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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 4, NO. 3, SEPTEMBER 2000
Optimal Zonal Wavelet-Based ECG Data Compression for a Mobile Telecardiology System Robert S. H. Istepanian, Senior Member, IEEE, and Arthur A. Petrosian, Senior Member, IEEE
Abstract—A new integrated design approach for an optimal zonal wavelet-based ECG data compression (OZWC) method for a mobile telecardiology model is presented. The hybrid implementation issues of this wavelet method with a GSM-based mobile telecardiology system are also introduced. The performance of the mobile system with compressed ECG data segments selected from the MIT-BIH arrhythmia data base is evaluated in terms of bit rrror rate (BER), percent rms difference (PRD), and visual clinical inspection. The compression performance analysis of the OZWC is compared with another wavelet-based (Discrete Symmetric Wavelet Compression) approach. The optimal wavelet algorithm achieved a maximum compression ratio of 18 : 1 with low PRD ratios. The mobile telemedical simulation results show the successful compressed ECG transmission at speeds of 100 (km/h) with BER rates of less than 10 15 , providing a 73% reduction in total mobile transmission time with clinically acceptable reconstruction of the received signals. This approach will provide a framework for the design and functionality issues of GSM-based wireless telemedicine systems with wavelet compression techniques and their future integration for the next generation of mobile telecadiology systems. Index Terms—ECG compression, GSM, mobile telemedicine, telecardiology, wavelets.
I. INTRODUCTION
I
T IS WELL known that electrocardiogram (ECG) signals are used extensively in different monitoring and diagnostic cardiology applications, including, among others, the transmission of ECG over telephonic channels, ambulatory monitors, and ECG recorders in intensive care units. In these monitoring environments, the ECG recording is typically sampled at frequencies of the order of 1 kHz. Thus, ECG compression is desirable if not necessary since aan average day of ECG recording typically requires the order of 100 megabytes of digital storage space. However, in mobile monitoring environments, compression is an essential tool to resolve and transmit the brief QRS complex, especially for real-time transmission scenarios. The QRS complex is a group of waves depicted on an ECG signal. It actually consists of three distinct waves created by the passage of the cardiac electrical impulse through the ventricles and occurs at the beginning of each contraction of the ventricles.
Manuscript received September 15, 1999; revised December 15, 1999. This work was supported by the Engineering and Physical Sciences Council (EPSERC), U.K., under Grant GR/L50419. R. S. H. Istepanian is with the Department of Electronic and Computer Engineering, Brunel University, Uxbridge UB8 3PH, U.K. (e-mail:
[email protected]). A. A. Petrosian is with the Health Sciences Centre, Texas Tech University, Lubbock, TX 79430 USA. Publisher Item Identifier S 1089-7771(00)03959-5.
In a normal electrocardiogram, the R wave is the most prominent of the three. The QRS complex is the most important clinical part of the cardiology system and determines the normal or abnormal arrhythmia occuring in the heart. The integration of digital cellular networks with telemedicine applications is the next most important phase in creating universal, mobile, and cost-effective telemedical systems. The future applications of mobile telemedicine systems are seen as useful and potentially powerful tools to improve the quality of healthcare, particularly in remote and underserved areas. In developing countries, for example, where standard telephone services are not universally available, cellular telephony is often seen as a better if not the only alternative to conventional wire services, and telemedicine is seen as an economically attractive method of healthcare delivery, especially in rural and impoverished areas [1]–[3]. The emergence of personal mobile telemedicine systems using wireless links with video capabilities is expected in the next few years [4]. It is well known that the Global System for Mobile Communications or Global Special Mobile (GSM), which is the European digital cellular standard in the 1800-MHz band, is emerging as the dominant and fastest-growing standard for cellular communications in use around the world [4]. Currently, a third generation of wireless networks (3G) is being defined in Europe, North America, and Japan with a view of providing mobile users not only with a high-speed Internet access, video and other communications services, but also compatible mobile medical services. Many of these wireless standards, such as DCS1800 (Digital Communications Systems in the 1800-MHz band) in Europe and PCS1900 (Personal Communication System in the 1900-MHz band) in the United States, will likely be GSM-based [4]. It is expected that, with the current growing demand on mobile phones, the GSM will have more than 52% of the world-wide market share by the end of 2000, and, by the year 2002, the European subscriber base for GSM mobile phones will approach 140 million [5]. However, the popularity and advances in cellular digital mobile systems are not paralleled with similar developments and integration of such systems for telemedicine applications and particularly for mobile telecardiology systems. In recent years, several mobile telemedicine systems design methodologies using different communication technologies were addressed in the literature [6]–[9]. However, these studies depended on the direct transmission of ECG and other medical data via the existing mobile bandwidths. In a recent clinical case study, the use of a commercial mobile phone system for clinical ECG transmission from a yacht was illustrated and the problems associated with the degradation in the received signal in certain mobile environments were shown [10]. Such studies
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ISTEPANIAN et al.: OPTIMAL ZONAL WAVELET-BASED ECG DATA COMPRESSION FOR A MOBILE TELECARDIOLOGY SYSTEM
Fig. 1.
