ECG Signal Classification & monitoring and alerting system using Machine Learning

Wearable medical devices with sensor continuously generate enormous data which is often called as big data mixed with structured and unstructured data. Due to the complexity of the data, it is difficult to process and analyze the big data for finding valuable information that can be useful in decision making. On the other hand, data security is a key requirement in healthcare big data system.

In order to overcome this issue, this project proposes a new architecture for the implementation of IoT to store and process scalable sensor data (big data) for health care applications. The Proposed architecture consists
of two main sub architectures, namely, Meta Fog-Redirection (MF-R) and Grouping and Choosing (GC)
architecture.

MF-R architecture uses big data collection and storage of the sensor data (big data) generated from different sensor devices.

The proposed GC architecture is used for securing integration of fog computing with cloud computing. This architecture also uses key management service and data categorization function (Sensitive, Critical and Normal) for providing security services.

The framework based prediction model to predict the heart diseases.

Performance evaluation parameters such as throughput, sensitivity, accuracy, and f-measure are calculated to prove the efficiency of the proposed architecture as well as the prediction model.

in this project we  proposes a new architecture for the implementation of IoT to store and process scalable sensor data (big data) for health care applications. The Proposed architecture consists of two main sub architectures, namely, Meta Fog-Redirection (MF-R) and Grouping and Choosing (GC) architecture. MF-R architecture uses big data technologies  for  collection and storage of the sensor data (big data) generated from different sensor devices.

The proposed GC architecture is used for securing integration of fog computing with cloud computing. This architecture also uses key management service and data categorization function (Sensitive, Critical and Normal) for providing security services.