28th May 2021
Gao Yuan, Zhang Tantan, Zhou Wei, Son Hyunwoo, Zhao Jianming, Tang Tao
IC Design DepartmentInstitute of Microelectronics
Challenges and Innovations in Sensor Interface Circuit Design for Next Generation Wearables
2
Wearable Device: A Fast Growing Market
Yole DéveloppementMarch 2019*
Market Value
$32B by 2024
*Medical Wearables: Market and Technology Trends report, Yole Développement, 2019
3
Trends in Wearable Devices Development
• Small size, light weight, easy to wear
• Comfortable, better user experience
• 24/7 continuous monitoring
• Multiple sensing modalities
• Towards medical grade accuracy
• Low power for longer battery life
Device Miniaturization
• Real-time feedback (alarm, parameter tuning)
• Data analysis for monitoring and diagnosis
Flexible and Stretchable Device
Smart DeviceMultifunction & Low-power
• Skin-conformal, better contact with skin
• Good performance during body movement
• Easy integration with smart textile
4
Sensing Modalities and Sensing Mechanisms
ECG (heart)
EEG (brain)
EMG (muscle)
Heart rate
Temperature
SpO2
Blood Pressure
Movement
• Body Electrolytes (Na+, K+, Ca2+ etc.)
• Glucose
• Urine acid
• Lactate And many other biomarkers!
Biopotential Signals Physical Signals
Biochemical Signals
Sweat Analysis
5
• Good signal quality, golden standard
• Low contact impedance
• Need skin preparation & conductive gel
• Signal degradation in prolonged usage
• Convenient to setup, no conductive gel
• High contact impedance
• Signal degradation due to motion artifacts,
impedance mismatch, etc
Wet Electrodes Dry Electrodes
Challenges in Biopotential Sensing
Wet Electrodes Dry Electrodes
6
• Normal amplifier input impedance <100MΩ.
• Signal attenuation due to voltage dividing effect.
• Need high amplifier input impedance.
Challenges in Biopotential Sensing
High Input Impedance of Dry Electrode Common-mode Interference
• Strong common-mode interferences
50/60 Hz power line
Body motion artifacts.
Common-mode interference
µV to mV level
Amplifier can be saturated by the strong interference
𝑉𝐼𝑁 = 𝑉𝑆𝑍𝐼𝑁
𝑍𝐸𝐿 + 𝑍𝐼𝑁
ElectrodeAmplifier
Amplifier
YM Chi, et al, IEEE Reviews in Biomedical Engineering, 2010
7
• Tunable Capacitive feedback reduces amplifier input current IEL.
• 1 to 2 orders of impedance boosting to hundred MΩ – 1 GΩ.
Core Amplifier
Tunable Capacitive feedback loop
Circuit Techniques for Biopotential Sensing:Amplifier Input Impedance Boosting Loop
16-channel EEG with Dry Electrodes
T. Tang, et al, IEEE Trans. Biomed. Circuits Syst., Jun. 2020
IFB = (VOUTP-VINP) / ZCB
IEL = IIN - IFB
Zin = VINP / IEL
0 20 40 60 80 100
100
200
300
400
500
600
700
800
900
ZIN
(M
)
0100
0011
0001
Frequency (Hz)
W/O impedance boosting
Cap bankCode
Amplifier Schematic Input Impedance Active Dry Electrode
Capacitive feedback control IFB and
reduces amplifier input current IEL
R Damalerio, MY. Cheng, ECTC 2020
8
Common-Mode Rejection Enhancement
T. Tang, et al, IEEE Trans. Biomed. Circuits Syst., Apr. 2020
Circuit Techniques for Biopotential Sensing:Area-efficient Driven-Right-Leg Circuit for Common-Mode Rejection
• Reuse of the amplifier gain stage for
driven right-leg circuit.
• Suitable for multi-channel biopotential
sensing system.
• More than 60dB common-mode rejection enhancement.
• 90% capacitor size reduction compared to the
conventional DRL circuits.