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Block diagram of the mobile wavelet-based telecardiology system.
highlighted the need for more efficient compression techniques to overcome the bandwidth limitations of the current generation of cellular telephonic channels for real-time transmission, especially larger amounts of ECG data compatible with future developments of mobile telephonic telemedical systems. The wavelet transform techniques for ECG data compression have received a great deal of attention over the past several years [11]–[16]. However, no study to date has addressed the integration issues of the wavelet-based ECG compression techniques with the design and functionality issues of the GSM-based mobile telecardiology system. These studies will present telemedicine providers with a better understanding of the engineering aspects and compatibility issues of the GSM mobile standard with relevant wavelet transform techniques and their future integration for next-generation integrated mobile telecadiology systems. The approach presented here will provide a new hybrid and interactive framework focusing on understanding the design and modeling issues of correlating wavelet compression theory with wireless telemedicine applications, especially in high mobility scenarios. The main advantage of the optimal zonal wavelet-based ECG data compression (OZWC) method presented here and its compatibility with the current application compared to existing ECG wavelet compression approaches can be summarized as follows. Typically, for a given wavelet transform, there exist some fixed zones of spectral vectors that are to be kept within the compression process. In the current approach, these can easily be updated and reprogrammed based on certain parametric variations within typical mobile communications scenarios by simply increasing or decreasing the sizes of the fixed selected zones of the spectral vector and the transmitted clinical ECG data. This methodology will provide minimized memory and less digital signal processing cycles with a more computationally efficient technique with simpler programmable concept for enhancing the performance of the hybrid telemedical system in real-time transmission scenarios with relevant GSM functional models. These are detailed in the following sections. The paper is organized as follows. The algorithm for the OZWC method for ECG data compression algorithm is described in Section II. The design and modeling issues of integrating the OZWC method with the GSM-based telecardiology system are presented in Section III. Section IV presents the
performance analysis of the OZWC method with the discrete symmetric wavelet compression (DSWC) algorithm [16]. The simulation results of the OZWC method and the reconstruction analysis from the mobile telecardiology system of selected ECG records from the MIT-BIH arrhythmia database [17] are also discussed. We conclude with some remarks and discussion in Section V. II. OPTIMAL ZONAL-BASED WAVELET ECG COMPRESSION A. General Description Fig. 1 shows the general structure of the integrated waveletbased ECG compression method for the mobile telecardiology monitoring system. The principal of the ECG compression algorithm used in this study is based on the optimal wavelet-based zonal coding method developed for the class of discrete “Lipschitzian” signals [18]–[20]. The details of the algorithmic issues and relevant derivations are described in [18]–[22]. A brief description is given here for completeness. The discrete “Lipschitzian” signal is defined as the following set of data vectors:
(1) For this class of discrete signals (a discrete analogy of the Lipschitzian class of functions), it can be shown that [20] (2) where
is the DFT of vector . The following Lemma provides a universal approach for the exact computation of the upper bounds . of real spectral transform coefficients’ moduli on the class Lemma: Let
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Fig. 2.
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 4, NO. 3, SEPTEMBER 2000
GSM mobile transmission structure of the OZWC algorithm.
where is the matrix of a real-valued discrete orthogonal transform that satisfies the following condition:
For the Haar transform of dimension from (5) that, for
, one can obtain ,
(3) Given
or
.. . .. .
.. . .. .
.. . .. .
.. . .. . (4)
where is the signal column vector and is the transform coefficient vector. Then the following relationship is satisfied: (6)
(5) The proof of this lemma is given in [19] and is based on the wellknown dynamic programming principle. The latter is based on the concept that the initial extremal task is being partitioned into a multiple-step process and the extrema are being sought at each step according to the optimality principle. Note that the conditions of (3) are satisfied for most of the classical trigonometric, Walsh, as well as Haar transforms. In addition, the relationship (5) remains true without the conditions (3) if the class of vectors , is considered inwith a fixed end, [19], [20]. Based on (5), we derived optimal zonal stead of for the above mentioned real orthogonal coding methods on transforms as well as for a number of widely known wavelet classes [18], [26]. The orthogonal discrete wavelet transform (DWT), similarly to DFT and DCT transforms, maps the input into a set of wavelet coefficients discrete signal where is the matrix of the linear orthogonal wavelet transform as defined above in the Lemma. The Haar wavelet was the first to provide an orthogonal wavelet series representation and it is the only symmetric orthogonal wavelet with compact support [23]–[25]. The Daubechies wavelet (daublets), on the other hand, was the first class of continuous orthogonal kernels with compact support [26]. In addition to daublets, two other wavelets kernels are considered here, namely, symmlets and coiflets. We have used the exact values of upper bounds of the discrete Haar transform obtained from (5), as well as numerical (DHT) on the class evaluations of those bounds for daublets, symmlets, and coiflets.
