real-time dsp-based conductance catheter measurement system for estimating ventricular volumes

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 58, NO. 10, OCTOBER 2009 3583

Real-Time DSP-Based Conductance CatheterMeasurement System for Estimating

Ventricular VolumesChia-Ling Wei, Member, IEEE, Chieh-En Chen, I-Ta Tseng, and Chin-Hong Chen

Abstract—Conductance catheter is a tool for estimating real-time ventricular volume by measuring time-varying ventricularadmittance, provided that myocardial contribution can be deter-mined and then removed. One approach for determining the my-ocardial contribution is to measure both the magnitude and phaseof the ventricular admittance rather than only its magnitude.However, the sensed signal waveform of the conductance cathetersystem is very special; thus, a deliberately designed admittancemeasurement system is developed for this application. A novelreal-time DSP-based conductance catheter measurement systemwas designed to measure time-varying ventricular admittancesignals, including both the magnitude and the phase. With somesignal-processing techniques, the system achieves a high degree ofaccuracy and high resolution with a relatively low sampling rate.

Index Terms—Admittance measurement, biomedical instru-mentation, conductance catheter, phase measurement, pressure–volume loops, ventricular volume.

I. INTRODUCTION

CONDUCTANCE catheter is a tool for getting real-timeventricular pressure–volume loop plots. Experimentally,

a catheter is inserted into the ventricle to inject an ac currentsignal and to continuously measure the instantaneous conduc-tance change as the ventricle fills and ejects blood [1]–[6].The measured conductance is proportional to the ventricularvolume, and two conductance-to-volume conversion equationshave been proposed to convert the measured conductance to thevolume signal [1], [7]. The following first equation, which as-sumes that the catheter-generated electric field is homogeneous,was proposed by Baan et al. in 1984 [1]:

Vol =1α

ρL2(Gmeas − Gp) (1a)

⇒ Vol =1α

ρL2gb (1b)

and the second one, which solves the issue of assuming a ho-mogeneous electric field, is proposed by Wei et al. in 2005 [7]

Manuscript received March 13, 2008; revised September 1, 2008. First pub-lished June 23, 2009; current version published September 16, 2009. This workwas supported by the National Science Council of Taiwan under Grant NSC-95-2218-E-006-038. The Associate Editor coordinating the review process forthis paper was Dr. George Giakos.

The authors are with the Department of Electrical Engineering, NationalCheng Kung University, Tainan 70101, Taiwan (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TIM.2009.2018690

and is given as

Vol =γ

γ − (Gmeas − Gp)ρL2(Gmeas − Gp) (2a)

⇒ Vol =γ

γ − gbρL2gb (2b)

where Vol is the instantaneous volume signal (l) 1/α and γ areempirical calibration factors, ρ is the blood resistivity (Ω-m),L is the distance between the voltage-sensing electrodes (m),Gmeas is the instantaneous measured conductance (S), Gp is thesurrounding myocardial conductance (S), and gb is the instan-taneous blood conductance (S). Both blood and myocardiumare conductive; thus, the measured result comes from both theventricular blood and the myocardium [8]–[10]. Notably, onlythe blood conductance should be used to estimate ventricularvolume, which means that the instantaneous myocardial con-tribution must be determined and removed from the combinedmeasured result, as shown in (1a) and (2a).

It has been proven that the myocardium is both resistiveand capacitive, whereas blood is only resistive [3], [10]–[15].Therefore, it is more appropriate to label the measured resultas admittance Ymeas instead of conductance Gmeas [10], [15],[16]. In fact, one method for estimating the myocardial admit-tance Ym is through the phase information of the measuredadmittance. We have

Cm =|Ymeas| · sin(φ)

2πf(3a)

gm =Cmσm

εm(3b)

Ym = gm + j2πfCm (3c)

where Cm is the myocardial capacitance, |Ymeas| is the magni-tude of the measured admittance, φ is the phase of the measuredadmittance, gm is the myocardial conductance, f is the injectedsignal frequency, σm is the myocardial conductivity, and εm isthe myocardial permittivity [10]. Then, the blood conductancegb can be calculated as

gb = |Ymeas| · cos(φ) − gm. (4)

In other words, if both the magnitude and the phase of theinstantaneous ventricular admittance, i.e., |Ymeas| and φ, canbe measured, the myocardial admittance can be determined andthen removed.

