Seizure Detection Algorithm in Neonates Using Limited Channel aEEG Team Members: Tahj Alli-Balogun, Kyle Thomas Client: Dr. Zachary Vesoulis

Background | Project Scope | Design Specs | Existing Solutions | Schedule Neonatal Intensive Care Unit (NICU) Term Births – Birth around 40 weeks Pre-term – Birth before 37 weeks Apnea Respiratory Distress Syndrome Hypoxic-Ischemic Encephalopathy Seizures

Electroencephalogram (EEG) Background | Project Scope | Design Specs | Existing Solutions | Schedule Electroencephalogram (EEG) Conventional EEG (cEEG) 10-256 Electrodes 1-2 hour use High Resolution Needs a trained neurologist to interpret Amplitude-Integrated EEG (aEEG) Usually 4 electrodes 12-24 hour use Simple interpretation training Quick Set-Up

Need Statement and Project Scope Background | Project Scope | Design Specs | Existing Solutions | Schedule Need Statement and Project Scope There is a need for an accessible monitoring system to alert clinicians in the neonatal intensive care unit (NICU) about the health status of seizure-prone neonates for potential life-saving intervention. We propose to deliver a system that will employ a limited channel-EEG to provide a less expensive alternate to current, commercialized software. A device that fulfills this need will include different responses based on the severity of the situation to guide the clinician towards an apt response. In addition, it will have an accessible device interface and be safe for use with infants. We propose to deliver, by the end of April 2019, a system that can both reliably detect seizures in neonates utilizing data from a limited-channel EEG and alert clinicians of an ongoing seizure with distinct and non-chaotic signals appropriate for the NICU. The prototype will include a user manual for detailed instructions on the reproduction and operation of the system. There is a need for an accessible monitoring system to alert clinicians in the neonatal intensive care unit (NICU) about the health status of seizure-prone neonates for potential life-saving intervention.

Design Specifications Background | Project Scope | Design Specs | Existing Solutions | Schedule Design Specifications Specification Metric Description Sensitivity 99% Seizures detected divided by total number of seizures Specificity 80% False seizures detected divided by total number of seizures Frequency Sampling > 240 Hz The frequency rate at which signals from the neonate's brain are sampled Device Responsivity < 10 seconds Delay between initiation of a seizure and device alert Operating Time 24 hours This device will operate over multiple neonatal sleep-wake cycles Device Setup Time < 10 minutes Non-expert clinicians should be able to set up system with ease Production Cost $200 (USD) Cost-effective device based on integration with NICU equipment Device Size 9 cm x 6.5 cm x 2.5 cm The device will be easily storable and portable for rapid, efficient use

Background | Project Scope | Design Specs | Existing Solutions | Schedule

cEEG with Physician Interpretation Background | Project Scope | Design Specs | Existing Solutions | Schedule cEEG with Physician Interpretation BioSemi ActiveTwo System Gold-standard for brain monitoring Incredible resolution which scales with electrode number Drawbacks Requires trained neurologist interpretation Real-time analysis difficult Electrodes can damage baby’s skin

aEEG with Detection Software Background | Project Scope | Design Specs | Existing Solutions | Schedule aEEG with Detection Software CFM Olympic BrainZ Monitor with RecogniZe software Electrodes record a limited channel EEG Display shows raw EEG and aEEG RecogniZe software Drawbacks Expensive (~ $25k-$30k) Does not integrate well with other signals

Other Clinical Signals Background | Project Scope | Design Specs | Existing Solutions | Schedule Other Clinical Signals Angelcare AC517 Baby Breathing Monitor with Video Detects motion with a sensor pad Video camera for real-time monitoring Drawbacks Does not work well with pre-term babies Requires constant monitoring

Background | Project Scope | Design Specs | Existing Solutions | Schedule Machine Learning Classical Machine Learning Random Forest Summation of Decision Trees Mursalin et al.: 97% Specificity Rate Support Vector Machines Hyperplane between datasets Kumar et al.: 95% Specificity Rate

Background | Project Scope | Design Specs | Existing Solutions | Schedule Machine Learning Classical Machine Learning Random Forest Summation of Decision Trees Mursalin et al.: 97% Specificity Rate Support Vector Machines Hyperplane between datasets Kumar et al.: 95% Specificity Rate

Background | Project Scope | Design Specs | Existing Solutions | Schedule Design Schedule

Group Responsibilities Background | Project Scope | Design Specs | Existing Solutions | Schedule Group Responsibilities Responsibilities Kyle Tahj Report Writing X Point Person for Client Contact   Web Page Upkeep Literature Search and Clinical Relevance Lead Software Designer Signaling and Systems Analyst Electronics and Machine Design Cataloguing of Results Preliminary Presentation Progress Presentation V&V Presentation Final Presentation and Poster

References https://i.ytimg.com/vi/TjaXUyQyH34/maxresdefault.jpg https://www.researchgate.net/profile/Murat_Tahtali/publication/263012343/figure/fig1/AS:296452153724933@1447691109407/Schematic- representation-of-the-electrodes-positions-in-the-a-19-Electrodes-b.png https://my.clevelandclinic.org/-/scassets/images/org/health/articles/epilepsy-monitoring-unit-2641.ashx https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor https://www.angelcarebaby.com/files/assets/images/large/1522781084_AC517_tracks_F_EN.jpg https://i0.wp.com/analyticsdefined.com/wp-content/uploads/2018/01/random-forests.png?fit=1965%2C942&ssl=1 http://www.statsoft.com/textbook/graphics/SVMIntro3.gif https://www.askideas.com/media/07/Cute-Babies-Group.jpg Mursalin, M., Zhang, Y., Chen, Y., Chawla, N. (2017). Automated Epileptic Seizure Detection Using Improved Correlation-Based Feature Selection with Random Forest Classifier. Neurocomputing, 241, 204-214 Kumar, Y., Dewal, M. L., Anand, R. S., Epiletic Seizure Detection Using DWT Based Fuzzy Approximate Entropy and Support Vector Machine, Neurocomputing, 133, 271-279

Thank You!

Background | Project Scope | Design Specs | Existing Solutions | Schedule

Background | Project Scope | Design Specs | Existing Solutions | Schedule Machine Learning Deep Machine Learning Convolutional Neural Networks (CNNs) More effective than classical machine learning in image recognition tasks Require a large dataset