IntelliHQ uses Machine Learning on AWS to create a web-based ECG live stream


The coming 4th Industrial Revolution – an exponential convergence of digital, physical, technological and biological advances – will transform industries over the course of the 21st century. No single innovation will lead this impending revolution, but one thing is clear: Artificial Intelligence (AI) will be at the forefront.

Healthcare is among the foremost industries ripe to be revolutionized by AI and the 4th Industrial Revolution. By enabling ground-breaking advances in healthcare digitisation, AI is expected to significantly contribute to medical advances and lead to marked improvements to healthcare delivery.

Embracing AI to revolutionize healthcare

A not-for-profit partnership between Gold Coast Health, industry and universities, IntelliHQ (Intelligent Health Queensland) is dedicated to promoting research, investment, and monetization of next-generation AI and machine learning technologies in the health sector. It aims to enhance patient outcomes, improve quality of care, create opportunities for skills, jobs and venture development, and encourage investment. By harnessing trusted AI, IntelliHQ aspires to become a globally recognised healthcare innovation and commercialization hub.

Building a global healthcare AI capability promises to deliver significant benefits, not only by relieving pressure on the medical system resulting from spiralling costs, but also by contributing to broader technological economic growth, global competitiveness, and skill creation.

But to realize its aspirations, IntelliHQ has to overcome several challenges and obstacles to AI adoption in healthcare. It needs to build community trust, and maintain secure access to patient data. As such, IntelliHQ needed a technology partner to enable it to achieve its goals.

IntelliHQ engages TechConnect

To enable its key initiatives, IntelliHQ worked with AWS Partner Network (APN) Advanced Consulting Partner, TechConnect to create a web-based ECG live stream with machine learning annotations as a proof-of-concept. This requires numerous cloud technologies for data security, storage, transformation, and means for deployment.

An ECG signal can be categorised into either a normal (healthy) signal or various types of abnormal (unhealthy) classifications, such as atrial fibrillation. Key health indicators are the standard deviation of the length of time between peaks and troughs in an ECG signal, and the ratio between low frequency vs high frequency signals.

It’s possible to generate annotations of these intervals and apply an algorithm to the resulting data to make classifications without machine learning. But given the ability for machine learning to make increasingly sophisticated classifications and predictions, applying these technologies to health data presents many advantages.

The Solution

To extract ICU data to the cloud, IntelliHQ used TechConnect’s Panacea toolset, a C# library and Windows service that connects to a GE Carescape Gateway High Speed Data Interface, subscribe to data feeds and push these data feeds to the cloud via AWS Kinesis Firehose, Amazon’s high speed data ingestion service.

As actual ICU data could not be streamed from a hospital to the cloud until all custodianship processes has been finalised, this proof-of-concept utilised a demonstration monitor to simulate a typical healthy heartbeat, several abnormal rhythms, and test with various combinations of leads connected.

Once subscribed to a data feed, the Windows service inspects each packet to identify source information, and then converts the ECG waveform and extra numeric data into a hexadecimal representation for transport to the cloud. The source information is parsed by an AWS Lambda function, which then writes the data into an Amazon S3 folder structure that uses folder names for a simple organisational scheme.

Parameters are extracted from the AWS Kinesis Firehose stream data by the Amazon Lambda function in a structure format that reflects the default naming convention for Hive partitions on Amazon S3. Many AWS and Hadoop compatible tools (like Amazon Athena) can use this partitioning scheme to efficiently select subsets of data when queried by these parameters.

Once the data was uploaded to Amazon S3 via Panacea in its proprietary raw hex format, they required conversion to a broadly supported file format using an Amazon EMR cluster to handle the parallel transform operations, for which Zeppelin was chosen. It exposes a web-accessible notebook interface to run code and display resulting visualisations, and each notebook consists of a sequence of cells which can utilise a different parser. This provided easy access to Spark, SQL and Scala for data analytics.

IntelliHQ then unpackaged the raw waveform and numerics data into a human readable tabular file format using a combination of PySpark and SparkSQL code. They chose the widely supported csv format, and created annotation files that labelled normal vs abnormal waveform periods. The next data preparation steps took place in Amazon Sagemaker, which gave access to serverless resources for training and deployment. After the addition of front end graphics, the resulting visualization displays in a simple UI. The code is then packaged with Amazon Elastic Container Service for deployment.

The Benefits

The proof-of-concept process results in significant query time improvements and, for tools like Amazon Athena, substantial query cost savings. Furthermore, the folder structure is self documenting and unambiguous, allowing a highly decoupled architecture that can handle any future growth in data volume.

Also the data is non-public, and AWS Identity and Access Management (IAM) credentials establish who has access rights. Security tags and security groups enable IntelliHQ to allocate who has access to data at different stages of preparation. Because these cloud services come with baked-in security, IntelliHQ can ensure they have strong control over health data.

The Outcome

The web-based ECG live stream proof-of-concept showed that efficient, secure web services enable IntelliHQ to build effective, scalable cloud solutions. By embracing AI and machine learning, IntelliHQ will continue to advance the possibilities of commercialized healthcare innovation.

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