A fast expert system for electrocardiogram ... - Semantic Scholar

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DOI: 10.1111/j.1468-0394.2010.00518.x

A fast expert system for electrocardiogram arrhythmia detection Sina Zarei Mahmoodabadi,1,2 Alireza Ahmadian,3,4 Mohammadjavad Abolhasani,3,4 Paul Babyn2,5 and Javad Alirezaie1,3,4 (1) Electrical and Computer Engineering, Ryerson University, Toronto, Ontario, Canada Email: [email protected] (2) Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada (3) Biomedical Systems and Biophysics Group, Tehran University of Medical Sciences, Tehran, Iran (4) Research Centre for Science and Technology in Medicine, Imam Hospital, Tehran, Iran (5) Medical Imaging, University of Toronto, Toronto, Ontario, Canada

Abstract: A fast expert system for electrocardiogram (ECG) arrhythmia detection has been designed in this study. Selecting proper wavelet details, the ECG signals are denoised and beat locations are detected. Beat locations are later used to locate the peaks of the individual waves present in each cardiac cycle. Onsets and offsets of the P and T waves are also detected. These are considered as ECG features which are later used for arrhythmia detection utilizing a novel fuzzy classifier. Fourteen types of arrhythmias and abnormalities can be detected implementing the proposed procedure. We have evaluated the algorithm on the MIT–BIH arrhythmia database. Application of the wavelet filter with the scaling function which closely resembles the shape of the ECG signal has been shown to provide precise results in this study.

Keywords: ECG, beat detection, Daubechies wavelets, fuzzy rules, fuzzy relational classifiers

1. Introduction Electrocardiogram (ECG) analysis is widely used in the diagnosis of many cardiac disorders. One cardiac cycle in an ECG signal consists of the P–Q–R–S–T waves. The development of rapid and automated methods for arrhythmia (abnormal beats) detection is of value, especially for the analysis of long recordings of patients with coronary diseases. Holter, ambulatory and remote monitoring systems may benefit from fast automated analysis. These systems consist of embedded architectures which require fast, 180

Expert Systems, July 2010, Vol. 27, No. 3

memory insignificant ECG analysing methods (Hu et al., 2007). We have designed and implemented fast and practical methods (Hu et al., 2007) to extract ECG signal features and classify different heart beats. Feature extraction provides most of the clinically relevant information in the ECG signal derived from the analysis of the P–Q–R–S–T waves throughout the cardiac cycle. Algorithms for ECG feature extraction are difficult to produce due to temporal variations from physiological conditions and the presence of noise (Addison, 2002). Beat or QRS complex detection is the most important part of c 2010 The Authors. Journal Compilation c 2010 Blackwell Publishing Ltd

an ECG feature extraction system (Huzar, 1988; ing in medical diagnosis. If the knowledge of a Reddy, 2000; Addison, 2002). R wave peak is physician or expert can be expressed in the form the QRS complex designator, so peak detection of an IF X THEN Y rule, easy computer algorithms are required. An excellent review of implementation is possible; extensive work has ECG peak detection algorithms is presented by now been performed on expert systems (Wang, Reddy (2000). Different shapes of the ECG 1985). It is critical to know how to deal with signal waves along with their high variations in ambiguity arising within the diagnostic process amplitude make their detection very demanding (Terano et al., 1992). Adlassnig et al. (1985) (Reddy, 2000). A single feature extraction meth- proposed a system in which X and Y of the rule od is not adequate for processing all sorts of are fuzzy propositions and ambiguity is left in. data, and classifiers are recommended for In this study, fuzzy classifiers are utilized to categorize ECG arrhythmias. Fuzzy classifier further processing (Sedaaghi, 1998). Fourier transform analysis provides the signal implementations are quite fast with respect spectrum or range of frequency amplitudes with- to their other counterparts (Kuncheva, 2000; in the signal; however, Fourier transform only Sapozhnikova & Rosenstiel, 2003) which is of provides the spectral components, not their tem- critical importance in processing long ECG poral relationships. Wavelets can provide a time records and ambulatory systems (Mahmoodaversus frequency representation of the signal badi et al., 2005b). (Grap, 1995; Mahmoodabadi et al., 2007b) and work well on non-stationary data. Wavelets also overcome the preset resolution problem of the 2. Materials short time Fourier transform by using a variable length window (Qian, 2001). The large number of 2.1. Matrix framework for multi-resolution decomposition and reconstruction different wavelet functions provides a rich space to search for wavelets which could efficiently Multi-resolution representation and wavelet represent a signal of interest. Although there are transformation allows signal information to be some methods available in order to select the best classified at different scales in a scale-space dowavelet for an application (Grap, 1995), no main. Different scales represent various aspects absolute solution has been reported. The ortho- of the signal so they correspond to different gonal Daubechies wavelet family, specifically resolution versions of the original signal. The Db4 and Db6, are used here (Daubechies, 1992). multi-resolution pyramid framework has been The Haar wavelet algorithm (Grap, 1995) is utilized here for the two-level dyadic subband computationally simple in comparison, but Dau- tree using Daubechies wavelet filters. The decombechies wavelets pick up more detail. Algorithms position process is accomplished by multiplicaused in implementing the discrete wavelet trans- tion of matrix A (transformation matrix) at m form (DWT) are even faster than the fast Fourier scale m with input data vector of length N (N is transform under the same conditions (Grap, even), i.e. A :S ¼ S~ where S and S~ are the m in out out in 1995). We have implemented the DWT in order input and output respectively of the data vector to extract ECG signal features (Mahmoodabadi of length N. At scale m ¼ 1, the output vector et al., 2005a). consists of two different outputs of the low-pass Biomedical signal processing algorithms re- and high-pass filters named the scaling coeffiquire appropriate classifiers in order to best cients c and the detail coefficients d as follows: 1k 1k categorize different ECG signals. In 1976, Shortliffe presented an early computer-aided ½A ½s ; . . . ; s T¼½c ; . . . ; c ; ; . . . ; c 1 1 N 11 1k 1N=2 ; diagnostic system for diagnosis and treatment of symptoms of bacterial infection. Koyama d11 ; . . . ; d1k ; ; . . . ; d1N=2 T ð1Þ and Kaibara (1984) also presented initial de1rkrN=2 scriptions about the role of knowledge engineer c 2010 The Authors. Journal Compilation c 2010 Blackwell Publishing Ltd


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(Addison, 2002) can be achieved by adding all low resolution signals plus the detail signals up to a certain scale M.