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3584 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 58, NO. 10, OCTOBER 2009

Fig. 1. Illustration of phase detection.

To the best of our knowledge, there is no commerciallyavailable conductance catheter system that can measure boththe magnitude and the phase of the ventricular admittance.Only the magnitude of the ventricular admittance is measured.Nevertheless, conductance catheter systems that were designedto measure both the phase and the magnitude of the admittancehave been reported in the literature. However, signals wereprimarily processed by analog circuits in previous literature[10], [15], [17], [18]. The drawbacks of analog circuits includethe issues of nonlinearity, aging, drifting, and mismatch, all ofwhich can be resolved using digital circuits. Nevertheless, whenhigh resolution is required for phase measurements, digitalimplementation is hesitated.

In fact, phase measurement of in vitro or ex vivo tissuesis typically performed by mixing the sensed signal with areference signal and then retrieving phase information fromits resulting dc value, provided that the measured admittanceor impedance is constant. However, this method is not ap-propriate for measuring time-varying ventricular admittancesignal, because the sensed signal has an amplitude modulation(AM)-like waveform and no longer a pure sinusoidal wave.One practical phase detection method is to measure the timedifference between the zero-crossing points of two signals,as illustrated in Fig. 1. Once the time difference td is mea-sured, the phase difference between two signals φ can becalculated as

φ =tdT

× 360◦ (5)

where T is the period of the reference signal, and φ is givenin the unit of degree. Hence, it implies that the resolution ofphase measurement is directly related to time resolution, whichis the reciprocal of the sampling rate in digital implementation.As a result, if this time-domain phase detection method isadopted, the sampling rate should be at least 360 times higherthan the signal frequency to achieve a 1◦ resolution. In fact,according to the Nyquist law, as long as the sampling rate is twotimes higher than the signal frequency, the phase informationis preserved and can be retrieved by performing fast Fouriertransform (FFT). However, performing FFT requires too muchcomputing resource, and thus, it is not suitable for the proposedsystem.

The fundamental frequency of ventricular admittance sig-nal is correlated with the heart rate, which may range from

1 Hz (60 bpm, human) to 10 Hz (600 bpm, mice). However, forin vivo measurements, the frequency of the injected ac currentsignal is much higher than that rate, which typically rangesfrom 1 kHz to 100 kHz [3]. Accordingly, the frequency of thesensed voltage signal is also in the range of 1–100 kHz, whichimplies that the low-frequency ventricular admittance signalshave been modulated to be around the carrier frequency.

For example, if a 30-kHz current signal is injected forphase measurement, an analog-to-digital converter (ADC) witha sampling rate of at least 10.8 MHz is required to convertthe sensed signals to digital for further processing. Due tothe necessary signal-processing time, the system clock rateof a digital instrument is, in fact, much higher than its ADCsampling rate. For example, if 100 clock cycles are required toprocess each sampled data before outputting it, the system clockrate must exceed the ADC sample rate by at least 100 times.The precision of an ADC generally decays as its samplingrate increases, and the cost and complexity of a digital instru-ment exponentially increases as its system clock rate increases.Conversely, the frequencies of ventricular admittance signals,including their harmonics, are generally lower than 100 Hz.Hence, it is ineffective to use a 10.8-MHz sampling rate tosample a signal with frequencies lower than 100 Hz.

In other words, the bottleneck of digital implementation isthe need for a high sampling rate, which, in turn, increases thesystem clock rate. For low-frequency ventricular admittancesignals, most output signals from a high-sampling-rate digitalinstrument are redundant. With some digital signal-processingtechniques, those redundancies can be traded for a lower sam-pling rate, which greatly reduces the complexity of digitalinstruments.

The sensed signal waveform of the conductance cathetersystem was analyzed, and a novel DSP-based admittance mea-surement system for the conductance catheter was developed,which has a relatively low sampling rate and high phase resolu-tion. In addition, a waveform emulator was constructed to testand measure the performance of the proposed system. Finally,the proposed system was tested by performing an in vivo ratexperiment.

