Using a deep learning-based signal super-resolution AI model and a challenge-best AI model to detect possible heart failure from electrocardiogram in the 2022 Data Science Competition hosted by Tri-Service General Hospital.

1. CHEN, TSAI-MIN(陳 在民) Ph.D. Candidate
Graduate Program of Data Science, National Taiwan University and
Academia Sinica
Supervisors: Dr. Tsao, Yu(曹 昱) &
Dr. Shen, Chun-Yen(沈 俊嚴)
Super-resolution for Signal
Enhancement of Low-Resolution ECG
in Automatic Cardiac Arrhythmias
Classification

2. Evaluation
Method
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Jellyfish-like Talent
Entomology
Microbiology Artificial
Intelligence
人工智慧學校
專案處-顧問
中研院生物醫學所
研究助理
日本京大學
分子微生物學所
台灣大學
昆蟲學系
政治大學
政治學系
Quantum
Computing
中研院&台灣大學
資料科學學位學程
Find a way out(O)
Cross-domain?(X)
https://www.linkedin.com/i
n/%E9%99%B3%E5%9C
%A8%E6%B0%91-tsai-
min-chen-4675449a/
Political
Science
Medicine

3. Evaluation
Method
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
3
• ECG is used to show the potential
changes in the rhythm of the heart
cycle
• Physicians can diagnose heart
disease from these potential
changes
• Study how AI sees these potential
changes
What is ECG for?
Akash Kumar Bhoi,
Basics of ECG, Published
on Sep 8, 2015

4. Evaluation
Method
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
2022 Data Science Competition Dataset
Records(9806):
Left ventricular
ejection rate label,
patient ID label
100Hz 12 lead
10 seconds ECG signal
Our
CPSC2018Net+ID
Analytical model
Enhanced 100Hz 12 lead
10 seconds ECG signal
: A.I.
12
Leads
A.I.
Our ESRNet
signal
enhancement
model
Left ventricular ejection rate label, patient ID label
:

5. Evaluation
Method
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Original ESRNet Signal Enhancement Model
https://arxiv.org/abs/2
012.03803
Original Enhanced
ESRNet

6. Evaluation
Method
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Original CPSC2018Net Analysis Model
https://doi.org/10.101
6/j.isci.2020.100886
2018 China Physiological Signal
Challenge World Champion
Me, before diet
CPSC2018
Net+ID
x10
One-hotted
patient ID
Concatenate

7. Evaluation
Method
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Original Distribution Adjustment (Scaling)
Before Adjustment After Adjustment (Scaled)
0.35(heart failure criteria) 0.50
左心室射出率(%)

8. Evaluation
Method
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Before Adjustment (Scaled) After Adjustment
0.50
Original Distribution Adjustment(Dragging)
0.50

9. Evaluation
Method
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
60-fold Training & Ensemble
Scoring with
60-fold Model Ensemble
on Official Private Test
Records(9806):
Left ventricular
ejection rate label,
patient ID label
60
x
60

10. Evaluation
Method
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Conclusion: ESRNet is Good
Private Leaderboard Results
1
Official Public Test Results
Method / Criteria Official Public Test
Baseline AUC 0.9523
+ ESRNet AUC 0.9555
+ ESRNet + Scaling AUC 0.9573
+ ESRNet + Scaling + Dragging AUC 0.9576
AUC

11. Evaluation
Method
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
NTU-Academia DS Program & CITI
Welcome to visit our places!!!

12. Evaluation
Method
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Question & Answer
Any questions are fine!