This implies that the optimal DHT-based zonal coding method replaces zero components of the last packets of on the class the spectral vector. The main advantage of the OZWC approach is that it provides a priori mechanism for the best selection pattern of the wavelet coefficients, thus eliminating the need to encode the addresses of other coefficients for a given transformed signal with potential computational advantages in the relevant ECG signal processing tasks. B. Details of Implementation The mobile wavelet-based ECG compression structure can be separated into two generic structures incorporated within the integrated mobile telecardiology system. Fig. 2 shows the block diagram of the mobile transmission model of the OZWC compression algorithm. The transmitter structure consists of the segmentation block, the transform (DCT, DFT, and DWT) block, the quantization block, and the coding block. The receiver structure consists of the inverse transform block, the reconstruction block, and the decoding block implemented in the receiver end of the system. The operations of these blocks are briefly described below. 1) Segmentation: The sizes of the wavelet matrices selected in this study were 128 128. For the ECG data, this dimension was selected from the different modeling studies to represent the best tradeoff choice between the best compression performance obtained and also providing less memory storage space for the digital signal processing computational requirements compared to larger dimensions. However, higher matrix dimensions can
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ISTEPANIAN et al.: OPTIMAL ZONAL WAVELET-BASED ECG DATA COMPRESSION FOR A MOBILE TELECARDIOLOGY SYSTEM
be appropriately selected for similar applications provided that the compression resolutions obtained are comparable for this processing phase. Accordingly, a typical segment block size of 2048 (128 16) samples was found to be appropriate for this study. This step minimizes the distortion amplitude and determines the maximum allowable compression ratio for the transmitted medical data. In the cases of block sizes larger than the selected 2048 sample, some signal distortion and degradation of the reconstructed ECG samples were obtained, especially if the number of ECG samples was chosen to be significantly larger. The selection of the ECG sampling rates is based on the ECG data extracted from the MIT database that were originally sampled between 250–360 Hz depending on the type of the clinical arrhythmia [17]. 2) Transform: Transforms are used to obtain a suitable signal representation for source coding and compression purposes. In this stage, the new OZWC algorithm was applied and suitable zones were selected to provide the minimum error in the compression and signal reconstruction process. These zones do not depend on or change with input vector once they are selected based on the obtained upper bounds from (5). For the telecardiology application, the size of the zonal mask was set to 32, i.e., of the original 128 coefficients in the block segment size, only 32 elements were selected as explained earlier, thus providing the required compression performance with considerable reduction in computational and memory cycles needed for the algorithmic processing steps as compared to the original 128 coefficients. Fig. 3 illustrates the zonal masking selection process of the optimal wavelet algorithm. 3) Quantization: Quantization reduces the accuracy of the representation of the output of the transform block. In this way, it removes irrelevancy and introduces source-coding errors. A uniform scalar quantizer with 16-bit resolution was used in this study as it presents the advantages of simplicity and ease of implementation. This type of quantization also reduces peak amplitude variations in the reconstructed signal. 4) Coding: The coding procedure performs two tasks: removing redundancy from the quantizer output symbols and mapping them on to a bit stream. A special modified entropy encoder was used to accommodate the bit-string variations and to provide adaptive codebooks from one frame to another. The aim of this adaptive mechanism is to transform the expected changes in the ECG signals into a format compatible with the subsequent mobile transmission phases. The contents of the adaptive change from one subframe to another depend on the transmitted ECG signal. This telemedical design modification is provided from the standard speech-based GSM encoder system. The telecommunication design details and modeling procedures of such codebooks are given in several GSM and speech processing texts and are beyond the scope of this work [28], [29], [32]. A brief description is given here for completeness. The principal procedure of the adaptive codebook is to turn the quasiperidoicity of ECG signals to transmission account. Since ECG data are quasi-peridical with certain PQRS periods, it is clear that the best excitation for a given subframe should be closest to the best excitation for a given subframe obtained from samples before the current ECG signal.. If the past excitation is kept in memory, it is sufficient to transmit to the decoder with the value
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of sampled and the value of a certain gain to apply to the past excitation. The telemedical decoder must also keep track of the past excitations. The memory contents of the past excitations are called the adaptive codebook. The value of here is called lag in the GSM standard [28], [32]. Usually, is calculated with a precision equal to a fraction of the original ECG sampling period within the range 250–360 Hz for the MIT clinical data base, and the terms “fractional delay” or “fractional lag” are used. The lag is then updated for each subframe. Its calculation involves two steps: first is the open loop estimation step used to correct the strong discontinuities between subframes, and the second is called the adaptive codebook search, which performs the closed loop search analysis using the synthesis approach to improve the results given in the open-loop search of the first step; usually the suboptimal iterative search is used to find the solution for the required samples ECG codebook. 5) Decoding: This is the inverse block of the coding block, and it is located at the receiving end of the system. This block maps the received bit stream on to quantizer output symbols in a similar approach to the encoder structure. 6) Reconstrcution: This is the counterpart block of the quantizer process. The reconstruction block returns the output symbols to the representation suitable for inverse transformation. 7) Inverse Transformation: This final step simply reverses the transformation step, ideally restoring the transmitted ECG signal to its original form. The computational steps andmodeling design flow diagram of the wavelet compression algorithm are shown in Fig. 3. These are summarized as follows: 1) Estimation of a reasonable value for the parameter defined earlier. This implies that, for a given number of samples of a segmented ECG signal , all the successive differences between samples must be calculated and the maximum of these differences are selected to be the parameter. 2) For a given -dimensional wavelet transform, the corwavelet matrix (i.e., the transform responding matrix which, if applied to the input signal, produces all wavelet coefficients) was next generated. In this study, seven wavelet matrices of 128 128 in size each were selected for best performance analysis. These were selected to represent the most commonly used classical families of the orthogonal wavelets [22]–[24]. Additionally, these wavelets also represent the best range of different smoothness and symmetrical parametric features of a typical mother wavelet. 3) The maxima of coefficients were then calculated using (5) to provide the spectral distribution of the wavelet coefficients for the aforementioned matrices. 4) The optimal “zonal mask” was selected for each wavelet, i.e., the spectral zones which have higher maxima values. The size of the zonal mask (the number of retained coefficients) was selected to be the same for all wavelet transforms throughout the implementation procedures. 5) The wavelet transform was then applied to the ECG samples and the compression was achieved by transmitting
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Fig. 3.