II. METHODOLOGY

Fig. 2 presents a block diagram of the proposed system,and Fig. 3 is a photograph of the proposed system. A four-electrode conductance catheter is used for illustration in Fig. 2.An ac current signal, which is typically 30 μA for a murineconductance catheter system, is applied to the two outer elec-trodes. The ac current signal can be generated by a voltage-to-current converter or from a current signal generator [19]. Thevoltage between the two inner sensing electrodes is amplifiedand converted to digital signals by the built-in ADCs of a digitalsignal processing (DSP) chip, i.e., TMS320F2812, which wasmanufactured by Texas Instruments, Inc., TX, USA [20], [21].The digital signals are then processed within the programmableDSP chip. The output signals of the DSP chips are sent to apersonal computer (PC) through a digital I/O card, i.e., LPCI7200S, which was manufactured by ADLINK Technology, Inc.,Taiwan [22]. LabVIEW 8.2 is used to postprocess and record

WEI et al.: REAL-TIME DSP-BASED CONDUCTANCE CATHETER MEASUREMENT FOR ESTIMATING VOLUMES 3585

Fig. 2. Block diagram of the real-time admittance measurement system.

Fig. 3. Photograph of the proposed system.

the measured data. The inverted execution of the magnitude1/x is performed in LabVIEW, because it can perform floating-point division rather than fixed-point operation in the DSP toavoid accuracy deterioration [23]. The signal-frequency rangefor each interface is also labeled in Fig. 2. The componentsand implementation method of each building block in Fig. 2are described as follows.

A. ADC and Band-Pass-Filter

The built-in ADCs of the DSP chip are 12 bits, and theirsampling rate fs is set at 20 times higher than the injectedcurrent signal frequency. For instance, a 600-kHz sampling rateis used when the injected signal frequency is 30 kHz. Twoanalog signals are converted to digital signals by the ADCs,including the reference signal that was copied from the injected


current signal and the sensed signal from the two inner sensingelectrodes. Following conversion, the reference and sensedsignals are filtered by bandpass filters (BPFs). Experimentally,there are many noise sources, e.g., electromagnetic interfac-ing noise from other medical instruments, to contaminate thesensed signal. Therefore, BPFs are required to remove the noiseoutside the signal band.

The injected current signal iref(t) can be represented as

iref(t) = Imag cos(2πfreft) ⇔ phasor : Iref = Imag∠0◦

(6)

where Imag is its magnitude, and fref is the injected signalfrequency. According to the previous literature, the instan-taneous ventricular impedance Zmi, which is the reciprocalof instantaneous ventricular admittance Y mi, is a parallelresistor–capacitor (RC) network, where the resistive portion Ri

comes from instantaneous blood and myocardial resistances,and the capacitive portion Ci comes from the instantaneousmyocardial capacitance [10], [15]. Thus, at the frequency fref ,Zmi can be expressed as

Zmi = Ri//

(1

j2πfrefCi

)=

Ri

j2πfrefRiCi + 1

≡Zmag_i∠φmi (7a)

⇒ Zmag_i =Ri√

1 + (2πfrefRiCi)2(7b)

∠φmi = −tan−1(2πfrefRiCi) (7c)

Ymi =1

Zmi=

1Zmag_i

∠ −φmi. (7d)

Both Ri and Ci are time varying due to the heart beating,and hence, Zmi, Zmag_i, and φmi are also time varying [10],[15]. However, the frequency of the heartbeat is extremely lowcompared to fref , as we have previously stated. Therefore,based on the viewpoint of the reference signal, the RC networkcan be viewed as a linear time-invariant system. Thus, thesensed voltage signal in phasor form and in time domain, i.e.,V sen and vsen(t), respectively, can be represented as

phasor: V sen = IrefZmi = ImagZmag_i∠φmi

⇔ vsen(t) = ImagZmag_i cos(2πfreft + φmi). (8)

