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IADL Assessment System Based on EEG Ren-Ying Fang Dept. Electrical Engineering National Cheng Kung University Tainan, Taiwan [email protected] Chi-Chun Hsia Cloud Service Technology Center Industrial Technology Research Institute Tainan, Taiwan [email protected] Jhing-Fa Wang 1,2 1 Dept. Electrical Engineering National Cheng Kung University Tainan, Taiwan 2 Department of Digital Multimedia Design, Tajen University, Pingtung County, Taiwan [email protected] Abstract— This paper aims to propose a system that used EEG to automatic measure human’s cognitive behavior ability, it can assist doctor long-term monitoring patients IADL (instrumental activities of daily living) abilities decline in daily life, and immediately give patient the care help they need, so it can save labor costs. For health care organization, the system can get more objective data to analyze. The system used a single channel wireless brainwave device that using Bluetooth connect PC device and capture subjects’ prefrontal brainwave to analysis. We use the improved adaptive PSR (Power Ratio) method as IADL assessment algorithm, according to different situations PSR can select the appropriate features to reduce unnecessary features and computational complexity, improve performance. Furthermore, the system used SVM as classifier and design fusion weights selection algorithm, the classifier selection based on different conditions in order to achieve optimum classification to get the best recognition rate. Our proposed system contains total 25 samples, including 15 male samples and 10 female samples for classifier training and testing, according to the experimental results show that the recognition rate can reach 68%, and the future, the system will collect more samples for training and algorithm modification to get better recognition results. Index Terms—Cognitive behavior ability, IADL, brainwave, SVM, ANN, PR . I. INTRODUCTION At present, most of developed countries have entered the aging society [1], more and more elder people and cognitive declined patients into the long term care institutions for long- term care. For long-term care institutions, patients need to conduct an cognitive abilities investigation and IADL scale assessment before admission, cognitive behavior ability assessment all rely on medical staff and doctors to do the assessment, so must consuming more manpower and material resources [2]. For the reasons, we propose the system expected to assist doctors to assess the elderly patient’s cognitive abilities and IADL scores. The system enables doctors determine cognitive abilities of the elderly and patients and IADL scores with more objective data. Moreover, it can reduce the cost of time and manpower. In this paper, we used EEG as the feature of assessment. The subjects through simple tests of cognitive behavior ability and behavior IADL scale, at the same time, using portable brainwave headset to capture subjects’ brainwave changes, so we can obtained subjects’ brainwave changes in cognitive behavior ability and IADL test [3]. Finally we can use support vector machine (SVM) to get cognitive behavior ability and IADL scale assessment to achieve the purpose assist doctors [4]. II. RELATED WORK In current years, more and more researchers have focused on declined of cognitive ability of human. The key issue for a method to assess cognitive ability is to analysis the state of brain with only a small amount of EEG data .Some of them used MRI to analysis; another used the EEG data to calculate the relation between activities of brain and AD (Alzheimer's disease). Du [4] use of MRI to detect hippocampus size and the correlation between the lesions of Alzheimer's disease; Bobinski [6] researched the differences from cerebral cortex MRI and brain wave between AD patients and general human; Golebiowski [7] used MRI to measure the cerebral cortex and hippocampus to analysis the differences between AD patients and general human. Above studies show that MRI can scan and measure the cerebral cortex and hippocampus of AD patients and ordinary people, but there is no research for IADL assessment based on EEG [8][9]. For these reason, we proposed a system that can used EEG to automatic analysis the score of IADL. This system can be divided into two models: EEG receiver module and IADL assess module. EEG receiver module includes single channel EEG sensor and Bluetooth module to capture EEG signal and feature selection [10]. IADL assessment module used Alpha, Beta, Theta, and Delta as the features, used the improved APSR (Adaptive Power Spectrum Ratio) method as IADL assessment algorithm, and SVM as the classifier to calculate the score of IADL [11]. In the process of proposed system, we can find the most significant EEG feature is adaptive power spectrum ratio. According to the feature, we can easily find the correlation between EEG and IADL assessment. III. FRAMEWORK OF THE PROPOSED SYSTEM We proposed the automatic IADL assessment system divided into two modules: EEG receiver module and IADL 978-1-4673-5936-8/13/$31.00 ©2013 IEEE 243