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 4, NO. 3, SEPTEMBER 2000
Design flow diagram of the OZWC algorithm.
only those coefficients that were within the selected zones whilst the other coefficients were discarded. The latter, as explained earlier, were replaced by zeros in the reconstruction phase in the receiver model. The retained coefficients were then quantized at a preset bit resolution and then coded for the mobile transmission purpose.
III. WAVELET COMPRESSION FOR MOBILE TELECARDIOLOGY The hybrid design and modeling platforms of the wavelet compression and the GSM mobile telecardiology system are shown in Fig. 4.
The detailed description of the system blocks and the associated GSM sub-blocks with the relevant simulation machinery and modeling details are described elsewhere [7]–[9], [27], [28]. However, a brief description of the main design modules is given here for completeness. The key functions performed in the transmit path in a GSM signal processing structure are the signal and channel coding, interleaving, and modulation. The receiver blocks are essentially the reverse of the transmitter blocks. The mobile model is based on the TCH/F9.6 data traffic GSM channel as the most widely used commercial standard with a data rate of 9.6 kbit/s. This provides enough bandwidth for clinical ECG real-time telemedical transmission. The basic GSM-based
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ISTEPANIAN et al.: OPTIMAL ZONAL WAVELET-BASED ECG DATA COMPRESSION FOR A MOBILE TELECARDIOLOGY SYSTEM
Fig. 4.
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Design framework and modeling environments of the hybrid wavelet-based mobile telecardiology system.
telecardiology system is comprised of the following generic cellular blocks: 1) A/D and GSM data encoder; 2) GSM transmitter; 3) GSM cellular channel model; 4) GSM receiver; and 5) D/A and GSM data decoder. A brief description of these modular blocks is given next. 1) A/D Conversion and Data Encoder: This block consists of several subblocks; these are the A/D quantizers, the GSM data encoder, channel encoder interleaving, and burst formatting blocks. For high resolution and sampling accuracy, a specific quantizer block is also modeled to accommodate the performance of the Hoffman-based adaptive entropy encoder described earlier and used for the ECG wavelet compression. The ECG signal source is represented by the wavelet-based compressed input ECG data. The A/D used in this study is a 16-bit resolution model determined for higher accuracy of the sampled data. The resultant bits are then encoded. It must be noted that the standard GSM system typically uses a 1/2 rate convolution encoder for the
speech processing purposes. This can be selected for the relevant MIT-ECG data with sampling rates higher than 250 Hz at the GSM channel bandwidth of 9.6 kbit/s. For the TCH/F9.6-GSM standard used in this study, the data field is formatted with various overhead fields to create a time-division multiple access (TDMA) frame required for the GSM burst structure. 2) GSM Transmitter: This part of the system is basically the Gaussian minimum shift keying (GMSK) modulator at an IF frequency of 900 MHz used in this study. A burst is received from the encoder block as 157 symbols in a vector that is 157 slots long. The resultant burst of 156 symbols as given in the GSM standard structure is then converted from a vector to a series of scalars [28], [29]. The scalar values are modulated by the GMSK modulator, which introduces a delay due to filtering. A complex bulk delay is added to give the transmitter a delay equal to one burst. The GMSK modulator mainly performs Gaussian low-pass-filtered MSK modulation with a
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digital FM or continuous-phase frequency shift keying modulation technique having a modulation index of 0.5. The modulated output is a complex envelope signal. 3) GSM Cellular Channel Model: The channel modeling is considered to be an important part of the telemedicine system to simulate realistic cellular channel conditions with delay profiles reflecting different mobile and noise conditions such as in urban or rural conditions. In order to obtain realistic performance results, the properties of the channel should closely match the characteristics of the real mobile environment using both urban and rural models and should consider all the relevant channel fading characteristics. The main modeling parameters considered in this study include a full channel model with multiple faders, co-channel or adjacent channel interference, and noise profiles [27]–[29]. These are discussed briefly here. The GSM fader basically implements the multipath fading profiles specified in the GSM 5.05 standard used in this study. The output of the fader is the sum of several independent paths in the mobile channel. Each path has a relative delay, a relative gain, and a fading type. The GSM 5.05 standard used here is typically specified as twelve path profiles. These include different profiles representing different cellular conditions such as hilly terrain, rural area, equal path, flat fading, and urban area profiles all used to study the effects of the different channel models on the received medical data tested. It also specifies two fading types, namely Rayleigh and Rician. These interpret different mobile environments and conditions. The other important channel modeling parameter is the Doppler frequency. This is a function of the carrier frequency and mobile speed. The GSM standard also specifies the performance of the receiver in the frequency-hopping method. When the frequency hopping parameter is on, the fading filters are re-initialized at each burst to simulate hopping to an uncorrelated frequency slot. Also, in order to maintain secure communications, the GSM standard specifies additional techniques that improve the performance of the radio link and enhances communications quality, which are important factors particularly for medical data transmission. The detailed issues are beyond the scope of this paper and can be cited in several new GSM texts [28], [29]. 4) GSM Receiver: This modeling block is used to modulate a GMSK signal by maximum likelihood sequence estimation using a Viterbi equalizer. An estimate of the channel impulse response is calculated for use by the Viterbi equalizer [28], [29]. The receiver demodulates different bursts described earlier. The bursts can contain different training sequences of various lengths in different locations within the burst frame. The main design elements in this block are: • baud Rate and frames/multiframe; • oversampling rate; • time advance; • truncation path length; • burst size; • decision type. These are selected according to the required system performance and standard GSM design procedures and compatibility with the medical data specifications. 5) D/A and GSM Data Channel Decoder: This block decodes the various fields of channel traffic used to carry
Fig. 5. Upper bounds of different level wavelets for
1 = 70.