As aforementioned, the variation of Zmi is caused by theheartbeat; thus, in a steady state, Zmi should be a periodicsignal with a fundamental frequency fm, which is the sameas the heart rate. Moreover, according to in vivo measurementdata, it is reasonable to model the magnitude of the ventricularimpedance Zmag_i as a sinusoidal wave with a dc value forsimplification, because the ventricular impedance cannot bezero or negative in any circumstance [1]–[10], [15], [24], i.e.,

Zmag_i = Z1 + Z2 cos(2πfmt + θ) (9)

where Z1 is the dc value, Z2 is the magnitude of the variation,and θ is its angle with respect to Iref . Thus, the magnitude of

the sensed voltage |vsen(t)| is

|vsen(t)| = ImagZmag_i = Imag · [Z1 + Z2 cos(2πfmt + θ)] .(10)

Therefore, based on the viewpoint of the magnitude of thesensed signal, it is an AM signal [24], [25]. In addition, thesensed voltage signal can be expressed as

vsen(t) = ImagZ1 cos(2πfreft + φmi)

+ImagZ2

2cos[2π(fref − fm)t + φmi − θ]

+ImagZ2

2cos[2π(fref + fm)t + φmi + θ]. (11)

Although fm is very low, this low-frequency signal is, infact, modulated to high frequencies in vsen(t), i.e., fref + fm

and fref − fm. Hence, as long as the BPF is centered at fref

with a bandwidth (BW) larger than 2fm, the information ofventricular admittance can be preserved after filtering, whereasnoise outside the passband is inhibited. The reference signal isalso filtered by the same BPF, and, thus, no time delay existsbetween the reference and sensed signals for the followingprocessing.

A second-order Butterworth filter is employed to constructthe BPFs. Butterworth filters provide the maximum flat re-sponse around fref , which minimizes waveform distortion forventricular admittance [26]. Although high-order filters providerelatively sharper transition bands, they require more calcula-tions. Hence, the second-order Butterworth filter is chosen.

B. Magnitude and Phase Detections

After passing the BPFs, the dc component is blocked, andonly signals with frequencies within the passband of BPFsremain. The magnitude of a no-dc-offset sinusoidal wave equalsthe maximum value of the data sampled during a cycle, whichcan easily be detected by comparison. Hence, AM-like signalscan be demodulated. As a result, an impedance-magnitude-proportional output signal M [n] is obtained. The reciprocal ofM [n] is taken in a PC to generate a signal proportional to themagnitude of the ventricular admittance. Fig. 4 illustrates theprocess of magnitude detection, where the ratio between fref

and fm is lowered to 10 for illustration purposes. In fact, theratio ranges from 100 to 100 000.

The principle of the adopted phase detection approach isillustrated in Fig. 1. The curve of a sinusoidal wave is approx-imately linear around zero crossings. Hence, the sampling ratecan be reduced using an interpolation technique. By using thistechnique, the sampling rate can be reduced to only 20 timeshigher than the reference signal frequency for a 1◦ phaseresolution.

C. Sequence Control

Based on Fig. 2, each sampled data of vsen(t) is subjectedto filtering, magnitude detection, and phase detection beforebeing transferred to the PC. Such processing requires an


Fig. 4. Illustration of the magnitude detection technique.

enormous amount of computations, which implies the need foran excessively high system clock rate, even for a sampling rateof 600 kHz. As aforementioned, the frequency of ventricularadmittance, including both magnitude and phase, are very lowwith respect to fref , and the detected signals are unlikely tosuddenly change, which means that the detected magnitudeof this cycle, for instance, may be the same as or very closeto the detected magnitude of the next 100 cycles. Moreover,the transfer rate between the DSP and the PC is set to only1 kHz; thus, outputting every detected magnitude and phaseis impossible. Hence, to reduce the required system clock rate,signals can first be sampled for a couple cycles, then sampling isstopped, and the data are processed; sampling is then resumed.That is, two phases—sampling and processing—are defined.Fig. 5 illustrates the sequence/event control for a 600-kHzsampling rate.

