In Year 2, we have edited a new textbook called "Tele-Healthcare Computing and Engineering: Principles and Design", developed one-semester (15 weeks) of lecture slides /notes, and 5 labs.

Note: In Fall 2012, we will offer a new course called "ECE 493/593 Special Topics: Telehealthcare Engineering". We will further improve our developed course materials based on students' feedback.

Quick Links:

Part 1. Textbook editing

Part 2. Lecture notes developed

Part 3. Lab materials

Part I: Edited a new textbook to be pusblished in Nov 2012 (Go back to Top Menu)

[Book] Fei Hu (editor), "Tele-Healthcare Computing and Engineering: Principles and Design," Publisher: SCIENCE PUBLISHERS, To appear in Nov 2012. Total 26 chapters. ~500 pages in total.

Motivation: To the best of our knowledge, so far there is still no suitable book for student reference purpose in the field of "telehealthcare engineering". Therefore, the PI (Hu) has invited over 20 world-famous tele-healthcare experts to write different topics in tele-healthcare. The PI himself also contributed a few chapters.

Features of The Book: Compared to other healthcare books, this book has the following special features:
(1) Emphasize both computing science and engineering design: Since Telehealthcare Computing and Engineering (TCE) is a multi-disciplinary field, we have invited over 20 scientists and engineers who have worked in TCE for a long term to describe all importance aspects in both science principles (such as healthcare signal analysis) and engineering design (such as medical sensor design). Such a science/engineering integration is important to TCE understanding since it needs to be built on both software and hardware.
(2) Explain both entire TCE system and individual components: This book has 4 parts. Part I introduces the entire TCE system architecture from remote health monitoring and TCE networking viewpoints. The system is built on the latest technologies such as e-textiles, wireless sensor networks, wireless and mobile networks, on-site medical imaging collections and processing, etc. The large amount of nuts and bolts of a complete TCE system, including medical sensors, implantable medical devices (IMDs), ECG signal analysis, medical pattern recognition algorithms, etc., will also be explained through circuit design and algorithms. Such a system/components integration approach makes our book suitable to both TCE R&D managers and design engineers.
(3) Covers both basic principles and deep research / development (R&D) details: The collected chapters could be tutorial nature or profound discussion of R&D design. Most chapters have discussions of both basic principles and deep knowledge. For example, when we discuss the medical sensor design, we first provide general guidelines on medical devices circuit design procedure; then we go to the concrete engineering design detail of each sensor component such as materials, CPU, healthcare signal sensing, and signal transmission.

Fei Hu, Meikang Qiu, Qingquan Sun, Mengcheng Guo, Yeqing Wu, Jiang Lu, " Chapter 15. RFID For Tele-Healthcare Applications," in Tele-Healthcare Computing and Engineering: Principles and Design, Science Publishers, to appear in Nov 2012.
Fei Hu, Xiaojiang Du, Xiali Hei, Alexandru Samachisa, Marcin Lukowiak, Dong Zhang, Shuhui Li, Jie Wu, Daniel Phillips, " Chapter 25. Implantable Medical Device (IMD) Access Authentication Through Access Pattern Classification: Algorithms and Hardware Implementation," in Tele-Healthcare Computing and Engineering: Principles and Design, Science Publishers, to appear in Nov 2012.

UA Fall 2012 starts in Wednesday. Therefore, there are only two lectures in week 1.

Lecture 1 will focus on the course syllabus. We will cover grading policy, discuss what topics will be covered in this course.

Lecture 2: (notes here) We will give an overview on telehealthcare system. Especially we will emphasize the use of telecommunication and signal processing knowledge for medical applications.

Week 2 will focus on Mobile Health (M-health) design. M-health is one of the most important telehealthcare implementation techniques. It uses mobile computing platforms to achieve anywhere anytime patient monitoring.

Lecture 1: (notes here) Overview of medical signal processing. This lecture is supposed to be given in week 1. But because UA has only two lectures in week 1, we move this "overview" topic in week 2. Because we will use 5 weeks to cover medical signal processing, it is important to use one lecture to talk about its big picture. In this lecture, we will briefly talk about wavelet, Fourial, de-noise, filtering, compression, Bayesian inference & learning, and so on.

Lecture 2: (notes here) The most important telecommunication technologies used for M-health, includes different types of wireless and mobile networks such as WiMax, cellular, WiFi, sensor networks, etc.

Lecture 3: (notes here) M-health case study. This case study is proposed by the PI to achieve a new generation telehealthcare platform. It is based on medical sensors, pervsaive computing, and medical privacy software. We have provided the price info. for each equipment.

