Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI-Powered Biosignal Analytics and Remote Patient Monitoring focuses on the analytics that make remote care possible. You learn to apply machine learning to biosignals — ECG, PPG, respiration and activity — for continuous monitoring: detecting arrhythmias and deterioration, tracking chronic conditions, and alerting clinicians to problems in patients at home. The course connects biosignal AI to the workflow and safety demands of remote patient monitoring. You finish able to reason about an AI biosignal-analytics system for remote care. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI-powered biosignal analytics and remote patient monitoring — turning wearable and sensor biosignals into continuous, intelligent monitoring of patients at a distance.
📋 Course Objectives
1. Apply AI to ECG, PPG and activity signals.
2. Detect arrhythmias and deterioration.
3. Monitor chronic conditions continuously.
4. Alert clinicians to at-home problems.
5. Design for remote-monitoring safety.
👥 Who Should Enroll?
• Health-tech and biomedical professionals
• Clinical and remote-care teams
• Health data scientists
• Students of digital health
🚀 Key Learning Outcomes
• An understanding of AI biosignal analytics.
• A remote-monitoring perspective.
• A digital-health project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals and Biosignal Analytics Foundations
Design and implement neural network architectures for biosignal processing using Python and TensorFlow • Analyze and visualize biosignal data using matplotlib and scikit-learn to identify patterns and trends • Develop and evaluate machine learning models for biosignal classification using cross-validation and metrics such as accuracy and F1-score
Data Engineering, Preprocessing, and Feature Pipelines
Configure and optimize data pipelines for biosignal data using Apache Beam and Google Cloud Dataflow • Implement data preprocessing techniques such as filtering, normalization, and feature extraction using Python and Pandas • Evaluate and compare the performance of different feature engineering techniques using metrics such as mean squared error and R-squared
Model Architecture, Algorithm Design, and Biosignal Analytics Methods
Develop and train deep learning models for biosignal analysis using Keras and TensorFlow • Design and evaluate algorithmic approaches for biosignal processing such as wavelet transforms and Fourier analysis • Implement and compare the performance of different machine learning algorithms for biosignal classification using metrics such as precision and recall
Training, Hyperparameter Optimization, and Evaluation
Implement hyperparameter tuning using grid search and random search for machine learning models • Evaluate and compare the performance of different machine learning models using metrics such as mean absolute error and coefficient of determination • Develop and implement early stopping and learning rate scheduling techniques for training deep learning models
Deployment, MLOps, and Production Workflows
Configure and deploy machine learning models using Docker and Kubernetes • Implement and manage production workflows for biosignal analytics using Apache Airflow and Zapier • Develop and evaluate monitoring and logging strategies for machine learning models in production using Prometheus and Grafana
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and evaluate the ethical implications of AI-powered biosignal analytics using case studies and scenarios • Develop and implement strategies for bias mitigation and fairness in machine learning models using techniques such as data preprocessing and regularization • Design and evaluate approaches for transparency and explainability in AI-powered biosignal analytics using techniques such as feature importance and partial dependence plots
Industry Integration, Business Applications, and Case Studies
Develop and evaluate business cases for AI-powered biosignal analytics in healthcare and medical devices • Implement and integrate AI-powered biosignal analytics with existing healthcare systems and infrastructure • Analyze and compare the performance of different AI-powered biosignal analytics solutions using case studies and benchmarks
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Apache Beam |
| Covered Tool / Platform | Google Cloud Dataflow |
| Covered Tool / Platform | Docker |
| Covered Tool / Platform | Kubernetes |
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