• No compromise in DRL circuit stability.
System Block Diagram
9
Intraocular Pressure E-Skin Sensor Array Bio-Impedance Sensorized Implant
LCR Meter Coherent I/Q Demodulation
• Conventional impedance measurement requires sinusoidal input and I/Q demodulation
(complex system structure, high power consumption)
• Need new solution with high sensitivity, wide measurement range and low power consumption
Challenges in Physical Signal Sensing: Impedance
10
Circuit Techniques for Impedance Sensing:
Time-domain Impedance Sensing
Ba
se
ba
nd
& D
igit
al
Inte
rfa
ce
ASK
Modulator
13.56MHz
CrystalOscillator
Envelope
Detector
Comparator
PA
Th
res
ho
ld
Display
Tissue
External
Monitoring Device
Power
Command
Data
Wireless
Sensor Interface ASIC
Rectifier
2
Co
il 1
Co
il 2
Rectifier
1
Load Modulator /
DC Limiter
Clock Extractor /
ASK Demodulator / Power-on-Reset
Digital Core
Clock
Rx Data
POR
Mod. Depth Control
Power
Management
LDO 2 Output (DVDD)2
R-to-I
Converter
Se
ns
or
Se
lec
t
Gating
Window
Relaxation
Oscillator
Bia
s
LDO 1 Output (AVDD)
Rectified DC
Tx Data
Se
ns
or
1
Se
ns
or
2
Cm
d/D
ata
Lin
k
Po
we
r
Lin
k
Se
ns
or
Inte
rfa
ce
2
Sensorized Graft
0 20 40 60 80 1000
20
40
60
80
100
5 psi
4 psi
3 psi
2 psi
1 psi
No
rma
lize
d N
o. o
f C
ou
nts
Time (s)
0 psi
JH. Cheong, et al, IEEE Trans. Biomedical Eng. Sep. 2012.
• Sensor is part of the oscillator
• Convert R/C to frequency change
• Mostly digital, low power consumption
RC Oscillator
In-vivo Animal Experiment
Wireless Powered Graft Pressure Sensing System
Measured Pressure Change
Time-domain Sensing
11
Circuit Techniques for Impedance Sensing:
Impedance Sensing with Square-Wave Current Source
• 40nm CMOS
• Chip area 0.6mm2
Chip Photo
0 1 2 3 413.8
13.9
14
14.1
14.2
Time (s)
Bio
Z (Ω
)
Heart Rate
Demo Video (Respiration Rate)
Bio-impedanceMeasurement Setup
T. Zhang, et al, International Solid-State Circuits Conference (ISSCC 2021)
• Relaxed accuracy requirement compared to
sine wave current source.
• Circuit techniques to suppress noises
• 6dB Signal-to-Noise ratio (SNR) improvement
compared to state-of-arts.
26 28 30 32 34
Time (s)
942
944
946
948
950
Bio
Z (Ω
)
Breathing
Hold
breathing
Respiration Rate
System Block Diagram Differential square wave current sources
12
Abbott FreestyleTM Glucose Sensor
Glucose
GOx
Gluconic
acid
O2
H2O2
PB
e- Circuit Requirements
• Stable electrode bias voltage
• Current sensing range: pA ~ µA
• Noise: pA noise floor within signal bandwidth
• Current to voltage gain > 10000
• Temperature sensing for compensation
• WE is functionalized with enzyme to catalyse
a reaction with the biomarker to be sensed.
• Enzymatic reaction occurs at specific voltage,
measure resulting current, proportional to
analyte concentration
Three-Electrode Electrochemical Sensor
Working
Electrode
(WE)
Reference
Electrode
(RE)
Counter
Electrode
(CE)
Chemical Reaction
Challenges in Electrochemical Sensing
Potentiostat and Readout Circuit
Adam Heller, et al, Accounts of Chemical Research 2010
13
Transient Response
• 0.1 – 30nA sensing range with accuracy of 0.06nA
• Integrated temperature sensor for auto calibration
Potentiostat for Electrochemical Sensing
Dissolved Oxygen Sensing Glucose Sensing
Sensitivity
W.P. Chan, et al, IEEE Journal of Solid-state Circuits, Nov. 2014.