Medical sensor networks are perhaps the most popular medical information collection platforms in local area (near the patient). Examples include different types of body sensor networks. In this week we will first introduce a famous medical sensor network system called CodeBlue. Then we will talk about a few example systems proposed by the PI (Hu) himself.

Lecture 1: (notes from CodeBlue group) Use CodeBlue as an example, detail the medical sensor network architecture, problems to be solved, and future trends.

Lecture 2: (notes here) Another architecture proposed by the PI (Hu) himself. It has wearable, non-invasive, and implanted medical devices.

Lecture 3: (material here) Case study based on the PI (Hu)'s IEEE journal paper. It is on a complete medical sensor system with robust medical data delivery architecture.

This week will focus on the medical sensor hardware design principles. Especially we will cover sensor circuit architecture, including CPU, transmitter, power supply, amplifer, filter, and so on.

Lectures 1 + 2 (notes here) These two lectures are based on the PI (Hu)'s textbook called "wireless sensor networks: principles and practice". They cover all circuit components in sensor design.

Lecture 3 (notes here): This part will detail each electronic component in sensor circuit.

RFID has been applied in medical monitoring for more than one decade. For example, we can attach RFID tag to the medicine bottle. When a patient approaches to the bottle, a speaker can automatically tell the patient the medicine name.

Lecture 1: (notes here; from Arkansas) RFID tutorial.

Lecture 2: (material here; a journal paper from others) RFID circuit design example.

Lecture 3: (PI's paper here) This is a journal paper from the PI himself. It is on the application of RFID for medicine-taking monitoring.

This week will focus on the most popular medical sensor - ECG (also called EKG) sensor hardware design details. We will first discuss CodeBlue EKG sensor architecture. Then we will use 2 lectures to cover a few important ECG circuit design examples.

Lecture 1: (material here): CodeBlue ECG sensor circuit design.

Lectures 2+3: (notes here): A few ECG sensor design examples.

Lecture 1: DSP concepts (material from Dr. Crawford) and Matlab (user manual from Matlab)

Lecture 2: (notes here) Wavelet basics

Lecture 3: (notes here) Wavelet for medical signal processing

Machine learning (ML) is an important way to learn the disease symptoms from medical signals. This week will cover the basic ML schemes.

Lectures 1+2 (notes from Christ textbook): Machine Learning Overview

Lecture 3 (material from a book chapter): Basic ML and applications

We have designed >300 slides from over 20 papers on ECG signal processing. Weeks 9 and 10 will cover all important aspects in that area.

Lectures 1+2 (slides): ECG recognition

Lectures 3+4 (slides): ECG processing (I)

EEG sensor can measure brain activity signals. It is also very popular medical sensor.

Lectures 1+2 (Materials here; from a Senior design group): EEG sensor design

Lectures 3+4: (slides): EEG sensor circuit design principle

Lectures: (240 slides) EEG signal processing

Lecture: (slides from Dr. R. E. Abdel-Aal) Data mining for medical infomatics

Lecture 1 (PI's paper on IMD): IMD concepts, design and applications

Lectures 2 and 3 (slides): IMD design examples

Medical privacy and security are very important issues!

Lecture 1: (materials 1 and 2) IMD security. For this part, we will first talk about the PI (Hu)'s journal paper on IMD security. Then we will introduce a well-written survey paper in this field.

Lecture 2: (materials from IEEE paper) Medical privacy.

Lecture 3: (materials from Dr. Goldman) Medical safety. Safety is different from security.

In this project, we will devide the entire class into different groups. Each group has 3~4 people. Each group should design a Printed Circuit Board (PCB) for a medical sensor (ECG or EEG). They need to demo the final board via wireless mote.

Here are some materials for this project:

PCBExpress user guide

Medical sensor PCB design specifications

PCB board testing process

Telehealthcare data transmission via different RF modulation methods

This lab (procedure here) is based on Matlab. It asks students to try different digital modulation schemes. Students need to display the received medical signals.

Medical sensor communications via RF motes

This lab (procedure here) will use Memsic motes to interface medical sensors in order to wirelessly transmit data through medical sensor networks.

Show medical sensor data in curves through a GUI

Lab procedure here

Use PhysioNet medical signals

PhysioNet is perhaps one of the biggest medical signal database. In this lab students will learn how to install Physio toolkit in Linux environment.

Installation Guide

Use PhysioNet

How do we find out the heart beat patterns from ECG signals? How do we know a patient is in coma status or not from his EEG signals? In this lab, we will conduct 3 mini-labs. They use signal processing functions (from Matlab) to extract the signal patterns.

Download procedure

This lab learns how to use compressive sensing technology to achieve low-cost medical signal compression. Especially we will learn the principle of subnyquist theorem, signal reconstruction, and low-cost sampling from ECG/EEG signals.

Download procedure