Year 1 outcomes include the following items: (7 weeks of lecture materials, 3 class labs, a conference paper, and others.)

(The following materials are based upon work supported by the National Science Foundation under Grant No. 0941020. Any opinions, findings, and conclusions or recommendations expressed in the materials are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.)

Teaching Goal: This part focues on ECG (Electrocardiogram) sensor hardware design: The students will learn the medical sensor design principle based on the following components: ECG measurement leads, amplifiers, resistors and capacitors, microcontrollers, RF transceivers, wireless communication principle, and so on.

Powerpoint slides: Download from here (copyright belongs to UA)

In Fall 2010, the PI taught ECE 408 "sensor networks".

Here is the lecture material on medical sensor design.

And we also made a homework assignment, and

an exam on such topics.

Suggested students' reading materials:

[1] Long Yan, Jerald Yoo, Hoi-Jun Yoo.; A 0.5-uVrms 12-uW Wirelessly Powered Patch-Type Healthcare Sensor for Wearable Body Sensor Network, IEEE journal of solid-state circuits, vol. 45, no. 11, Nov 2010

[2] Amy D. Droitcour, Olga Boric-Lubecke Gregory; Signal-to-Noise Ratio in Doppler Radar System for Heart and Respiratory Rate Measurements, IEEE transactions on Microwave theory and techniques, vol 57,no.10, OCT 2009.

[3] Refet Firat Yazicioglu; Patrick Merken; Robert Puers; A60 uW60 nV/ Hz Readout Front-End for Portable Biopotential Acquisition Systems, IEEE journal of solid-stae circuits, Vol,42, No.5, May 2007

[4] Anna Gruetzmann, Stefan Hansen and J¨ org M¨ uller; Novel dry electrodes for ECG monitoring, Journal of Physiological Measurement, 2007 Page(s): 1375–1390

[5] Long Yan; Joonsung Bae; Hoi-Jun Yoo; A 3.9 mW 25-Electrode Recon?gured Sensor for Wearable Cardiac Monitoring System, IEEE Journal of solid –state circuits, Jan 2010.

[6] Olav E. Liseth, Daniel Mo, Håkon A. Hjortland; ower-Ef?cient Cross-Correlation Beat Detection in Electrocardiogram Analysis Using Bitstreams, IEEE Transactions on Biomedical Circuits and Systems, Vol 4, No.6, Dec. 2010.

[7] Rita Paradiso, Giannicola Loriga, and Nicola Taccini; A Wearable Health Care System Based on Knitted Integrated Sensors, IEEE Transactions on Information Technology in Biomedicine, vol. 9, No. 3, Sep. 2005.

[8] Yong Gyu Lim, Ko Keun Kim, and Kwang Suk Park; ECG Recording on a Bed During Sleep Without Direct Skin-Contact, IEEE Transactions on Biomedical Engineering, Vol. 54,No. 4, April 2007.

[9] Richard R. Fletcher; Sarang Kulkarni ; Clip-on Wireless Wearable Microwave Sensor for Ambulatory Cardiac Monitoring , 32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina, August 31 - September 4, 2010

Weeks 3~4: ECG signal processing

Teaching Goal: Bio-signal processing is an important component in tele-healthcare engineering. Different medical signals have different processing requirements. For ECG signals, we need to recognize over 20 different heart diseases. The following materials will cover the most important ECG signal processing skills: decision tree, Support Vector Machine, neural networks, Kalman filtering, etc.

Powerpoint slides: Download from here

Suggested Students' Reading materials:

[1] Understanding 12 Lead EKGs ,A Practical Approach, BRADY: Understanding 12 Lead EKGS Ch. 14

[2] Data Mining and Medical Informatics , R. E. Abdel-Aal,November 2005

[3] Factor and Component Analysis, esp. Principal Component Analysis (PCA)

[4] Algorithms for Distributed Supervised and Unsupervised Learning, Haimonti Dutta
The Center for Computational Learning Systems (CCLS),Columbia University, New York.

