This book presents the theoretical basis and applications of biomedical signal analysis and processing. Initially, the nature of the most common biomedical signals, such as electroencephalography, electromyography, electrocardiography and others, is described. The theoretical basis of linear signal processing is summarized, with continuous and discrete representation, linear filters and convolutions, Fourier and Wavelets transforms. Machine learning concepts are also presented, from classic methods to deep neural networks. Finally, several applications in neuroscience are presented and discussed, involving diagnosis and therapy, in addition to other applications.


  • Explains signal processing of neuroscience applications using modern data science techniques.
  • Provides comprehensible review on biomedical signals nature and acquisition aspects.
  • Focusses on selected applications of neurosciences, cardiovascular and muscle-related biomedical areas.
  • Includes computational intelligence, machine learning and biomedical signal processing and analysis.
  • Reviews theoretical basis of deep learning and state-of-the-art biomedical signal processing and analysis.

This book is aimed at researchers, graduate students in biomedical signal processing, signal processing, electrical engineering, neuroscience and computer science.

section Section 1|112 pages

Physiological Signal Processing—Challenges

chapter 2|19 pages

Automated Recognition of Alzheimer's Dementia

A Review of Recent Developments in the Context of Interspeech ADReSS Challenges

chapter 3|28 pages

Electrogastrogram Signal Processing

Techniques and Challenges with Application for Simulator Sickness Assessment

section Section 2|109 pages

EEG—ECG Signal Processing

section Section 4|13 pages

Wearables—Sensors Signal Processing