At each step the low-pass version of the original signal is divided into a low- and a high-pass band and the detail signal will be kept. By repeating this procedure m times the scaling coefficients at scale m are provided. In order to reconstruct the original signal at different resolutions from the scaling coefficients, the related wavelet coefficients must be interpolated to become the detail signals and then added to the interpolated scaling coefficients. The reconstructed signal can be achieved by multiplying the inverse of matrix Am with the output data vector of T ~ ~ analysis stage S~out , i.e. Sin ¼ A1 m qSout ¼ Am Sout . Because of the orthogonality of matrix Am (as in the case of Daubechies wavelets), its inverse is simply its transpose. It can be simply shown (Qian, 2001) that the approximated output at discrete location k can be written as X Fm ð2m k nÞc"m ðnÞ m 2 N ð2Þ Sm ðkÞ ¼

Sin ðkÞ ¼ SM ðkÞ þ

M X

D Sm ðkÞ

ð4Þ

m¼1

We will select the proper details of the ECG signal and use equation (4) to extract the signal features. 2.2. Fuzzy classification

n

where Sm is the approximated or blurred version of the original signal with n samples at scale m and Fm is the proper scaling function. c"m is the upsampled version of the scaling coefficients. This is the main equation which has been used here to perform the approximation of the original signal at different resolutions in the proposed multiresolution pyramid framework. In a similar way, by putting zeros instead of the scaling coefficients in equation (2), the detail signal at scale m can be expressed as X D ðkÞ ¼ Cm ð2m k nÞdm" ðnÞ m 2 N ð3Þ Sm

Crisp sets have a rather strict sense of membership; in contrast, fuzzy sets have many degrees of allowed membership. The degree of membership to a set is indicated by a membership function. The inference section of a fuzzy classifier implements expert designed fuzzy rules. Fuzzy rules consist of antecedent and consequent parts in the form of IF–THEN clauses. Depending on the type of antecedent and consequent parts, two main fuzzy IF–THEN systems exist: the Mamdani–Assilian (MA) system and the Takagi–Sugeno–Kang (TSK) system (Kuncheva, 2000). While the antecedent and consequent parts in MA systems are Boolean expressions, in TSK systems the consequent part is a function of the input. If we assume that all the features are used in the antecedent part of the nr fuzzy rules, for the rule Rk we can write in the case of MA systems Rk: IFfx1 is Lx1 g AND fx2 is Lx2 g AND f. . .g THENfy1 is Ly1 g AND fy2 is Ly2 g

n D is the proper detail signal, Cm is the where Sm wavelet function at scale m or resolution 2m and dm" is the upsampled version of the wavelet coefficients. The proper wavelet functions at different resolutions can be obtained by using equation (3). This equation has been used here to compute the wavelet functions at different scales. Thus, two basic equations to compute the approximation of signals and the detail signals at each resolution in the multi-resolution pyramid decomposition and reconstruction framework have been derived. Because of the orthogonality of the wavelet transform, a perfect reconstruction

182


AND f. . .g

ð5Þ k ¼ 0; 1; . . . ; nr

where x and y are the corresponding inputs and outputs and the parameter k looks for any selection of the linguistic terms in Lx and Ly fuzzy sets (Kuncheva, 2000). TSK fuzzy systems have the following rule format: Rk: IFfx1 is Lx1 g AND fx2 is Lx2 g AND f. . . g THENY ¼ f ðXÞ

ð6Þ k ¼ 0; 1; . . . ; nr

c 2010 The Authors. Journal Compilation c 2010 Blackwell Publishing Ltd

where f is a function with respect to the input vector X which generates the output vector Y. Fuzzy IF–THEN systems along with their characteristics and fuzzy rules are not as easy to codify as neural networks, where processes like matrix operation and iteration are predominant (Welstead, 1994); however, fuzzy relational classifiers (Kuncheva, 2000) can be easily implemented. A design example of a fuzzy inference system in order to process the ECG signal is provided in Berkan and Trubatch (1997). Here we describe a design method presented by H.J. Zimmermann (1976; Zimmermann & Setwstian, 1994) and incorporated in the form of a fuzzy relational classifier. The constraints for a fuzzy problem may be given as XtB by implementing the fuzzy inequality t. It means that all the elements of vector X are about their respective elements of vector B, and they are all positive. If we consider the ith element of the vector xi and the membership function mi(xi) for the fuzzy inequality less than or about a value bi with the maximum possible value for the right-hand side of the inequality di, then 8 xi rbi < mi ðxi Þ ¼ 1 0rmi ðxi Þr1 bi < xi < bi þ di XtB : xi Zbi þ di mi ðxi Þ ¼ 0 ð7Þ In addition, let wij define the connection between xi and yj and its weight. 2 3 w11 w1no 6 7 6 .. 7 W ¼ 6 ... Wij . 7 4 5 w1ni

wni no

ð8Þ

X ¼ ½x1 ; . . . ; xi ; . . . ; xni Y ¼ ½y1 ; . . . ; yi ; . . . ; yno where W, X and Y represent the weight matrix and the input and output vectors, respectively, and i ¼ 1, . . ., ni and j ¼ 1, . . ., no where ni and no are the total number of input and output units, respectively. Using fuzzy notations, the fuzzy

relation (Kuncheva, 2000) is defined as Y ¼ XoW

ð9Þ

where o stands for the process involved and usually contains a few fuzzy related operations. Depending on the diagnostic process, we may find Y from X and W. In words we may say that xi ¼ 1 when primary factor i happens, yj ¼ 1 when symptom j shows up, and when both of them appear at the same time, then wij ¼ 1. In order to achieve the inference process, we calculate the input and output weights of the rules and find the inference results for each of them; the final weighted mean of the result is found next. Now, consider xi as an input to the fuzzy set with mi(xi); if the corresponding weight is wij, then the weighted-mean membership function of the output yj is found using the equation P i wij mi ðxi Þ ð10Þ mj ðyj Þ ¼ P i jwij j i ¼ 1; . . . ; ni ; j ¼ 1; . . . ; no In order to conclude the inference process, the result of the fuzzy relation and the defuzzification scheme y can be defined as y ¼ fyj jmj ðyj Þ ¼ maxf[n1o mj ðyj Þgg

ð11Þ

where the operation maxf g looks for the output yj in the union [mj ðyj Þwith the highest membership function. There are three different inference methods which each has its own type of membership function (Welstead, 1994). Piecewise linear trapezoidal membership functions are often selected for ease of use with acceptable results with respect to others (Welstead, 1994). These functions may appear anywhere with respect to the vertical axis while the regular type should not be symmetrical (Terano et al., 1992). They have a maximum of 1 and their shape is determined by the points that define the linear segments (Welstead, 1994). Membership functions define fuzzy sets and are the mechanism through which the fuzzy system interfaces other systems. Input values to membership functions might be any range of possible values for a given



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variable, but the output is usually normalized to be between 0 and 1 (Tsoukalas & Uhrig, 1994; Welstead, 1994) or 1 and 1 (Tsoukalas & Uhrig, 1994). The proposed fuzzy classifier which is used to categorize arrhythmias is designed based on equation (7).

