Arrhythmia classification with the LSTM-FCN SageMaker Algorithm¶
Arrhythmia classification based on electrocardiogram (ECG) data involves identifying and categorizing abnormal patterns of cardiac electrical activity detected in the ECG signal. Arrhythmia classification is important for diagnosing cardiac abnormalities, assessing the risk of adverse cardiovascular events and guiding appropriate treatment strategies.
Machine learning algorithms can automate the process of ECG interpretation, reducing the reliance on manual analysis by healthcare professionals, a task that is both time-consuming and prone to errors. The automation provided by machine learning algorithms offers the potential for fast, accurate and cost-effective diagnosis.
Different neural network architectures have been proposed in the literature on ECG arrhythmia classification [1]. In this post, we will focus on the Long Short-Term Memory Fully Convolutional Network [2], which we will refer to as the LSTM-FCN model. We will demonstrate how to use our Amazon SageMaker implementation of the LSTM-FCN model, the LSTM-FCN SageMaker algorithm, for categorizing the ECG traces in the PhysioNet MIT-BIH Arrhythmia Database [3].
Model¶
The LSTM-FCN model includes two blocks: a recurrent block and a convolutional block. The recurrent block consists of a single LSTM layer followed by a dropout layer. The convolutional block consists of three convolutional layers, each followed by batch normalization and ReLU activation, and of a global average pooling layer.
The input time series are passed to both blocks. The convolutional block processes each time series as a single feature observed across multiple time steps, while the recurrent block processes each time series as multiple features observed at a single time step (referred to as dimension shuffling). The outputs of the two blocks are concatenated and passed to a final output layer with softmax activation.
Data¶
We use the pre-processed version of the PhysioNet MIT-BIH Arrhythmia Database made available in [4] where the ECG recordings are split into individual heartbeats and then downsampled and padded with zeroes to the fixed length of 187. The dataset contains 5 different categories of heartbeats where class 0 indicates a normal heartbeat while classes 1, 2, 3, and 4 correspond to different types of arrhythmia.
The dataset is split into a training set and a test set. The train-test split is provided directly by the authors. The training set contains 87,554 time series while the test set contains 21,892 time series. Both the training and test sets are imbalanced, as most time series represent normal heartbeats.
MIT-BIH dataset class distribution.
Code¶
Environment Set-Up¶
We start by importing all the requirements and setting up the SageMaker environment.
Warning
To be able to run the code below, you need to have an active subscription to the LSTM-FCN SageMaker algorithm. You can subscribe to a free trial from the AWS Marketplace in order to get your Amazon Resource Name (ARN). In this post we use version 1.14 of the LSTM-FCN SageMaker algorithm, which runs in the PyTorch 2.1.0 Python 3.10 deep learning container.
import io
import sagemaker
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from imblearn.under_sampling import RandomUnderSampler
from sklearn.metrics import accuracy_score, confusion_matrix
# SageMaker algorithm ARN from AWS Marketplace
algo_arn = "arn:aws:sagemaker:<...>"
# SageMaker session
sagemaker_session = sagemaker.Session()
# SageMaker role
role = sagemaker.get_execution_role()
# S3 bucket
bucket = sagemaker_session.default_bucket()
# EC2 instance
instance_type = "ml.m5.2xlarge"
Data Preparation¶
After that we load the training data from the CSV file.
Warning
To be able to run the code below, you need to download the datasets (“mitbih_train.csv” and “mitbih_test.csv”) from Kaggle and store them in the SageMaker notebook instance.
# load the training data
training_dataset = pd.read_csv("mitbih_train.csv", header=None)
To speed up the training process, we undersample the training data using imbalanced-learn. After resampling, the training data contains 641 instances of each class.
# resample the training data
sampler = RandomUnderSampler(random_state=42)
training_dataset = pd.concat(sampler.fit_resample(X=training_dataset.iloc[:, :-1], y=training_dataset.iloc[:, -1:]), axis=1)
We then proceed to moving the class labels from the last column to the first column, as required by the LSTM-FCN SageMaker algorithm.
# move the class labels to the first column
training_dataset = pd.concat([training_dataset.iloc[:, -1:], training_dataset.iloc[:, 1:]], axis=1)
Once this is done, we can save the training data in the S3 bucket in CSV format.
