Measuring Ocular Microtremor

The Brainstem Has 3 components 4 cranial nerves on each section

Assessing Brainstem Activity “High Tech”  BIS Technology Expensive (sensors & trained technicians) Can’t use if swelling from head injuries  Glasgow Coma Scale “Low Tech”  Cranial Nerves

Unconscious Patients Very uncomfortable procedures to assess intactness of cranial nerves  Flush 60 oz. of ice cold water in ear

Ocular Microtremors (OMT) OMT have been shown in clinical studies to accurately reflect brainstem activity Ocular motor nerves originate in the brainstem where sensory signals are pre- processed and sent to the cerebrum Oculo-motor nerves originate in the brainstem These signals are necessary for consciousness

Packaging & Architecture Incorporate a disposable element into the sensor mounting apparatus. Redesign and re- package the electronics for miniaturization and packaging for in-line dongle-type element on the cable. Objective 1

Objective 2 Signal Processing - Signal processing for signal amplification and/or noise reduction

Objective 3 Integration Into Patient Overhead Monitor Research and understand the standard(s) used to integrate into this technology Implement in software with potential hardware modifications for the physical interface.

Current Trend Analysis Software

Component Overview Waveform Generator OMT Simulator Sensor (Provided) Power Supply Analog Input/ Preprocessing Circuitry - Amp Test MUX XRAM Microprocessor with Integrated A/D, UART, and DSP Bedside Monitor (Philips MP-60/70 or Agilent V24/26) Philips VueLink Module – M1032A For MCU, currently looking at Atmel AVR32 with 12-bit A/D and integrated DSP functions,50 MHz clock.

OMT Sensor Composed of a piezoelectric transducer, and a surface mount IC amplifier The sensor generates voltage when it undergoes stress, in this case eye movement The piezoelectric transducer was designed to be really sensitive to slight amounts of stress

Power Management The current “Blackbox” is powered by a medical grade power supply which provides ±12V for the sensor, and 5V for the processing unit The Atmel AVR32 processor has a maximum voltage rating of 3.6V The interface module to the bedside monitor will be powered by the monitor itself

Originally the bedside monitor was going to be used to power both the processor and sensor Power options Dual PS/2 – output voltage 5V ±10% Dual MIB/RS232 – output voltage 5V ±5% USB 2.0 – low power mode, output voltage 4.4V It is more convenient to construct our own custom power supply that is capable of powering both the sensor and processor Power Management continued

Power Flow Sensor Power supply Signal processing unit Monitor interface Bedside monitor

Sample Signal until XRAM Buffer Full Perform DSP Algorithm on Buffer Contents Extract Necessary Data From DSP Output Send Wave Samples, Freq, and Amp. to UART(Monitor) Broad Overview of Software

Sampling Sampling will be interrupt driven, and will continue until a buffer a buffer is filled. Buffer must be large enough for an adequate analysis of signal. When the first buffer is full, the DSP algorithm will be started on the signal and sampling will continue to a secondary buffer of equal size. Sampling and DSP will alternate between buffers so no data is lost. The buffer size and sampling rate will be configurable over the UART. A/D DSP Buffer 1 Buffer 2 XRAM DSP Output Bus

FFT – Generate Output Array in XRAM Search Valid Range of Array for Max Energy Zero Out Locations Outside of OMT Range (Filter) Inverse FFT to Obtain Filtered Waveform in Time Domain FFT Method Amplitude/ Frequency Output Filtered Waveform Output -An FFT is performed on the contents of the sample buffer, with spectrum data saved to an array. -The array locations corresponding to the valid OMT range are searched for the maximum energy, giving the Frequency and Amplitude of the OMT signal. -An inverse FFT on only the valid OMT frequency range is required to reconstitute a time-domain waveform that can be sent to the bedside monitor.

Digital HPF/LPF/Notch Filter Routines Modify Buffer Filtered Waveform Output Peak-Count and Amplitude- Averaging Algorithm Amplitude/ Frequency Output Peak-Detection Method -Digital filter routines modify the contents of the buffer, giving a filtered signal that could be sent to the bedside monitor. -Positive-to-negative changes in slope are counted, giving the frequency. The amplitudes at which this occurs are averaged. Wavelets – Short Oscillations of a given frequency. Can be compared to our signal to determine if energy at that frequency is present.

Bedside Monitor Interface Philips VueLink Module 1032A RS-232 Input – Open Interface Protocol Allows 1 Waveform (OMT Signal), 2 Numerics (Amplitude and Frequency), Diagnostic Messages (ex. OMT out of valid range), and Alarms (ex. OMT below certain value) SDM_VueLink_Installation_Manual.pdf

Testing and Data Mining Important in order to determine the pattern and/or trend of OMT signals Determining a pattern is vital to the signal processing component The process involves a subject (one of us) to lie down and have the sensor mounted on a closed eyelidusing surgical sensitive tape Due to a lack of FDA approval and other required certification, we will not be able to gather data on a large scale basis

Vivian’s OMT

Mark’s OMT

Division of Labor

Schedule First MilestoneSecond MilestonePre-Expo GrantsThroughout Semester

Budget

Interference Concerns Accidental patient movement Electrical noise from other equipment in the ICU room Noises in excess of 95 dB Heartbeat

Contingencies Software implementation of display if bedside monitor is not able to be acquired Scaling number of processors to ensure proper operation Purchasing additional components to ensure minimal downtime especially before expo

Risks During application of sensor patients’ eye may be agitated Additional cord is hanging off of patient Possibility of dongle removing sensor from patients’ eye if too heavy Components may be destroyed if wired incorrectly

Extension possibilities Add through connections in order to minimize hanging cables Adding accelerometer to measure patient movement to find bad samples

Questions?