TABLE I PRD PERFORMANCE OF OZWC FOR THREE DIFFERENT WAVELETS AND ECG ARRYTHMIA SIGNALS
TCH-GSM encoded data [28], [29]. One burst of each TDMA frame is decoded. The decoder function is summarized as follows. The parsing process of the multiframe signal is used to extract each TDMA frame. Each burst of the TDMA frame is parsed to extract the individual fields contained in the GSM burst. The data field is de-interleaved and the de-interleaved main data field is then decoded using a modified Viterbi decoder model. The de-interleaving is performed to restore the sequence of the received data in the receiver model. The resultant bits from the decoder are fed to the A/D model to retrieve the original ECG data. As shown in Fig. 4, the mobile telemedical system GSM and relevant models were simulated using Cadence-Alta Group’s Signal Processing Work System (SPW) and the associated GSM design libraries on a UNIX-HP computing environment [30]. The OZWC described in Section II was simulated on using MATLAB and the associated Wavelet and Signal Processing Toolboxes [31]. IV. RESULTS A. Optimal Wavelet Compression Results The 10-min segments of ECG data were extracted and tested from a subset of different ECG records contained in the MIT-BIH arrhythmia database [17]. Three ECG records were selected both on clinical advice and to provide a variety of clinical rhythms, QRS complex morphologies, and ectopic beats with varying levels of noise and other artifacts.
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TABLE II COMPRESSION PERFORMANCE OF THE OZWC ALGORITHM FOR DIFFERENT ECG RECORDS AND QUANTIZATION LEVELS
These data were MIT/BIH record 100, Noise Stress Test record 118e00 (sampled at 360 Hz and 12-bit resolution), and the Creighton University Tachyarrythmia record cu01 (sampled at 250 Hz and 11-bit resolution) [17]. The signals were compressed using the annotation files provided with the database. In order to provide a comparative analysis of the performance of the OZWC algorithm with other discrete wavelet compression methods, the same ECG test records were also compressed using the discrete symmetric wavelet transform method developed at the University of Wisconsin-Madison [16]. It is well known that the most efficient criterion for judging the performance of any ECG compression methodology is the clinical quality and visual inspection of the reconstructed signals by cardiologist. However, an accepted standard measurement of signal distortion is also the percent rms difference (PRD) that forms complementary performance criteria of the relevant ECG compression method in addition to the visual inspection, since reconstruction with a low PRD does not necessarily mean clinical acceptance. The performance of the OZWC with different wavelet kernels (daublets, symmlets, and coiflets) and relevant values of the parameter were tested to provide the best wavelet and the corresponding best spectral zones for this telemedical application. The parameter can be easily calculated from any given signal database or to be statistically estimated from a class of random signals. The best tradeoff choice of the values in this case was based on the ECG data samples selected from the MIT database and used in this study. Fig. 5 presents the upper bounds of the tested wavelets at dif. The experiments ferent coefficients for a given value of in this study has shown that for, the tested ECG arrhythmia
records, the values of were bounded between 60 and 100 and represent the best tradeoff values for the operational parameter. Table I summaries the comparative performance of different wavelets (before the quantization and Huffman coding steps) and the corresponding PRD values tested for a compression ratio of 4 : 1 on different ECG segments. The results also show that the Haar wavelet provides the highest PRD ratios and coiflet 4 (12 point 4 level Coiflet) provides the optimal performance for all the ECG samples tested with corresponding low PRD’s. The latter structure was used for the mobile telecardiology application. Table II shows the comparative performance results of the OWZC algorithm for the three selected MIT records using 8-, 12-, and 16-bit quantization levels, respectively. It can be seen from these results that a maximum compression ratio of 18 : 1 is achieved with a low PRD ratio for the data of Record 100, although this does not correlate to the lowest PRD value of the other arrhythmia types. However, the 16-bit quantization used for GSM transmission was also found to provide lower PRD’s and retain most of the clinically important features of the original signals. Figs. 6 and 7 show the original and the reconstructed beats selected from record 100 and the Ventricular Tacharrhythmia (CU01), respectively. It can be seen from these results that all the main features of the selected rhythms, underlying QRS morphologies, and ectopic beats are preserved after the reconstruction. Table III shows the performance of the DSWC algorithm [16], for the tested ECG segments. The comparative results show that both approaches successfully reconstruct the main structural components of the ECG rhythms with clinically acceptable quality and minimum distortion as determined
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TABLE III COMPRESSION PERFORMANCE OF OZWC AND DSWC ALGORITHMS
Fig. 6.
Original and reconstructed ECG segment (MIT100) at CR of 18 : 1.
Fig. 7.
