Blind Signal Separation for Medical Data Recording Using · PDF fileBlind Source Separation (BSS) application for medical field, especially during medical data recording. ... signal

Blind Signal Separation for Medical Data Recording Using Self-Organizing Neural Network

Abstract—This paper present a study of signal processing in Blind Source Separation (BSS) application for medical field, especially during medical data recording. There are two main techniques that will be investigated; Natural Gradient Method (NGM) and Self-Organized Neural Network (SONN). The main source signals are Electrocardiograph (ECG), and Electroencephalograph (EEG) that linearly mixed with noise signal. This study related to doctor’s assistant for remote outpatient and their patients to communicate each other in separate places, as well as the doctor will be able to monitoring patient’s condition using a telephone/mobile phone that have been attached with an embedded system. Hopefully, while implementing BSS technique into an embedded system for signal processing application will increase the quality of medical outpatient system remotely. This investigation conclude that SONN provide about 80% effectively than NGM while separating mixed source signals of EEG, ECG, and noise Signal.

Keywords—BSS; Neural Network; EEG; ECG; Neural Network; Signal Processing; Computation

I. INTRODUCTION Telemedicine has a broad range type of application such as

an interactive website, android application, and portable devices. However, there is still limitation of investigation reported in telemedicine application that using an event/condition while patient and doctor are communicating using telephone/mobile phone. The idea is how the human body signal will be able to be transmitted and monitored by a doctor when they are calling each other using phone. Certain artifacts and unnecessary mixed signals will be occurred during the phone calling simultaneously with transmitting biological signals. This paper is a part of a project to design an embedded system to process medical data recording and send the data while two people are communicating using communication network using voice call. Previous research have been developed by [9] that EEG, EMG, and Blood pressure are possible to be monitored using Bluetooth, Wi-Fi, and Mobile Internet. However, it will spend a lot of bandwidth and depends on distance and network infrastructures. This study will investigate the possibility of GSM network or conventional cable’s telephone to transmit EEG, and ECG data while the conversation between patient and doctor was held. Blind signal or mixed signal problem between medical data, voice data, and artifacts/noise are occurred while implementing this technique. Regarding to the problems, BSS techniques will be able to improve the quality of telemedicine system and provides potential of extracting information which coherent and identifiable signal features that can be more easily tied up to certain and specific bodily functions [1].

In signal separation, multiple streams of information are extracted from linear mixtures of these signal streams. This process is blind in example of the source signals along with their corresponding mixtures [2]. Artificial Neural Network (ANN) provide a contribution to develop ICA method by conducting self-organized algorithm of Neural Network (SONN) that a neural architecture based on linear predictability is used to separate linear mixtures of signals [6]. There is several reasons why BSS is need to be developed, such as the development of several BSS techniques and task, the identification of many potential application of BSS. One of the most useful applications of BSS was in signal enhancement in telemedicine.

Signal decomposition and separation are two important and fundamental problems in signal processing with a broad range of applications including communications, speech signal processing, biomedical signal processing, and sensor networks [3][4][5][7]. The extraction of signals or components from observed data is a fundamental and challenging problem in many digital signal-processing applications. In many practical situations, observations may be modeled as linear mixtures of a number of source signals, for instances, a Linear Multi-Input-Multi-Output (MIMO) signals [8]. This paper is organized as follows. Section 2 reviews the problem formulation and mathematical background of BSS problem; also the proposed method will be presented in section 3. Section 4 presents investigation result while performing BSS based on SONN algorithm in medical data recording. Section 5 will concludes the final result of this study.

II. LITERATURE STUDY BSS concept was shown on Figure.1. This section will

present basic information of BSS properties and the basic algorithm for separating linearly mixed signals of EEG, ECG, and noise signal.

