Research Project Title: Machine-to-Machine Application of Telehealth Using Automatic
Electrocardiographic Analysis System by Digital Signal Processing Methods
1. Team member
Principal Investigators: Assoc Prof. Yi-Lwun Ho
Co-PI: Assist Prof. Jian-Jiun Ding; Assist Prof. Yen-Hung Lin; Dr. Ying-Hsien Chen
2. Discussion with champions
Time: 4/26 10:00-11:00
a. discuss the current work: ECG analysis (the algorism for differentiation of normal and
abnormal ECG including APC, VPC, atrial fibrillation)
b. Assess different criteria which
differentiate atrial fibrillation fgrom ventricular fibrillation
(Cited from reference: Liu CS,
Tseng WK, Lee JK, Hsiao TC, Lin CW. The differential method of phase space matrix for
AF/VF discrimination application. Medical Engineering & Pysics 2010;32: 444–453)
3. Progress between last month and this month
(the algorithm for extracting the feature points and
differentiation of normal and abnormal ECG including APC, VPC, atrial fibrillation)
a. Summary of key findings and innovation, and/or
 The results of the previous month:
For the database acquired from NTHU
Feature Points
R Points
Q and S Points
P and T Points
Sensitivity
98.06%
97.09%
93.93%
FP rate
0%
0%
2.60%
Computation time: 0.000264 second for a one-second length ECG signal (by Matlab).
 The results of this month:
For the database acquired from NTHU
Feature Points
R Points
Q and S Points
P and T Points
True Number
206
412
346
Detected (TP)
206
412
343
False Detected (FP)
0
0
2
Sensitivity
100%
100%
99.13%
FP rate
0%
0%
0.58%
Computation time: 0.000292 second for a one-second length ECG signal (by Matlab).
If only the R points should be detected, the computation time is 0.000130 second for a one-second
length ECG signal (by Matlab).
For MIT/BIH Arrhythmia Database (used worldwide)
True number of R points: 110159
Sensitivity: 99.82%
Detected R Points: 109964
a.We have done the paper survey and found that the best result in the open literature is 99.80%. The
computation time of our method is about 0.3 second (by Matlab). However, the computation time of the
existing method is 3 second.
We develop an advanced method for base line extraction. The accuracy of base line extraction
significantly affects the performance of ECG feature point extraction. We find that, with the proposed base
line extraction method, the P, Q, R, S, and T points can be detected very accurately.
b. Negative results and their consequences, and/or
There are some cases that the algorithm does not work perfectly, such as the cases where the interval
of R points is less than 1/3 second and the baseline varies too fast.
c. Cross-project synergy
We propose the idea that the ECG sensor can be implemented in the steering wheel of the car. This
can monitor the condition of the driver. If the driver feels sleepy, the RR interval becomes larger. If the
driver is drunk, the RR interval becomes smaller. In these cases the system in the car will inform the driver
not to derive again. This is helpful for improving the safety of the driver.
4. Brief plan for the next month
(1) We will calculate the number of instructions for ECG signal analysis and try to minimize it.
(2) We will further improve the accuracy of the ECG feature point extraction algorithm.
(3) We will try to design the algorithm to analyze the arrhythmia problems, including the cases of atrial
fibrillation (AF), ventricular fibrillation (VF), and the atrioventricular block (AV block).
a. Updated view of planned milestones, deliverables, and success criteria
Date
Milestone
Deliverables
Technical Success
Criteria/ Objectives
 Confirm the research
direction
Study and environment
 Presentation of the study
setting. Make solid power
estimation to re-address the
first risk of this project
2012Q4
Finish the first version of  Technical report of the system
 A technique report
the ECG interpretation
analysis result
with detailed
algorithm.
 Software for the interpretation
description
 Define some joint develop items  Clinical trial IRB
with other subproject.
application
2012Q5
Develop a very accurate and  Technical report of the system

For MIT/BIH
very fast algorithm for ECG
analysis result
Arrhythmia Database,
feature point extraction.
 Software for the interpretation
sensitivity = 99.80% and
 Define some joint develop items computation time for
with other subproject.
each 30 minute length
 Discuss the arrhythmia problem ECG data is 0.3 second.
5. Research byproducts
2012Q2
Since the ECG feature point extraction algorithm we develop has both higher accuracy and very less
computation time than the existing methods, we plan to write it as a journal paper in the summer.