FPGA implementation of fractal patterns classifier for multiple cardiac arrhythmias detection

13 Pages·2013·1.55 MB·English

J. Biomedical Science and Engineering, 2012, 5, 120-132 JBiSE
http://dx.doi.org/10.4236/jbise.2012.53016 Published Online March 2012 (http://www.SciRP.org/journal/jbise/)
FPGA implementation of fractal patterns classifier for
multiple cardiac arrhythmias detection
Chia-Hung Lin, Guo-Wei Lin

Department of Electrical Engineering, Kao-Yuan University, Kaohsiung City, Taiwan
Email: [email protected]

Received 2 December 2011; revised 30 December 2011; accepted 29 January 2012
ABSTRACT and uses a cardiogram to identify arrhythmic types of the
patients. Stationary monitors, such as dedicated or PC-
This paper proposes the fractal patterns classifier for
based devices, have also been used on healthcare systems,
multiple cardiac arrhythmias on field-programmable
which results in expensive solutions and limiting the port-
gate array (FPGA) device. Fractal dimension transfor-
ability [1,2]. Since telemedicine has also been used to
mation (FDT) is employed to adjoin the fractal fea-
acquire patient’s information, and has portable configu-
tures of QRS-complex, including the supraventricular
ration for patient monitoring in remote non-clinical en-
ectopic beat, bundle branch ectopic beat, and ventricu-
vironments, it can be used in home healthcare, elder com-
lar ectopic beat. FDT with fractal dimension (FD) is
munities, and public place to acquire patient’s information.
addressed for constructing various symptomatic pat-
It has become a commonly used technology due to its
terns, which can produce family functions and enhance
low-cost, compactness, and short design-cycle. This re-
features, making clear differences between normal and
mote device is an embedded hardware with FPGA, Blue-
unhealthy subjects. The probabilistic neural network
tooth, RFID, and Zigbee. Via wireless transmission be-
(PNN) is proposed for recognizing multiple cardiac
tween the biosensor and supervision device, FPGA makes
arrhythmias. Numerical experiments verify the effi-
the ECG signal processing and sends digital signal to a
ciency and higher accuracy with the software simula-
remote monitor [3]. For a portable monitor design,
tion in order to formulate the mathematical model
FPGA device provides a promising solution to hardware
logical circuits. FDT results in data self-similarity for
implements.
the same arrhythmia category, the number of dataset
FPGA device provides the flexibility and potential al-
requirement and PNN architecture can be reduced.
ternative to design hardware, including built-in DSP, analog
Its simplified model can be easily embedded in the
and digital I/O, signal generation, communication, and spe-
FPGA chip. The prototype classifier is tested using the
cific functions for signal processing and controllers. It has
MIT-BIH arrhythmia database, and the tests reveal
been applied for digital filters design [4,5], signal analy-
its practicality for monitoring ECG signals.
sis and classification [6,7], fault detection [8], and Fuzzy

