6.4 MCI 20 (54%) 17 (46%) ± ± ± 9.4 Tot 54 (54%) 46 (46%) ± ± ± / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

8 Introduction Methods Experiments Conclusions Recording Pre-processing Features Extract Classification Step 2. Pre-processing Aim: to reduce the EEG background artifacts and noise EEG signals are pre-processed by converting each one at a sampling frequency of 256 samples per second extracting 180 seconds (from 60 seconds to 240 seconds) 7 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

9 Introduction Methods Experiments Conclusions Recording Pre-processing Features Extract Classification Step 3. Features extraction Time-Frequency Analysis Aim: to extract desirable features from the time domain EEG signals We apply the Fast Fourier Transform (FFT) to the signal in order to estimate its spectrum with: S 1 X [k] = x[s]e k [s] (1) s=0 x : the time series signal (the data), s = 0, 1,..., S 1; S : the total number of samples in signal x; X : the frequency domain representation of the time-series signal x; k : the k-th frequency component, k = 0, 1,..., S 1; s : the s-th sample in the time domain; e k [s] = e jks2π S : the k-th basis function. 8 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

10 Introduction Methods Experiments Conclusions Recording Pre-processing Features Extract Classification Step 3. Features extraction FFT coefficients as features We apply the FFT functions to EEG collected data: taking into account the 180-seconds signal and extracting N Fourier Coefficients (N equal to 16 and 32) dividing the signal into 6 epochs of 30 seconds and extracting for each one N Fourier Coefficients (N equal to 16 and 32) Data collected and processed by extracting the Fourier Coefficients (N) require to be arranged in a comma-separated matrix file 9 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

11 Introduction Methods Experiments Conclusions Recording Pre-processing Features Extract Classification Step 4. Classification Supervised Learning paradigm Aim: to assign an unknown object (patient) into a given class (AD, MCI, healthy) by examining its features (EEG signal) Leave-one-out cross-validation schema (for t samples performs t experiments by using t 1 samples for training and the remaining one for testing) Classification algorithms: Support Vector Machines (SVM), Decision Trees, and Rule-based classifiers 10 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

12 Introduction Methods Experiments Conclusions Experimental Design Results Experimental Design Dataset and Classifiers Example of the experimental data set with P= num of patients, M= num of electrodes, N= num of Fourier Coefficients We tested: Patient Fourier (1,1) Fourier (M,MN) Class sample 1 value (1,1) value (1,MN) AD sample 2 value (2,1) value (2,MN) MCI sample P value (P,1) value (P,MN) Control Support Vector Machines (SMO that implements the SVM, polykernel = 2, 3) Decision Tree (J48 that implements the C4.5 algorithm, minnumobj = 10) the DMB rule-based machine learning algorithm ( in order to handle the classification problem of: AD vs CT MCI vs CT AD vs MCI 11 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

13 Introduction Methods Experiments Conclusions Experimental Design Results Results I 180-seconds EEG signals Sampling AD vs CT MCI vs CT AD vs MCI SVM J48 DMB SVM J48 DMB SVM J48 DMB Accuracy [%] Precision [%] Recall [%] Specificity [%] F-measure [%] Decision Tree (J48) achieves better classification results DMB and J48 provide a classification model containing the involved EEG electrodes, which are taken into account for performing the patient identification N = 16 Leave-one-out cross-validation (=63,51,86 folds for AD vs CT, MCI vs CT, AD vs MCI, respectively) 12 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

14 Introduction Methods Experiments Conclusions Experimental Design Results Results II 30-seconds EEG epochs Sampling AD vs CT MCI vs CT AD vs MCI SVM J48 DMB SVM J48 DMB SVM J48 DMB Accuracy [%] Precision [%] Recall [%] Specificity [%] F-measure [%] Decision Tree (J48) achieves better classification results DMB and J48 a classification model containing the involved EEG electrodes, which are taken into account for performing the patient identification Better performances on AD than those of 180-seconds EEG signals N = 16 Leave-one-out cross-validation (=63,51,86 folds for AD vs CT, MCI vs CT, AD vs MCI, respectively) 13 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

