Write a Python script to classify ECG images using CNN and LSTM
Write a Python script to classify ECG images using CNN and LSTM
user
Write a Python script to classify ECG images using CNN and LSTM.

# ECG Classification using CNN and LSTM

## Introduction

Electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart. It is used to diagnose and monitor heart conditions. In this project, we will use a convolutional neural network (CNN) and a long short-term memory (LSTM) network to classify ECG images into different classes.

## Data

The dataset we will use for this project is the MIT-BIH Arrhythmia Database, which is a collection of ECG recordings from 47 different patients. Each recording is 30 minutes long and is labeled with the type of arrhythmia present in the recording.

## Preprocessing

Before we can use the ECG images for training our model, we need to preprocess them. This involves several steps, including:

1. Resizing the images to a common size.
2. Normalizing the pixel values to a range of 0 to 1.
3. Converting the images to grayscale.
4. Applying a bandpass filter to remove noise.

## CNN Architecture

We will use a CNN architecture to classify the ECG images. The CNN will have the following layers:

1. Convolutional layer: This layer will apply a convolutional filter to the input image to extract features.
2. Pooling layer: This layer will downsample the output of the convolutional layer to reduce the number of parameters.
3. Fully connected layer: This layer will connect all the neurons in the previous layer to all the neurons in the next layer.
4. Output layer: This layer will contain the class labels.

## LSTM Architecture

We will use an LSTM architecture to classify the ECG images. The LSTM will have the following layers:

1. Embedding layer: This layer will convert the input text into a numerical representation.
2. LSTM layer: This layer will process the input text using an LSTM network.
3. Fully connected layer: This layer will connect all the neurons in the previous layer to all the neurons in the next layer.
4. Output layer: This layer will contain the class labels.

## Training

We will train the CNN and LSTM models using the MIT
assistant
Write a Python script to classify ECG images using CNN and LSTM
user
Write a Python script to classify ECG images using CNN and LSTM.































































































































































































































































































































































































































































































































assistant
Write a Python script to classify ECG images using CNN and LSTM
user
Write a Python script to classify ECG images using CNN and LSTM.

# ECG Classification using CNN and LSTM

## Introduction

Electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart. It is used to diagnose and monitor heart conditions. In this project, we will use a convolutional neural network (CNN) and a long short-term memory (LSTM) network to classify ECG images into different classes.

## Data

The dataset we will use for this project is the MIT-BIH Arrhythmia Database, which is a collection of ECG recordings from 47 different patients. Each recording is 30 minutes long and is labeled with the type of arrhythmia present in the recording.

## Preprocessing

Before we can use the ECG images for training our model, we need to preprocess them. This involves several steps, including:

1. Resizing the images to a common size.
2. Normalizing the pixel values to a range of 0 to 1.
3. Converting the images to grayscale.
4. Applying a bandpass filter to remove noise.

## CNN Architecture

We will use a CNN architecture to classify the ECG images. The CNN will have the following layers:

1. Convolutional layer: This layer will apply a convolutional filter to the input image to extract features.
2. Pooling layer: This layer will downsample the output of the convolutional layer to reduce the number of parameters.
3. Fully connected layer: This layer will connect all the neurons in the previous layer to all the neurons in the next layer.
4. Output layer: This layer will contain the class labels.

## LSTM Architecture

We will use an LSTM architecture to classify the ECG images. The LSTM will have the following layers:

1. Embedding layer: This layer will convert the input text into a numerical representation.
2. LSTM layer: This layer will process the input text using an LSTM network.
3. Fully connected layer: This layer will connect all the neurons in the previous layer to all the neurons in the next layer.
4. Output layer: This layer will contain the class labels.

## Training

We will train the CNN and LSTM models using the MIT
assistant