Figure 1. BSS Block Diagram [2]

A. Problem Formulation Current technology has provides many applications to

improve quality of human’s life. Telemedicine is one of the most useful advantages of technology development. In medical

Alvin Sahroni and Hendra Setiawan

DOI: 10.5176/2251-3043_4.2.322

GSTF Journal on Computing (JoC) Vol.4 No.2, March 2015

©The Author(s) 2015. This article is published with open access by the GSTF

45

Received 26 Nov 2014 Accepted 24 Dec 2014

DOI 10.7603/s40601-014-0009-5

field, it is very difficult to maintain a large of patient data at the same time using human assistant. Important issue in telemedicine is collecting biological signal with high flexibility and robust system to be located in particular areas/room. Engineer aspects should be able to fulfilled several requirements in medical data recording to prevent misinterpreted of data while doctor is reading the data. Artifacts or it will be commonly known as noises are occurred during telemedicine system. Reducing the artifacts such as power line noise (50/60 Hz) and another biological interference during recording should be considered while develop particular system. Biological sensor will be able to collects various signal that comes from inside human’s body to provide precious information for medical analysis/diagnosis and with the rapid development of signal processing, it will be possible to create future devices for outpatient remotely.

Electroencephalograph (EEG) and Electrocardiograph (ECG) are one of the most basic instruments to generalize medical condition of human’s body, for instances, using EEG and ECG data, doctor will be able to diagnose sympathetic and parasympathetic conditions which is related to the central nervous system within human’s body. Presently, the idea of telemedicine application will be able to help people by monitoring EEG and ECG using their mobile phone/portable devices which is embeds small devices to transmit medical data for certain doctor so they will be able to analyze and diagnose several abnormalities within human’s body. The problem occurred when transmitting two or more sources signals, which is commonly known as mixed source signals, while the data was transmitted simultaneously over voice data. However, the medical recording will be misinterpreted while the medical data cannot be transmitted with small of error detection. EEG and ECG are two independents sources signals that can be separated into their original independent signal using Blind Source Separation (BSS) method. BSS structure contains source signal eq(1).

si(k) s1(k), s2(k),� , sm(k) T (1)

Where si(k) is the i-source signal. These m source signals

have linearly mixed properties by the nm unknown

mixing matrix of A with entries aij , based on the observation

of vector x(k) signal as eq(2).

x(k) As(k)v(k) (2)

v(k) is an n-dimensional noise vector sequence that is unrelated to the source signal s(k) . An important characteristics that should be fulfilled and also an important feature in BSS is the relative values of m and n . We can classify into 3 conditions as the scenario/case in BSS approach such as:

When m n , It shows that the independent sources as equal to the number of measurements sensors

When m n , It shows that the independent sources is less than to the number of measurement sensors

When m n , It shows that the number of independent sources is more than the number of measurement sensors.

B. Blind Criteria and Independent Component Analysis (ICA) Algorithm

The procedure for adjusting )(kB depends on the assumption about the sources in )(ks . Firstly we have to decide the assumption of the problem that will be investigated later. Signal Separation Using Spatial Independence is BSS method, which is stated that each )(ksi is assumed to be

statistically independent of )(ks j for ni1 . Then, the marginal possibility density function (PDFs) of at least

)1(m of the sources should be non-Gaussian properties. These assumption can be formulated at eq(3).

nsssms pspspssp ...)()(),...,( 211 21 (3)

Where )( is sp

i, the marginal PDF of )(ksi , cannot be the

Gaussian kernel for more than one value of i . Non-Gaussianity of the sources is due to certain ability to identify characteristics of conditions that must be satisfied for this BSS formulation to work optimally. The general terms of ICA algorithm has been showed on Figure.2.

Figure 2. Illustration of ICA algorithm [10]

Based on eq(2), which assume D to represent a linear transformation of the unknown source signal vector )(ksi on

eq.(1) and noise vector of )(kv . AM is unknown but constant mixing matrix. The goal of BSS is to find separating matrix of B such that the components iy of the outputs

vectors )()( tButy have the maximal degree of statistical independence. It can be shown that the separating matrix of pre-whitened vectors x which transform u into uFDx T1 such that x has as its covariance matrix )( T

xx xxEC the

identity matrix I . Any neural or non-neural algorithm, can achieve pre-whitening process, where columns of matrix F are the n principal eigenvectors of xxC , and the diagonal matrix contains the square roots of the n largest eigenvalue. The next phase (dashed) box represents the BSS part. Each output of



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niyi ,...1, can be regarded as an approximation of one type of the source signals. The last part will provide ICA algorithm to find separation matrix by estimating the mixing matrix. In many practical situations, the source signals are either sub-Gaussian or Super-Gaussian, i.e. distributions with densities flatter or sharper that that of Gaussians, respectively.

