PhysOnline

Online Feature Extraction and Machine Learning of Streaming Physiological Data
This is a novel near real-time machine learning pipeline used to classify Paroxysmal atrial fibrillation (PAF) in an online streaming environment. This method uses the scalable and parallelized Apache Spark platform.

Developed by Rishikesan Kamaleswaran and Jacob Sutton
Center for Biomedical Informatics, University of Tennessee Health Science Center

Citations:

• Please cite the tool using the following:
  - Sutton, J.R., Mahajan, R., Akbilgic, O. and Kamaleswaran, R., 2018. PhysOnline: an open source machine learning pipeline for real-time analysis of streaming physiological waveform. IEEE journal of biomedical and health informatics, 23(1), pp.59-65.
  https://ieeexplore.ieee.org/abstract/document/8353460/

Prerequisite Software:

• Apache Spark 2.2.0
• MongoDB 3.6
• Amqp-client 4.1.0
• SBT
• Scala 2.1.1

Build instructions

1. Extract directory.
2. Modify the credentials in the psprSpark.scala file in the source directory. You will need to alter the credentials for the RabbitMQ connection.
3. Run "sbt package" in the root directory. No errors should appear.
4. Once the step #3 has been run, a target file should have been created. Then use the command "spark-submit" in the root directory to execute the spark file.

Spark Submit

The spark-submit command will not work unless you have listed the coinciding jar files to be run in tandem:

• amqp-client-4.1.0.jar
• spark-rabbitme-0.5.1.jar
• akka-actor_2.11-2.4.11.jar
• config-1.3.1.jar
• slf4j-nop-1.7.25.jar
• mongo-spark-connector_2.11-2.2.0.jar
• mongo-java-driver-3.4.2.jar
  
  These can all be found in the in the lib directory. For more information on how the spark streaming process works, please check out https://spark.apache.org/docs/latest/streaming-programming-guide.html