3. Methods We wished to design a fast and efficient algorithm to extract ECG features and detect arrhythmias. Wavelet analysis is a proper choice for ECG signal analysis and fuzzy classifiers are well suited for ECG arrhythmia detection. The algorithm consists of two parts: ECG feature extraction, and classification (Figure 1). Basic

ECG signal features are extracted first utilizing wavelet transforms including P, Q, R, S, T waves and the zero level of the signal. These basic features are used later to evaluate the six final classification related features: QRS, P–R, R–R time intervals, heart rate, R–R time interval variation and T–P and S–T interval voltage levels. The final features constitute a feature vector of length 6 which will be the input to the fuzzy classifier. Final features are then compared to medically accepted normal cases using the fuzzy classifier, and arrhythmias will be detected accordingly. Only a certain number of known arrhythmias (14 out of 24) (Huzar, 1988; Conover, 1996; Springhouse, 2005; Guyton & Hall, 2006) were selected for analysis which are easier to detect implementing our algorithm: FDB, first degree block; SDB-I, second degree block type I; TDB, third degree block; Myo. In., myocardial injury; Isc., ischaemia; VER, ventricular escape rhythm; SAr, sinus arrest; SB, sinus bradycardia; ST, sinus tachycardia; VF, ventricular fibrillation; VT, ventricular tachycardia; VA, ventricular asystole; PAC, premature atrial contraction; and PVC, premature ventricular contraction. Four other types of arrhythmias are also studied for further comparisons: AT, atrial tachycardia; AFr, atrial flutter; AFn, atrial fibrillation; and SDB-II, second degree block type II. 3.1. Feature extraction

Figure 1: The schematic feature extraction and classification procedure presenting premature ventricular contraction arrhythmia as a sample output. QRS, P–R, R–R, between waves time intervals; HR, heart rate; RRV, R–R time interval variation; T–P and S–T, interval voltage levels; FDB, first degree block; SDB-I, second degree block type I; TDB, third degree block; Myo. In., myocardial injury; Isc., ischaemia; VER, ventricular escape rhythm; SAr, sinus arrest; SB, sinus bradycardia; ST, sinus tachycardia; VF, ventricular fibrillation; VT, ventricular tachycardia; VA, ventricular asystole; PAC, premature atrial contraction; PVC, premature ventricular contraction. 184


Selecting a wavelet function which closely matches the signal to be processed is of utmost importance in wavelet applications (Grap, 1995). The Daubechies wavelet family is similar in shape to QRS complexes and their energy spectra are concentrated around low frequencies (Addison, 2002). Figure 2(a) shows the details d21–d28 of a sample ECG signal x[n] with a short burst of noise at its end using Db4 wavelets for decomposition. Db6 wavelet application is also utilized and the result is shown in Figure 2(b). Below the original signal (at the top of the plot), the eight subsequent wavelet details are shown and are scaled for better illustration. Addition of all these details plus the remaining c 2010 The Authors. Journal Compilation c 2010 Blackwell Publishing Ltd

Figure 2: Multi-resolution decomposition of a sample ECG signal containing a short burst of noise: (a) using Db4; (b) using Db6; (c) signal approximation using Db6. Source signal x[n], and signal details at scale m (d2m). The () refers to the absence of the corresponding detail signals. signal approximation (which is the signal mean) returns the original signal. Four points are noticeable from the plots. First, important characteristics of the signal (the ECG morphology and beat locations) are contained at detail d25. The second point is that the high frequency burst of noise is captured at the low order details, namely d22 and d21. The third is the similar appearance of the signal at higher details to Daubechies Db4 and Db6 scaling functions. The last one is the greater similarity of Db6’s scaling function to QRS complexes than Db4’s which can be seen by the detail of the signal at d25. Because of the latter property the Db6 wavelet is implemented here (Mahmoodabadi et al., 2005a). While the higher coefficients of the Daubechies wavelet family with higher filter widths (Press et al., 2002) are quite small in value, it is not practical to use them as it increases computation overheads. The number of computations of the fast Fourier transform algorithm is known to be nFFT ¼ N log2 N where N is the number of samples of the signal. According to the matrix implementation of the Daubechies wavelets (Press et al., 2002), we can compute the number of computations of the DWT. The total number of computations will be nDb4 ¼ 7N

and nDb6 ¼ 11N in the cases of Db4 and Db6 respectively. The number of computations is 1.57 times Db4’s in the case of Db6 implementation. We can easily see that the number of computations is less than for the fast Fourier transform if the number of samples is greater than 128 (25) and 2048 (29) in the cases of Db4 and Db6, respectively. In this study, the ECG signals consist of about 650,000 data samples per record. Figure 2(c) describes the approximation of the signal shown in Figure 2(b). The signal is approximated by omitting the details beginning from d21 to d28. It is clear that high frequency components (abrupt signal transitions and noise) decrease as low details are removed from the original signal. As these details are removed, the signal becomes smoother and the noise on the T wave disappears. We can also see that the peaks of QRS complexes flatten while P and T waves, containing lower frequencies, become more visible. If the frequency distribution of the details is provided, appropriate understanding of the wavelet transform can be achieved. A normal ECG signal x[n] along with its frequency distribution X[n] is shown in Figure 3. The signal is sampled at 360 samples per second and the range of the real frequency component of



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Figure 3: A normal ECG signal x[n] and its frequency distribution X[n]. The frequency distributions of Db6 wavelet details at scale m (d2m) are also shown.

the signal will be between 0 and 180 Hz. A normal signal is used here to omit the interference of abnormal beats in the frequency distribution. The frequency response of the original signal shows its main concentration in the lower frequency range 2–40 Hz. The signal had a large DC component which was eliminated to enhance other frequency components. The frequency responses of the Db6 wavelet details are also presented, normalized to clearly show their distribution. It can be seen that low details of the signal contain the higher frequency components. Although the frequency components of the details do not overlap each other up to d23, they obviously overlap at higher details; while working with details of the signal, these overlaps among discrete distributions should be taken into consideration. The last plot shows the overall frequency response of the signal approximation including details up to d28. The plot matches the frequency response of the original signal well, and the DC and low frequency components which are not of concern in processing are removed. 186


The algorithms presented in this section are applied directly in one run over all the digitized ECG signals that are available from the MIT–BIH arrhythmia database [class 1, core] (AAMI, 1999). There are four separate algorithms, each of which is designated to extract certain features of the ECG signal. First, the peaks of the QRS complexes with their high dominated amplitude in the signal were registered. Then Q and S waves were detected. The zero voltage level of the signal was found next. P and T waves along with their onsets and offsets were the last features to be found. The process is shown in Figure 4(a).