Potentiostat Circuit
Chip PhotoWE
Transient ResponseOutput v.s O2 Change
RE
CE
Glucose Sensing Chip Photo Commercial Sensing Device
Reference Reading
Chip Reading
Glucose Concentration (mg/dL)
14
• Wearable devices have huge market potential, especially in healthcare and medical
device areas.
• Challenges and innovations in circuit design for next-generation wearables
Electrode-tissue interface for biopotential sensing
Input impedance boosting loop
Area-efficient DRL circuit for common-mode interference rejection
Low-power impedance measurement for physical sensing
Time-domain sensing with RC oscillator
Square-wave current source based impedance measurement
High resolution current sensing for electrochemical sensors
Reliable potentiostat bias voltage
Low noise, wide range current sensing circuit
Cross-disciplinary collaboration is the key to success!
Conclusions
15
Acknowledgements
Collaborators
Prof. Goh Wang Ling (NTU EEE)
Prof. Heng Chun-Huat (NUS ECE)
Prof. Chen Xiaodong (NTU MSE)
Prof. Aaron Thean (NUS ECE)
Dr. Yu Jing (NTU MSE)
Dr. Yang Le and IMRE Team
And many other colleagues in IME, A*STAR and universities.
Funding
A*STAR Biomedical Engineering Programme
BMRC IAF-PP and JCO-DP Grant
AME Programmatic Funds
IME Team
IC Design Department colleagues and
Cheng Ming-Yuan, Chen Weiguo,
Maria Damalerio, Lim Ruiqi
THANK YOU
www.a-star.edu.sg
Spyder-Verse:DigitalECGmonitoringforBetterpatient
Outcomes
www.spyderecg.com
WEBBiotechnologyPteLtd
Pain-points in Ambulatory ECG Diagnosis & Monitoring
Tradi>onalWired24-HourHolter SpyderAllDigital,WirelessECGmonitor
TheSpyderECGEcosystem3Proprietarycomponentsseamlesslyintegratedinto1completesystem:
1. SpyderECGWearable:Light-weight, inconspicuous,wire-free,re-usable,self-administered,single-channelECGsensorallowingextendeduse
2. Smartphoneapp(Androidbased):FullECGdisplay,interactivetime-stampingofcardiacevents,&continuoustransmissionofdatatoDoctorSpyderCloud-Database
3. Doctor Spyder: Secure Cloud-Based, ECG data-storage & Web-based Physician dashboard withAlgorithm-Drivenreview&reportingplatform
✓ Product ismedicalCEmarkedsince2013¤tlyMDDcertified till2025. It isHSAapproved(2014)
IPRights:PatentgrantedinSingapore(2013)&theEU(2021)andinNationalPhaseinIndia.
