Multimedia InfoRmation systems & Advanced Computing Laboratory
MCI/Healthy classification based on deep learning using EEG signals
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Plan
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Context
Materials and methods
Experimental results
Conclusion
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Plan
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Context
Materials and methods
Experimental results
Conclusion
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Context
Mild Cognitive Impairement (MCI)
Amnestic MCI
Non-Amnestic MCI
Forget
Memory
Thinking skills
Decision
Visual perception
Complex tasks
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Plan
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Context
Materials and methods
Experimental results
Conclusion
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Materials and methods
BrainAmp DC characteristics
➢ Designed for neurophysiology research and used in
neurophysiological research and the clinical world.
➢ Contains 32 active electrodes: Fp1, Fp2, AFz, Fz, F3,
F4, F7, F8, FC1, FC2, FC5, FC6, Cz, C3, C4, T7, T8, CP1,
CP2, CP5, CP6, Pz, P3, P4, P7, P8, O1, O2, reference:
Fig. 1 BrainAmp DC device.
nose, ground: FCz).
➢ Sampling rate: 500 Hz.
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Materials and methods
5 (Morning)
Participants
14 MCI
9 (Evening)
13 (Morning)
38 Females
24 Healthy
11 (Evening)
61
participants
11 (Morning)
14 MCI
3 (Evening)
23 Males
5 (Morning)
9 Healthy
4 (Evening)
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Materials and methods
✓
Brainstorm is a collaborative, open-source application dedicated to the analysis of brain recordings.
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Dedicated to magneto-encephalography (MEG) and electroencephalography (EEG) data visualization and processing.
Fig. 2 Visualization of raw EEG data with Brainstorm.
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Materials and methods
Fig. 3 Raw EEG data visualization with the annotation.
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Materials and methods
Proposed Approach
Start
Rest State (10 min)
Eyes open (5 min)
Stroop (20 min)
Eyes closed (5 min)
Condition 1
+
Break 1
End
N-back (20 min)
Rest State (5 min)
Condition 2
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Break 2
Condition 3
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Break 3
Condition 1
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Break 1
Condition 2
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Break 2
Condition 3
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Break 3
Verbal fluency (10 min)
Eyes open
Condition 1
Condition 2
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Plan
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Context
Materials and methods
Experimental results
Conclusion
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Experimental results
Database
✓ 70% for training set
✓ 10% for validation set
✓ 20% for testing set
✓ Split data into 1min duration
✓ 20 patients
Binary Classification
MCI
Healthy
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Experimental results
Layer (type)
Output Shape
Param #
Conv1d (Conv1D)
(None, 1, 64)
1984
Conv1d_1 (Conv1D)
(None, 1, 128)
8320
Conv1d_2 (Conv1D)
(None, 1, 256)
33024
Batch_normalization
(BatchNormalization)
(None, 1, 256)
4
Max_pooling1d
(MaxPooling1D)
(None, 1, 256)
0
Flatten (Flatten)
(None, 256)
0
Dense (Dense)
(None, 128)
32896
(None, 1)
129
Dense_1 (Dense)
Table 1 The proposed CNN model.
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Experimental results
Run
Accuracy Train
Accuracy Validation
Accuracy Test
1
96. 85 %
96.94 %
95.02 %
2
96.91 %
96.89 %
95.04%
3
96.87 %
96.88 %
95.07 %
4
96.93 %
96.92 %
95. 01 %
5
96.86 %
96.84 %
95.06 %
Average
96.88 %
96.89 %
95.04 %
Table 2 Experimental results for binary classification.
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Experimental results
Binary classification results
Fig. 4 Accuracy and loss curves for binary classification.
Classes
Healthy ‘0’
MCI ‘1’
Healthy
1669144/1751600 = 95.29 %
(TP)
3/4200 = 0.07 % (FP)
MCI
82456/1751600 = 4.70 % (FN)
1142624/1208000 = 94.58 %
(TN)
Table 3 Confusion matrix results for binary classification.
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Experimental results
Run
Precision
Recall
F1 score
1
93.23%
94.61%
95.03%
2
94.45%
95.12%
94.16%
3
96.25%
94.84%
94.03%
4
95.34%
95.20%
95.43%
5
94.21%
94.16%
94.64%
Average
94.69%
94.78%
94.65%
Table 4 Experimental results for binary classification.
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Plan
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Context
Materials and methods
Experimental results
Conclusion
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Conclusion
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A new method for early MCI detection is targeted.
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The data is collected by the BrainAmp DC headset which contains 32 channels.
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A Deep learning technique is used in this study.
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Various signal segments are used with a CNN algorithm for binary classification.
Future works
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Focus on the brain-waves responsible for detecting MCI.
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Try to develop an automatic application for early brain disease prediction.
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Multimedia InfoRmation systems & Advanced Computing Laboratory
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