logic controller [9]. For signal analysis, transform meth-
Keywords: Field-Programmable Gate Array (FPGA);
ods have been used to extract features in the transform
Fractal Dimension Transformation (FDT); Fractal
domain, such as fast Fourier transform (FFT) and wave-
Dimension (FD); Probabilistic Neural Network (PNN)
let transform (WT). However, the FFT technique is lim-
ited by the processing time, computational complexity, and
1. INTRODUCTION
the number of samples. The more samples, the more ac-
The electrocardiogram (ECG) signal can be measured curate parameters will be estimated. It will increase the
non-invasively by placing electrodes on the body surface memory requirement. WT can be chosen with a very desir-
that provides information of myocardium electric activity able frequency and time characteristics, allowing the visu-
and heart physiological function. An ECG signal shows alization with the short/broad window at the high/low
as an almost periodic signal, and can reveal symptomatic frequencies. It is a dilated or constricted function by chang-
information in the dysfunction duration. Currently, port- ing dilation and translation coefficients for feature ex-
able and stationary monitoring devices have been used traction. WT process uses a several-structured filter bank
on healthcare systems. Holter recorder is a well-known to generalize several frequency bands containing decom-
portable device and used to record the electrical activity posed signals [6,7]. Significant features are obtained at
with surface electrodes placed on the chest. However, its specific wavelet coefficients with a trial procedure of wave-
diagnostics is off-line analysis from the recorded data, let decomposition and experiences.
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C.-H. Lin et al. / J. Biomedical Science and Engineering 5 (2012) 120-132 121
temT o( IoFvSe)r ciso mpreo pthoes eddr afworb macokdse, lainng i ttehrea tnedo nf-ulinncetaior ni nstyesr-- e  Np2 W x tx t2 (3)
p px p p
polation function [10-14]. Its modification, the so-called tNp1
FDT function, is simple in form and consists of sinusoid-  tN  
dal terms to the affine maps, making the model more flexi- where nint p1 N 1, N = N – N + 1,
ble for processing irregular signals. FDT functions with Np2Np1  p p2 p1
FD are used to construct the fractal patterns from ECG and N = N + N + N + ⋯ + N + ⋯ + N . To improve the
1 2 3 p P
signals in the time-domain, including the “Q-R Segment” constraint, non-linear interpolation is used to adjoin the
and “R-S Segment”. The transform method results in data data among the interpolation points, which makes the model
self-similarity and enhances the features for the same cate- more flexible for processing non-linear and irregular
gory. The PNN-based classifier is developed to perform signals. Non-linear terms as sinusoidal functions can be
the classification tasks. The performance of this method added to W . The non-linear interpolation function can be
p
is presented and promising results are given for classifi- represented as
cmaatitoicna la opppelircaattiioonn,s f, lesxuicbhl ea ps atthteer ns tmraeigchhtafnoirswma,r da nmd ahthigeh- Wpxxptcpndpxpt fpgpsinNn (4)
tolerance capability [15-17]. These algorithms can be
easily programmed into the FPGA chip. The FPGA de- A fractal pattern of ECG signal can be adjoined with
vice has an inherent parallel architecture allowing de- several segments such as P-R interval, P-R segment,
signers to execute the multiple inputs and control loops Q-R-S complex, S-T segment, or Q-T interval. The non-
simultaneously without slowing down the execution time linear function with fractal dimension (FD) will change
and applications. From the test results, they appear to be the ECG signals into fractal patterns at different scale pa-
computationally efficient and accurately recognize for rameters. FD must be a parameter between 1 and 2 for
patterns classification. processing one-dimensional signals. The non-linear in-
terpolation function, FDT with FD, can be modified as
2. MATHEMATICAL BACKGROUND
2.1. Fractal Dimension Transformation (FDT) ptxptcpndpxpt fp gpsinDn (5)
An IFS has been proposed for image compression and  D1 
signal modeling, and is capable of producing family func- n t (6)
N 1
tions with different fractal dimensions (FDs). It is a finite  p 
set for contraction mappings, and has been used to create where D is a FD parameter (1 < D < 2). The remaining
images, various waveforms and patterns for medical im- map parameters c , d , f , and g can be solved by
p p p p
age classification and biomedical signal analysis [10,11].
c 
IFS is implemented with similarity maps, and the result- p
 