During the sampling phase, reference and sensed signalsare sampled and then saved in buffers. No calculations areperformed during this phase; consequently, high-speed real-time sampling is practical. The sampling phase lasts for only acouple of signal cycles and is followed by the processing phase,during which no sampling is performed. Signal processing—filtering, magnitude detection, and phase detection—is per-formed in the foreground during the processing phase, anddata are read from the buffers. After the processing phase iscomplete, the sampling phase starts again.

The adopted interrupt service routine (ISR) is a timer un-derflow interrupt, which generates interrupts at a fixed rate andis suitable for real-time data acquisition [20]. Keeping ISRsas short as possible is the key to real-time data acquisition.Therefore, sequence control and most signal processing are per-formed in the foreground, whereas only sampling, outputting,and low-pass filtering are done in the ISRs. In addition tosampling data, transferring data between the DSP and thePC should also be set in real time. Hence, this task is alsoperformed within the ISR. Moreover, a low-pass filter (LPF) isalso in the ISR to suppress noise outside the system BW. Thus,the 3-dB BW of the LPF should be the same as the desiredsystem BW. Due to the possible low level of sensed signals,limiting the system BW is important such that it is just sufficientto adequately pass the sensed signal. In this manner, optimalsignal-to-noise ratios (SNRs) can be obtained.

D. Waveform Emulator

To measure the performance of the DSP system, a waveformemulator that mimics the sensed signal was constructed. As pre-viously stated, based on the viewpoint of the magnitude of thesensed voltages, the waveform is an AM signal. Theoretically,an AM signal is generated by multiplying a low-frequencysignal with a high-frequency carrier. An analog multiplier chip,i.e., MPY634, which was manufactured by Analog Devices,Inc., USA, is used to generate the AM signal.

E. In Vivo Rat Experiment

A white Wistar rat with a weight of 252 g was anesthetizedwith 10-cc Ketalar and 3-cc Rompum. The rat was mechani-cally ventilated at a rate of 46.7 breaths/min. A rat conductancecatheter, i.e., SPR-838, which was manufactured by Millar In-struments, Houston, TX, USA, was used in the experiment. Thecatheter was inserted into the carotid and then fed retrogradeinto the left ventricle (LV) of the rat. The other end of thecatheter was connected to the proposed system, and fref is setto 30 kHz. The magnitude and phase signals of the rat LVadmittance were measured and recorded.

III. SYSTEM PERFORMANCE

A. Accuracies of Magnitude Measurement andPhase Measurement

Fig. 6(a) shows the relationship between the true and themeasured magnitudes of admittance. The magnitude rangeis the same as the measurement range of a commercialpressure–volume system, i.e., MPCU 200, which was manu-factured by Millar Instruments, Inc. A linear regression linewas added to emphasize the linearity of the system, and thecorrelation coefficient is 0.999. The maximal error is 3.2%,which occurs at the low end of the admittance magnitude range.

An inductance–capacitance–resistance (LCR) meter, i.e.,MT4090, which was manufactured by Motech Industries, Inc.,Taiwan, is used to calibrate the system. Fig. 6(b) shows therelationship between the true and the measured phase angles ofthe admittance. A linear regression line is added to emphasizethe linearity of the system, and the correlation coefficient is0.999. The maximal difference between the true and the mea-sured phase angles is 0.8◦, which occurs at the low end of themeasured phase range.

B. System BW and Frequency Ranges

The system BW was measured as follows. A sinusoidalsignal with a 1-V magnitude and 1.5-V dc offset was mul-tiplied by a 30-kHz carrier to generate an AM signal in thewaveform emulator, and then, this AM signal was input intothe DSP system. By altering the signal frequency, the frequencyresponse of the DSP system can be obtained. Fig. 7 shows thefrequency response of the DSP system. With the LPF beinglocated in the ISR (see Fig. 2), the 3-dB system BW is 50 Hz,which is determined by the 3-dB BW of the LPF. A 50-Hz BWis adequate for measuring ventricular admittance signals. For


Fig. 5. Sequence control for a 600-kHz sampling rate.