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Page 1: [IEEE 2013 1st International Conference on Orange Technologies (ICOT 2013) - Tainan (2013.3.12-2013.3.16)] 2013 1st International Conference on Orange Technologies (ICOT) - IADL assessment

IADL Assessment System Based on EEG

Ren-Ying Fang Dept. Electrical Engineering

National Cheng Kung University Tainan, Taiwan

[email protected]

Chi-Chun Hsia Cloud Service Technology Center

Industrial Technology Research Institute Tainan, Taiwan

[email protected]

Jhing-Fa Wang1,2 1Dept. Electrical Engineering

National Cheng Kung University Tainan, Taiwan

2Department of Digital Multimedia Design, Tajen University, Pingtung

County, Taiwan [email protected]

Abstract— This paper aims to propose a system that used EEG to automatic measure human’s cognitive behavior ability, it can assist doctor long-term monitoring patients IADL (instrumental activities of daily living) abilities decline in daily life, and immediately give patient the care help they need, so it can save labor costs. For health care organization, the system can get more objective data to analyze.

The system used a single channel wireless brainwave device that using Bluetooth connect PC device and capture subjects’ prefrontal brainwave to analysis. We use the improved adaptive PSR (Power Ratio) method as IADL assessment algorithm, according to different situations PSR can select the appropriate features to reduce unnecessary features and computational complexity, improve performance. Furthermore, the system used SVM as classifier and design fusion weights selection algorithm, the classifier selection based on different conditions in order to achieve optimum classification to get the best recognition rate.

Our proposed system contains total 25 samples, including 15 male samples and 10 female samples for classifier training and testing, according to the experimental results show that the recognition rate can reach 68%, and the future, the system will collect more samples for training and algorithm modification to get better recognition results.

Index Terms—Cognitive behavior ability, IADL, brainwave, SVM, ANN, PR .

I. INTRODUCTION At present, most of developed countries have entered the

aging society [1], more and more elder people and cognitive declined patients into the long term care institutions for long-term care. For long-term care institutions, patients need to conduct an cognitive abilities investigation and IADL scale assessment before admission, cognitive behavior ability assessment all rely on medical staff and doctors to do the assessment, so must consuming more manpower and material resources [2].

For the reasons, we propose the system expected to assist doctors to assess the elderly patient’s cognitive abilities and IADL scores. The system enables doctors determine cognitive abilities of the elderly and patients and IADL scores with more objective data. Moreover, it can reduce the cost of time and manpower.

In this paper, we used EEG as the feature of assessment. The subjects through simple tests of cognitive behavior ability and behavior IADL scale, at the same time, using portable brainwave headset to capture subjects’ brainwave changes, so

we can obtained subjects’ brainwave changes in cognitive behavior ability and IADL test [3].

Finally we can use support vector machine (SVM) to get cognitive behavior ability and IADL scale assessment to achieve the purpose assist doctors [4].

II. RELATED WORK In current years, more and more researchers have focused

on declined of cognitive ability of human. The key issue for a method to assess cognitive ability is to analysis the state of brain with only a small amount of EEG data .Some of them used MRI to analysis; another used the EEG data to calculate the relation between activities of brain and AD (Alzheimer's disease).

Du [4] use of MRI to detect hippocampus size and the correlation between the lesions of Alzheimer's disease; Bobinski [6] researched the differences from cerebral cortex MRI and brain wave between AD patients and general human; Golebiowski [7] used MRI to measure the cerebral cortex and hippocampus to analysis the differences between AD patients and general human.