www.spyderecg.com
SpyderDataFlow=LiquidECG(Complete Medical Grade Wireless End to End System for Cardiac Rhythm Diagnostics and Monitoring )
4
SecureWeb-BasedPhysicianReportingInterface
CaseWorkflow:Tradi>onalHolterVsDigitalSpyderHolter(Timeandvisitssavedcomparison)
First Visit : consultation, a r range ho l te r tes t and arrange for holter Appointment
2nd Visit : picks up holter, med-tech sets up , cannot bathe
3rd Visit : returns holter, necessary to download information
4th Visit : consults doctor, makes diagnosis and treats
3 to 4 months waiting time wait 1 - 2 week till next TCU for holter reviewOnly 1 day monitoring; max 2 days
1.Tradi>onalMethodsWiredHolterof1daydura>on(En>reprocessrequires4visitsand4to6months>metocomplete)
2.UsingSpyderWirelessDirectpa>enttodatabaseECG(En>reprocessrequiresonly2visitsand1to4weekstocomplete)
First Visit : consultation, arrange spyder pick up immediately as no limitation to number of sets held
3 days of monitoring; can be extended to 1 week, 2 weeks, up to 30 days as per clinical indication
ECG goes ‘live’ to cloud, data analysed and reported via web-based portal with 48 to 72 hours. Patient keeps Spyder till next visit and returns during 2nd consult with Report
2nd Visit : consults doctor, makes diagnosis and treats
1 week- 2 weeks for DiagnosisNo waiting time for Spyder
Cloud DatabaseDoctor Spyder
‘LiquidECG’System:ScalableacrossmultipletimezoneswithDATAconsolidatedtoaCloudDatabase
Spyder’sInnovation(USP)✓ Full Digital ECG Eco-System; 100%wireless, use can be extended forWeeks. ( Longest non-invasive Holter
monitorinmarket).CanbeadministeredContact-Free/Counter-Lessfromanyhealthcarefacility.
✓ OffersTrueDiagnosticANDcontinuousambulatoryMonitoringforacriticalvitalsignacrosslargegeographicaldistances.
✓ Mobilephoneclockrationalisestimeofeventsacrossmultipletimezones&thereforeusableglobally.
✓ Highly scalable, ’Direct Patient to Database, Hub and Spoke’ architecturewith all ECG data consolidated inCloud.
✓ Easysystemsetup,Zero‘brickandmortar’infrastructurerequired.(Nocapitalinvestmentrequiredforsetup)
✓ Continuous transmission through (WIFI/3G/4G/5G) allowing for real-time ECG rhythm Access, Analysis,Reporting&Diagnosis.
✓ BridgesGapbetweenhospital-basedDiagnosticHolterMonitoring&HomeRemoteECGmonitoring
✓ Full physician/clinical engagement model with secured web-based dashboard & real-time access to dataallowing‘on-the-fly’review,analysis&fullreportingfromanyremotePC
✓ MassivelysimplifiesECGrhythmdataanalyticswhicharetypicallylargedatasets
SpyderECG: SingleDevice,OnePlatform,MultipleClinicalIndications
1. DiagnosticExtendedHolterAcademic/Service
Package
Advantages:1. Flexiblenon-invasivemonitoringperiodupto30days2. Fullservicereportorinstitutionbasedself-reporting3. Increaseddiagnosticyieldforinfrequentarrhythmia4. Fullreportavailablebeforereturnofdevice
3.EventMonitoringSPYDEREventor
Personal
Advantages:1. SamedeviceplatformasPro2. Onlyeventmonitorproviding2.5minstripbefore&after
activation3. OTC/E-commerceready4. CanbeconvertedtobacktoAcademicplatformbasedon
requirement
2.HighRiskHomeMonitoringSolution
HomeMonitoring/RehabPackage
Advantages:1. Highriskpatients:PostSurgery,Post-strokePatients2. EarlydetectionofParoxysmalAfiboutofhospital3. Responsetimedcanbemanagedbasedonserviceplatform4. UsedclinicallytomonitorhighriskrehabPatients
ApplicationsofSpyderforRemoteECGMonitoring,Rehab&Telemetry
1. World’s First commercial available device that allows ‘Live ECG monitoring’ from remote location by aqualifiedspecialist: Forhigherriskpatients,ECGrhythmappearsontheserver~1min+afterbeingsentbyPhone,allowingHealthcareprovidertomonitorrhythm‘live’fromatabletorconnectedPC.
2. Compact,lightweight:-APairedSpyderphonecanalsobeusedtodirectlyvisualiseECGrhythm,eitherbytheuser,orbythePhysiotherapist/care-giverinthesameroom.
3. Usable in any remote, ambulatory or home setting: Device can be lease to individual for home orcommunityrehabforrhythmmonitoringoutsidethehospital.