3.1.1. R wave detection ECG signals from Modified Lead II (MLII) (Huzar, 1988) were chosen for processing. Peaks of the R waves in signals from the MLII leads have the largest amplitudes amongst the other leads (Guyton & Hall, 2006). Two sample ECG signals are shown in Figure 5(a). In order to detect the peaks, specific details of the signal were selected. The


Figure 4: Description of the ECG feature extraction algorithm: (a) the overall procedure; (b) R wave detection; (c) Q and S wave detection; (d) zero level detection; (e) P and T detection. The ECG signal is the input from the left. F-DWT and R-DWT stand for forward and reverse wavelet applications. procedure of the R wave detection algorithm is shown in Figure 4(b). Details of the signal in the range d23–d25 were kept and all the other details were removed (Figure 5(b)). Although this range was found to be the most practical choice, we can give good reason by looking at the multiresolution decomposition of the signal. As seen in Figure 2(c), removing the signal details below d23 does not affect the beat locations (peaks), while low frequency components of the signal do not contribute to R peaks either. Following this procedure, the DC and low frequencies along with very high frequency components were removed (Figure 5(b)). The attained signal samples were squared (Figure 5(c)). High amplitude transitions of the signal were then more noticeable, even if the R peaks were deformed

(Figure 5, right). Then an empirically selected lower threshold was applied to remove unrelated small amplitude peaks. The proper threshold value was determined to be 1.5% of the maximum detected peak’s value which was found throughout the record under consideration; the small threshold value seems quite interesting. Later, because no subsequent beats may happen within less than 0.25 seconds, pseudo-beats were also removed. It should be emphasized that the R peaks define cardiac beats, and the precision of all forthcoming algorithms depends on their accurate detection.

3.1.2. Q and S wave detection Q and S peaks, which are characteristics of their waves, occur



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Figure 5: Detection of P–Q–R–S–T waves of two samples (A and B) of ECG signal: (a) original signal; (b) removing DC and low frequencies; (c) detecting R peaks; (d) detecting Q and S waves along with zero levels of the signal; (e) detecting P and T waves. around the R peak within 0.1 seconds (Guyton & Hall, 2006). In order to make these peaks noticeable (Figure 5(d)), all the details of the signal up to detail d25 were removed. Mid and low frequency components of the signal contribute to these waves. Figure 3 shows that frequency components of the details over (and including) d25 have less overlap with those of the lower details. In this case the unrelated details of the signal may be removed in order to pick up Q and S peaks. The approximation signal that remained was later searched for extremum (maximum or minimum) points about the previously determined R peaks. The left point denotes the Q peak while the right one denotes the S peak. The procedure of Q and S wave detection is shown in Figure 4(c).

3.1.3. Zero level detection One would think that the electrocardiograph machines used 188


for recording ECGs could determine when no current is flowing around the heart. However, many stray currents exist in the body, including currents resulting from skin potentials and from differences in ionic concentrations elsewhere in the body. These stray currents make it impossible for one to predetermine the exact zero reference level in the ECG. At the end of the QRS complex, no current is flowing around the heart. Even the current of injury (Guyton & Hall, 2006) disappears at this point and the potential of the ECG at this instant is zero. This point is known as the J point. Most people, however, are conditioned to consider the T–P segment of the ECG as the reference level rather than the J point which is much easier to detect properly (Guyton & Hall, 2006). The zero crossings (Mallat, 1991) of the approximation signal represent the slope change while the DC and low frequency components are removed. If we generate the approximation signal by keeping c 2010 The Authors. Journal Compilation c 2010 Blackwell Publishing Ltd

details d21–d25, we are removing all low frequency components while keeping the important characteristics of the signal unchanged. We have found that the voltage of the ECG signal at the T–P level is closely equal to the one at the zero crossing of this approximation signal before the Q peak. There is another zero crossing after the S peak. The voltage at this point shows the S–T level of the signal. Comparing the voltage levels at these two points is essential for detecting what is called the S–T segment shift (Figure 5(d)) (Guyton & Hall, 2006). Detecting the shift is required for diagnosis of ischaemia and myocardial injuries. The detection procedure of the zero level is shown in Figure 4(d).

3.1.4. P and T wave detection These waves are characterized by their peaks and rising=falling times (Figure 5(e)). They contain low frequencies and are more noticeable while keeping details in the range d24–d28. Very low frequencies and high frequency ripples of the signal are removed in this range (Figure 3). The extrema of the approximation signal before and after the zero crossings about each R peak are selected as the P and T peaks. Later, the zero crossings of the same approximation signal about the P and T peaks are characterized as the onset and offset points of the waves, respectively. The detection procedure of the P and T waves is shown in Figure 4(e). 3.2. Classification In order to identify arrhythmias, we have to classify different heart beats. In practice, we are only provided with the signal under test; then it is not possible to judge the classification result with extra knowledge of the expected output required by supervised approaches. Supervised neural networks need appropriate training in order to shape the input–output mappings of the networks according to a given data set. These networks cannot respond correctly to unpredictable and abrupt changes encountered in patients because of varying shapes of arrhythmias. One big concern for any neural network

(supervised or unsupervised) is its learning time. Time is needed to adjust weight functions and this procedure should be done separately for every input ECG signal. The classification procedure may contain huge errors as a result of improper learning routines. We recommend that the weight functions be predetermined with respect to different classes of the signal in order to eliminate the learning process. According to the ECG standard (AAMI, 1999), the maximum beat detection time allowed is somewhat greater than the time span between two beats or 1 second, which is the capability of fast fuzzy classifiers. With no learning, designing a proper network (weight functions and number of nodes at different layers) and selecting correct inputs are of high concern, because the network will be unable to adapt itself to each situation, which is the required capability of neural networks. As was mentioned before, the application of fuzzy sets is firmly tied to human judgement; therefore the study of human behaviour towards a problem is very important to achieve reasonable results. Here we have proposed a novel fuzzy feedforward network which is designed with the idea of competitive neural networks (Terano et al., 1992) in order to classify different ECG beats. Evaluation of ECG signals for arrhythmia detection contains the determination of several characteristics of the signal. Identifying these factors puts forward the rules for proper classification of the different heart beats. If we look through medical texts (Huzar, 1988; Conover, 1996; Springhouse, 2005) we see that ECG evaluation systematically consists of nearly ten steps.