[5]Applications of the DWT in beat rate detection,
Ding jian,Jun, DISP lab, NTU

[6] Kyriacou, E.; Pattichis, C.; Pattichis, M.; Jossif, A.; Paraskevas, L.; Konstantinides, A.; Vogiatzis, D.; An m-Health Monitoring System for Children with Suspected Arrhythmias, 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007 Page(s): 1794 – 1797

[7] Wang Zhiyu; Based on physiology parameters to design lie detector, International Conference on Computer Application and System Modeling (ICCASM), 2010 Page(s): V8-634 - V8-637

[8] Cutcutache, I.; Dang, T.T.N.; Leong, W.K.; Shanshan Liu; Nguyen, K.D.; Phan, L.T.X.; Sim, E.; Zhenxin Sun; Tok, T.B.; Lin Xu; Tay, F.E.H.; Weng-Fai Wong; BSN Simulator: Optimizing Application Using System Level Simulation, Sixth International Workshop on Wearable and Implantable Body Sensor Networks, 2009 Page(s): 9 – 14

[9] Chareonsak, C.; Farook Sana; Yu Wei; Xiong Bing; Design of FPGA hardware for a real-time blind source separation of fetal ECG signals, IEEE International Workshop on Biomedical Circuits and Systems, 2004 Page(s): S2/4 - 13-16

Week 5: EEG (brain) circuit design

Teaching Goal: This part focuses on EEG (Electroencephalogram) sensor design. EEG can be used to measure brain signals. It can be used to recognize sleep, wake-up, drowsiness and some other brain status. It has much lower cost than MRI-based measurement. EEG sensor needs a high-precision instrumentation amplifier and a good Right Leg Circuit (to remove ground signal fluctuations).

Powerpoint slides: Download from here

Suggested Students' Reading materials:

[1] http://en.wikipedia.org/wiki/EEG

[2] Design of a Compact Amplifier and Signal Conditioning Module for Wireless EEG Monitoring, Ashwin K. Whitchurch Member, IEEE, Jose K. Abraham

[3] Novel Hydrogel-Based Preparation-Free EEG Electrode, Nicolas Alexander Alba, Robert J. Sclabassi, Mingui Sun, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 18, NO. 4, AUGUST 2010

[4] Design Of An Electronic Device For Brain Computer Interface Applications, . Palumbo,P. Vizza,P. Veltri, MeMeA 2009 - International Workshop on Medical Measurements and Applications

[5] Design and Evaluation of a Capacitively Coupled Sensor Readout Circuit, toward Contact-less ECG and EEG, Daniel Sard, Andrzej Cichockiand Atila Alvandpour, 2010 IEEE

[6] Design of Portable Multi-Channel EEG Signal Acquisition System , Lin ZHU, Haifeng CHEN, Xu ZHANG, IEEE

[7] Design and Implementation of a Wireless Multi-Channel EEG Recording, R. Dilmaghani, M. Ghavami, K. Cumar, A Dualeh, S. Gomes Da Sousa, R. Salleh Mohd, CSNDSP 2010

[8] Development of a Low-Cost FPGA-Based SSVEP BCI Multimedia Control System, Kuo-Kai Shyu, Member, IEEE, Po-Lei Lee, Ming-Huan Lee, Student Member, IEEE, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, VOL. 4, NO. 2, APRIL 2010

[9] Joseph Brooks (ICN)Maria Joao (FIL) Methods for Dummies 2007Wellcome Department For Neuroimaging

[10] A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System, Naveen Verma, Ali Shoeb, Jose Bohorquez, IEEE JOURNAL OF SOLID-STATE CIRCUITS, VOL. 45, NO. 4, APRIL 2010

[11]Design of a Compact Amplifier and Signal Conditioning Module for Wireless EEG Monitoring, Ashwin K. Whitchurch (a), Member, IEEE, Jose K.Abraham (a), Senior Member, IEEE, Meghana A. Lonkar , 2007 IEEE Region 5 Technical Conference, April 20-21, Fayetteville, AR

[12]Design on Sampling Circuit of EEG Signal Based on AT89C2051 Single-chip, Xiao-dong Zhang,Zhen-hai Zhang, 2009 Fourth International Conference on Innovative Computing, Information and Control

Week 6: EEG (brain) signal processing

Teaching Goal: The EEG signals have very different waveforms from ECG signals. Some advanced signal processing methods (such as Bayesian learning) need to be used here in order to find out whether the patient is in drownsiness, sleep, anger, or other brain status.

