winslow-ncbo dbp mtg

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Ontology-Based Annotation of BiomedicalOntology-Based Annotation of Biomedical

Time Series DataTime Series Data

Raimond L. Winslow, PhD

Stephen J. Granite, MS, MBAThe Institute for Computational Medicine

Johns Hopkins University


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MotivationMotivation

Electrocardiogram (ECG) is most common type of

cardiovascular (CV) data collected

No tools exist for managing, sharing and annotating

ECG data Having these tools will:

Facilitate further data analysis

Contribute to exchange and re-use of data

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Project AimsProject Aims

Develop comprehensive ECG ontology

Develop ECG data model and data service

Extend capabilities of CardioVascular Research Grid (CVRG) ECG datamanagement and analysis portal to allow users to:

Upload, query, and retrieve data from ECG data service

Scroll through ECG waveforms View the annotations generated by ECG analysis algorithms

Edit these annotations (& correct algorithm mistakes)

Create new annotations for ECG waveform temporal features (...missed by the algorithms)

Create new annotations for ECG waveform analysis derived data

Return annotated ECGs and derived data back to data service as part of multi-scale data federation

Enable further ontology development through BioPortal 2.0 features

Allow Subject Matter Experts (SMEs) to comment on or add new terms

Put a new term adjudication process in place


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ECG Data and Analytic ServicesECG Data and Analytic Services

HL7aECGXML

Repository(Raw andAnalyzed

Data)

Norav ECGData (RDT)

BergerAlgorithm

s

PhysionetECG Data(WFDB)

Meta-data

XML

WFDB

Physionet

Algorithms


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ECG Visualization PortletECG Visualization Portlet


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Automated ECG AnnotationAutomated ECG Annotation

Data passed to ECG analysis services onupload

ECG properties are computed, for example Q, R, S, T wave peaks QRS width, RR & QT intervals QT and RR interval variability ST segment elevation many other properties

Computed features are displayed Term definition returned from NCBO

BioPortal via web services Reference to algorithm used to compute each

feature Users can scroll through the annotated data

electronic rather than paper ECG

chart recording


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ECG ProgressECG Progress

Collected terms from HL7aECG Vocabulary

Organized terms within Protg

We are: Reviewing standardization

recommendations proposed byAHA and other organizations forECG interpretation

Comparing terms to ECG journalarticles and papers

Integrating other ontologies intoECG Ontology Foundational Model of Anatomy

(FMA)

Units of Measurement (UO)7


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Cardiac Electrophysiology (EP) OntologyCardiac Electrophysiology (EP) Ontology

Cardiac EP data is another important type of time-

series data in CV research

No ontology/data model for disseminating EP data

No culture of even sharing this data

Develop an ontology and data model

Protg

Single-channel electrophysiological experiments and data

Voltage-clamp

Current clamp

Fluorescence imaging

Multi-channel fluorescence imaging8


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Referenced OntologiesReferenced Ontologies

NCBO BioPortal

Foundational Model of Anatomy (FMA)

Units of Measurement (UO)

Cellular Components (GO) Chemical Entities of Biological Interest (ChEBI)

NCBI Organismal Classification

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EP ProgressEP Progress

Created an NCBO BioPortalproject (http://bioportal.bioontology.org/pr)

Ontology version 1.0 availablefor download (http://bioportal.bioontology.org/on)

We: Have invited Cardiac EP

community SMEs to reviewontology

Are revising ontology and adding

more terms 10
http://bioportal.bioontology.org/projects/26http://bioportal.bioontology.org/ontologies/39008http://bioportal.bioontology.org/ontologies/39008http://bioportal.bioontology.org/projects/26


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ECG Ontology

ECG data collection protocols Number of leads, lead name, lead placement

ECG acquisition instrumentation

deskside, ICD, pacemaker, Holter monitor

Software version #, raw data format Signal attributes Duration, Filtering, sampling, # bits, signal processing

Data analysis algorithms

Name features computed from ECG (large number) Name specific algorithms used to compute ECG

features (large number)

Name reference describing the algorithm(s)


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Challenges

Arking et al (2006). A common genetic variant in the NOS1 regulator

NOS1AP modulates cardiac repolarization. Nat. Genetic. 38(6): 644

Bioset 9000 instrumentation

12 lead ECG

Digital caliper electrodes

ECG computed from leads II, V2, V5

Hannover ECG Analysis Software Version

V3.22-12 Software specific definition of QT interval

QT interval correlated with genotype is

corrected for heart rate, age, and sex of subject


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Challenges (cont.)

Arking et al (2006). A common genetic variant in the NOS1 regulator

NOS1AP modulates cardiac repolarization. Nat. Genetic. 38(6): 644

QTc_RAS=QT

1g(RR 1000)

2g(A 60) 4.58

QTc_RAS is Average QT interval corrected for heart rate R,

age A, and sex S

is mean QT interval, is mean RR interval

1 and 2 are sex dependent factors

** Definition of computed ECG features is algorithm dependent

and currently there is no unique name for each quantity

QT RR


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EP Ontology LayoutEP Ontology Layout

EPStudy

Creator Biomateria

Procedure

ObservationEPObservationEPData

EPProcedureEPTechnique

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EPprocedureEPprocedure

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EP Ontology TestingEP Ontology Testing

Creation is an iterativeprocess

Describe experimental

methods section

Web-based submissionform for novel EP related

terms (in future)

Version 1 ready for

submission

Use this experience in

development of the ECG

ontology13


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Manage the Ontology

Deposit the ontology at the NCBO andmanage using NCBO tools (BioPortal)

First level of review

Expert review panel to triage terms

Members - Ary Goldberger, Ron Berger, Gordon Tomaselli

Add members as needed over time

Second level of review

User feedback capabilities of BioPortal 2.0

Third level of review

Annual meetings of the CVRG ECG Working Group

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