Asanin, Bogdan

Karadaglic, Dejan

Knezevic, Sasa

Stojanovic, Radovan

English

Sasa Knezevic

Radovan Stojanovic

Bogdan Asanin

Dejan Karadaglic

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A single chip solution for pulse transmit time measurement

This paper proposes a methodology for Pulse Transmit Time measurement for purposes of cuff-less monitoring of Blood Pressure. The ECG and PPG signals are simultaneously acquired and Pulse Transmit Time is determined as a time between R peak in ECG and maximal slope in PPG. To distinguish characteristic points Discrete Wavelet Transform, Derivative Filtering, Modulus Maxima and additional functions are employed and translated in VHDL code and then embedded in a single chip from FPGA technology. The system works on-line, achieving accuracy of 97.53% and 97.09% for R peak and slope detection respectively. The emphasis is given to the description of signal processing approach, system architecture and working principles of fundamental components. Also, the preliminary testing results are presented

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A single chip solution for pulse transmit time measurementKnezevic, Sasa; Stojanovic, Radovan; Asanin, Bogdan; Karadaglic, DejanPublished in:2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE)DOI:10.1109/BIBE.2013.6701625Publication date:2013Document VersionPeer reviewed versionLink to publication in ResearchOnlineCitation for published version (Harvard):Knezevic, S, Stojanovic, R, Asanin, B & Karadaglic, D 2013, A single chip solution for pulse transmit timemeasurement. in 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE).,14026341, IEEE, pp. 1. https://doi.org/10.1109/BIBE.2013.6701625General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.Take down policyIf you believe that this document breaches copyright please view our takedown policy at https://edshare.gcu.ac.uk/id/eprint/5179 for detailsof how to contact us.Download date: 29. Apr. 2020   Abstract—This paper proposes a methodology for Pulse Transmit Time measurement for purposes of cuff-less monitoring of Blood Pressure. The ECG and PPG signals are simultaneously acquired and Pulse Transmit Time is determined as a time between R peak in ECG and maximal slope in PPG. To distinguish characteristic points Discrete Wavelet Transform, Derivative Filtering, Modulus Maxima and additional functions are employed and translated in VHDL code and then embedded in a single chip from FPGA technology. The system works on-line, achieving accuracy of 97.53% and 97.09% for R peak and slope detection respectively. The emphasis is given to the description of signal processing approach, system architecture and working principles of fundamental components. Also, the preliminary testing results are presented. I. INTRODUCTION upplemented by other physiological parameters, the Blood Pressure (BP) is considered as an invaluable diagnostic parameter to access the condition of certain illnesses, especially cardiovascular, which are a leading cause of death worldwide [1]. Continuous monitoring of BP is highly recommended in order to respond timely. The measurement techniques are divided into two categories: direct and indirect. The direct technique is in fact invasive, used when a high level of accuracy, dynamic response and continuous monitoring are required. A catheter with pressure transducer is directly inserted into blood vessel. The indirect techniques, almost existing more than one century, rely on concept of inflatable cuff to the arm which applied auscultatory or oscillometry principle. Presence of the cuff reduces the efficiency and flexibility of the meters in terms of power consumption, measurement frequency, patient mobility, ease of use, etc. The Pulse Transit Time (PTT) method is a cuff-less alternative to the classical indirect methods [2]. The PTT is the time which BP pulse takes to travel from one arterial site to another. It is inverse proportional to the BP value. Typically, the PTT is initiated by the R-wave of the ECG signal and the arrival, at finger or ear, is, usually, detected  Manuscript received August 21, 2013. This work was supported by National project of Ministry of Science of Montenegro, MESI, and EU TEMPUS project BioEMIS. The authors are thankfull for support.     