Hospitalizers

Problem: In the critical setting of an ambulance, where every second counts, ECG readings are vital. Yet, the process of analysis often introduces delays. Nurses, though dedicated, face difficulties in interpreting these readings swiftly. With the absence of a doctor on board, there is a cumbersome process to communicate with doctors to make critical decisions, and unfortunately, this communication is not standardized.

Solution: When the ECG readings come in, our tool employs signal processing to filter noise and extract crucial features validated by research. Our embedded machine learning model processes these features, automating decision-making without the need for manual communication, because results are securely uploaded to FHIR, ensuring standardized and secure data exchange. HeartSnap transforms ECG analysis in ambulances, bringing speed, accuracy, and standardized data exchange to the forefront of healthcare.

Impact: Every second counts in healthcare. What if we told you we've found a way to make those crucial moments even more effective?

Feasibility & financials: Is it practical? Absolutely. Our tool doesn't just save time; it enhances the accuracy of screening patients. HeartSnap, as an AI-enabled decision support system, analyzes ECG readings through signal processing and machine learning techniques, providing a feasible solution ready to make a real-world impact.

What is new about your solution?: Innovative, useful, non-trivial, and practical.

What you have built at the hackathon - text explanation + code (e.g. GitHub link): https://github.com/Eiad21/ecg_hospitalization

What you had before the hackathon, please mention open source as well: Brain age prediction using EEG: https://github.com/abdulkaderghandoura/brain-age

What comes next and what you wish to achieve: Employing self-supervised learning and training on a compute cluster.

Video: https://www.youtube.com/embed/FZojtQpPNBA