1. P–P: variation of different P–P interval durations (atrial rhythm) 2. R–R: variation of different R–R interval durations (ventricular rhythm) 3. Atrial rate: number of occurrences of P waves per minute 4. Ventricular rate: number of occurrences of R waves per minute 5. P wave: existence, configuration, size and shape of P wave for every QRS complex Expert Systems, July 2010, Vol. 27, No. 3

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6. P–R: the time duration between successive P and R waves in each beat 7. QRS duration: the time duration from the beginning of the Q wave to the end of the S wave 8. T wave: existence, shape, amplitude and its deflection with respect to its QRS in a heart beat 9. QT interval duration: this is the time from the beginning of the Q wave to the end of the T wave 10. ST segment shift: it might be elevated or depressed When any combination of these factors is not within normal range, one can recognize the type of arrhythmias related to an ECG beat; not all of these factors have to be checked in order to identify all the medically recognized arrhythmias. Here we have implemented six out of ten of these factors in order to detect 14 out of 24 medically recognized arrhythmias. Table 1 defines the conditions of a normal beat and 18 different arrhythmias. This presentation recommends the use of fuzzy classification. In order to express this information in a form suitable for fuzzy classifier implementation, we have designed novel piecewise continuous trapezoidal fuzzy membership functions and the defuzzification scheme shown in Figure 6, as in Welstead (1994). ECG final features available from the feature extraction section constitute the universe of discourse (input). They are also arranged in up to five ordinal groups: very low (VL), low (L), normal (N), high (H) and very high (VH). The normal groups are located in the middle and have the membership value of 0. The other groups have a value selected from the graph. L and VL are negative while H and VH are positive. These values will be further explained when weight values are introduced later. The fuzzy membership functions are used in a fuzzy feedforward network, displayed in Figure 7. The network from the left consists of the input unit which includes the ECG final features, fuzzy input membership functions, discrete weight functions, the output membership functions, the 190


fuzzy output unit and the defuzzification unit which prepares the classification result. The network has been designed according to the procedure described by Berkan and Trubatch (1997). ECG final features provided from the feature extraction section are mapped into the range [ 1 1] using fuzzy input membership functions (Figure 6). The resulting values will be multiplied by the corresponding weight functions and the output fuzzy membership function is calculated using equation (10). The weight values wij are represented as a matrix, shown in Figure 7. Weights represent the degree to which a feature is related to an output unit and are chosen by medical consultations and review of textbooks (Huzar, 1988; Conover, 1996; Springhouse, 2005; Guyton & Hall, 2006). Fourteen classes resembling different arrhythmias are included in the output unit. In the defuzzification unit, each output node (class) which is assigned a greater value is considered as the winner class and will then present the result of the fuzzy classification (or the detected arrhythmia); this process is accomplished implementing equation (11).

4. Results and validation The MIT–BIH arrhythmia [class 1, core] database consists of 48 ECG signal records. Records are identified by three-digit record names. Each record comprises several files, the signals, annotations and specifications of signal attributes. MIT–BIH database records are each 30 minutes in duration, and are annotated throughout; by this we mean that each beat is described by a label called an annotation. Typically an annotation file for an MIT–BIH record contains about 2000 beat annotations, and smaller numbers of rhythm and signal quality annotations (AAMI, 1999). The test protocols described in EC57 standard (AAMI, 1999) require that, for each record, the output of every arrhythmia detection algorithm is recorded in an annotation file (the test annotation file), in the same format as the reference annotation file for that record. The programs released by MIT or equivalent should be used to perform the comparisons between the c 2010 The Authors. Journal Compilation c 2010 Blackwell Publishing Ltd



191

Normal Normal – > 0.16*

Normal > 0.16 – – – Normal > 0.16 Normal Normal – Normal – > 0.16 –

< 0.16

R–R interval variation (s)

0.24– 0.4 – – Normal

Normal 0.33– 0.6 Normal – – >1 > 0.33 >1 0.33– 0.6 – – – 0.33– 0.6 –

0.6–1

– Normal 0.33– 0.6 >1

Normal 0.33– 0.6 >1 – – >1 > 0.33 >1 0.33– 0.6 1–1.5 Normal > 1.5 0.33– 0.6 –

0.6–1

P–P R–R interval interval duration (s) duration (s)

150–250 250–400 > 400 Normal

Normal Normal Normal – – < 40 Normal < 60 100–180 – – – – –

60–100

Atrial rate (1=s)

– Normal 100–180 Normal

Normal Normal < 60 – – < 40 Normal < 60 100–180 – > 100 – – –

60–100

Ventricular rate (1=s)

– Abnormal Abnormal Normal

Present, upright, round and smooth in lead II Normal Normal Normal – – Normal Normal Normal Normal – Absent – – Absent

P wave

– – – Normal*

> 0.2 > 0.2 – – – Normal Normal Normal Normal – – – – –

0.12–0.2

Normal Normal Normal Normal

Normal Normal Normal* – – > 0.1 Normal Normal Normal – > 0.1 – Normal > 0.1

0.06–0.1

QRS P–R interval interval duration (s) duration (s)

Q–T interval duration (s)

Normal Normal Normal – – Normal Normal Normal Normal – – – – Opposite in direction to QRS – – – Normal

Normal – – Normal

Normal Normal Normal – – – Normal Normal Normal – – – Normal –

Present, 0.36–0.44 upright and round in lead II

T wave

– – – –

– – – > 0.1 < –0.05 – – – – – – – – –

–0.05–0.1

ST segment shift (mV)

FDB, first degree block; SDB-I, second degree block type I; TDB, third degree block; Myo. In., myocardial injury; Isc., ischaemia; VER, ventricular escape rhythm; SAr, sinus arrest; SB, sinus bradycardia; ST, sinus tachycardia; VF, ventricular fibrillation; VT, ventricular tachycardia; VA, ventricular asystole; PAC, premature atrial contraction; PVC, premature ventricular contraction. Four other types of arrhythmias are also studied for further comparisons: AT, atrial tachycardia; AFr, atrial flutter; AFn, atrial fibrillation; SDB-II, second degree block type II. –, not applicable, exactly discernable or measurable. * The common case.

