Скачать с ютуб EEG Signal Processing and Machine Learning for Coma Recovery Prediction in Critical Care в хорошем качестве

EEG Signal Processing and Machine Learning for Coma Recovery Prediction in Critical Care

🌟🎙️ This February, we had the honor of hosting Dr. Morteza Zabihi for the 15th installment of our webinar, Case Studies in Neurocritical Care AI! Talk Title: EEG Signal Processing and Machine Learning for Coma Recovery Prediction in Critical Care ✨ Dr. Morteza Zabihi is a biomedical engineer and scientist specializing in physiological signal processing and machine learning (AI/ML). After earning a Ph.D. with distinction in Finland, Dr. Zabihi was recruited to the Massachusetts General Hospital (MGH) Department of Neurology as a postdoctoral research fellow. Demonstrating exceptional expertise, he transitioned to a junior faculty position at the MGH Neurology Data Science & AI Center under the mentorship of Dr. Eric Rosenthal. Now, he is serving as the lead scientist in Dr. Rosenthal's lab and associate director of MGH NeuroAI Center. Dr. Zabihi's research centers on leveraging physiological time series data to develop robust and equitable AI/ML methodologies. His work is particularly focused on creating reliable and trustworthy prognostic tools for clinicians, families, and patient surrogates dealing with post-acute brain injuries, including hemorrhagic stroke and cardiac arrest. Throughout his academic and professional journey, Dr. Zabihi has honed his skills in designing AI/ML algorithms tailored to diverse clinical applications. His contributions encompass algorithms for coma recovery prediction, intracranial pressure waveform analysis, seizure detection, cardiac anomaly detection, and early sepsis prediction in critically ill patients. Dr. Zabihi has also been honored with five international awards for his contributions to scientific competitions, including those hosted by PhysioNet and the Brain-Computer-Interface challenge at the IEEE EMBS Neural Engineering Conference. 🎙️ About the Talk: ‼️ This talk begins with an overview of EEG signal processing and machine learning techniques. It then examines two recent approaches developed for EEG analysis in critical care: one focuses on long-term outcome prediction in post‑cardiac arrest patients, and the other on coma recovery prediction in patients with acute brain injury. Finally, Dr. Zabihi discusses the key challenges of applying machine learning in healthcare and explores strategies for building trustworthy, robust AI models. If you're interested in joining the webinar series, please register via this link: https://us06web.zoom.us/webinar/regis... You can view recordings of past webinars here:    • Webinar Series - AI in Neurocritical ...   This webinar series is designed to help clinicians learn how to get more from their neurocritical care data. No prior experience is necessary. Each session focuses on a real-world case study from us or from your colleagues. We walk participants through the steps to re-create the analytics that answers the neurocritical care question presented. We cover tools, data handling, feature engineering, cohort selection, model training, model tuning, visualization, and other topics. Sample code, Jupyter notebook, and data are provided to enable you to experiment and apply these methods to your own data sets and problems. Please feel free to let us know if you have any questions about the concepts that were presented in this video or if you have case studies or suggestions for upcoming sessions. Website: https://moberganalytics.com LinkedIn:   / moberg-analytics   Twitter: https://twitter.com/moberganalytics?l... Disclaimer: The views and opinions expressed by the presenter and other third parties do not necessarily reflect those of Moberg Analytics, Inc. Moberg Analytics, Inc. makes no clinical claims regarding information described by the presenter and other third parties.