"Continuous Digital Biomarkers from Wearable Devices" - Brandon Ballinger (Co-Founder, Cardiogram) Delivered at the inaugural Hyper Wellbeing Summit, 14th November 2016, Mountain View, California. For more information including details of subsequent events, please visit http://hyperwellbeing.com The summit was created to foster a community around an emerging industry - Wellness as a Service (WaaS). Consumer technologies, in particular wearables and mobile, are powering a consumer revolution. A revolution to turn health and wellness into platform delivered services. A revolution enabling consumer data-driven disease risk reduction. A revolution extending health care past sick care towards consumer-led lifelong health, wellness and lifestyle optimization. WaaS newsletter sign-up http://eepurl.com/b71fdr @hyperwellbeing

1. NOVEMBER 14-16
MOUNTAIN VIEW, CA

2. Continuous Digital Biomarkers from Wearable Devices
Brandon Ballinger, @bballinger
@AppCardiogram

3. 2

4. 3
TRADITIONAL BIOMARKERS CONTINUOUS DIGITAL BIOMARKERS
Biomarkers are Becoming Continuous and Digital
Necessary but not fun Engaging
Point-in-time 100,000+ data points per person per year
Clinician-interpreted Hybrid of human and artificial intelligence
Well-understood science based on Framingham
(1948) and other historic studies
Emerging field at the intersection of medicine,
artificial intelligence, and mobile design.

5. Fitbit Huawei
Apple
Fossil Motorola

6. What’s your ♡ telling you?
Examples from Cardiogram for Apple Watch
5
Fitness Stress
Running
Hockey
Driving in Rush Hour
Microsoft interview
Illness
Atrial Fibrillation
Atrial Flutter
Sleep
Restful Sleep
Sleep after Alcohol

7. Heart rate biomarkers
known from the medical literature

8. Reverse commute
(SF to Palo Alto in AM)
Regular commute
(SF to Palo Alto in PM)
8:00AM 8:30AM 4:30PM4:00PM
60
100
140
Heart rate
60
100
140
Heart Rate Variability

9. 11:00AM
80
130
180
Heart rate
12:00PM11:30AM
Intense workout StretchingRecovery
Heart Rate Recovery & Resting Heart Rate
HEART RATE RECOVERY
RESTING HR

10. What can heart rate metrics tell you about yourself?
Metric Associated with (not exhaustive)
Resting heart rate (RHR)
(Physical fitness, stress)
• Mortality (1.3x in pts with
coronary artery disease)
• Heart attack
• Mortality (1.3x women)
Heart rate recovery (HRR)
(Physical fitness, vagal
activity)
• Mortality (4x)
• Coronary artery disease
Heart rate variability (HRV)
(Stress response, vagal
activity)
• PTSD
• Depression
• Anxiety
• Sudden death after heart attack
Heart rhythm abnormality
e.g. atrial fibrillation (AF)
• Mortality (1.5x men, 1.9x women)
• Stroke (5x)

11. 40
50
60
70
80
90
100
110
0 200 400 600 800 1000 1200 1400
Movement vs Resting Heart Rate
40
50
60
70
80
90
100
110
0 5 10 15 20
Standing vs Resting Heart Rate
40
50
60
70
80
90
100
110
0 20 40 60 80 100 120
Exercise vs Resting Heart Rate
RestingHeartRate(beatsperminute)
Exercise (minutes per day) Stand (hours per day) Move (calories per day)
Exercise vs Resting Heart Rate Standing vs Resting Heart Rate Movement vs Resting Heart Rate
Do Apple Watch’s activity rings drive cardiovascular improvements?

12. Can we use artificial intelligence to design
novel biomarkers?

13. Semi-Supervised Sequence Learning for Continuous Digital Biomarkers
5 12 5 28 30 30
dt
heart rate
steps
86 0 74 0 85 83
0 5 0 97 0 0
5
83
0
…
…
…
1 0 0 -1 1
atrial fibrillation
hypertension
sleep apnea
0 0 -1 0 0 1
0 0 1 0 0 -1
-1
0
0
…
…
…
…
Multi-channel,
multi-timescale
sensor input
Multi-task Output
0
time ▶
Recurrent Neural
Network
Fully connected tanh
Long Short-Term Memory
1D Convolution
Fully connected ReLu

14. 13
…
AF Not-AF
86 79 74 82 85 83 74
Raw Heart Rate Measurements
1 0
Clinical Gold Standard
Continuous Digital Biomarkers
Applying deep learning to health
Atrial Fibrillation Normal Heart Rhythm

15. Accuracy on Cardioversions
TruePositiveRate
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
False Positive Rate
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

16. What else can you detect?

17. Why does this matter?

18. 17

19. Thanks!
brandon@cardiogr.am