Normal Normal – Normal

Normal Normal – – – Normal > 0.16 Normal Normal – – – > 0.16 –

FDB SDB-I TDB Myo. In. Isc. VER SAr SB ST VF VT VA PAC PVC

AT AFr AFn SDB-II

< 0.16

P–P interval variation (s)

Normal

Beat type

Table 1: Clinical characteristics of the ECG signal used in designing the fuzzy classifier

Figure 6: Designed fuzzy input membership functions: very low (VL), low (L), normal (N), high (H) and very high (VH). test annotation files and the reference annotation files. The analysis results for 46 records using the ‘bxb’ program (AAMI, 1999) are shown in Table 2. The table provides the accuracy of the beat detection algorithm implementing Db4 and Db6 wavelets. The classification 192


results (arrhythmia detection) are reported utilizing the Db6 wavelet; not all the records contain the selected arrhythmia. The results for the other two records (102 and 104) of the database were not reported, because they do not contain the signal of the MLII lead. Beat c 2010 The Authors. Journal Compilation c 2010 Blackwell Publishing Ltd

Figure 7: The fuzzy feedforward network designed for ECG arrhythmia detection. Weight functions are presented in a 14 6 matrix.

detection outcomes in the case of Db6 implementation were shown to surpass Db4 implementation.

5. Discussion and conclusion The proposed wavelet based detector, in contrast to most QRS detectors found in the literature,

takes advantage of characteristics of time-scale analysis (Mallat, 1991). Implementing multiresolution analysis was our basic tool. We have utilized successive stripping of the signal (signal details, d2m) with the combination of signal approximation in order to extract the ECG signal features. The Db6 family of Daubechies wavelets showed superior results to Db4 as can easily be seen in the region of convergence scatter plot, shown in Figure 8. This supports the fact that the best option in wavelet signal processing is to select a mother wavelet which is similar to the signal under test. With reasonable results in the case of Db6 application, there would be no need to implement other Db wavelet family members with higher filter widths and their higher computation cost. Most signal details are contained at lower scales (mostly scale d25) which need less decomposition, so faster application of the wavelet is possible. The absence of very low ( 40 Hz) frequency concentrations of the ECG signal helped us to denoise the signal from movement artifacts and external interfering noises easily. Preserving certain details (scales d23–d25) and squaring of the remaining signal approximation resulted in appropriate detection of R peaks. The peaks were detected among several types of beats in signal durations longer than 30 minutes. Choosing certain details (d21– d25) of the signal made Q and S peaks easy to find. The zero level detection procedure described above helped us to find the ST segment shift without the need for the very challenging J point detection. Again, using certain details (d24–d28) made P and T waves more noticeable. The proposed wavelet based feature extraction system achieved good detection performance on the MIT–BIH database. All the formerly proposed (to the best of our knowledge) algorithms are based on beat by beat detection which is more easily achievable (Li et al., 1995; Sahambi et al., 1997; Silipo et al., 1999; Li et al., 2003; Prasad & Sahambi, 2003). In the time consuming beat by beat approach, a limited time interval (usually within a second) is selected for processing. Later, the maximum of the signal, the R peak, is recognized. The R peaks may



193

Table 2: Sensitivity (Se) and positive predictivity (PP) results of the algorithm Beat detection Db6

Arrhythmia detection

Db4

Myo. In.

Isc.

PAC

PVC

Type Rec.

Se (%)

PP (%)

Se (%)

PP (%)

Se (%)

PP (%)

Se (%)

PP (%)

Se (%)

PP (%)

Se (%)

PP (%)

100 101 103 105 106 107 118 119 200 201 202 203 205 207 208 209 210 212 213 214 215 217 219 x_108 x_109 x_111 x_112 x_113 x_114 x_115 x_116 x_117 x_121 x_122 x_123 x_124 x_220 x_221 x_222 x_223 x_228 x_230 x_231 x_232 x_233 x_234 Ave

100 99.87 100 82.77 99.63 99.94 100 100 99.91 100 99.94 94.73 99.49 99.62 96.67 99.92 97.99 100 99.96 99.73 99.63 99.94 94.52 99.12 100 100 100 100 100 100 100 100 100 100 100 100 99.77 100 100 100 99.06 100 100 100 100 100 99.18

100 99.61 100 81.49 99.94 96.96 99.67 99.07 98.2 99.64 99.87 98.76 99.95 98.73 96.99 100 99.71 100 100 99.84 100 96.34 94.3 93.13 99.81 99.76 98.48 79.55 99.41 100 100 86.93 99.19 100 100 100 100 100 99.55 100 96.79 99.79 100 96.77 99.84 100 98.00

49.05 51.2 48.28 49.36 50.48 54.46 47.86 49.65 49.91 45.45 50.95 49.17 49.48 51.03 49.39 47.52 49.53 47.3 49.81 50.26 50.09 49.42 48.7 40.98 38.12 35.36 34.08 34.04 38.46 29.7 38.39 32.43 32.89 33.33 40.74 34.86 33.15 35.35 33.16 33.64 35.52 32.47 34 41.03 32.58 37.82 42.40

37.74 31.15 34.67 42.65 35.26 37.16 37.83 34.57 43.4 37.88 32.56 50.53 44.69 34.38 49.55 50 44.49 44.33 53.37 38.07 57.72 36.08 35.41 22.22 33.86 25.3 29.12 19.59 21.13 20.85 31.52 15.89 19.61 29.51 20.25 19.59 22.99 28.79 23.68 29.08 24.71 25.61 19.84 25.81 33.73 35.16 33.07

– – – – – – – – – – – – – – – – – – – – – – – – – – – 100 – 100 – 100 – – – – – – – – 99.06 – – – – – 99.77

– – – – – – – – – – – – – – – – – – – – – – – – – – – 78.03 – 100 – 86.30 – – – – – – – – 94.90 – – – – – 89.81

– – – 82.77 99.63 – – – 99.91 – – 94.73 – – – – 97.99 – 99.96 – – – 94.52 – – – 100 – 100 – – – – – – – 99.77 100 – 100 – – – – 100 100 97.81

– – – 81.2 99.32 – – – 98.10 – – 98.65 – – – – 98.96 – 100 – – – 93.25 – – – 97.88 – 99.39 – – – – – – – 100 100 – 100 – – – – 99.71 100 97.60

100 100 100 – 91.74 – 100 – 56.03 – 88.89 – 44.44 – – 100 – – 1.69 – 33.87 – 93.18 33.33 – – – – – – – – – – – – 100 – – 100 – – 100 100 100 – 80.17

100 100 100 – 97.49 – 14.52 – 100 – 3.32 – 100 – – 100 – – 100 – 100 – 12.69 11.11 – – – – – – – – – – – – 100 – – 100 – – 66.67 100 100 – 78.10

– – – 77.14 – 23.81 75 99.18 55.14 – 88.89 49.06 45.95 71.29 26.53 0 50.98 – 1.67 93.81 33.33 48.23 80.39 33.33 0 – – – 0 – 91.67 – – – – 5 – 97.85 – 31.58 41.33 – 100 – 74.4 – 51.69

– – – 55.1 – 16.13 13.85 95.76 95.97 – 3.31 33.7 89.47 94.74 89.08 0 50 – 20 98.5 97.67 49.28 12.62 9.09 0 – – – 0 – 100 – – – – 33.33 – 94.79 – 60 72.09 – 66.67 – 96.9 – 53.64

–, not applicable.

