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  1. Team Measuring Ocular Microtremor Thomas Rutkowski Vivian Phinney Salman Al-Saif Mark Hasemeyer Steve Carroll

  2. Ocular Microtremors (OMT) -Low-amplitude, high-frequency tremors of the eye. (20-100Hz) -Caused by random, ground-state signals on oculo-motor nerves -Oculo-motor nerves originate in brain stem. OMT indicates brainstem health. -OMT correlated with patient’s level of consciousness. -Gives strong indication of the chances of recovery for coma patients.

  3. System Module Overview System Overview Waveform Generator Bedside Monitor (Philips MP-60/70 or Agilent V24/26) Test MUX OMT Simulator Philips VueLink Module – M1032A Sensor (Provided) Analog Input/ Preprocessing Circuitry - Amp Microprocessor with Integrated A/D, UART, and DSP For MCU, currently looking at Atmel AVR32 with 10-bit A/D and integrated DSP functions,50 MHz clock. Power Supply LCD SD Card

  4. Basic CPU Architecture and Connectivity 3.5” Graphic LCD 16-bit Parallel Interface AVR32UC3B1256 Microcontroller Sensor GPIO Controller MAX3380E UART Level Shifter Signal Conditioning 10-bit A/D UART Controller 16-bit Timer 256kB Flash RealTerm/ Bedside Monitor PLL – 50 Mhz CPU Clock SPI Controller 3.3 V Power Supply 16Mhz Crystal SD Card

  5. Start Sampling Buffer 0 Wait until Buffer 0 Full (1 sec) Start Sampling Buffer 1, Start FFT on Buffer 0 Find OMT frequency from FFT output just completed Wait until Buffer 1 Full (1 sec) – FFT on Buffer 0 finishes ~1 ms Perform inverse FFT on frequencies within valid range. Wait until Buffer 0 Full (1 sec) – FFT on Buffer 1 finishes ~1 ms Send frequency and waveform samples to UART(monitor), LCD, and SD card. Start Sampling Buffer 0, Start FFT on Buffer 1 Processing Algorithm Currently using 1024 sample buffer and 1024 kHz sampling frequency, giving 1 second of data in buffer.

  6. Signal Processing – General Overview • Using FFTs, much faster than DFT algorithms • Atmel studio has several built in FFT algorithms that require minimal modification • But… it’s not working as we had anticipated • Further work in DSP is needed • Just recently got sampling, buffers, and FFT working • Further testing required to find optimal DSP algorithm

  7. Signal Sampling • Using the built in ADC in the Atmel processor to sample data • The input voltage ranges from 0V to 3.3V (adjustable), the signal needs to be scaled and clamped to avoid causing damage to the processor • Use of a 8:1 voltage divider, or an op-amp circuit with gain K = 0.125, assuming a maximum 12V input from the sensor IC • A 3V zener diode is used to clamp the analog input, or a comparator circuit

  8. Signal Sampling (contd.) • Sensor output is in the range ±12 V • A/D takes 0 - 3.3 V • First stage inverts and scales sensor output to ±1.5 V • Second stage adds 1.5V DC offset for new range of 0 – 3 V for A/D

  9. Signal to be sampled • Frequency components at 15Hz, 90Hz, and 165Hz

  10. Signal recreated with MATLAB

  11. FFT • As with other things involving signal processing, we are currently experimenting with the built in FFT functions • The FFT routines functioned properly with minimal modifications • Going forward, two possible solutions to processing that we are considering • After determining the frequency of the OMT signal, a sinusoidal wave is generated with varying amplitudes proportional to the OMT • Using wavelet processing • We will know more once we’ve done more careful testing, since our sensor is very sensitive, there is a lot of room for error

  12. FFT (contd.) • Filtering and Noise Reduction • As mentioned previously in PDR, the OMT frequency range lies between 40Hz and 120Hz • Use of a high pass filter at 20Hz, and a low pass filter at 150Hz • We identified the main source of noise as that coming from various power lines across the room • A notch filter is used (bandstop) for the frequency range 57Hz-62Hz • Sensor is sensitive, picks up the slightest movement, another sensor might be used to provide a differential solution • The other main artifact that affects the signal is heartbeat and voice, we are still considering solutions to both problems

  13. FFT (contd.) • The following slide represents an example of the same signal sampled above • We used the FFT algorithms to represent the frequency content of the analog signal • Simple processing was done to filter out unwanted frequency content (below 20Hz, above 150Hz, and 60Hz) • The slide after that is a capture of Vivian’s OMT frequency content

  14. Processed signal

  15. Captured OMT Signal over 1 Second Interval

  16. We believe that thorough testing and experimentation is very important to the success of our project • After CDR, we will continue with OMT processing

  17. Power Management • Utilizing a medical grade power supply, since project is not power centric, plus it is safer to go with this option and avoid any risk or harm to the experimental subjects (basically us) • ELPAC MED113TT, provides three regulated outputs, 5V, ±12V • LP2951 voltage regulator from TI will be used to power the Atmel processor

  18. Power Management (contd.)

  19. Bedside Monitor • Leasing from Medelco • Lease will include: • Philips MP70 Touch Screen Monitor • M1032A Vuelink Interface Module • M8048A FMS (Flexible Module Server) • M3001A MMS (Multiple Monitoring System) • Possibly: • M1032A#K66 Cable with DSUB-9 connector

  20. VueLink Cable Pinout Showing UART Tx/Rx Lines VueLink Connector DB-9 Connector

  21. Backup Plan - Analog Input Signals

  22. LCD Display • Parallel or SPI Interface to LCD • 4-Wire Resistive Touch Panel • Will display small waveform and OMT frequency, in addition to bedside monitor display. • Touch-screen menu may allow user to configure data-logging options, sampling rate, and buffer size.

  23. SD Card Socket Board Routes SD card pads to solder able through-hole connector. We will use SPI mode to read/write from card. Card requires 3.3 V Data Logging can be started using touch screen menu on LCD. Breakout Board for SD-MMC Cards

  24. Risks and Contingencies • Blowing the sensor piezo-transistor or integrated IC amp • Eyetect shipped a backup sensor earlier this week • The FFT signal processing algorithm is a concern • Waveform spiky and aperiodic • Now that we have the sampling, buffers and basic FFT working, can test other processing strategies: • Wavelets • FIR Filters on Buffer followed by Peak-Count Algorithm • Statistical Analysis

  25. Current Budget

  26. Division of Labor

  27. Deliverables

  28. Timeline

  29. Questions? In Order of Appearance: Tom Steve Salman Mark Vivian