Powerpoint slides: Download from here

Suggested Students' Reading materials:

[1] A 2.4-GHz Low-Power/Low-Voltage Wireless
Plug-and-Play Module for EEG Applications; IEEE SENSORS JOURNAL, VOL. 7, NO. 11, NOVEMBER 2007

[2] A 200 ..W Eight-Channel EEG Acquisition ASIC
for Ambulatory EEG Systems; IEEE JOURNAL OF SOLID-STATE CIRCUITS, VOL. 43, NO. 12, DECEMBER 2008

[3] Global Sensitivity Analysis of Three- and
Four-Layer EEG Conductivity Models; IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 56, NO. 4, APRIL 2009

[4] A Model-Based Objective Evaluation of Eye
Movement Correction in EEG Recordings; IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO. 2, FEBRUARY 2006

[5] A Multistage, Multimethod Approach for Automatic
Detection and Classification of Epileptiform EEG; IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 49, NO. 12, DECEMBER 2002

[6] A Nonstationary Model of Newborn EEG; IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 54, NO. 1, JANUARY 2007

[7] A Prediction Approach for Multichannel EEG
Signals Modeling Using Local Wavelet SVM; IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 59, NO. 5, MAY 2010

[8] A Wavelet-Chaos Methodology for Analysis of EEGs
and EEG Subbands to Detect Seizure and Epilepsy; IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 54, NO. 2, FEBRUARY 2007

Week 7: IMD (implantable medical device) principle

Teaching Goal: Implantable medical devices (IMD) are becoming one of the most important medical devices today. So many people are relying on pacemakers and drug pump today. This part will provide a comprehensive introduction on the hardware and software design principle of IMDs.

Powerpoint slides: Download from here

Suggested Students' Reading materials:

[1] Roland Gubisch, Intertek ETL SEMKO,
EMC for active implantable medical devices

[2]http://en.wikipedia.org/wiki/Implant_(medicine)

[3] http://en.wikipedia.org/wiki/Medical_device

[4] http://en.wikipedia.org/wiki/VeriChip

[5] “American Innovation Forum” , March 31st, 2008

[6] www.americanhear t.org/heartattack

[7] Chen-Hua Kao, Kea-Tiong Tang , Wireless Power and Data Transmission with ASK Demodulator and Power Regulator for a Biomedical Implantable SOC, 2009 IEEE

[8] Ming Yin, Maysam Ghovanloo , Using Pulse Width Modulation for Wireless Transmission of Neural Signals in Multichannel Neural Recording System, IEEE Transactions on Neural Systems and Rehabilitation engineering, august2009

[9] Pichitpong Soontornpipit, Cynthia M. Furse, ,Design of Implantable Microstrip Antenna for Communication With Medical Implants, IEEE Transactions on Microwave theory and techniques 2004

[10] Rizwan Bashirullah , Wireless Implants

[11] Mohamad Sawan, Yamu Hu, and Jonathan Coulombe , Wireless Smart Implants Dedicated to Multichannel Monitoring and Microstimulation

Teaching Goal: How do we transmit healthcare signals from a remote patient to the doctor's office or hospital? Communication technologies (such as wireless cell phone or Internet) should be used. In this lab, we will learn how digital and analog modulation schemes work in ECG signal communication systems. The entire lab is built on Matlab language.

Lab requirements: Download from here (note: lab solutions will not be posted in the website to prevent students' copy. Instructors please email to fei@eng.ua.edu for solutions if needed).

Powerpoint slides on Modulation : Download from here

Teaching Goal: 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.

Lab requirements: Download from here

Teaching Goal: 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.

Lab requirements: Download from here

Download from here (copyright belongs to ASEE 2011)

Paper Title: Multi-Dimensional Tele-healthcare Engineering Undergraduate Education via Building-Block-based Medical Sensor Labs

Abstract - The entire world is facing healthcare challenges. Human society is in critical need of trained tele-healthcare engineers due to the fast expanding bioengineering industries. In a project (sponsored by U.S. National Science Foundation), we are developing a new course called ECE 493 Tele-healthcare Computing. This paper reports our lab design and teaching experiences. Especially we will discuss our educational development of medical networks and bio-signal processing. We have designed three class labs on ECG sensor and ECG signal processing. Those class labs are developed from a building-block approach. When we offer the lectures to students, we have used a multi-dimensional approach: Dimension-1: Multi-student-level adaptive materials: To meet different schools' course setup requirements, we design basic, intermediate and advanced labs for different levels of undergraduate students. Dimension-2: Medical-application-driven, practical learning: Engineering students show much greater enthusiasm to materials that are closely connected to their lives (i.e. application-driven learning) than pure theoretical lab topics (such as writing a program to verify an algorithm). Dimension-3: Multi-solution-based, creative engineering learning: We propose to use level-to-level question-based, non-instructional lab style to motivate students to seek for solutions from out-of-classroom materials such as web resources. And we will make flexible grading policy to encourage students' out-of-box thinking and creative engineering designs.

Index Words - Tele-healthcare engineering, undergraduate education, Building Blocks, Labs, embedded system