4. Dual RemoteMonitoring and Diagnostic Reporting is available as a service for high risk individuals whorequiredetailedhomeholterreportingoverthemonitoredperiod.
GrowthOpportunitiesforSpyderDigitalCounter-less&ContactFreeRemoteAmbulatoryECGSystem
Markets
✓ LargeunmetclinicalneedinECGambulatorydiagnostics&remotemonitoringofcardiacarrhythmia
✓ 7to15BUSDannualsalesmarketdominatedbycumbersome,antiquatedwired&non-transmittingsystems
✓ Global drive to ‘Digitize’ all portablediagnostic systems to ‘Hub&Spoke’ virtual platforms that are scalableacrossgeographicalbarriers&simplifiesanalyticsusingAI
✓ COVID19Pandemic‘distancing’measurespreventtraditionaltravelandcontactbetweenHospitalandPatients.
✓ Covid19 post pandemic will accelerate conversion of traditional Hospital-Based test to those that are fullyambulatory&contactfreetoCommunityBasedCaretopreventresurgenceofthePandemic
10
Spyderinthenews:Singapore’sMinisterofTransport&formerMinisterofHealthcommentsonSpyderinFBafterhisrecentdischargefromHospital
11
PotentialuseofArtificialIntelligenceinECG&inSpyder
Circ Arrhythm Electrophysiol. 2019;12:e007284. DOI: 10.1161/CIRCEP.119.007284 September 2019 1
BACKGROUND: Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related differences on the ECG. We hypothesized that a convolutional neural network (CNN) could be trained through a process called deep learning to predict a person’s age and self-reported sex using only 12-lead ECG signals. We further hypothesized that discrepancies between CNN-predicted age and chronological age may serve as a physiological measure of health.
METHODS: We trained CNNs using 10-second samples of 12-lead ECG signals from 499 727 patients to predict sex and age. The networks were tested on a separate cohort of 275 056 patients. Subsequently, 100 randomly selected patients with multiple ECGs over the course of decades were identified to assess within-individual accuracy of CNN age estimation.
RESULTS: Of 275 056 patients tested, 52% were males and mean age was 58.6±16.2 years. For sex classification, the model obtained 90.4% classification accuracy with an area under the curve of 0.97 in the independent test data. Age was estimated as a continuous variable with an average error of 6.9±5.6 years (R-squared =0.7). Among 100 patients with multiple ECGs over the course of at least 2 decades of life, most patients (51%) had an average error between real age and CNN-predicted age of <7 years. Major factors seen among patients with a CNN-predicted age that exceeded chronologic age by >7 years included: low ejection fraction, hypertension, and coronary disease (P<0.01). In the 27% of patients where correlation was >0.8 between CNN-predicted and chronologic age, no incident events occurred over follow-up (33±12 years).
CONCLUSIONS: Applying artificial intelligence to the ECG allows prediction of patient sex and estimation of age. The ability of an artificial intelligence algorithm to determine physiological age, with further validation, may serve as a measure of overall health.
VISUAL OVERVIEW: A visual overview is available for this article.
ORIGINAL ARTICLE
Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs
© 2019 The Authors. Circulation: Arrhythmia and Electrophysiology is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial-NoDerivs License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited, the use is noncommercial, and no modifications or adaptations are made.
Zachi I. Attia, MScPaul A. Friedman, MDPeter A. Noseworthy, MDFrancisco Lopez-Jimenez,
MD, MScDorothy J. Ladewig, BSGaurav Satam, MS, MBAPatricia A. Pellikka, MDThomas M. Munger, MDSamuel J. Asirvatham, MDChristopher G. Scott, MSRickey E. Carter, PhDSuraj Kapa, MD
https://www.ahajournals.org/journal/circep
Key Words: artificial intelligence ◼ coronary disease ◼ electrocardiography ◼ hypertension ◼ neural network
Circulation: Arrhythmia and Electrophysiology
June302019
Dow
nloaded from http://ahajournals.org by on Septem
ber 19, 2019
1. Existing Spyder: -UseofDiagnosticAlgorithm,withfurther scrubbing and reporting by qualifiedindividuals-AugmentedIntelligence
2. Population:>10,000individualsinourdatabase,canbeused to look at e.gpredictive signals for variousarrhythmiaswithinthepopulation.