C. Self-Organized Neural Network Self-Organized Neural Network (SONN) is a

type unsupervised neural network that is able to re-organize its architecture related to the features extraction purpose. There are several types of SONN methods, for instances Kohonen, LVQ, Self-Organizing Map with Hebbian Rule, etc. This paper will investigate several approach of SONN using unsupervised neural network to obtain )( nm weight matrix to separate mixed source signals.

In addition to the general assumption of independent Component Analysis (ICA), neural network approach presents several learning algorithms. This paper will provide 4 types of neural network approach to self-organized source signals into separated independent sources such as Natural Gradient Algorithm (NGM), Bells and Sejnowski Neural Model (BSICA), fastICA Neural Network, and Multi-layer Neural Network. Previously, conventional ICA method was implemented to separate mixed source signals. But, ICA approach cannot correctly reconstruct its original signals. If X AS, then:

If )')('( 1 MSMAAS : scaling problem

If )')('( 1 PSPAAS : ordering problem Thus, this study will compare the most feasible and effective neural network method for separating the mixed source signals. Thus, ICA is typically posed as an optimization problem. However, Neural Network can be cast into optimization problems due to its iterative approach.

III. PROPOSED METHOD This section will explains how to implement and measures

neural network method while separating blind signals problems. This section will include the procedures to measure of each method.

A. Source Signals This paper will investigate BSS application using neural

network. Source signals simulation was obtained from free web access database provided by PhysioNet. There four types of signals such as ECG, EEG, voice, and noise signal. There are three scenarios for each neural network method. The scenarios are:

2 sources: voice and noise

3 sources: ECG, voice, noise

3 sources: ECG, EEG, voice + noise

Figure.3 shows mixed signals that contains voice and noise signal. The two mixed source signals will be used for

preliminary test for each BSS algorithm, where indicating whether the algorithms are able to separate simple mixing signals (cocktail problem) or not. On Figure.4, the three mixed source signals have been linearly mixed by matrix of A . The three mixed source signals consists of EEG, ECG, voice + noise signals. In this step, neural network method which passed the first preliminary test (two sources mixing signals) will separate the mixed signals using Multi Layer Neural Network (MLNN), Bell and Sejnowski Neural Network (BSICA), Natural Gradient Method (NGM), and fastICA with Neural Network algorithm (fICANN) respectively. Please notice that this study has using raw data provided by Physionet and to generate the mixing sources signals are by doing calculation based on eq.(2) To measure the performance of BSS application using neural network, Mean Square Error (MSE) value will be considered between the original value and separation result for each signal type respectively based on eq.(4).

n

iseparationoriginal iyiy

nMSE

1

2))()((1 (4)

For measuring performance, is by calculating the average MSE value in the end of investigation.

Figure 3. Simulation data for 2 sources signal



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B. Multi-Layer Neural Network (MLNN) The general assumption of ICA assumes that the input

vector x is already pre-whitened and that the source signals have super-Gaussian distributions [10]. Figure.5 was proposed for performing BSS consists of an input and an output layer, each of it consisting of n units. Both layers are fully connected to each other, as illustrated on Figure.5.

Figure 4. Simulation data for 3 sources signal

Figure 5. Network Architecture [10]

The connection weights are the n dimensional vectors wi, i 1,...,nwhich form the rows of the nnmatrix W. the

output of the i th output unit yi, i 1,2,...,n are updated according to the learning rule.

xwy Tii or Wxy (5)

Where x (x,..., xn )T is the input vector and

y (y1,..., yn )T is the output vector respectively. The matrix

of weights wi, i 1,2,...,n are updated according to learning algorithm

wi (t 1) wi (t)(t).G.Kwhere :G wi (t)

T x(t)3

K (I wj (t).wj (t)T ).x(t)

j1

i1

(6)

And then, updated weight matrix will be

wi(t 1) wi(t 1)wi(t 1)

(7)

To be noticed that G on eq.(5) is nonlinear function that included in the learning rule. The nonlinear function of the outputs yi is required to perform Super-Gaussian source signals [10].