The original and reconstructed ECG segment (CU01) at CR of 9.83 : 1.
by the cardiologist’s visual inspection. However, the OZWC method provides higher compression ratios for most of the tested data at the same bit/sample ratios as shown in Table III. For completeness, the function between the CR and bit/sample ratio used in this study can be clarified by examining the following example for the compression of the MIT 100 record with an 8-bit quantization used for a block size of 2048 samples bits. at 12-bit resolution, i.e., This is illustrated as follows: bits before compr. CR bits after compr. bits bits after compr. sample of samples CR
(7)
B. Mobile Telecardiology Compression Results In this section, we present the performance results of the mobile telecardioloy system using the compressed ECG data with the OWZC compression algorithm. The mobile telecardiology
Fig. 8. Transmitted ECG data (record 100) and received signals from the mobile system at a compression ratio of 3.66 : 1.
system was tested under different mobile and channel conditions simulating different mobility environments. Fig. 8 shows a typical transmitted ECG (record 100) compressed using the OZWC algorithm at a ratio of 3.66 : 1 with a PRD of 0.7029% and the corresponding received signal from the GSM-based mobile system transmitted at a mobile speed of 100 km/h. The signal-to-noise ratio (SNR) represents here the basic degree of the distortion and interference-free parameter, i.e., the degree of “error-free” mobile telemedical communication link of the signal (ECG) power to the noise power, with the noise taking the form of additive white Gaussian noise (AWGN) within the tested GSM cellular link. Figs. 9 and 10 clearly show that the main clinical features for both ECG segments are reconstructed in the receiver end with a relatively low PRD with the specified mobile characteristics. Table IV shows the corresponding mobile results
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TABLE IV COMPRESSION AND MOBILE TRANSMISSION PARAMETERS (RECORD (100) AT A MOBILE SPEED OF 100 km/h)
TABLE V TIME REDUCTION
FOR THE MOBILE TRANSMITTED AND WITHOUT OZWC
ECG DATA
WITH
Fig. 9. Mobile transmitted and received ECG sample (record 100).
Fig. 10. CU01).
Mobile transmitted and received Ventricular Tachyarrhythmia (record
for the transmitted record 100. Table V summarizes the results of the performance of the integrated mobile telecardiology model for the tested ECG records. The results indicate that an average reduction of approximately 73% of the total transmission time of the tested records is achieved by using the OZWC algorithm compared with no compression tranmission case. At the same time, a higher CR can be integrated within the GSM mobile model with lower clinically acceptable quality in the received signals. Further simulation studies on the functionality and mobile performance of the integrated optimal wavelet-mobile telecardiology system under different mobility and cellular channel conditions were also carried out. Fig. 11 shows the BER performance of the system at different mobile speeds for the ECG record 118e00. The simulation re-
Fig. 11. BER performance of the mobile telecardiology system at different mobile speeds.
sults clearly show that the system performance at mobile speeds higher than 55 km/h reaches a steady error plateau with lower . A similar performance is also recorded BER values of for other MIT arrhythmia GSM transmissions. Hence, the relative speed of 100 km/h was chosen as the maximum high-mobility cut-off limit for further speed performance tests. Fig. 12 shows the comparative mobile performance of the BER as a function of the SNR at a mobile speed of 100 km/h. Similarly, Fig. 13 shows the mobile test results for different signal-to-interference ratios (SIR’s) representing the degree of
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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 4, NO. 3, SEPTEMBER 2000
Fig. 12. Mobile BER versus wavelet-telecardiology system.
the
SNR
performance
of
the
Fig. 13. Mobile BER versus wavelet-telecardiology system.
the
SIR
performance
of
the
mobility distortion in the cellular channel. These results demonstrate the good performance of the system in terms of the lower at the receiver end) with all three tested BER (of less than 10 clinical ECG data segments with preserved clinical quality of the received data. Further simulation tests showed that the increased mobile speeds (in the range of 150–200 km/h for the urban channel model) provide lower BER performance (less than 10 ) for all the tested data types. Thus, as was expected, the successful system performance and clinical quality of the received signals for compressed ECG data depends on the mobility conditions, the proper channel conditions, and the degree of interference present in the cellular communication path. V. CONCLUSIONS This paper describes the modeling and design concepts for an integrated wavelet-based mobile telecardiology system. The integrated concept of using compressed ECG data for mobile telemedical applications is vital for future exploitation of direct
transmission scenarios of electrocardiograms via mobile telephones in emergency, remote patient management, and afterhours consultation scenarios. The main contributions of this paper are of twofold. First is the demonstration of a hybrid design and modeling methodology for the application of an optimal wavelet-based ECG compression algorithm based on the zonal coding for mobile telecardiology transmission scenarios. The second contribution is the presentation of a new framework for investigating the effects of biomedical signal transmission using the GSM-based wireless telephonic standard under high mobility conditions. The clinical review of the received samples suggest that ECG compression ratios of up to 18 : 1 can be achieved without reducing the clinical quality of the transmitted ECG signals whilst retaining the necessary features for clinical diagnosis at the receiver end. The work sought to examine the most widely occurring ECG arrhythmia in telemedical emergency and post-hospitalization cases and is based on the clinical advice from the hospitals and cardiology centers in Portsmouth, U.K. The other main advantage of the presented OZWC method is the simplicity of retuning the design parameters of the algorithm depending