Above studies show that MRI can scan and measure the cerebral cortex and hippocampus of AD patients and ordinary people, but there is no research for IADL assessment based on EEG [8][9].

For these reason, we proposed a system that can used EEG to automatic analysis the score of IADL. This system can be divided into two models: EEG receiver module and IADL assess module.

EEG receiver module includes single channel EEG sensor and Bluetooth module to capture EEG signal and feature selection [10].

IADL assessment module used Alpha, Beta, Theta, and Delta as the features, used the improved APSR (Adaptive Power Spectrum Ratio) method as IADL assessment algorithm, and SVM as the classifier to calculate the score of IADL [11].

In the process of proposed system, we can find the most significant EEG feature is adaptive power spectrum ratio. According to the feature, we can easily find the correlation between EEG and IADL assessment.

III. FRAMEWORK OF THE PROPOSED SYSTEM We proposed the automatic IADL assessment system

divided into two modules: EEG receiver module and IADL

978-1-4673-5936-8/13/$31.00 ©2013 IEEE 243

Page 2: [IEEE 2013 1st International Conference on Orange Technologies (ICOT 2013) - Tainan (2013.3.12-2013.3.16)] 2013 1st International Conference on Orange Technologies (ICOT) - IADL assessment

assessment module that included training phase and recognition phase, we depicted in Figure1. In the following statements in sections A and B, we will introduce the related components.

Fig. 1. Framework of automatic IADL assessment system

A. EEG receiver module We can use single channel EEG sensor with 512 Hz

sampling rate to capture Alpha, Beta, Delta and Theta waves while brain wave signal via fast Fourier transform to do spectrum conversion, then the system used the power spectrum to do feature extraction and calculate the Power Ratio. The step shows in Figure 2.

Fig. 2. Brainwave signal process in EEG receiver module

(1)

B. IADL assessment module It is divided into two kinds of procedures, training and

recognition. We can work out the whole module into four parts,

EEG signal feature extraction, SVM classifier, IADL assessment database, and IADL score, such as shown in the figure above. The training and recognition procedures in the first half of route are the same. After EEG signal is input from the EEG sensor, it will pass through the EEG signal feature extraction first. Then the feature extraction adopted in this thesis is the perceptual feature, training phase and recognition phase. In training phase, EEG data as input IADL scale, EEG signal feature extraction, and SVM classifier. It is easily known that the distribution of vector parameter of similar EEG signals in the vector space must be very close. Afterwards, we can utilize the above features to deliver the collected brainwave into classification generator and start training, and establish the classification database of system. In the future, when new EEG signal is coming, it also has to pass through the process of EEG signal extraction and feature extraction. In the end, it is delivered into recognizer and recognizer will compare the features vector parameter of the newly coming EEG signal with the power ratio in database to find out the most similar classification and output the result to complete the IADL assessment work.

IV. EXPERIMENTAL SETUP AND RESULT The IADL assessment database consists of 5 classes is

used for our experiments. The names of these IADL classes are close eyes and silence (25), open eyes and silence (25), chatting with people (25), ability of using telephone (22) and finance management (22). The number describes how many files in each class. There are totally 119 EEG files in this database. The sampling rate is 512 Hz and each sample is 16 bits. For each IADL class, half of the EEG files are utilized for training and the others are used for testing.

Two sets of brainwave features are considered and combined for the task, 1) Alpha, Beta, Thrta, and Delta and 2) perceptual feature sets including total spectrum power, subband energies.

EEG files were mutually divided into two exclusive sets. For each experiment, the one sets for training and the remaining one set for testing. Therefore, we totally have two combinations.