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Figure 8: The region of convergence scatter plot for the beat detector.

happen at intervals’ end points which present errors in R peak detection. There might be many low amplitude R peaks in a long duration signal which are not visible, while other high amplitude peaks exist throughout the signal. The novelty of our feature extraction procedure resides in the capability of its application in one run of the algorithm. That is, the whole signal samples are processed all at once, taking the possibility of any kind of beats into account. The algorithm is faster than those which are based on spectral analysis using Fourier transform and comply with the EC57 standard. It also provides us with the results using lower wavelet details, allowing fewer wavelet decompositions. The required learning time for the algorithm is omitted by not implementing neural networks. The MIT–BIH database is not annotated for P, Q, S, T waves and S–T segment shift detections, although the database is annotated for the PVC arrhythmia which is closely dependent on R and P wave detections (Conover, 1996). As the R detection output of the algorithm is reasonably high, we expect that the result for P detection will be closely equal to or higher than the result of the PVC detection algorithm.

Feature extraction using Daubechies wavelets was the basic step for automatic ECG arrhythmia detection. Fast fuzzy classifiers are used for ECG beat type detection and 14 different arrhythmias and abnormalities can be detected using our procedure. These arrhythmias were mainly recognized by our detected final features. Fuzzy membership functions presented here are design specific and novel. It was not possible to evaluate the arrhythmia detection algorithm for all the 14 arrhythmias because they were not all present in the MIT–BIH records. Only five types of beat (including the normal beat) are annotated in the MIT–BIH database (AAMI, 1999). PAC and PVC arrhythmias are among those for which we are able to report our results implementing the standard software. Myocardial injury and ischaemia arrhythmias were also present in some records but the results are not reported using standard software because they were not annotated throughout the database. As the arrhythmia recognition algorithms are highly dependent on precise beat detection, we may easily expect very low values in implementing Db4 wavelets for ECG characteristic detection. We have encountered a high value of standard deviation (nearly 30%) in detecting PVC arrhythmia. This represents the fact that we were not able to detect PVC in some records properly, while we had appropriate results in others. We believe this to be the result of the multiform morphology of the PVC and more features should be required for its appropriate identification. The beat and arrhythmia detection algorithms present precision of over 99.8% for wavelet based methods (Li et al., 1995, 2003; Sahambi et al., 1997; Prasad & Sahambi, 2003; Martı´ nez et al., 2004). Neural networks methods reported a precision of over 96% (Maglaveras et al., 1998; Gamlyn et al., 1999; Silipo et al., 1999; Dokur & Olmez, 2001; Papaloukasa et al., 2002). Time based analyses of the signal showed a precision of over 99.3% (Pan & Tompkins, 1985; Sternickel, 2002). Other approaches implementing support vector machines (SVMs) (Osowski et al., 2002), morphological filters (Sedaaghi, 1998) and knowledge-based systems



195

(Kundu et al., 2000) reported precisions higher than 81%. None of these methods provided algorithms for detection of 14 different types of arrhythmias and they did not follow the standard in order to report their results. The time consuming routines are the other shortcomings for them. The presented algorithms have been applied successfully in low-cost remote cardiac monitoring systems (Hu et al., 2007) which support our procedure. A comprehensive study has been published recently on ECG signal analysis by U¨beyli (2008b). U¨beyli has comparatively investigated the classification procedure and the corresponding outcomes of various algorithms: SVMs (U¨beyli, 2007b), neural networks and mixture of experts (ME) (U¨beyli, 2008a). Various derivations of these algorithms such as probabilistic neural networks (PNNs) (U¨beyli, 2007c), multilayer perceptron neural networks (MLPNNs) (U¨beyli, 2007b) and modified ME (U¨beyli, 2008b) have also been reported. U¨beyli showed that SVMs surpass other methods in total classification. The SVM is a linear classifier in decision making while it is also considered as a nonlinear classifier due to the nonlinear mapping of the data into a high dimensional space. The SVM can be used as a multiclass classifier, but all the SVMs involved in the classification process should categorize the data correctly. Beside the higher complexity of the processing routine of the SVM due to increased dimensionality, the training time of the system is also of concern. The MLPNN has been extensively used in pattern recognition, but combined neural networks (CNNs) and PNN extensions were shown to be more reliable. The CNN utilizes MLPNN at consecutive levels for higher accuracy while a single PNN handles multiclass problems in distinction to a single SVM required per class. ME perceives a problem differently: ME is composed of a gating and several expert networks. It cleaves a classification problem into simpler problems and later combines every solution. The ME’s processing time is higher than for a regular MLPNN. A modified version of ME accepts different input features simultaneously 196


through each expert network and presents the result as a linear combination of every expert network’s output. In support of our approach, U¨beyli indicates that an ECG analysis system should consist of a feature extraction and a classification section for the best result. While the eigenvector method has also been selected as a feature extraction routine, a hybrid implementation of wavelet and fuzzy sets (U¨beyli, 2007a) with close similarity to our approach has been examined by U¨beyli. In the corresponding study, the wavelet coefficients were selected as the features. This is in contrast to our approach where the wavelets have been utilized as filter banks. In our study, wavelets are used for denoising and ECG wave detection with the results later used in the classification step. Although U¨beyli (2007a) showed that Daubechies wavelets with two coefficients (Db2 or Harr wavelet) are more suitable for detecting changes in ECG beats, we showed that Db6 is the appropriate Db family to be used in wavelet filtering. The Db6 detail and scale functions are much more similar to the ECG signal than Db4’s and their corresponding frequency spectrum is also concentrated in the low frequency with less high frequency ripples (Addison, 2002). The Db2 wavelet is totally different in shape from ECG beats. It is simply an edge detection operator and is not suitable for denoising. An edge detector (high pass filter) can emphasize the noise drastically. For denoising purposes a smooth basis function is required. We also support the fact that wavelet analysis is an appropriate tool for time–frequency analysis of non-stationary signals if proper attention has been paid to the frequency content concentration of the details (Figure 3) and their order in the frequency plane (Jensen & Cour-Harbo, 2001; Mahmoodabadi et al., 2007b). As U¨beyli showed, most of the ECG signal characteristics can be acquired at low wavelet details (d21–d24) but we have also found the detail d25 to be beneficial in our study. In contrast to U¨beyli’s approach, our analysis is based on the run-by-run method (AAMI, 1999) which is investigated on a different database of Physiobank, the MIT–BIH [class 1, core] c 2010 The Authors. Journal Compilation c 2010 Blackwell Publishing Ltd