on the mobile speed and environment, i.e., by simply reselecting the wavelet family and increasing or decreasing the sizes of the fixed zones of the wavelet spectral vectors depending on the type of the medical data transmitted. In the case of the ECG data, the size was set to 32 to be used for the transmission out of the selected size of 128 coefficients. Emphasis should be given to the fact that, although the PRDs were somewhat higher for the OZWC method in some cases, the main advantage remains in that the zones of the spectral coefficients used for the compression are fixed and are totally independent of the input signal. Thus, the need for additional addressing information of those coefficients is eliminated within the algorithmic process. An adaptive wavelet algorithmic approach is currently being studied for such mechanisms. The simulation results of the mobile telecardiology system show the successful transmission of the compressed ECG at the standard GSM data rate of 9.6 kbit/s. The algorithmic approach presents a computationally fast method compared to standard wavelet algorithms and can be implemented on standard DSP architectures allowing less processing and faster computational time for real-time transmission. Ongoing work is currently underway to implement the compression methodology on the TMS320C6201 DSP hardware-testing platform and to test the performance of the algorithmic approach by selecting multiple zones within the coefficient spectra. The simulation results presented in Section IV indicate that the quantization level and particular wavelet used have an impact on the quality and performance of the reconstructed signals. The algorithm also performs favorably in comparison with the DSWC algorithmic approach for the same tested ECG data. It provides relatively higher compression ratios for most of the tested data while retaining clinical quality. The results also show that clinically acceptable signals can be retrieved for an ambulatory speed of up to 100 km/h in urban channel profiles with with an average reduction of 73% in a BER of less than 10 the transmission time compared to noncompressed signals for most of the tested ECG data. Further studies will target a larger
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ISTEPANIAN et al.: OPTIMAL ZONAL WAVELET-BASED ECG DATA COMPRESSION FOR A MOBILE TELECARDIOLOGY SYSTEM
selection of the ECG arrhythmia samples from the MIT database to test the current algorithmic approach in other clinical scenarios. Practical implementation of the system is currently underway and further improvements to the current encoder/decoder models are also currently underway to achieve higher mobile compression ratios. Future studies will also target new and emerging cellular standards representing the rest and third generation (3G) mobile systems, such as the upgraded IS-136 and DCS1800 (essentially GSM at 1800 MHz bandwidth) and the new UMTS/IMT2000 standards. The modular design framework discussed in this paper presents the first phase for the development of the next generation of wireless telecardiology monitoring systems using digital cellular links.
[19] [20] [21] [22] [23] [24] [25] [26]
ACKNOWLEDGMENT The authors are grateful to Dr. J. Watkins, Senior Cardiologist at the Heart and Lung Unit, St. Mary’s Hospital, Portsmouth, U.K., for his clinical advice. The authors would also like to thank the anonymous reviewers for their constructive comments.
[27] [28] [29] [30]
REFERENCES [1] E. Rosen, “Mobile telemedicine arrives,” Telemedicine Today, pp. 14–42, Oct. 1997. [2] R. H. Istepanian and H. Nikogosian, “Telemedicine in Armenia,” J. Telemedicine & Telecare, vol. 6, to be published. [3] D. A. Perendina and A. Allen, “Telemedicine technology and clinical applications,” J. Amer. Med. Assoc., vol. 273, no. 6, pp. 483–488, 1995. [4] M. Mouly and M. Pauter, “Current evolution of GSM systems,” IEEE Personal Commun., pp. 9–19, Oct. 1995. [5] W. Tuttlebee, “You will never talk alone,” IEE Rev., pp. 99–100, May 1997. [6] R. H. Istepanian, M. Brien, and P. Smith, “Modeling of photoplethysmography mobile telemedical system,” in Proc. 19th Annual IEEE Int. Conf. Engineering in Medicine and Biology, Chicago, IL, Oct. 30–November 2, 1997, pp. 987–990. [7] R. H. Istepanian, B. Woodward, E. Gorilas, and P. Balos, “Design of mobile telemedicine systems using GSM and IS-54 cellular telephone standards,” J. Telemedicine Telecare, vol. 4, pp. 80–82, 1998. [8] R. H. Istepanian, “Modeling of GSM-based mobile telemedical system,” in Proc. 20th IEEE Annual Int. Conf. Engineering in Medicine and Biology, Hong Kong, Nov. 1998, pp. 1166–1169. [9] R. H. Istepanian, P. Balos, B. Woodward, S. Chen, and B. Luk, “The comparative performance of mobile telemedical systems using the IS-54 and GSM standards,” J. Telemedicine Telecare, vol. 5, no. 2, pp. 97–104, 1999. [10] S. B. Feedman, “Direct transmission of electrocardiograms to a mobile phone for the management of a patient with acute myocardial infarction,” J. Telemedicine Telecare, vol. 5, no. 1, pp. 67–69, 1999. [11] M. Akay, Time Frequency and Wavelets in Biomedical Signal Processing, ser. Biomedical Engineering. New York, NY: IEEE Press, 1998. [12] B. Bradie, “Wavelet packet-based compression of single lead ECG,” IEEE Trans. Biomed. Eng., vol. 43, pp. 493–501, May 1996. [13] M. L. Hilton, “Wavelet and wavelet packet compression of electrocardiograms,” IEEE Trans. Biomed. Eng., vol. 44, pp. 394–402, May 1997. [14] T. Blanchett, G. C. Kember, and G. A. Fenton, “KLT-based quality controlled compression of single lead ECG,” IEEE Trans. Biomed. Eng., vol. 45, pp. 942–945, July 1998. [15] W. Philips, “ECG data compression with time-warped polynomials,” IEEE Trans. Biomed. Eng., vol. 40, pp. 1097–1100, Nov. 1993. [16] A. Djohan, T. Q. Nguyen, and W. J. Tompkins, “ECG compression using discrete symmetric wavelet transform,” in Proc. 18th Annual IEEE Int. Conf. Engineering in Medicine and Biology Soc., Montreal, Canada, Oct. 1995, DSWT toolbox http://saigon.ece.wisc.edu/ waveweb/QMF.html, pp. 899–900. [17] MIT-BIH Arrhythmia Database CD-ROM, Third ed. Cambridge, MI: Harvard-MIT Division of Health Sciences and Technology, May 1997. [18] A. Petrosian, “Upper bounds of wavelet spectra on the class of Lipschitzian signals,” in SPIE Conf. Wavelet Applications in Signal and Image Processing, Denver, CO, Aug. 8, 1996.
