Häftad, Engelska, 2026
Signal Processing-Driven AI for Healthcare
Av Ildar Rakhmatulin, Ganesh R. Naik
2081 kr
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Beskrivning
- Integrates AI-driven signal processing across EEG, EMG, EOG, and ECG
- Presents end-to-end workflows from data acquisition to deployment
- Demonstrates multimodal fusion and clinical decision support
- Emphasizes interpretability, validation, and regulatory considerations
- Features supplementary website to host the dataset
- Offers ready-to-use Python notebooks and scripts optimized for Google Collaboration
Produktinformation
- Utgivningsdatum: 2026-09-07
- Mått: 191 x 235 x 22 mm
- Vikt: 450 g
- Format: Häftad
- Språk: Engelska
- Antal sidor: 362
- Förlag: Elsevier Science
- ISBN: 9780443492761
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Mer om författaren
Dr. Ildar Rakhmatulin is a scientist and the creator of several popular open-source brain-computer interface (BCI) projects. He is the founder of PiEEG, a low-cost BCI solution. His experience includes working as a BCI developer at Imperial College London, a machine learning researcher at Heriot-Watt University, and a researcher in the neurotechnology group at the University of Edinburgh, UK. Additionally, he is the author of neuroscience courses on Udemy.Dr. Ganesh R. Naik is a leading researcher in biomedical engineering and signal processing, ranked among the top 2% of scientists globally (Stanford University). He earned his PhD in Electronics Engineering from RMIT University, Melbourne (2009), and is currently a Senior Academic and Researcher in Computer Science and IT at Torrens University Australia. Dr. Naik has edited 16 books and published over 150 peer-reviewed papers. He serves as Associate Editor for IEEE Access, Frontiers in Neurorobotics, and two Springer journals. His career is distinguished by fellowships from Baden–Württemberg (Germany), ISSI (Australia), the BridgeTech Program, and the Royal Academy of Engineering (UK). Previously, he held research roles at Flinders University, Western Sydney University, and UTS, contributing to major projects in sleep technology, wearable sensors, and AI-driven biomedical signal processing.
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