ing data are self-similar. The significant removal of re- NpS STdpNpS x t (7)
dundancy is related to the self-similarity of natural pat-  t t f  t p
terns. In modeling the pattern of a function or data se- t1  p t1
quence x[t], t = 1, 2, 3, , N, the pth interpolation map gp
W , p = 1, 2, 3, , P, can be presented as T
p  n
 t  a b  t  e  St n xpt 1 sin D  (8)
Wpxtcpp dppxtfpp (1) Apply sequence data xp [t] are the sampling data from
the ECG signals. The fractal patterns can be reconstructed
W ta tb xte as [14]
 p p p p
Wpxxtcptdpxt fp (2) ptPptxpt t1,2,3,,Np (9)
p1
For each map, W maps the data sequence x [t] onto
p p Equations (5) and (6) are used to extract the features
the subsequences with N sampling data in the interval
p from the ECG signals, and Equation (9) is utilized to con-
[N , N ], and the maps can be constructed side by side.
p1 p2 struct the fractal patterns of cardiac arrhythmias.
The remaining map parameters c , d , and f can be
p p p
solved by minimizing the sum of squared errors between
2.2. Artificial Neural Network (ANN)
the transformed data and the original data in the range of
the pth map, and can be justified by the Collage Theorem The PNN consists of four layers, including the input
[12,13]: layer, hidden layer, summation layer, and output layer, that
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122 C.-H. Lin et al. / J. Biomedical Science and Engineering 5 (2012) 120-132
has the parallelism distributed process, learning, and pattern ton-Raphson method [15-17], is employed to adjust the
recognition ability. In this study, we have considered the parameter  and minimize the error with iteration pro-
sequence data x [t], t=1, 2, 3, , N , with P segments cedures. It is intended to minimize the predicted squared
p p
from the ECG signal, and a fractal pattern  can be rep- error function e qT O q2, and is updated by
resented as j  j j 
using pattern learning as
 , , ,,  , , ,,
 11 12 13 1N1 21 22 23 2N2 e q
 , , ,, P x t (10) q1q j (13)
P1 P2 P3 PNP pt p
p1 where T is the desired output for training pattern k,
j
The vector  can be also combined as  = [ ,  ,
11 12  is the learning rate, and q is the iteration number. The
 , ,  | ,  ,  , ,  |…| ,  ,  , ,
13 1N1 21 22 23 2N2 P1 P2 P3 PNN based classifier has a dynamic and fast adaptation
 ] = [, , , , , , ], i = 1, 2, 3, , N, N
PNP 1 2 3 i N ability with continuity add-in or delete-off training data
= N + N + N +  + N , and each feature  is con-
1 2 3 P i by automatically tuning the desired outputs and parame-
nected to the input nodes of input layer. The number of
ters  of hidden nodes.
input nodes is equal to the number N. The number of hidden
nodes Hk (k = 1, 2, 3, , K) is equal to the number of 3. FPGA IMPLEMENTATION
training data, while the number of summation nodes S and
j
3.1. FPGA Development Environment
output nodes O (j = 1, 2, 3, , m) equals to the types of
j
cardiac arrhythmias. Therefore, its architecture can be easily
The field-programmable gate array (FPGA) is a semi-
to determined without any trial-and-error procedure. The
conductor device that can be configured by the customer
weights wIH (connecting the kth hidden node and the
ki or the designer after manufacturing. It has the array ar-
ith input node) and wHS (connecting the jth summation
jk chitecture of logical elements, which can be used to de-
node and the kth hidden node) are determined by K in-
sign any logical functions to implement the given appli-
put-output training pairs. In the hidden node, a normal-
cations, such as an application-specific integrated circuit
ized Gaussian function H is applied to the Euclidean
k (ASIC). FPGA is also a programmability device, like pro-
distance E between the unknown pattern  and K train-
d grammable ROMs, and its programming techniques in-
ing patterns wIH. The function Hk is inversely propor-
ki clude antifuse-based device (Programmed Once) and sta-
tional to the distance E . If E approaches zero, the un-
d d tic-memory-based device (Reprogrammed an Unlimited
known pattern  is similar to any training data. This
Number of Times). A typical FPGA consists of con-
concept can be used for analyzing pattern relations. The
figurable logic blocks (CLB), input/output blocks (IOB),
output O can be computed by
j and programmable interconnects [4-9]. Each CLB can
 N wIH2  E2  design the logical functions and latching data with com-
H exp i ki exp d  (11) binational logic and sequential logic (AND, OR, Flip-
k  i1 2k2   2k2 flops, and Registers). IOBs provide the interface between
external pins and internal logics. Programmable intercom-
K nects link the CLBs, I/O pins, and other resources on-chip
wHSH
jk k memory through the routing paths, where interconnection
O  k1 j1,2,3,,m (12)
j K among these blocks can be programmed by using hardware
H
k description language (HDL). Then, the designs can trans-
k1
ferred the descriptions to gate-level netlists and dataflow-
where the weights wIH are created by training data (k)
ki flow on a chip. The prototype device can be implemented,
= [(k), (k), (k), , (k)], k=1, 2, 3, , k, , K;
1 2 3 N tested, debugged, and modified as needed in a short de-
the weights wHS are the desired outputs associated with
jk sign cycle.
each stored pattern wIH. The value of wHS will be equal Custom-design platforms have been provided to the
ki jk
to “1” or “0”. The value will be set to be “1” when the FPGA implementation, such as Verilog, ModelSim, and
kth training data belonged to the jth class;  is the smooth- Quartus II, and its development environment can simu-
k
ing parameter. Finally, the maximum output O = [O , late and verify the logic designs before initiating the compi-
max 1
O , O , , O, , O ] indicates the type of cardiac lation process [18-20]. However, HDL is a low level text-
2 3 j m
arrhythmias. based programming language used for FPGA hardware
In Equation (11), the smoothing parameter  = = design. Currently, LabVIEW extends the graphical pro-
1 2
 =  =  =  (  0) would refine the classification gramming tools to FPGA-based hardware. It has parallel
3 K
accuracy. The non-linear optimization method, such as gra- execution, data acquisition, and floating-point arithmetic
dient descent method, steepest descent method or New- (Data Word Length: 8, 16, and 32 bit) functions to proc-
Copyright © 2012 SciRes. OPEN ACCESS
C.-H. Lin et al. / J. Biomedical Science and Engineering 5 (2012) 120-132 123
ess signals on FPGA, and embeds specific functions for constant coefficients; R, r = 0, 1, 2, , R, is the number
math, signal analysis/generation, comparison logic, linear/ of series term (R = 10 in this study); z, r = 0, 1, 2,, R,
r
non-linear control, analog and digital I/O, and timing. In represent the series coefficients and are also constant
this workspace, designers can quickly develop specific values. These coefficients can be computed and com-
and reliable embedded systems containing FPGA-chips, pleted to verify the expanded function accuracy in the
real-time processor, and human machine interface (HMI). Matlab workspace, as shown in Table 1.
Graphical user interface for windows application has shorter Figure 1 shows the structure of FDT logical block, in
design cycle, reprogrammability, and flexibility, which is which 13 adders, 26 multipliers, and 17 constant coefficients
much better than text-based languages. Under this deve- are required to implement each FDT. Its CLB can design
lopment environment, we use saturation arithmetic func- the function of feature extraction with combinational logical
tions to configure the combinational logics without prior elements, and constant coefficients are stored in the mem-
complex digital design or electronic design automation ory elements. When each sampling data is applied to the
(EDA) tools, and then to embed intelligent algorithms on FDT, each fractal feature is computed with multiplica-
the compact chip. tion and addition. This parallelism process completes the
fractal pattern, and then each feature is applied to the
3.2. FDT Implementation with FPGA PNN logical block.