S. K. is with the Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro (e-mail: sasaknezevic@live.com).  R. S. and N.L. are  with the Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro (e-mail: stox@ac.me). B.A. is with the Faculty of Medicine, University of Montenegro, Podgorica, Montenegro (e-mail: basanin@ac.me). D. K. is with the School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow, United Kingdom (e-mail: Dejan.Karadaglic@gcu.ac.uk).  from photoplethysmograph (PPG) signal, in a point of its maximal slope MS, Fig. 1.  RMSPTTECGPPGECGPPG Fig. 1.  The principle of PTT measurement.  The suspension of cuff and beat-to-beat tracking are the main advantages that recommend this method for application in wearable and telemedical devices, which are autonomous, miniature, low-cost, ultra-low-power devices and very often in form of a single chip. In practice, it is not a trivial task to embed all necessary processing operations in a single chip of limited arithmetic, memory and power consumption performances. An additional request is on-line operation.  This paper presents a trial in this direction. It proposes a methodology to implement an on-line system for PTT measurement in a single chip from FPGA technology. First, the characteristic points in ECG and PPG signals are distinguished using optimized Discrete Wavelet Transform (DWT), Derivative Filtering, Modulus Maxima and additional functions. Second, all signal processing functions are developed in VHDL code and embedded into functional modules which are later integrated in FPGA chip. Third, the approach is verified using real signals and obtained results are presented and discussed. II. METHODOLOGY Signal processing and operation flowchart consists of two parallel tracks: QRS detection and PPG slope detection, Fig. 2. Initially, the both signals are passed through the DWT decomposition module which consists of low-pass (L) and high-pass (H) filters according to the Mallat’s scheme [3].  Ai(n) and Di(n), known as approximation and detail coefficients, are the outputs from these filters for ith decomposition level. In this research the integer modification of Haar wavelet transform, here marked as Haar DWT, is used for wavelet decomposition [4].  In case of ECG, Haar DWT, as any other wavelet transform, is capable to distinguish the QRS complexes within ECG signal. The Di(n) coefficients across the scales show that the R peak of the QRS complex corresponds to the A Single Chip Solution for Pulse Transmit Time Measurement Saša Knežević, Radovan Stojanović, Bogdan Ašanin and Dejan Karadaglić S 978-1-4799-3163-7/13/$31.00 ©2013 IEEE  zero crossing (ZC) between two local modulus maxima. In case of PPG, the approximation coefficients Ai(n) are fed to the first order derivative filter producing signals Difi(n).   Maximal slope in original PPG signal, MS, corresponds to the local maxima in D1fi(n). To improve accuracy of detection the coefficients D2fi(n) and D3fi(n) are also examined on local maxima. When R or MS are detected the adequate trigger signals are generated and forwarded to the PTT calculator module, which is in fact a timer for PTT measurement.  HaarDWTA1(n)D1(n)A2(n)D2(n)A3(n)D3(n)Modulus Maxima&Zero CrossingModulus Maxima&Zero CrossingModulus Maxima&Zero CrossingORQRS detectionECG[n]HaarDWTD1(n)A1(n)D2(n)A2(n)D3(n)A3(n)Diff(A(n))PPG slope detectionPPG(n)Diff(A(n)) MaxMaxMaxPTTCalc.PTTRRRMSMSMSD1fi(n)D2fi(n)Diff(A(n))ORD3fi(n)Fig. 2.  