3. Individuals: asdatabeingreceivedissignificant,upto 80,000 to 100,000 heartbeats/day, high qualitydatacanbeusedasabaselinetolookforindividualvariationandchangesinthefuture.
Cloud Database
Doctor Spyder
SpyderECGnowAIreadyasIndividuals’ECG‘Live-streamed’toCentralCloudDATABASE
PersonalMedicinewithPrecisionMedicine/DatawithAIgivesPredic>veMedicine
TransitingtheSpyderECGPlatformtootherNon-ContactPlatforms
www.spyderecg.com
WEBBiotechnologyPteLtd
TheSpyderECGremotemonitoring&diagnosticEcosystemcanbeconsideredasasingleVerticalSilowhereaCriticalVitalSignParameter(ContinuousECG)iscapturedandstored
Existing Doctor Spyder Servers &
Databases
WEB Databases & Servers are currently self-autonomous & are fully connected from patient to database within its own
vertical Silo
ExpandingWEB:DifferentVerticalsinCVSpacecanbeExpanded&IntegratedintoaHealthDelivery&ServicePlatform
Existing Imaging Database
/PACS
1. Echo Data 2. CT Data 3. MRI Data 4. Angiographic Data 5. Carotid Imaging Data
Existing Doctor Spyder Servers & Databases
( Livestream ECG heart rhythm data )
Chronic Disease Database
1. Hypertension 2. Diabetes 3. High Cholesterol 4. Family/Genetic History
Health Delivery Service Platform (HDSP) : (Mother of all Databases)
Data send via APIs, other secure channels
*N.B. Other Disease Verticals e.g Oncology, Geriatrics,
Inflammatory Diseases can be added later
HealthDelivery&ServicePlatformOrganization(HealthMetaVerse)&itsUses
Health Delivery & Service Platform (HDSP) : Mother of all Databases (MOAD)
1. Data from different verticals will be integrated & combined into an Individuals own de-centralised data wallet ( or PEHR ) Personal Electronic Health Records.
2. Such data for individuals will be build up over time and individuals’ trend data can be derived. This will be faster for continuous data sets like Spyder ECG.
3. Using Machine Learning/AI & data analytics, Risk Assessment & Profiling Data can be obtained to Predict events and Onset of disease.
4. Patients hold access rights to their data & can open their wallets to allow Doctors’ access for second opinions.
5. No Geographical barriers ( Contact-Free ) to Individuals seeking opinions/consultations or for Doctors assessing data.
6. Individuals in full control of their own data & granting rights to doctors to view their data.
WhyaDigitalSpyderEcosystem?(BetterPatientOutcomes)
1. PatientAccessibility :Device issmall,user friendly,self-administered&canbedeliveredanywhereoutside
thehospital,increasingthenumberofpatientswhocanbenefitfromit.ThereisNOrequirementforpatients
totraveltohospitaltopickupandhavedevicefittednortoreturndevicesforDoctorstoreviewthedata.
2. DataConnectivity: ECG signal is ‘Fluid’ and ‘live streamed’ to theCloudacross theglobe, allowingalmost
instantaneousrecognitionofabnormalrhythmsacrosslargegeographicaldistances.Afully’LiquidECG’data
system.
3. Doctor Reactivity: Secure Cloud-Based log-in &Web-Based Dashboard (Doctor Spyder) allows viewing of
individualdatabyHealthcareprofessionalfromanyconnectedPCortablet,inanylocationworldwide.
4. Accessibility,Connectivity,ReactivityenhancesHealthcaredeliveryintheCommunityandAmbulatorysetting
Enhanced*Accessibility,*Connectivity,*Reactivity&*HealthcareDelivery
Thankyou
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