TABLE I. EXPERIMENTAL RESULT AT FIVE SENERIOS

Senerio Train sample

Test sample

Accuracy

Close Eyes and Silence

15 10 70.0%

Open Eyes and Silence

15 10 60.0%

Chatting with People

15 10 70.0%

Ability of Using

Telephone

12 10 70.0%

Finance Management

12 10 70.0%

Total 69 50 68.0%

( )( )

delta power theta power

Power Ratioalpha power beta power

+=

+

244

Page 3: [IEEE 2013 1st International Conference on Orange Technologies (ICOT 2013) - Tainan (2013.3.12-2013.3.16)] 2013 1st International Conference on Orange Technologies (ICOT) - IADL assessment

V. CONCLUSION In this paper, we construct a IADL assessment system

based on EEG signal. IADL events such as ability of using telephone, finance management, man talking and etc can be detected by the proposed system. Experimental results demonstrate the proposed IADL assessment system can effectively classify the IADL score; the average accuracy rate is 68.0%. In the future, we’ll combine HRV sensor into our proposed system to acquire much better IADL assessment recognition accuracy.

VI. REFERENCES [1] D. Par´e, D. R. Collins, and J. G. Pelletier, “Amygdala

oscillations and the consolidation of emotional memories,” Trends in Cognitive Sciences, vol. 6, no. 7, pp. 306–314, 2002.

[2] R. C. Petersen and S. Negash, “Mild cognitive impairment: anoverview,” CNS Spectrums, vol. 13, no. 1, pp. 45–53, 2008.

[3] C. DeCarli, “Mild cognitive impairment: prevalence, prognosis, aetiology, and treatment,” Lancet Neurology, vol. 2, no. 1, pp. 15–21, 2003.

[4] Clifford R. Jack, Jr., Ronald C. Petersen, Yue Cheng Xu, Stephen C. Waring, Peter C. O'Brien, Eric G. Tangalos, Glenn E. Smith, Robert J. Ivnik, and Emre Kokmen, “Medial temporal atrophy on MRI in normal aging and very mild Alzheimer’s disease,” Neurology, vol. 49, no. 3, pp. 786–794, 1997.

[5] A T Dua, N Schuffa,b, D Amenda, M P Laaksof, Y Y Hsug, W J Jagusth, K Yaffec,d, J H Kramerc, B Reedh, D Normanb, H C

Chuii, M W Weiner, “Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease,” Journal of Neurology Neurosurgery and Psychiatry, vol. 71, no. 4, pp. 441– 447, 2001.

[6] Bobinski, Maciek De Leon, Mony J. Convit, Antonio Santi, Susan De Wegiel, Jerzy Tarshish, Chaim Y. Saint Louis, L.A Wisniewski, Henryk M., “MRI of entorhinal cortex in mild Alzheimer’s disease,” Lancet, vol. 353, no. 9146, pp. 38–40, 1999.

[7] M. Golebiowski, M. Barcikowska, and A. Pfeffer, “Magnetic resonance imaging-based hippocampal volumetry in patients with dementia of the Alzheimer type,” Dementia and Geriatric Cognitive Disorders, vol. 10, no. 4, pp. 284–288, 1999.

[8] M. P. Laakso, M. Lehtovirta, K. Partanen, P. J. Riekkinen, and H. Soininen, “Hippocampus in Alzheimer’s disease: a 3-year follow-up MRI study,” Biological Psychiatry, vol. 47, no. 6, pp. 557–561, 2000.

[9] T. den Heijer, M. I. Geerlings, F. E. Hoebeek, A. Hofman, P. J. Koudstaal, and M. M. Breteler, “Use of hippocampal and amygdalar volumes on magnetic resonance imaging to predict dementia in cognitively intact elderly people,” Archives of General Psychiatry, vol. 63, no. 1, pp. 57–62, 2006.

[10] L. Cahill, “Neurobiological mechanisms of emotionally influenced, long-term memory,” Progress in Brain Research, vol. 126, pp. 29–37, 2000.

[11] L. Cahill and J. L. McGaugh, “Mechanisms of emotional arousal and lasting declarative memory,” Trends in Neurosciences, vol. 21, no. 7, pp. 294–299, 1998.

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