From Heartbeats to Networks: How Complex Network Theory is Revolutionizing ECG Analysis

Python Implementation Example

import numpy as np
import networkx as nx
from sklearn.ensemble import RandomForestClassifier

# ECG to Network transformation
def visibility_graph(ecg_signal):
    G = nx.Graph()
    n = len(ecg_signal)
    # Add nodes
    for i in range(n):
        G.add_node(i, value=ecg_signal[i])
    # Add edges based on visibility criterion
    for i in range(n):
        for j in range(i+1, n):
            if is_visible(ecg_signal, i, j):
                G.add_edge(i, j)
    return G

# Calculate network features
def extract_network_features(G):
    features = {}
    features['clustering'] = nx.average_clustering(G)
    features['degree_entropy'] = degree_distribution_entropy(G)
    features['efficiency'] = local_efficiency_entropy(G)
    return features

# AF classification
def classify_af(ecg_signals):
    features = [extract_network_features(visibility_graph(sig)) 
                for sig in ecg_signals]
    clf = RandomForestClassifier()
    # Training and prediction code here
    return predictions

Research Workflow

1. Data Collection

   Acquire ECG signals from reliable sources like PhysioNet
2. Preprocessing

   Filter noise and extract relevant segments
3. Network Transformation

   Apply VG, LPVG, or electrostatic methods
4. Feature Extraction

   Calculate network metrics and distribution entropies
5. Model Training

   Develop classifiers for condition detection
6. Validation

   Test performance on independent datasets