PTT Signal processing approach. Fig. 3 illustrates the decomposition of real discrete signal ECG(n) up to the 4th  decomposition level of details,  D1(n), D2(n), D3(n) and D4(n). Simultaneously the signal PPG(n) is decomposed till 4th level of approximations, A1(n), A2(n), A3(n) and A4(n) and then fed to the derivative filter, producing signals D1fi(n), D2fi(n), D3fi(n) and D4fi(n). It is seen how R peak corresponds to the zero crossing of Di(n) and MS to local maxima of D1fi(n).   Fig. 3. (a) ECG signal and detail coefficients from 4 levels of decomposition (b) PPG signal with first derivations of 4 levels of approximations. III. FPGA IMPLEMENTATION A. System Architecture FPGA implementation of PTT meter follows the functional diagram in Fig. 4.  It consists of several modules and runs by 50MHz clock. Analog signals ECG(t) and PPG(t) are digitalized by 12bit A/D convertor TLC2543 (TI) under sampling frequency of fs=800Hz. A/D converter is driven by A/D controller module producing the digital samples ECG[n][11..0] and PPG[n][11..0]. These samples are then fed to the ECG processing core and PPG processing core respectively. ECG processing core produces QRS_pulse which indicates detected R wave while PPG processing core detects maximal slope MS in PPG and generates PPG_slope_pulse. QRS_pulse and PPG_slope_pulse are inputs to the PTT_calculator module which calculate PTTI[10..0], an integer equivalent of PTT. PTTI[10..0] is sent out by UART controller.   ECG[n][11..0]PTTI(TX pin for serial transmision)Controller for A/D conversion50 MHz clockCyclone II FPGA deviceTLC2543 A/D converterQRS complexRESETPPG[n][11..0]ECG processing corePPG processing corePTT calculator UARTQRS_pulsePPG_slope_pulsePTTI[10..0]ECG(t)PPG(t)Maximal slopePTT PTTEnable_measuring Fig. 4.  Block diagram of PTT extraction by FPGA chip. B. QRS Detection The internal structure of the ECG processing core is given in Fig. 5. The digital samples ECG[n][11..0] are fed  to the Haar DWT block whose outputs are detail coefficients  Di(n)[11..0] and approximation coefficients Ai(n)[11..0], i=1..3. The Di(n)[11..0], collected during 1s, from all levels, make arrays D1[NC1…1][11…0], D2[NC2…1][11…0] and D3[NC3…1][11…0], NCi=1s*fs/2i,  NC1=400, NC2=200 and NC3=100. Each of these arrays is then searched by module Local_min., Local_max. and Zero_cross. detector in order to locate characteristic points, local min, ZC and local max. In case of their occurrence in true sequence, the Pulse_i is generated. Condition for successful detection of QRS complex is appearance of at least one Pulse_i at the inputs of OR gate. Afterwards, the Final_pulse generator produces a QRS_pulse which is sent out of the ECG processing core.  HaarDWTA1(n)[11..0]D1(n)[11..0]A2(n)[11..0]D2(n)[11..0]A3(n)[11..0]D3(n)[11..0]Read_en_out_1Read_en_out_2Read_en_out_3Local_min., Local_max. and Zero_cross. detector for D1Local_min., Local_max. and Zero_cross. detector for D2Local_min., Local_max. and Zero_cross. detector for D3Pulse_1Pulse_2Pulse_3Enable_measuringEnable_measuringEnable_measuringORQRS_pulseOR_gate_pulseECG processing coreFinal_pulse generatorECG[n][11..0] Fig.  5.  Internal structure of ECG processing core. 500 1000 1500 2000 2500 3000 3500 4000 450005001000odbirci(n)200 400 600 800 1000 1200 1400 1600 1800 2000 2200-50050odbirci(n)100 200 300 400 500 600 700 800 900 1000 1100-1000100odbirci(n)50 100 150 200 250 300 350 400 450 500 550-2000200odbirci(n)50 100 150 200 250-2000200odbirci(n)ECG(n)D1(n)D2(n)D3(n)D4(n)PPG(n)D1f i(n)D2f i(n)D3f i(n)D4f i(n)a) b)  Each module Local min, Local max and Zero-cross. consists of 5 sub-modules: Local_min. detector; Local_max. detector; Zero_cross. detector; State machine for QRS recognition and Pulse generator, Fig. 6. Operation of Local_min. detector and Local_max. detector follows the logic from Fig. 7. They generate signals Min_detected and Max_detected.     