An ECG signal is measured with the modified limb lead
Table 1. Related data for FDTs.
II (ML II), and its typical waveform consists of the P-
wave, QRS-complex, and T-wave. The QRS-complex pro- Function The Number of FDT Remaining Map Parameter
vides distinct information in monitoring heartbeats, which
c 0.8811360429
can be used to discriminate the arrhythmic types. Cen- 1
tered on the R-wave peak, the QRS-complex can be di- d 0.8485988992
1
vided into the Q-R segment and R-S segment (P = 2). W1x 25
f 0.3445652340
For 50 sampling points (25 points before R-peak and 25 1
points after R-peak, N = N1 + N2 = 50), the remaining g1 0.3857726869
map parameters can be solved by Equations (7) and (8)
c –0.6759430538
in the Matlab workspace. By using FD between 1 and 2, 2
the fractal patterns are constructed with Equations (5), d 0.9296483879
2
(6), and (9). The FDTs with fractal dimension D = 1.6 is W2x 25
f –0.2087852761
chosen in this study. For a QRS-complex of normal beat 2
(Patient Number: MIT-103), related parameters cp, dp, fp, g2 0.5826642888
and g , p = 1, 2, are computed as shown in Table 1. The
coeffipcients of four terms are assigned to construct FDT Function The Number of Series Coefficient
Expanded Term
functions with 4 remaining map parameters for Q-R seg-
ment and R-S segment, respectively. Following the mul-  1.9634954084
tiplication and addition of the FDT, the fractal fractures n' 0.0250000000
t
can be computed. However, the logical blocks only sup-
port the four fundamental operations of arithmetic. In Z0 1.0000000000
n' Z –0.1666666666
Equation (5), special function sin  must be ex- 1
 D 
Z 0.0083333333
2
panded a function into finite terms of Maclaurin series as
Z -0.0001984126
n2r1 3
n R  D  sin() 11 Z4 0.0000027557
sin (1)r
 D  r0 (2r1)! Z5 –2.5052108385E–8
n 1 n3 1 n5 1 n2R1
          Z6 1.6059043836E–10
D 3! D  5! D  (2R1)! D 
 z ntz nt3z nt5z nt2R1 Z7 –7.6471637318E–13
0 t 1 t 2 t R t
(14) Z8 2.8114572543E–15
 D1 Z9 –8.2206352466E–18
where  and n ,  and n are the
D t N 1 t Z 1.9572941063E–20
p 10
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124 C.-H. Lin et al. / J. Biomedical Science and Engineering 5 (2012) 120-132