database. Every ECG record in the MIT–BIH database consists of nearly 2000 beats (46 2000 in total) in contrast to the total of 90 beats (4 90 in total) reported by U¨beyli (2008b). The beat detection and the classification outcomes have been reported for every record accordingly. The result of the beat detection algorithm is very close to U¨beyli’s superior method utilizing the SVM, but a considerable number of ECG beats are investigated in our application. In the classification design, we have chosen to emulate the procedure the clinicians follow and prefer for arrhythmia diagnosis. It is usually quite demanding to convince clinicians to accept an engineering approach towards a medical problem. Our classification process is appropriately emulated by fuzzy reasoning which is the closest counterpart to human judgement. Unfortunately, we are not able to compare our classification results due to the different types of arrhythmias present in the two different databases used. In the classification step, U¨beyli utilized fuzzy sets and proposed a similarity index as a required parameter for differentiating between normal and abnormal ECG beats. She also utilized predefined Gaussian membership functions in the fuzzy classification routine. As a different approach, we have designed a fuzzy relational classifier (Kuncheva, 2000) utilizing fuzzy relations. We have also designed the necessary fuzzy membership functions in order to classify different heart beats in a close similarity to the human decision making process. The proposed fuzzy membership functions can be easily implemented in embedded system architectures. While U¨beyli utilized a reference signal for comparison and arrhythmia detection, in our analysis the normal signal features are used as reference parameters and utilized in fuzzy relation design. There are a large number of medically recognized ECG arrhythmias and it is unlikely that a single approach could provide reasonable results for all cases. We have proposed a hybrid technique to achieve better performance in this regard. It may be possible to increase the precision of the algorithm by utilizing different

wavelets and increasing the number of features. Additionally, the combination of wavelets and fuzzy classifiers technique could be improved by proper selection of the weight functions. Inclusion of a neural network to form a neuro-fuzzy system would be more appropriate if the time constraints are met. The combination of wavelets and fuzzy classifiers has been utilized in the analysis of other biomedical signals which strengthen their vast capabilities (Mahmoodabadi et al., 2008a, 2008b).

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Tehran University of Medical Sciences. He has also been the deputy head of the Research Centre for Science and Technology in Medicine (RCSTIM) since 2004. He is the head of the medical informatics research group at RCSTIM as well. The most advanced research projects in the field of biomedical engineering are conducted at RCSTIM. Dr Ahmadian received his PhD degree in biomedical engineering from the Imperial College of Science, Technology and Medicine in 1997. He obtained his MSc and BSc in the field of communication systems from Tehran University and Isfahan University of Technology, respectively. In 1999 he was nominated for a postdoctoral position at Kings College London, Multimedia Lab, where he spent two years working on three-dimensional medical image compression and transmission using wavelet functions. He is also a senior member of IEEE. His research interests are multi-resolution wavelet analysis of biomedical signals and images, analysis of ECG signals, medical image segmentation, texture analysis, medical image compression and transmission and image-guided surgery systems.

The authors Mohammadjavad Abolhasani Sina Zarei Mahmoodabadi Sina Zarei Mahmoodabadi is currently a postdoctoral research fellow at the Diagnostic Imaging Department of the Hospital for Sick Children (2010). He graduated from Ryerson University with a PhD degree in electrical and computer engineering (2009). He holds an MSc degree in nuclear engineering (2003) and a BSc in applied physics (2000) received from Amirkabir University of Technology. He also has a Masters degree in biomedical engineering (2005) received from Tehran University of Medical Sciences. His research interest is in computerassisted medical decision support systems.

Alireza Ahmadian Alireza Ahmadian is an associate professor at the Biomedical Systems and Biophysics Group,

Mohammadjavad Abolhassani received his PhD degree in the Department of Biomedical Engineering of the Imperial College of Science, Technology and Medicine, London, in 1994. He obtained his MSc in the field of communication and digital electronics from University of Manchester Institute of Science and Technology in 1990 and his BSc in electrical and electronic engineering from University College London in 1989. He is currently an Associate Professor at Tehran University of Medical Sciences, and the head of the Biomedical Systems and Medical Physics Group. He is also the head of the Biomedical Engineering Group of the Research Centre for Science and Technology in Medicine, where most of the advanced research projects in the field of biomedical engineering in Iran are ongoing. His research interests are medical instrumentation design,



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ultrasound imaging systems and biomedical signal processing.

Paul Babyn Paul Babyn, MD, FRCPC, is an associate professor at the Department of Medical Imaging, University of Toronto. Dr Babyn has been on the staff at the Hospital for Sick Children since 1988 and is currently the Radiologist-in-Chief. He graduated from McGill University in 1982 with his MD, CM, and from Massachusetts Institute of Technology in 1978 with an SBEE (Electrical Engineering) and SB (Biology). He completed his residency in diagnostic imaging at the Pennsylvania Hospital in Philadelphia in 1986 and had fellowships in General Radiology and the Division of Neuroradiology=Special Procedures at the Hospital for Sick Children in 1988. Dr Babyn has published over 120 publications to date and is involved in a variety of funded research projects. He has an active role in resident and fellow teaching. He is a member of the Radiological Society of North America, the Canadian Association of Radiology and the Society of Pediatric Radiology. Dr Babyn’s main research interests are in musculoskeletal radiology and cross-sectional imaging.

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Javad Alirezaie Javad Alirezaie received his BSc degree in electrical and electronic engineering from Tehran University in 1988 and his MASc and PhD degrees in systems design engineering (specializing in biomedical engineering) from the University of Waterloo in 1993 and 1996, respectively. From 1997 to 2000 he was a postdoctoral fellow and an assistant professor. He joined Ryerson University in 2001. He is currently an associate professor in the Department of Electrical and Computer Engineering and an adjunct professor with the Department of Systems Design Engineering at the University of Waterloo. Dr Alirezaie holds a cross appointment with the Biomedical Systems and Biophysics Group at Tehran University of Medical Sciences and is a scientific member of the Research Centre for Science and Technology in Medicine. His research interests include biomedical signals and image processing, neural networks, pattern recognition, bioinformatics and bioengineering. He is author and co-author of numerous technical papers in these areas. Dr Alirezaie is a licensed Professional Engineer (P.Eng.) and a member of IEEE, SPIE and EMB society.
