1. Making Sense of my Bio-SignalsMaking Sense of my Bio-Signals
Fu-Chieh Hsu, Ph.D.

2. Who am I
• retired technology geek (i.e., speed freak)
– top manager at IDT, MoSys, TSMC
• passion and curiosity to measure, quantify
and understand oneself

3. Who am I
– apply latest tech capability
– wonderful 2-year journey of learning & un-
learning
– build tools and SHARE
– Raspberry Pi of Bio-sensors

4. My Story to Share
• Not about the tools
• All about the data
– quantity of data is a good start
– quality of data tells the real story
– real story leads to real understanding
– real understanding leads to real improvement
(need for quality data => drives tools)

5. What I Did
• Wired myself up with EEG, ECG, Motion
and Posture Sensors
• Getting on with my normal day, and night
• Study and compare the “stories” told by
various sensors

6. How I did it (1)
• Miniature wireless
bio-sensor like this

7. How I did it (2)
• On these body locations

8. How I did it (3)
• Data streams like this

9. How I did it (4)
• Checking on others
Sigma peak @
11.2Hz, 12.2Hz, 29Hz?
Alpha anomalies too

10. What I Learned (1)
• Don’t believe everything you read from
“…” (textbook, news, ads, …)
• Don’t believe everyone is the same
• Don’t believe those “scores”

11. What I Learned (2)
• Quality is more important than quantity
• Explore, experience, discern and form
your own well-informed decision
• on What-to-Track and Why

12. The Details, Please
• Outrageous, you are attacking everyone!

13. The Myth: EEG (1)
• Are these
really my
brainwaves?
– if you are still,
not making
faces, no
muscle activity,
no tension or
stress, …
then maybe

14. The Myth: EEG (2)
• How to
identify
artifacts?
– Movement
artifacts at
low
frequency
– Muscle
artifacts at
high
frequency

15. The Myth: EEG (3A)
• Are Delta waves slow moving?
– ONLY after the fast moving parts are filtered out
– They often contain large spikes (pulses) more widely
spaced

16. The Myth: EEG (3B)
• Are Delta waves slow moving?
– ONLY after the fast moving parts are filtered out
– They often contain large spikes (pulses) more widely spaced

17. The Myth: EEG (4)
• Do Beta and Gamma waves have broad
bandwidth?
– ONLY as calculated from FFT
– They often contain small random sharp spikes
(pulses) very closely spaced

18. The Myth: EEG (5)
• Do most people conform to textbook
bands?
– ONLY after the non-conforming ones are
filtered out
– Highly varied Alpha band, many are weak or
none
– Occasional oddly placed Sigma band

19. The Myth: EEG (6)
• Can EEG tells a good Sleep story?
– ONLY after artifacts removed, energy bands calibrated
– and supplemented by posture, movement data
– Still can’t reliably tell between REM and (calmly) Wake

20. The Myth: ECG/HRV (1)
• Do most people conform to textbook bands?
– ONLY after the non-conforming breathing ones are
filtered out (highly modulated by deep breathing)
– LF/HF highly sensitive to natural breathing rate

21. The Myth: ECG/HRV (2)
• Do 5min ECG/HRV test tells a good ANS story?
– LF/HF highly sensitive to movements and arousal
– Wide variations over daily (24Hrs) cycle

22. The Myth: ECG/HRV (3)
• Can ECG/HRV tells a good Sleep story?
– Maybe, but NOT quite
– YES with EEG, posture, movement data
– Clear indicator for REM (if not active Wake)

23. The Myth: Activity (1)
• Can Activity tells a good Sleep story?
– NO WAY

24. The Myth: Activity (2)
• Can Activity tells a good day story?
– Well…
– Posture is better (on body trunk, not limb)

25. Activity+Posture+EEG

26. What I Learned
• Read with curiosity from “…” (textbook,
news, ads, …), but THINK afterwards
• Know I (and everyone else) am unique
• Accept “score” only after understanding
the data

27. What I Learned
• Quality is more important than quantity
• Explore, experience, discern and form my
own well-informed decision
• on What-to-Track and Why

28. Thank You
• Fu-Chieh Hsu, Ph.D.,
OP-Innovations, Ltd.
• fhsu@op-innovations.com
• www.op-innovations.com
• www.bioshare.info
• Hsinchu, Taiwan, Republic of China