Figure 1. The structure of FDT logical block.

3.3. PNN Implementation with FPGA data. The optimal parameter  = 0.10942 can be com-
puted in the Matlab workspace. For the convergent con-
In this section, we focus on classifier design and collec-
dition (Squared Error  10–4), PNN converges to the
tion of the annotated ECG beats for PNN training data.
nearest local minimum for less than five learning cycles.
The ECG signals are obtained from the MIT-BIH ar-
The related data of the PNN-based classifier are shown
rhythmia database, including patient numbers: 100, 103,
in Table 2.
107, 109, 111, 118, 119, 124, 200, 202, 207, 209, 212,
213, 214, 217, 221, 231, 232, and 233 [21]. ECG signals
3.3.1. Hidden Node CLB
have various morphological information and waveforms,
In the hidden layer, a normalized Gaussian function must
which can be classified into seven categories, including
be also expanded into a Maclaurin series as
normal beat (), premature ventricular contraction (V),
atrial premature beat (A), right bundle branch block beat 1
(R), left bundle branch block beat (L), paced beat (P), expE 1 R Edkr 
and fusion of paced and normal beat (F). Centered on the  dk   r! 
r0 
R-wave peak, the QRS-complex is divided into the Q-R
1
 1 1 
segment and R-S segment. The FDTs with FD are utilized 1E  E 2 E R
to construct various fractal patterns as shown in Figure 2.  dk 2! dk (R)! dk  (15)
With data self-similarity, the preprocess resulting in the y y E y E 2y E R1
fractal patterns are similar for the same category, which  0 1 dk 2 dk R dk 
can reduce the requirement of training data. The total num- div

ber of fractal patterns are selected to be 1-, 6-, 2-, 3-, 4-, y y E y E 2y E R
 0 1 dk 2 dk R dk 
4-, and 2-set data (K = 22) for the seven categories, re-
spectively. These associated patterns could be expressed
1
as weights wIH, k = 1, 2, 3, , 22, i = 1, 2, 3, , 50, where div is dividend, div = 1 in this sudy, 
ki 22
between the input and hidden layer. The weights wHS,
m = 1, 2, 3, , 7, between the hidden and summatijkon and E N wIH2,  is the constant coefficient, and
dk i ki
layer are encoded as binary values with signal “1” de- i1
noting the seven categories while the rest of the weights R is the number of series term (R = 6 in this study); y are
r
are zero. The smoothing parameter  was adjusted by the series coefficients. Related data can be computed and
using the gradient descent method with 22-set training stored in the memory elements as shown in Table 2. The
Copyright © 2012 SciRes. OPEN ACCESS
C.-H. Lin et al. / J. Biomedical Science and Engineering 5 (2012) 120-132 125
dition. Then input the results to Gaussian function H.
k
Figure 3 shows that the structure of hidden-node CLB,
106 adders, 63 multipliers, 1 divider, and 59 constant
coefficients are required to implement each node H.
k
3.3.2. Summation-Node and Output-Node CLBs
For seven categories, the weighting factors wHS, k = 1,
jk
2, 3, , 22, j = 1, 2, 3, , 7, are encoded as binary val-
ues with signal “1” belonging to category j and the rest
of the factors are zero. The weighting matrix [wHS] is
jk 722
sparse with 22 nonzero elements. The numerator of
Equation (12) is a matrix vector computation, where each
output S is formed by multiplying each of 22 outputs of
j
the hidden node by one of 22 weighting factors, and can

be presented as
Note: 1) No. 1: ; 2) No. 2~7: V; 3) No. 8~9: A; 4) No. 10~12: L; 5)
No. 13~16: R; 6) No. 17~20: P; 7) No. 21~22: F. H 
1
 