C. Bell-Sejnowski Neural Model (BSICA) Bell and Sejnowski have proposed a new approach for

blind signal separation using single layer neural network. They proposed infoMAX algorithm that addresses the problem of maximizing the mutual information );( xyI between the input vector x and an invertible nonlinear transform of it, and y obtained as:

y h(u) h(Wx) (8)

Where W is nn de-mixing matrix and h(u) [h1(u1),...hn(un )]

T which consists of N nonlinear function. For updating the new weights of de-mixing matrix is:

W (W H (u)xH ) (9)

D. Natural Gradient Algorithm (NGM) Natural Gradient Method (NGM) provides more simple and

easy for implementing BSS method. Iteratively, updated weight will be calculated as follows:

)()]())(()[()()1( kWkykyfIkkWkW T (10)



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E. fastICA with Neural Network (fICANN) ICA and fastICA are the most common method to separate

blind signal application. fastICA also expanded using neural network approach and provide better solution with more efficient calculation that ICA. This method is a block-based procedure for maximizing the kurtosis the updates for a given N sample of data are:

)()(

)(

)(3))()(()(1

0

3

kwkw

Nkw

kwlkvlkykw

i

ii

N

liii

(11)

IV. RESULTS AND DISCUSSIONS This section will provide simulation results of four-type

neural network approach to separate blind signals (EEG, ECG, Voice, and noise). The model and algorithms are implemented using public EEG and ECG free web access database provided by PhysioNet. The simulation using 5000 samples data and EEG, ECG sampling rate is 128 KHz respectively. Each simulation using different approach while updating mixed weight matrix iteratively. We assume the blind signals were linearly mixed with randomize matrix of Awith (mm)size where m is the number of sources. There are two scenarios in this investigation, 2 sources and 3 sources signal separation. 2 sources separation consist Voice and Noise that will be used for preliminary test of neural network algorithm whether it can be continue for 3 sources separation test or not. Figure. 6, 7, 8, 9, 10, 11, and 12 are the separation result using MLNN, BSICA, NGM, and fICANN respectively. Special case is related to NGM method while separating 2 sources signals. Based on Figure.10, we can conclude that NGM is not able to separate 2 linear mixed sources. It is because ordinary NGM cannot provide a good solution for parameter space, which is not orthogonal in Riemannian space, and the ordinary gradient does not indicate the steepest ascent direction for a desired solution. MLNN, BSICA, and fICANN are able to separate 3 sources that consist of (EEG, ECG, and voice + noise signals. Table.1 provide information that BSICA method is better than another neural network method. Previously, ICA and fastICA are common method to extract artifacts from biological signals and sensor. Bell and Sejnowski proposed a better approach to separate blind signal by using InfoMAX algorithm. We can conclude that infoMAX algorithm will works on blind signal, which consist of medical data/signals. For future investigation, we will provide infoMAX algorithm by modifying another BSS method and then compare between original method and the modified method.

V. CONCLUSIONS Separating blind signal problems using BSS application for

telemedicine field has been investigated in this paper. EEG, ECG, and voice + noise signal was identified as independent sources. There are four types of neural network to separate mixed source signals in this investigation, Natural Gradient Method (NGM), fastICA with Neural Network learning

algorithm (fICANN), Bells and Sejnowski Neural Network (BSICA), and multi-layer neural network (MLNN).

Figure 6. MLNN 2 sources separation result

Figure 7. MLNN 2 sources separation result

Figure 8. BSICA 2 sources separation result



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Figure 9. BSICA 3 sources separation result

Figure 10. NGM 2 sources separation result

Figure 11. fICANN 2 sources separation result

Figure 12. fICANN 3 sources separation result

TABLE 1. SIMULATION RESULT

Types of Algorithm

Number/Types of Sensors

Average Mean Square Error

(Avg(MSE)) in μV

Multi Layer Neural Network (MLNN)

2 types of sensors (voice, noise) 0.0106

3 types of sensors (ECG, voice, and

noise) 1.3081

Bell-Sejnowski Neural Model (BSICA)


3 types of sensors (ECG, voice, noise) 0.1962

3 types of sensors (EEG, ECG, voice+noise)

0.6726

Natural Gradient Method (NGM)


fastICA with neural network algorithm (fICANN)


3 types of sensors (ECG, voice, noise) 2.6677

3 types of sensors (EEG, ECG, voice+noise)