15 Introduction Methods Experiments Conclusions Experimental Design Results Classification Model Example of J48 tree classification model for AD vs CT Four Four Four : Control (8.0) Four44 > Four : AD (13.0) Four172 > : Control (2.0) Four458 > 0.051: AD (36.0) Four272 > : Control (4.0) 14 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

16 Introduction Methods Experiments Conclusions Concluding Remarks Acknowledgments Conclusions and Future works Conclusions EEG signals of patients affected by Alzheimer s disease, Mild Cognitive Impairment, and healthy control samples were analyzed and processed by applying a time-frequency analysis through Fourier Transform Well-known supervised learning methods (i.e., SVM, Decision Trees, and Rule-based classifiers) allowed an accurate classification of the human samples Decision Tree methods stood out among the other ones the EEG partition into 6 epochs of 30 seconds identifies AD samples with better accuracy than the whole 180-seconds signal Future works to extend the analysis of the EEG signals by applying the Wavelet Transform to design more accurate automatic artifacts rejection procedure in order to save time consuming human analysis 15 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

17 Introduction Methods Experiments Conclusions Concluding Remarks Acknowledgments Acknowledgments Joint work with people of IRCCS Centro Neurolesi Bonino-Pulejo of Messina (Italy) and the Institute for Systems Analysis and Computer Science (IASI) of the Italian National Research Council (CNR) Funding: GenData 2020 Project, EPIGEN Flagship Project, Sapienza University of Rome, IASI-CNR A really sincere thankyou to The Organizing Committee of the IEEE SSCI-2014 Dr.ssa Paola Bertolazzi (head of IASI-CNR) Dr. Giovanni Felici (of IASI-CNR) Ing. Emanuel Weitschek (of Roma Tre University of Rome) Dr.ssa MC De Cola, Dr.ssa S. De Salvo, P. Bramanti (of IRCCS) 16 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

18 Appendix 17 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

19 Supervised learning algorithms SVM separating hyperplane, which maximizes the minimum distance between the data of different classes in a new space that has been obtained by applying a kernel function to the original data. Limitations: the high computational requirements for multi-class problems; the classification model cannot be interpreted easily in terms of the original variables by domain experts; the choice of the right type of kernel function We run the experiments in parallel We did not consider the classification model of SVMs as additional knowledge for AD/MCI we tested different kernel functions (i.e., linear and polynomial with coefficients 2 and 3) 18 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

20 Supervised learning algorithms (II) Decision Trees DMB used to model sequential decision problems internal nodes represent the predicate of the objects in the data set each edge represents a splitting rules over one attributes The attribute classes are represented in the tree by leaf nodes Limitations: the computation of an optimal decision tree is an NP-complete problem decision trees can be very sensitive to changes in the training data and outliers complexity (too many splits and large trees) minimum instances per leaf set to 10, which limits the growth of the tree and the too many splits that may lead to over-fitting we use a leave-one-out cross validation sampling scheme for testing all the different training data sets. collection of data mining tools (such as BLOG, MALA, and DMIB) engineered for the classification of biological data ( Rule-based method (building of logic formulas as a model to characterize data) 19 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014

21 Performance measures Measure Formula Accuracy (A) or correct rate A = Precision (P) or Positive Predictive Value (PPV) P = TP TP+FP Recall (R) or True Positive Rate (TPR) R = TP TP+FN Specificity (S) or True Negative Rate (TNR) S = TN TN+FP F-measure (F) TP+TN TP+TN+FP+FN F = 2P R P+R True Positives (TP): objects of that class recognized in the same class; False Positives (FP): objects not belonging to that class recognized in that class; True Negatives (TN): objects not belonging to that class and not recognized in that class; False Negatives (FN): object belonging to that class and not recognized in that class. 20 / 16 Giulia Fiscon Alzheimer s EEG signals processing IEEE SSCI 2014