# save the training data in S3
training_data = sagemaker_session.upload_string_as_file_body(
body=training_dataset.to_csv(index=False, header=False),
bucket=bucket,
key="MITBIH_train.csv"
)
Training¶
We can now run the training job.
# create the estimator
estimator = sagemaker.algorithm.AlgorithmEstimator(
algorithm_arn=algo_arn,
role=role,
instance_count=1,
instance_type=instance_type,
input_mode="File",
sagemaker_session=sagemaker_session,
hyperparameters={
"num-layers": 1,
"hidden-size": 128,
"dropout": 0.8,
"filters-1": 128,
"filters-2": 256,
"filters-3": 128,
"kernel-size-1": 8,
"kernel-size-2": 5,
"kernel-size-3": 3,
"batch-size": 256,
"lr": 0.001,
"epochs": 100,
},
)
# run the training job
estimator.fit({"training": training_data})
Inference¶
Once the training job has completed, we can deploy the model to a real-time endpoint.
# define the endpoint inputs serializer
serializer = sagemaker.serializers.CSVSerializer(content_type="text/csv")
# define the endpoint outputs deserializer
deserializer = sagemaker.deserializers.CSVDeserializer(accept="text/csv")
# create the endpoint
predictor = estimator.deploy(
initial_instance_count=1,
instance_type=instance_type,
serializer=serializer,
deserializer=deserializer,
)
After that we load the test data from the CSV file.
# load the test data
test_dataset = pd.read_csv("mitbih_test.csv", header=None)
To avoid confusion, we move the class labels from the last column to the first column, even though they are obviously not used for inference.
# move the class labels to the first column
test_dataset = pd.concat([test_dataset.iloc[:, -1:], test_dataset.iloc[:, 1:]], axis=1)
Given that the test dataset is relatively large, we invoke the endpoint with batches of time series as opposed to using the entire test dataset as a single payload.
# define the batch size
batch_size = 100
# create a data frame for storing the model predictions
predictions = pd.DataFrame()
# loop across the test dataset
for i in range(0, len(test_dataset), batch_size):
# invoke the endpoint with a batch of time series, make sure to drop the first column with the class labels
response = sagemaker_session.sagemaker_runtime_client.invoke_endpoint(
EndpointName=predictor.endpoint_name,
ContentType="text/csv",
Body=serializer.serialize(test_dataset.iloc[i:i + batch_size, 1:])
)
# save the predicted class labels in the data frame
predictions = pd.concat([
predictions,
pd.DataFrame(
data=deserializer.deserialize(response["Body"], content_type="text/csv"),
dtype=float
)
], axis=0)
After generating the model predictions, we can calculate the classification metrics.
# calculate the accuracy
accuracy_score(y_true=test_dataset.iloc[:, 0], y_pred=predictions.iloc[:, 0])
# calculate the confusion matrix
confusion_matrix(y_true=test_dataset.iloc[:, 0], y_pred=predictions.iloc[:, 0])
We find that the model achieves 99.79% accuracy on the test data.
LSTM-FCN confusion matrix on MIT-BIH test dataset.
After the analysis has been completed, we can delete the model and the endpoint.
# delete the model
predictor.delete_model()
# delete the endpoint
predictor.delete_endpoint()
References¶
[1] Ebrahimi, Z., Loni, M., Daneshtalab, M., & Gharehbaghi, A. (2020). A review on deep learning methods for ECG arrhythmia classification. Expert Systems with Applications: X, vol. 7, 100033. doi: 10.1016/j.eswax.2020.100033.
[2] Karim, F., Majumdar, S., Darabi, H., & Chen, S. (2018). LSTM fully convolutional networks for time series classification. IEEE Access, vol. 6, pp. 1662-1669, doi: 10.1109/ACCESS.2017.2779939.
[3] Moody, G. B., & Mark, R. G. (2001). The impact of the MIT-BIH arrhythmia database. IEEE engineering in medicine and biology magazine, vol. 20, no. 3, pp. 45-50, doi: 10.1109/51.932724.
[4] Kachuee, M., Fazeli, S., & Sarrafzadeh, M. (2018). ECG heartbeat classification: A deep transferable representation. 2018 IEEE international conference on healthcare informatics (ICHI), pp. 443-444, doi: 10.1109/ICHI.2018.00092.