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Sal Ortiz: Software Engineer Ruben Sifuentes: Hardware Engineer Brent Taylor: Project Manager T.

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Presentation on theme: "Sal Ortiz: Software Engineer Ruben Sifuentes: Hardware Engineer Brent Taylor: Project Manager T."— Presentation transcript:

1 Sal Ortiz: Software Engineer Ruben Sifuentes: Hardware Engineer Brent Taylor: Project Manager T

2 Overview  Scope, application and users  EEG signals  Epileptic absence seizures  Market analysis  Hardware & Software design  Risks and contingencies  Future directions T


4 Scope, Application and Users  Single channel EEG machine  Records EEG data for later analysis  Detects patterns typical of epileptic absence seizures  Used by trained medical professionals  Designed for use outside of a clinical environment T

5 EEG Signals  EEG is short for electroencephalogram  A trace of electrical activity in the brain  It appears in regular, repeated patterns  Frequencies and amplitudes of these waves are an indication of brain activity T Normal EEG rhythm

6 Brain Rhythm Detector  Takes a single EEG signal from electrodes attached to the scalp  Records brain rhythms and displays traces on a monitor  Detects epileptic absence seizures O

7 Normal EEG O

8 Epileptic Absence Seizures  Absence seizures are just one type of epileptic seizure  The primary characteristic of absence seizures is a brief loss of awareness  May be accompanied by subtle symptoms, such as blinking of the eyes or a fluttering of the eyelids O

9 Absence Seizure Rhythm  The frequency is about 3 Hz  Amplitude is much larger than normal brain waves at this frequency O

10 EEG During an Absence Seizure O

11 Market Analysis DevicesZmachineBioSomniaBrain Rhythm Detector Patient population Adult Adult, and Children Application Sleep monitoring system Absence Seizure Detector, Sleep Monitoring System EEG Channels 111 Battery powered Yes Real-Time Data Analysis Yes S

12 Labor Costs Estimated Project Budget Materials: $509 Labor: $37,548 S

13 Brain Rhythm Detector Design S

14 Brain Rhythm Detector – Signal Pickup  Signal acquired from electrodes attached to the scalp and ear  EEG signals are on the level of 5-100μV and are susceptible to noise S

15 Brain Rhythm Detector – Amplification and Filtering  Signal feeds into an instrumentation amplifier and 2 nd stage amplifier  An isolation amplifier protects the subject from line voltage.  A band-pass filter limits the frequency range between 1.6mHz and 40Hz  From there it’s digitized and sent to the computer  The EEG signal is shown on the computer monitor  The signal is recorded to a file S

16 Hardware Components I nstrumentation Amp Secondary Amp & BP Filter S

17 Brain Rhythm Detector – Processing  BRD software will record EEG data as it’s received  The analysis component examines the data for absence seizures  When a seizure is detected it will be indicated on the screen and recorded to the log O

18 Software  Three components Data acquisition Data Display and Analysis Epileptic seizure simulator

19 Data Acquisition Component O

20 Normal Signal Acquired from Subject

21 Data Display & Analysis Component O

22 Epileptic Seizure Simulator

23 Simulated Absence Seizure Signal Normal Epileptic Normal

24 Risks and Contingencies TRiskCause Native Risk (RPN1) Risk Mitigation Modified Risk (RPN2) No Data Displayed Leads dirty or are not correctly connected, or power is off 10 Clean leads before placement and check battery connection 2 False readings Noise being picked up or calibration errors 28 Calibration before placement, proper shielding from external noise 8 Electrical ShockPower short/malfunction or exposed to liquids 16 Patient is isolated from electrical power 2 Leads being pulled or damaged Loose lead wires can be caught on objects 18 Lead wires are tied together preventing loose wires 1 Severity Range(S): 1=Negligible Harm, 2=Minor Injury, 3=Major Injury, 4=Death Frequency Statistical Analysis (F): 1=Virtually Never, 2=Remote, 3=Occasional, 4=Probable, 5=Frequent, 6=always Detectability Range(D): 1=Obvious, 2=Noticeable, 3=Obscure, 4=Undetectable RPN=Risk Probability Range Acceptable Risk 8 and below *RPN1= S1xF1xD1 *RPN2=S2xF2xD2

25 Safety Issues  Patient isolation from powered components  Sealed enclosure to protect device from moisture T

26 Future Enhancements  Miniaturization  USB port for data transfer  Encryption of identifying patient information  Sleep monitoring T

27 References  Chappell, B., & Crawford, P. (2001). Epilepsy: The ‘At Your Fingertips’ Guide. Longod,GBR: Class Publishing.  Consolidated Research of Oakland, Inc. (2011, March 31). ZMachine 510(K) Summary (PDF). Retrieved October 06, 2011, from FDA 510(k) Premarket Notification DB:  National Instruments. (2011). NI USB-6008. Retrieved October 5, 2011, from National Instruments:  OnTheHub. (n.d.). LabView Development Environment 2011 (32-bit). Retrieved October 5, 2011, from On The Hub Store: bb6c-0030485a6b08&vsro=8&srch=labview&JSEnabled=1  Oxford Biosignals Limited. (2002, September 11). Biosomnia 510(K) submission (PDF). Retrieved October 6, 2011, from FDA: 510(k) Premarket Notification DB:  The Royal Children's Hospital, M. (2011). Routine EEG - Video presentations: Normal EEG movie. Retrieved from  The Royal Children's Hospital, M. (2011). Absence Seizure EEG - Video presentations: Normal EEG movie. Retrieved from  Tatum, W. O. (2007). Handbook of EEG Interpretation. New York, NY, USA.  Tong, S., & Thankor, N. V. (2009). Quantitative EEG Analysis Methods and Clinical Applications. Norwood, MA, USA: Artech House.  The Center for Epilepsy & Seizure Education, B.C., Canada. Retrieved from T

28 Q&A? T

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