We Build an AI to Detect Atrial Fibrillation With Smartwatches

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In this episode, we dive into our machine learning project designed to detect atrial fibrillation (AFib) using ECG signals.

The project leverages MIT and Beth Israel Hospital’s arrhythmia database and implements the Pan Tompkins algorithm for advanced signal processing. By extracting key features like QRS duration and PQ interval, our model achieves impressive accuracy, offering a cost-effective solution for real-time AFib detection.

Learn how AI-powered smartwatches can transform heart health monitoring and provide an accessible way to detect AFib, empowering better health outcomes for all.

00:00 Introduction to the Project
00:21 Meet Uncle John and Auntie Anne
00:37 Understanding Atrial Fibrillation
01:29 The Problem in the Philippines
01:45 Our Cost-Effective Solution
01:56 Developing the Machine Learning Model
03:10 Signal Processing Steps
05:20 Feature Extraction and Analysis
05:47 Model Accuracy and Interpretability
07:13 Conclusion and Future Implications
07:37 Final Thoughts and Call to Action

#MachineLearning #ArtificialIntelligence #AtrialFibrillation
#HealthTech #ECGAnalysis #AIInHealthcare #SmartwatchTechnology #SignalProcessing #HeartHealthAI #MedicalAI
Category
Cardiology
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