Sold by: MphasisÂ
Deployed on AWS
This solution analyses ECG waveform data and classifies each peak as normal or one of the types of arrhythmia.
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.
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.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.4xlarge Inference (Batch) Recommended | Model inference on the ml.m5.4xlarge instance type, batch mode | $10.00 |
ml.m5.4xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.4xlarge instance type, real-time mode | $10.00 |
ml.m5.4xlarge Training Recommended | Algorithm training on the ml.m5.4xlarge instance type | $20.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $10.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $10.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $10.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $10.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $10.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $10.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $10.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is version 2.1
Additional details
Inputs
- 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/zip
https://github.com/Mphasis-ML-Marketplace/Arrhythmia-identification-from-ECG/tree/main/input/test
https://github.com/Mphasis-ML-Marketplace/Arrhythmia-identification-from-ECG/tree/main/input/test
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