Fig. 6. (a) Relationship between the true and the measured magnitudes ofadmittance. (b) Relationship between the true and the measured phase anglesof admittance.

applications that require high BW, the LPF can be redesignedor removed to increase the system BW. Data that were measuredwithout an LPF in the ISR are also shown in Fig. 7. Themeasured system BW is 440 Hz. To avoid aliasing, the systemBW should be limited up to half the transfer rate between theDSP and the PC ftrans, which is 500 Hz for ftrans = 1 kHz. Thesystem-to-PC transferring rate of the Millar pressure–volumesystems is set to 1 kHz; thus, ftrans is also set to 1 kHz in theproposed system. However, it can, in fact, be decreased for a50-Hz BW.

Fig. 7. Frequency response of the DSP system.

As previously stated, the required frequency range of theinjected/reference signal is 1–100 kHz. However, due to thelimited performance of TMS320F2812, the proposed algorithmand system only works for frequencies of up to 50 kHz. Thelow frequency bound is limited by the DSP–PC transfer rateftrans, which is 1 kHz in our current design. The frequencyranges of the reference signal frequency fref , the measuredsignal frequency fm, the transfer rate ftrans, and the samplingrate fs of the proposed system are all labeled in Fig. 2.

C. SNR

SNR is a ratio of the signal root-mean-square (RMS) valueto the RMS sum of all other spectral components over aspecified BW. Typically, a pure sinusoidal wave is directly fedinto a system to measure its SNR. However, in the proposedsystem, an AM waveform, instead of a sine wave, is requiredas an input. Therefore, while measuring the SNR, the signalsthat were generated by the waveform emulator are used asinput signals for the proposed system, which implies that themeasured SNR is affected by the waveform emulator perfor-mance. Any distortion or harmonic components that originatein the waveform emulator, primarily from the analog multiplier,deteriorate the measured SNR.

One technique can minimize this effect. An SNR measure-ment over a specified BW is made using a −50 dB signal,i.e., 50 dB lower than a full-scale signal, and then, 50 dB is


Fig. 8. Measured signal frequency fm versus the measured SNR. fref is setto 30 kHz, and ftrans is 1 kHz.

Fig. 9. In vivo measurement of (a) magnitude and (b) phase of the rat LVadmittance.

added to the resulting measurement to refer the measurement tofull scale. This technique ensures that distortion and harmoniccomponents are below the noise level and do not affect mea-surements. In fact, distortion and harmonic components shouldnot be included, by definition, when measuring the SNR. Thismeasurement technique has been approved by the ElectronicIndustries Association of Japan (EIAJ CP-307) and the AudioEngineering Society (AES17-1991). Fig. 8 is a plot of themeasured SNR versus the measured signal frequency, whereasthe reference signal frequency fref is set to 30 kHz, and theDSP–PC transfer rate ftrans is 1 kHz. The built-in ADCs of theDSP are 12 bits, which puts a theoretical upper limit of 72 dBfor the system SNR. The measured SNR are 66–68 dB withinthe system BW (see Fig. 8).

D. Measured Magnitude and Phase of theIn Vivo Rat LV Admittance

An in vivo rat experiment was performed to test the proposedsystem. Fig. 9 shows the measured magnitude and phase of therat LV admittance. The phase is inversely proportional to the

Fig. 10. Relationship between the number of samples per reference cycle andthe maximal phase error from Matlab simulations.

magnitude, which is consistent with the findings of the previousliterature [10], [15].

IV. DISCUSSION

A. Tradeoff Between the Sampling Rate fs and Accuracy

One may ask why 20 is chosen as the ratio between the sam-pling rate and the reference signal frequency in the proposedsystem. In fact, a larger ratio results in a lower sampling ratefor the same reference signal frequency, thereby extending thetime interval between two successive samples. On the otherhand, the accuracy of phase detection relies on the accuracyof linear interpolation, which assumes that a sinusoidal waveis approximately linear around zero crossings. However, thisassumption weakens for a low sampling rate. In other words,a tradeoff exists between the sampling rate and the accuracyof phase detection. Other factors that affect the accuracy andresolution of phase detection include the resolution of the built-in ADCs and the magnitudes of sensed and reference signals.By taking the ADC resolution into account, the relationshipbetween the number of samples per reference cycle and themaximal phase error from Matlab simulations is shown inFig. 10, where the magnitudes of both the sensed and referencesignals are assumed to reach the maximum of the ADC inputrange. According to Fig. 10, the ratio of 20 was chosen for thebest compromise.

