Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Arrhythmia Identification from ECG
By:
Latest Version:
2.1
This solution analyses ECG waveform data and classifies each peak as normal or one of the types of arrhythmia.
Product Overview
Early detection of arrhythmia is crucial as it could be life threatening while showing no symptoms. This solution helps in classifying each peak in the electrocardiogram waveform data (even from multiple leads) into the following categories: Normal, Premature ventricular contraction, Paced beat, Right bundle branch, Left bundle branch, Atrial premature beat, Ventricular flutter wave, Ventricular escape beat. The input format for training and inference is standard Waveform Database Format (WFDB). The solution uses a CNN based deep learning model that can be personalised for each patient. On the inference data each peak is timestamped and classified into the above mentioned categories and presented as a json. The solution is intended to be used for auxillary/support only.
Key Data
Version
By
Type
Algorithm
Highlights
The solution can be used for remote patient monitoring by providing early warning and alerts. With wearable devices collecting ECG data, the solution also extends to Federated Machine Learning scenarios with hyper-personalised models for patients.
The solution adheres to standard Waveform Database file format which can be easily integrated with other healthcare data platforms. The pre-processing mechanism can identify peaks in the waveform data and automatically split in format required for the deep learning format.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
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Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Algorithm Training$20/hr
running on ml.m5.4xlarge
Model Realtime Inference$10.00/hr
running on ml.m5.4xlarge
Model Batch Transform$10.00/hr
running on ml.m5.4xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Algorithm Training$0.922/host/hr
running on ml.m5.4xlarge
SageMaker Realtime Inference$0.922/host/hr
running on ml.m5.4xlarge
SageMaker Batch Transform$0.922/host/hr
running on ml.m5.4xlarge
Algorithm Training
For algorithm training in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Algorithm/hr | |
|---|---|---|
ml.m4.4xlarge | $20.00 | |
ml.c5n.18xlarge | $20.00 | |
ml.g4dn.4xlarge | $20.00 | |
ml.m5.4xlarge Vendor Recommended | $20.00 | |
ml.m4.16xlarge | $20.00 | |
ml.m5.2xlarge | $20.00 | |
ml.p3.16xlarge | $20.00 | |
ml.g4dn.2xlarge | $20.00 | |
ml.c5n.xlarge | $20.00 | |
ml.m4.2xlarge | $20.00 | |
ml.c5.2xlarge | $20.00 | |
ml.p3.2xlarge | $20.00 | |
ml.c4.2xlarge | $20.00 | |
ml.g4dn.12xlarge | $20.00 | |
ml.m4.10xlarge | $20.00 | |
ml.c4.xlarge | $20.00 | |
ml.m5.24xlarge | $20.00 | |
ml.c5.xlarge | $20.00 | |
ml.g4dn.xlarge | $20.00 | |
ml.p2.xlarge | $20.00 | |
ml.m5.12xlarge | $20.00 | |
ml.g4dn.16xlarge | $20.00 | |
ml.p2.16xlarge | $20.00 | |
ml.c4.4xlarge | $20.00 | |
ml.m5.xlarge | $20.00 | |
ml.c5.9xlarge | $20.00 | |
ml.m4.xlarge | $20.00 | |
ml.c5.4xlarge | $20.00 | |
ml.p3.8xlarge | $20.00 | |
ml.m5.large | $20.00 | |
ml.c4.8xlarge | $20.00 | |
ml.c5n.2xlarge | $20.00 | |
ml.p2.8xlarge | $20.00 | |
ml.g4dn.8xlarge | $20.00 | |
ml.c5n.9xlarge | $20.00 | |
ml.c5.18xlarge | $20.00 | |
ml.c5n.4xlarge | $20.00 |
Usage Information
Training
For model training, the ECG waveform data for each patient must have the following files: .atr, .hea and .dat The model uses all the patients' data for training The data should be in a directory having the following naming convention: patient_id_1.atr, patient_id_1.hea, patient_id_1.dat, patient_id_2.atr, patient_id_2.hea, patient_id_2.dat, etc
Channel specification
Fields marked with * are required
train
*Input modes: File
Content types: application/zip
Compression types: None
Model input and output details
Input
Summary
For inference, ECG data for each patient must have the following files: .atr, .hea, .dat All the patient data must be put together in a .zip file and name it as Input.zip
Input MIME type
application/zipSample input data
Output
Summary
Output will be a .json file
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
Arrhythmia Identification from ECG
For any assistance reach out to us at: https://www2.mphasis.com/AWS-Marketplace-Support-LP.html
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