0.0295



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Figure 13. Two-source separation (voice and noise)

Figure 14. Three-source separation (ECG,voice, noise)

Each method presents different performances by measuring

average value of MSE between original and separated sources. It concludes the main problem of previous ICA method is scaling and ordering data. Generally, based on simulation result, BSICA approach based on neural network provide better result while separate EEG, ECG, and voice + noise signal, but another result shows that fICANN is better than the others while separating certain of three types of independent sources. The challenging field for further investigation is to improve separation performance (ordering and scaling problems) while many sensors should be separated at once. For future investigation, there should be another hybrid method between Neural Network and nonlinear perspective method to measuring the robustness of BSS method based on neural network.

ACKNOWLEDGMENT Thanks to Electrical Engineering Department, FIT UII

Yogyakarta Indonesia, dr. Erlina Marfianti in Medical Faculty, for supporting this research and discussions. It will give benefits for future works and continuous development.

REFERENCES

[1] M.J Mckeon, et al, "Analysis of fMRI data by blind separation into

independent spatial components," Human Brain Mapping, pp. 160-188, 1998.

[2] Jeng-Neng Hwang Yu Hen Hu, "Blind Signal Separation and Blind Deconvolution," in Handbook of Neural Network Signal Processing.: CRC Press, 2002, ch. 7.

[3] D. Le Guennec A, A. Rouxel, and O. Machi, "Unsupervised adaptive separation of Impulse Signals applied to EEG analysis," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, 2000, pp. 420-423.

[4] S. Talvar A.J, Van der Veen, and A. Pauraj, "A Subspace approach to Blind Space-Time Signal Processing for Wireless Communication Systems," IEEE Trans. on Signal Processing, pp. 45:173-190, 1997.

[5] Michael J. Kirby, Charles W. Anderson, David A. Peterson, and Kames N.Knight, "Feature selection and blind source separation in an eeg-basedbrain computer interface," EURASIP Journal on Applied Signal Processing, pp. 19:3128-3140, 2005.

[6] Raul Rojas and ernesto Tapia, "A neural architecture for blind source separation.," Freie Universitat Berlin, Institut f ur Informatik, Berlin, Technical Report 2006.

[7] C.B. Papadias and A. Pauraj, "A constant modulus algorithm for multi user signal separation in presence of delay spread using antenna arrays," IEEE Trans. on Signal processing, pp. 4:178-181, 1997.

[8] Zeyong Shan, "Multichannel signal decomposition and separation in the time-frequency domain," Proquest, 2009.

[9] S. Saravanan, "Remote Patient Monitoring in Telemedicine using computer communication network Bluetooth, Wifi, Internet Android Mobile," International Journal of Advanced Computer and Communication Engineering, vol. 3, no. 7, pp. 7590-7596, July 2014.

[10] Bernd Freisleben, Claudia Hagen, and Markus Borschbach, "Blind Separation of Acoustic Signal Using a Neural Network," Electrical Engineering Department and Computer Science, University of Siegen, Germany, Technical Report.

AUTHOR’S PROFILE

Alvin Sahroni. received B.Eng (2008) and M.Eng(2011) degree from Electrical Engineering UII and Joint Program in Resource Engineering between Gadjah Mada University, Yogyakarta-Bandung Institute of Technology, Bandung-Karlsruhe Institute of Technology(KIT), Germany, respectively. Presently, he is pursuing his doctoral degree in Kumamoto University, Japan in Computer Science and Electrical

Engineering Department. His research interests include Development and Applied of Artificial Neural Network and Artificial Intelligence. He is a member of IEEE

Dr. Hendra Setiawan received B.Eng. (2002) and M.Eng. (2007) degree from Gadjah Mada University and Bandung Institute of Technology, Indonesia, respectively. His Ph.D. degree is from Department of Computer Science and Electronics at Kyushu Institute of Technology, Japan, in 2011. Currently, he is an associate professor in Electrical Engineering

Department, University Islam Indonesia. His research interests are physical layer design of wireless communication system and its implementation on programmable devices. He is a member of IEEE



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Blind Signal Separation for Medical Data Recording Using · PDF fileBlind Source Separation (BSS) application for medical field, especially during medical data recording. ... signal