B. Comparison Between the Previous Analog System andthe Proposed System

Compared with the analog conductance catheter system thatwas used in previous studies, the accuracies of the analog andthe proposed digital systems are similar, whereas the maximuminjected frequency of the proposed system fref is 50 kHzcompared with 100 kHz in the previous analog system [10],[15], [17]. However, the proposed system can perform real-timecontinuous magnitude and phase measurements, whereas thephase measurement of the previous analog system was doneoffline, and the phase cannot continuously be measured [10],


[15], [17]. Moreover, the phase delay due to the previous analogsystem increases with frequency (15◦ for fref = 10 kHz and 97◦

for fref = 100 kHz), and hence, phase calibration is extremelyimportant and should carefully be performed for every injectedfrequency before each experiment. On the contrary, the phasedelay due to the proposed system is relatively negligible andconstant (2◦ for fref = 30 kHz). Finally, the most importantsuperiority of the proposed system is that it does not havecomponent aging problems, and hence, its performance doesnot drift with time. On the other hand, the center frequencyof BPFs, offsets, and gains of the operational amplifier in theanalog system drift with time, which makes the instrumentmaintenance a burden.

In in vivo experiments, real-time admittance-pressure plotscan be obtained by the proposed system if the pressure signalis incorporated. To incorporate the pressure signal, a drivingsignal for the pressure transducer and an extra ADC inputchannel for recording the pressure signal are required [23].Then, by using (2a-b)–(4), real-time pressure–volume plots canbe obtained [7], [10].

V. CONCLUSION

A novel DSP-based conductance catheter system for mea-suring time-varying ventricular admittance signals has beenpresented. The frequency of the injected signal is 1–50 kHz,and the system can acquire time-varying admittance signals upto 50 Hz, i.e., a 50-Hz system BW. Higher BW is achievablewith slight modifications to the DSP code. With 12-bit ADCs,the measured SNR achieves 66–68 dB within the system BW.High accuracy and linearity are achieved for both magnitudeand phase measurements in the proposed system. Notably, byincorporating pressure signals and the conductance-to-volumeconversion equation, plots of real-time pressure–volume loopscan be obtained.

ACKNOWLEDGMENT

The authors would like to thank Dr. C.-D. Kan andDr. M.-L. Tsai for performing the in vivo animal experiment.

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Chia-Ling Wei (S’02–M’04) received the B.S. andM.S. degrees in electrical engineering from theNational Taiwan University, Taipei, Taiwan, in 1995and 1997, respectively, and the Ph.D. degree in elec-trical and computer engineering from the Universityof Texas, Austin, in 2004.

From 1997 to 1999, she was with Taiwan Semi-conductor Manufacturing Company Ltd., Hsinchu,Taiwan, where she was engaged in integrated circuitdesign. In 2004, she joined Silicon Laboratories Inc.,Austin, TX, as a Design Engineer. In 2006, she

joined the faculty of National Cheng Kung University, Tainan, Taiwan, whereshe is currently an Assistant Professor of Electrical Engineering. Her researchinterests include biomedical instrumentation and power management integratedcircuit design.

Chieh-En Chen was born in Chung-Hwua, Taiwan,in 1984. He received the B.S. and M.S. degreesin electrical engineering from the National ChengKung University, Tainan, Taiwan, in 2007 and 2008,respectively. His research is focused on the develop-ment of conductance catheter measurement system.

I-Ta Tseng received the B.S. degree in electricalengineering from the National Taipei University ofTechnology, Taipei, Taiwan, in 2006 and the M.S.degree in electrical engineering from the NationalCheng Kung University, Tainan, Taiwan, in 2008.

Chin-Hong Chen received the B.S. degree in elec-trical engineering in 2008 from the National ChengKung University, Tainan, Taiwan, where he is cur-rently pursuing the M.S. degree in the Department ofElectrical Engineering.

real-time dsp-based conductance catheter measurement system for estimating ventricular volumes

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