Local_min. detectorDi(n)[11..0] Min_detectedZero_cross. detectorDi(n)[11..0] ZC_detectedLocal_max. detectorDi(n)[11..0]Max_detectedQRS_detectedState machine for QRS recognition Pulse_iEnable_measuringPulse generator5 sLocal_min., Local_max. and Zero_cross. detector for Di Fig. 6.  QRS detection from Di(n) - structure. Decision on local min/max is based on adaptive thresholds Tpi or Tni and the contents of three registers that contain three successive points R1=Di(n)[11..0], R2=Di(n-1)[11..0] and R3=Di(n-2)[11..0]. The decision rules are as following: first, IF ((R3<R2) and (R2>Tpi) and (R1<R2)) then Local_max=R2 and second, IF ((R3>R2) and (R2<Tni) and (R1>R2)) then Local_min=R2. ZC circuit works under principle Di(n)[11..0]>0 and Di(n-1)[11..0]<0 and only sign bits are compared.  0 500 1000 1500 2000 2500 3000 3500 4000 450005001000EKG(n)odbirci(n)0 500 1000 1500 2000-1000100KD1(n)odbirci(n)0 200 400 600 800 1000-2000200KD2(n)odbirci(n)0 100 200 300 400 500-5000500KD3(n)odbirci(n)0 50 100 150 200 250-5000500KD4(n)odbirci(n)TpiTniDi[NCi…1][11…0] ZCR1R2R3R1R2R3 Fig. 7.  Finding modulus maxima from Di(n). QRS recognition is carried out using a state machine (SM) whose inputs are Min_detected, ZC_detected and Max_detected. The initial state lasts for 5 seconds and it serves for initial suiting of the adaptive thresholds, Fig 8. After initial adaptation, the signal Enable_measuring becomes high and process of QRS detection begins. Detection of QRS complex is successful if local max is detected after local min and ZC. Then QRS_detected synchronized output of SM is generated. QRS_detected from SM is fed to the Pulse_generator, which generates Pulse_i of 30 ms duration.  Pulse_1, Pulse_2 and Pulse_3 are passing through the OR gate creating OR_gate_pulse which is in fact the overlapped summation. OR_gate_pulse is fed to the Final_pulse generator which produces QRS_pulse  of 20 ms duration. QRS_pulse happens only if between two sequent OR_gate_pulses passed 200 ms that is considered as down physiological limit for RR interval.   Fig.  8.  State machine for QRS detection. C. PPG Slope Detection PPG samples, PPG(n)[11..0], are decomposed up to 3rd level. Approximation coefficients A1(n)[11..0], A2(n)[11..0] and A3(n)[11..0] are fed to the derivative filter, producing D1fi(n)[11..0], D2fi(n)[11..0] and D3fi(n)[11..0],  and then to the Max. detector 1, Max. detector 2, and Max. detector 3, Fig. 9. The sequences of D1fi[NC1..1][11..0], D2fi[NC2..1][11..0] and D3fi[NC3..1][11..0] (NC1=400, NC2=200, NC3=100) are searched in order to find a local maxima by using thresholding technique. For each Difi(n)[11..0], Ti (i= 1..3) is an adaptive threshold.   Decision rule for local maxima detection for one Difi is: IF ((Difi(n-2)[11..0]<Difi(n-1)[11..0]) and (Difi(n-1)[11..0]>Ti) and (Difi(n)[11..0]<Difi(n-1)[11..0])) then Local_max=Difi(n-1)[11..0]. When local maxima are detected, pulses Max_detected_1, Max_detected_2 and Max_detected_3 are generated and fed to state machine, SM for detection of maximal slope. This SM has initial state of adaptation of Ti which lasts for five seconds. From detection state, after appearance of Max_detected_1, Max_detected_2 or Max_detected_3, the SM goes into PPG_slope_pulse generation state which lasts for 20 ms. After this state, SM goes into standby mode for 180 ms. From standby mode SM returns to detection state.  Differentiator for A1Differentiator for A2Max. detector 1State machine for detection of maximal slopeMax_detected_1PPG(n)[11..0] HaarDWTA1(n)[11..0]D1(n)[11..0]A2(n)[11..0]D2(n)[11..0]A3(n)[11..0]D3(n)[11..0]Read_en_out_1Read_en_out_2Read_en_out_3Differentiator for A3Max. detector 2Max. detector 3Max_detected_2Max_detected_3PPG_slope_pulsePPG processing coreD1fi(n)[11..0]D2fi(n)[11..0]D3fi(n)[11..0]Fig. 9.  