12.3 ECG feature extraction and classification

ECG signals provide crucial insights into heart function. By analyzing features like heart rate, R-R intervals, and QRS duration, we can detect abnormalities and assess cardiovascular health. Signal processing techniques extract these features for further analysis.

Machine learning algorithms like SVMs and neural networks can classify ECG patterns as normal or abnormal. The process involves preprocessing data, selecting relevant features, choosing and training a model, and evaluating its performance on unseen data.

ECG Signal Processing and Feature Extraction

Features of ECG signals

• Heart rate represents the number of heartbeats per minute (bpm) calculated from the number of R-peaks in the ECG signal, with a normal resting heart rate ranging from 60 to 100 bpm
• R-R intervals measure the time between consecutive R-peaks in the ECG signal, and the variability in these intervals can indicate heart rate variability (HRV), which provides insights into the autonomic nervous system function
• QRS duration corresponds to the width of the QRS complex in the ECG signal, with a normal duration ranging from 0.06 to 0.10 seconds, and prolonged QRS duration can suggest conditions like bundle branch block or ventricular hypertrophy
• Other important features of the ECG signal include P-wave amplitude and duration (atrial depolarization), T-wave amplitude and duration (ventricular repolarization), and ST segment elevation or depression (myocardial ischemia or infarction)

Signal processing for ECG extraction

• Wavelet transform is a time-frequency analysis technique that decomposes the ECG signal into different frequency components at various scales, enabling the identification and localization of specific waveform features (QRS complex, P-wave, T-wave)
• Principal component analysis (PCA) is a dimensionality reduction technique that transforms the original set of ECG features into a new set of uncorrelated features called principal components, which helps in reducing the dimensionality of the feature space while retaining most of the relevant information for classification purposes

ECG Classification using Machine Learning

Machine learning in ECG classification

• Support vector machines (SVM) are supervised learning algorithms that find an optimal hyperplane to separate different classes (normal vs. abnormal ECG patterns) in the feature space, and the kernel trick allows for the creation of non-linear decision boundaries to handle complex class distributions
• Neural networks consist of interconnected nodes (neurons) organized in layers, with the input layer receiving the ECG features, hidden layers learning the patterns and representations, and the output layer producing the classification result, while activation functions introduce non-linearity, and the backpropagation algorithm is used for training the network weights

Classifier development for ECG patterns

1. Data preprocessing involves removing noise and artifacts from the ECG signals, normalizing the extracted features to ensure consistent scales, and splitting the dataset into training, validation, and testing sets for model development and evaluation
2. Feature selection aims to choose the most relevant features that best discriminate between normal and abnormal ECG patterns using techniques like correlation analysis (identifying highly correlated features), mutual information (measuring the dependence between features and the target variable), or wrapper methods (evaluating feature subsets based on classifier performance)
3. Model selection involves choosing an appropriate classifier based on the problem characteristics and data properties, as well as performing hyperparameter tuning to optimize the model's performance (regularization strength in SVM, number of hidden layers and neurons in neural networks)
4. Training and evaluation consist of training the selected classifier using the training set, evaluating its performance using metrics such as accuracy, sensitivity, specificity, and F1-score on the validation set, and finally testing the trained model on an independent testing set to assess its generalization ability to unseen data