Figure 2. The the various fractal patterns for multiple cardiac S  1 0 0  0 0  0 0 H
1  2 
arrhythmias. S  0 1 1 0 0 0 0H 
 2   3 
Table 2. Related data for PNN-based classifier. S3 0 0 0  0 0  0 0  
S40 0 0 1 1 0 0H11
Network Topology S  0 0 0  0 0  0 0H 
Training Initial  5   12
Method I H S O Data Learning Rate SPmaroaomtheitnerg SS76 00 00 00  00 00  10 10H21
 
H22
PNN 50 22 8 7 22 0    1  (q = 0) = 1.0 S  H 
1 1
   
V S H H H H H H
 2  2 3 4 5 6 7
The Number of
Function Related Coefficient AS  H H 
Expanded Term  3  8 9 
 LS4H10H11H12 
 41.7615475105 RS  H H H H 
 5  13 14 15 16 
PS  H H H H 
6 17 18 19 20
div 1.0000000000    
FS7 H21H22 
(16)
y 1.0000000000
0
The number of addition and multiplication operations
can be reduced leading to an increased speed in the
y 1.0000000000
1 arithmetic process. In the summation layer, the outputs
of summation node H are computed with 15 adders. For
k
exp() 8 y 0.5000000000 the same category, their outputs of hidden nodes are
2
summed in the summation-node CLB. The implementa-
tion of output node O would require 6 adders and 7 di-
y 0.1666666666 j
3
viders, and can be presented as
O  S 
y4 0.0416666666  1  1
V O S
 2  2
y 0.0083333333 AO  S 
5  3  1  3
y 0.0013888888 RLOO4S1S2S3S4S5S6S7SS4 (17)
6  5  5
PO  S 
6 6
   
term Edk can be computed with multiplication and ad- FO7 S7
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126 C.-H. Lin et al. / J. Biomedical Science and Engineering 5 (2012) 120-132

Input Layer
Multiplier
Divider
Adder
Input/Output
Constant Coefficient
Hidden Layer

Figure 3. The structure of hidden-node logical block.

as shown by the denominator of Equation (12), the num- structure of summation-node and output-node CLBs are
ber of the addition operations can also be reduced. The shown in Figure 4.
Copyright © 2012 SciRes. OPEN ACCESS
C.-H. Lin et al. / J. Biomedical Science and Engineering 5 (2012) 120-132 127

Summation Layer
Output Layer
Divider
Adder
Input
Output

Figure 4. The structure of summation-node and output-node logical block.

3.4. Overall Structure of the Proposed Classifier roughly the width of a typical QRS-complex. This window
is at least 150 ms wide to allow for the wide QRS-com-
In Figure 5, the overall structure of the proposed classifier
plexes produced by V-heartbeats [22-24]. Centered on the
divides into three stages: 1) signal preprocessing; 2) fractal
detected R-peak, 25 sampling data are acquired including
feature extraction; and 3) heartbeat recognition with PNN-
both Q-R segment (140 ms) and R-S segment (140 ms),
based classifier. In the preprocessing stage, ECG signals
respectively. In the second stage, these sampling data are
are acquired by using the amplifier and filter. The band-
converted to digital form (Signed 8-bit, 16-bit, or 32-bit
pass filter is used to remove unwanted frequency compo-
Length Data) and then sent directly to the FDTs. The FDT
nents, which comprise supply line frequency interference
CLBs with parallelism process are used to extract features
(50 Hz/60 Hz), baseline wander, and muscle noises. Thus,
and construct the fractal pattern. Finally, the classifier mo-
these noises do not affect the performance of the proposed
dule integrates hidden-node, summation-node, and output-
classifier. ECG records are composed by a modified limb
node CLBs for cardiac arrhythmias recognition.
lead II (ML II) sampled at 360 Hz. Then R-peak waves are
detected by the Peak detection algorithm. It begins by
4. EXPERIMENTAL RESULTS AND
scanning for local maxima in the absolute value of ECG
DISCUSSIONS
data. For certain window, the search continues to look for
next larger value. If this search finishes without finding a The FPGA implementation of the proposed classifier is
larger maximum, the current maximum is assigned as the applied to examine multiple cardiac arrhythmias. With
R-peak wave. The averaging window was chosen to be MIT-BIH arrhythmias database, the records of patient
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128 C.-H. Lin et al. / J. Biomedical Science and Engineering 5 (2012) 120-132

