Applied Topological Signal Processing with Python: Persistent Homology and Feature Extraction for Temporal Data - Softcover

Marwood, Helena K.

Softcover

ISBN 13: 9798176996296

Publisher: Independently published, 2026

Synopsis

Reactive Publishing

Traditional signal processing relies heavily on Fourier transforms and time-frequency methods—tools that often fail when dealing with non-stationary, noisy, or high-dimensional real-time data. Applied Topological Signal Processing with Python bridges the gap between abstract mathematical topology and practical engineering, providing a hands-on guide to analyzing complex temporal data streams.

This book delivers a concrete framework for implementing Topological Data Analysis (TDA) directly in real-world signal workflows. Through complete Python examples, you will learn how to transform raw physical measurements and time-series arrays into persistence diagrams, extract robust structural features, and strip out background noise without losing critical phase information.

Inside, you will explore:

• Fundamentals of Persistent Homology: Construct Vietoris-Rips and filtration complexes from numerical time-series.

• Noise Reduction & Filtering: Separate true topological signal signatures from random ambient noise.

• Feature Vectorization: Convert persistence landscapes and diagrams into ML-ready inputs for Scikit-Learn and PyTorch models.

• Real-Time Signal Workflows: Implement sliding-window algorithms designed for streaming data pipelines.

• Python Tooling: Practical implementations using Gudhi, Ripser, SciPy, and NumPy.

Whether you are a data scientist working with sensor networks, a biomedical engineer analyzing ECG/EEG signals, or a quantitative developer processing financial ticks, this text provides the exact code patterns and mathematical foundations needed to deploy topological methods into production.

Edition details

• Publisher: Independently published
• Publication date: 2026
• Language: English
• ISBN 13: 9798176996296
• Binding: Paperback
• Number of pages: 444
• Editor: Schwartz Alice