@Sanjeev Bansal, good morning from Uganda.

What we are is that We are building a smart ECG support system that uses artificial intelligence specifically machine learning models Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to automatically detect when ECG wires are placed incorrectly. The models will be trained on many ECG recordings, both correctly and incorrectly performed, and they learn to recognize patterns that signals like unusual wave shapes or changes in direction. The ANN learns complex patterns in the ECG signal, while the SVM sets a boundary that separates correct from incorrect recordings based on clear rules it learns from data.

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‎When an ECG is taken, the system first uses these models to check whether the signal looks correct. If it detects a likely misplacement or interchange of leads, it activates a second step; signal correction. This part uses learned relationships between the ECG leads to estimate what the proper waveform should have looked like undoing the mistake virtually. The final output is a corrected ECG signal, helping clinicians make safer, faster diagnoses.

To explain the predictions of models based on artificial neural networks (ANNs) or support vector machines (SVMs) to non-technical stakeholders, it is essential to use simple language and practical analogies. Start by describing ANNs as a system that mimics how the human brain processes information, while SVMs identify the best boundaries to separate different classes based on their characteristics. Visual aids, such as graphs or charts, illustrate how the models analyze data and make predictions, making complex concepts easier to understand. Focus on the practical implications of the predictions and directly relate them to business outcomes, such as improved efficiency or enhanced customer experiences. Finally, encourage questions to foster engagement and clarify any doubts, helping stakeholders understand the value of this technology.

Thank you, Mr. Farkad