Figure 5. The overall structure of proposed fractal patterns classifier.

numbers 107, 118, 119, 200, 209, 211, 214, 217, and 231 them into the FPGA chip. The average fractal patterns from
are selected for testing. Tested ECG signals were gener- the same category are obtained from the selected patients.
ated using LabVIEW and Matlab software on a PC Pen- For training data, fractal patterns are computed by FDT
tium-IV, 3.0 GHz, 480 MB RAM. The design platform module and high-level programming language (HLPL) in
of SOPC-NIOS II EDA/SOPC series (NIOS II-EP2C35 the Matlab workspace, respectively. Figure 6 shows the
Chip, 700 K system gates, EEPROM) was used in this comparison fractal patterns in FDT module and Matlab
study. The ECG signals have been digitized and band- workspace. The asterisk-line stands for the computed data
pass filtered, thus low- and high-frequency noises appears with HLPL, and plus-sign-line is the computed data with
smaller in amplitude [21]. Signal preprocesses, such as FDT module Through cursory observation, nice inoscu-
R-peak detection and QRS-complex extraction, can be lations can be seen between them, such as the fractal pat-
performed in LabVIEW and Matlab software. It is im- terns of normal heartbeat, V-heartbeat, and F-heartbeat (Pa-
portant that the signals delivered to the FPGA must be tient Numbers: 100, 107, 119, and 200). The differences
digital type. As they are intrinsically digital formats (8-bit are less than 0.2 as shown by the circle-line in Figure 6.
Format), they can be directly sent to FPGA design plat- This confirms that the proposed FDT module has high
form. For recorded (Training Data) and unrecorded data, confidence of computation performance for reconstruct-
the proposed classifier was tested with accuracy and com- ing fractal patterns.
putational efficiency, and compares with the results in
FPGA module and Matlab workspace. 4.2. Classification Tests
The learning performance of the proposed classifier was
4.1. Comparison with the Fractal Patterns in
tested with 22-set training data. In the learning stage, the
FDT CLB and in Matlab Workspace
optimal parameter  can be computed in the Matlab work-
The proposed classifier is tested with the software simu- space. The proposed method has a fast learning process
lation, in order to examine the implementation of FPGA with slight iteration for adjusting parameter . It takes
modules. After the designated modules, the designated 0.195 seconds to classify the 22-set training data into 7
circuit can be programmed into the FPGA chip through categories. Then this optimal value was used to deter-
the download cable. In feature extraction, fractal features mine the parameter  of 22 hidden-node modules and
are extracted by using 50 FDTs. Then 50 features are was obtain to minimize the misclassification errors. Under
reconstructed into one fractal pattern. Since the FDT mo- the optimal parameter, decision boundaries can become
dule is a simplified module, it is worth noting that each increasingly non-linear, the network approaches a nearest
FDT function must be first examined before programming neighboring classifier. In the recalling stage, all outputs
Copyright © 2012 SciRes. OPEN ACCESS
C.-H. Lin et al. / J. Biomedical Science and Engineering 5 (2012) 120-132 129

Figure 6. Compare with the fractal patterns in FDT module and in Matlab workspace.

approach the desired targets for 7 categories. The output This confirms that the proposed classifier has nice learn-
values of the proposed classifier are shown in Figure 7. ing performance.
Copyright © 2012 SciRes. OPEN ACCESS