New Frontiers of Cardiovascular Screening using Unobtrusive Sensors, AI, and IoT
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New Frontiers of Cardiovascular Screening using Unobtrusive Sensors, AI, and IoT provides insights into real-world problems in cardiovascular disease screening that can be addressed via AI, IoT and wearable based sensing. Non-Communicable Diseases (NCD) are surpassing CDS and emerging as the foremost cause of death. Hence, early screening of CVDs using wearable and other similar sensors is an extremely important global problem to solve. The digital health field is constantly changing, and this book provides a review of recent technology developments, offering unique coverage of processing time series physiological sensor data. The authors have developed this book with graduate and post graduate students in mind, making sure they provide an accessible entry point into the field. This book is particularly useful for engineers and computer scientists who want to build technologies that work in real world scenarios as it provides a practitioner’s view/insights /tricks of the trade. Finally, this book helps researchers working on this important problem to quickly ramp up their knowledge and research to the state-of-the-art.
Key Features
- Maps digital health technology to real diseases that are relevant to the medical community
- Supported with patient data and case studies
- Gives practitioners insights into the real-world implementation of signal conditioning, signal processing and machine learning
Readership
Graduate and post-graduate students in signal processing and biomedical engineering, Researchers in other sub-domains of healthcare with a cardiovascular focus, such as doctors, medical device manufacturers, biomedical and electrical engineers
Table of Contents
- PART A: Sensors, AI and IoT in Cardiovascular Diseases
Chapter 1 – Cardiovascular Diseases and Conditions
Silent Killer and its Global Impact
Digital Unobtrusive Screening
Leveraging AI and IoT
Bibliography
Chapter 2 – Unobtrusive Sensing
Proliferation of new generation sensors: smartphones and wearable
Clinical Devices
Pulse oximetry: Photoplethysmogram (PPG)
Digital Stethoscope: PhonoCardiogram (PCG)
Echocardiogram (ECG)
Breathing Flow meter
Brain Signal: Electroencephalogram (EEG)
Bibliography
Chapter 3 – Process Flow for Automated Screening
Introduction
Data Collection Steps
Signal Conditioning
Noise: Discarding or Reconstruction
Statistical Analysis
Handling Unbalanced Data
Clinical Knowledge Augmentation: Using Meta-information
Machine Learning
Deep Learning Architecture on multi-sensor time series data
Bibliography
PART B: Disease Screening
Chapter 4 – Abnormal Heart Rhythms
Heart Rate using PPG and ECG
Noise Cleaning: Smartphone, Wearable and Nearable
Frequency and Time Domain Analysis
Arrhythmia Detection using PPG and ECG
Signal Conditioning
Peak Detection
AI Based Rhythm Analysis
Bibliography
Chapter 5 – Heart Blockage
Correlation of Heart Blockage with ECG, PPG and PCG
Detection of Chronic Ischemic Heart Diseases
AI Based Fusion of Multiple Sensors for Classification
Patient Metadata based Knowledge Modelling
Bibliography
Chapter 6 – Other Cardiac Conditions
Screening of Hypertension from PPG and ECG
Electrical Modelling of Cardiovascular System
Regression Modelling from PPG
Pulse Transit Time Analysis from PPG and ECG
Cardiac Fatigue from PPG and ECG
AI Based Anomaly Detection
Bibliography
Chapter 7 – Associated Conditions and Diseases
Sleep Apnoea from PPG, ECG, Breathing and EEG
Deep Learning based Prediction
COPD from Flow meter
AI Based Classification
Bibliography
PART C: Future Challenges
Chapter 8 – Looking at the Future
Trends for Physiological Sensing
Trends for Analytics and AI
Security and Privacy Challenges
Ethics and Liability Issues
Future Vision for Cardiovascular Health
A day in the Life of a Cardiologist in 2030
A day in the Life of a Patient in 2030
Bibliography
Product details
- No. of pages: 250
- Language: English
- Copyright: © Academic Press 2022
- Published: July 1, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780128244999
About the Authors
Anirban Dutta Choudhury
Anirban Dutta Choudhury received his B.E. in Electronics and Telecommunication Engineering in 2005 from Jadavpur University, India and M.Sc. in Embedded System Design in 2008 from University of Lugano, Switzerland.
He has more than 15 years of experience, all in R&D departments in various geographies and scale of companies such as NXP Semiconductors Netherlands, PIXY AG Switzerland and Tata Consultancy Services India. His research interests encompass Noninvasive Physiological sensing in Digital Health and AI-driven Sensor Signal Informatics.
Anirban has more than 40 publications till date in reputed Journals and Conferences. He has filed for more than 25 unique patents and has 10 patents granted to him.
Affiliations and Expertise
Senior Scientist, Research and Innovation, Tata Consultancy Services, Kolkata, India
Rohan Banerjee
Rohan Banerjee has more than 10 years of experience in digital signal processing and machine learning. Currently, works as a scientist at TCS Research. He received his M.Tech from the department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur in 2011. His main research area includes signal processing with a strong focus on biomedical signal processing, pattern recognition, machine learning in digital healthcare and cardiology. Rohan has more than thirty papers published in peer-reviewed conferences and journals and has five granted patents.
Affiliations and Expertise
Scientist, Research and Innovation, Tata Consultancy Services, Kolkata, India
Sanjay Kimbahune
Sanjay Kimbahune received his BE degree in Electronics from Amravati University with distinction. He is working as a senior scientist in TCS Research and Innovation. He has experience of 32 years in diverse fields of CTI, Video Conference solutions and Mobile applications for masses. He is passionate about solving problems faced by the masses by developing frugal and scalable technology solutions. He has published 33 papers and has 48 granted patents. His current research focus is on engineering innovative devices for screening cardiovascular diseases in a scalable and frugal way. He is mentoring many startups working in the healthcare domain. He was instrumental in developing mKRISHI - platform for farmers. mKRISHI has reached about 1 million farmers as well has received many prestigious awards.
Affiliations and Expertise
Senior Scientist, Research and Innovation, Tata Consultancy Services, Thane, India
Arpan Pal
Arpan Pal has more than 29 years of experience in the areas of Intelligent Sensing, Signal Processing & AI, Edge Computing and Affective Computing. Currently, as Distinguished Chief Scientist and Research Area Head, Embedded Devices and Intelligent Systems, TCS Research, he is working in the areas of Connected Health, Smart Manufacturing and Remote Sensing.
He is on the editorial board of notable journals like ACM Transactions on Embedded Systems, Springer Nature Journal on Computer Science and is on the TPC of notable conferences like ICASSP and EUSIPCO. He has filed 180+ patents (out of which 95+ granted in different geographies) and has published 140+ papers and book chapters in reputed conferences and journals. He has also authored two books – one on IoT and another on Digital Twins in Manufacturing. He is on the governing/review/advisory board of some of the Indian Government organizations such as CSIR, MeitY, Educational Institutions like IIT, IIIT and Technology Incubation Centers like TIH.
Prior to joining Tata Consultancy Services (TCS), Arpan had worked for DRDO, India as Scientist for Missile Seeker Systems and in Rebeca Technologies (erstwhile Macmet Interactive Technologies) from as their Head of Real-time Systems. He is a B. Tech and M. Tech from IIT, Kharagpur, India and PhD. from Aalborg University, Denmark.
Affiliations and Expertise
Distiguished Chief Scientist, Research and Innovation, Tata Consultancy Services, Kolkata, India
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