[31] [32]
211
, “Optimal zonal compression of signals with bounded first and second order finite differences,” in 8th IEEE Digital Signal Processing Workshop, Bryce Canyon, UT, Aug. 9–12, 1998. , “Optimal zone encoding of digital signals with transform” (in Russian), Problemi Peredachi Informatsii, vol. 27, no. 2, pp. 46–58, 1991. A. Petrosian, “Optimal zone encoding of digital signals with transform,” Problems of Information Transmission, pp. 128–140, October 27–2, 1991. Y. Meyer, “Wavelets, Algorithms & Applications,” SIAM Publication, 1993. C. S. Burrus, R. A. Gopinath, and H. Guo, Introduction to Wavelets and Wavelet Transforms, A Primer. Englewood Cliffs, NJ: Prentice-Hall, 1998. G. Erlebacher, M. Y. Hussaini, and L. M. Jameson, Wavelets Theory and Applications. New York, NY: Oxford University Press, 1996. M. Vitally and J. Kovacevic, Wavelets and Subband Coding. Englewood Cliffs, NJ: Prentice-Hall, 1995. I. Daubechies, Ten Lectures on Wavelets. Philadelphia, PA: SIAM, 1992. E. G. Gorilas, “Design of mobile telemedical systems using the GSM standards,” M.Sc. thesis, Univ. of Portsmouth, U.K., 1997. M. Mouly and M. B. Pautet, The GSM System for Mobile Communications. Norwood, MA: Artech House, 1992. S. M. Redhl, M. K. Weber, and M. W. Oliphant, An Introduction to GSM: Artech House Publishers, 1995. Cadence design Systems-, Alta Business Unit, SPW Signal Processing Worksystem Communications and DSP Library: Cadence Design Systems Inc., 1997. MATLAB Wavelet Toolbox. Natick, MA: Mathworks, Inc., 1994. B. Atal, Advances in Speech Coding. Boston, MA: Kluwer Academic, 1991.
Robert S. H. Istepanian received the Ph.D. degree from Loughborough University, U.K., in 1994. From 1984 to 1988, he worked in different overseas industrial and academic positions. In 1988, he was a Visiting Research Fellow in the Department of Electronic and Electrical Engineering, Loughborough University. He then joined the same department in 1994–1995 as a Post-Doctoral Research Fellow. From 1996 to 1999, he was a Senior Lecturer with the Department of Electrical and Electronic Engineering, University of Portsmouth, U.K. In 1999 he was a visiting Associate Professor in the Department of Electrical and Computer Engineering, Ryerson Polytechnic University, Toronto, ON, Canada, and Adjunct Professor in the Department of Electrical and Computer Engineering, University of Western Ontario, Canada. He is currently with the Department of Electronic and Computer Engineering, Brunel University, Uxbridge, U.K., leading research in the areas of mobile telemedicine and E-MED computing systems. He has published more than 90 refereed journal and conference papers and is currently editing three books in the areas of wireless telemedicine, biomedical signal processing, and control systems. His current research interests include the next generation of wireless E-MED systems, biomedical signal processing, interface issues between medicine and control, finite precision control theory, and computational intelligent systems. Dr. Istepanian is a member of the Institution of Electrical Engineers and a Chartered Engineer in the U.K. He serves on the Editorial Board of the IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE and was one of the founding Special Area editors. He has also served on the IEEE Control Systems Society Conference editorial board from 1996–2000. He has served on technical committees and been an invited speaker and sessions chair for several international conferences, including the American Control Conference and the IEEE Engineering in Medicine and Biology Conferences (EMBS’98 and EMBS’99), and the 2000 World Medical Congress. He is a recipient and investigator of research grants from the Royal Society of London, British Council, the Engineering and Physical Sciences Research Council (U.K.), and the Natural Sciences and Engineering Research Council of Canada. He was the recipient of the UK Universities Committee of Vice Chancellors Ph.D. award (1991–1993). He was also awarded the 1999 Heaviside Premium Award for the best paper from the Institution of Electrical Engineers (U.K.) for his work on finite precision systems and control theory.
Arthur A. Petrosian (M’93–SM’99), photograph and biography not available at the time of publication.
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