Internal structure of PPG slope detection core. D. PTT calculation The circuit for PTT calculation consists of two counters (C1 and C2), one latch registers and control logic, Fig. 10. When QRS_pulse appears at input of PTT_calculator, counters C1 and C2 are enabled by control logic. C1 is clocked by system clock, while C2 is clocked each 1 ms using overflow (of.) of C1, and  latched by Initial adaptation processLocal minimum searchZero crossing searchLocal maximum searchPower up or resetLocal minimum detectedZero crossing detectedLocal maximum detectedQRS_detected='1'QRS_detected='0'QRS_detected='0'QRS_detected='0'QRS_detected='0'QRS_detected='0'QRS_detected='0'  PPG_slope_pulse. Output of C2, Nms represents time elapsed from QRS_pulse appearance and it is latched to PTTI[10..0] each time the  PPG_slope_pulse appears.  PTT_calculatorC1 C2of. clkLetch register1 msreadQRS_pulseClock PTTI[10..0]new_intNmsPPG_slope_pulseControll logicEnable signalEnable signalReset ResetEnable_measuring Fig.  10.  PTT calculator structure. IV. TESTING AND RESULTS Overall system was developed in VHDL code and implemented in Cyclone II FPGA chip EP2C70F896C6N [5]. PTT intervals were transferred out via serial port, while QRS_pulses and PPG_slope_pulses were observed by oscilloscope.  The records from MIT-BIH Arrhythmia and MIMIC databases were used to test the system. Fig. 11 (a) shows the oscilograph of ECG signal and digital pulses, QRS_pulses, at output of the QRS detector, at the pin of the FPGA chip. Fig. 11 (b) shows the result for the detection of maximal slopes in PPG signals. QRS detector test was conducted on 10 records from MIT-BIH Arrhythmia database and it lasted for five hours. Testing was conducted on a total of 22031 beats, where there were 105 false detections and 424 missed detections. The average accuracy of the QRS detector is 97.53%. PPG processing core was tested on records from MIMIC database. 5 x 10-minute PPG records were examined, achieving average detection accuracy was 97.09%.  (a) (b)Fig. 11.  (a) QRS detection from ECG (b) MS detection from PPG.          PTT calculation was tested on records from MIMIC database. ECG and PPG signals were processed in parallel in order to extract PTT intervals. Fig. 12 shows result for calculation of PTT intervals for MIMIC record no. 041 and confirms inverse correlation between PTT and systolic BP. Interval numberSample numberBlood pressure (mmHg)PTT (ms)0 50 100 150 200 250 300 350 4002503003504004505005506006507000 0.5 1 1.5 2 2.5 3x 104405060708090100110120a)b) Fig. 12.  (a) PPT intervals and their correlation with (b) BP signal. V. CONCLUSION A system and methodology for real-time ECG and PPG processing and PTT interval calculation are presented. All tasks are implemented in a single FPGA chip. The system is achieving an accuracy of more than 97% for ECG and PPG signals. As such, it is ideal for embeded systems or wearable health care devices where cuff-less measuremnt of blood pressure is necessary.   REFERENCES [1] WHO, “Cardiovascular diseases (CVDs)”, Fact sheet N°317, March 2013, accessible at http://www.who.int/mediacentre/factsheets/fs317/en/. [2] M. H. Voelz, C. F. Babbs, J. D. Bourland, "Pulse Transit Time as an Indicator of Arterial Blood Pressure '', Psychophysiology, vol. 18, pp. 71-74, 1980. [3] S.G. Mallat, "A theory for multiresolution signal decomposition: the wavelet representation'', IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674-693, 1989. [4] R. Stojanović, D. Karadaglić, M. Mirković, D. Milošević, "A FPGA  system for QRS complex detection based on integer wavelet transform", Measurement Science Review, vol. 11, issue 4, pp. 131-138, 2011. [5] Altera Corporation, www.altera.com.  

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A single chip solution for pulse transmit time measurement

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