Conference proceeding
Spiral Wave Clustering using Normalized Compression Distance
2014 COMPUTING IN CARDIOLOGY CONFERENCE (CINC), VOL 41, Vol.41(January)
Computing in Cardiology Conference
01 Jan 2014
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Cardiac fibril/atory dynamics are identified with spiral waves in mathematical modeling of cardiac electrical propagation. Automatic identification of spiral wave dynamics is essential for patient specific cardiac modeling.
In our work we used normalized compression distance (NCD). an information theoretical distance measure. in order to cluster the simulated spiral waves as stable. meandering and break up. Different representation of the data was introduced to NCD in the form of raw time series, fast Fourier transform (FFT), feature summarization and symbolic quantization of the simulated electrograms. Clustering was done in an unsupervised way using spectral method. Clustering analysis was performed using different validation methods. Gap statistics was used to find optimal number of groups. Jaccard coefficient was used in order to evaluate accuracy of clustering.
We had a perfect evaluation results from the raw data representation and Fourier transformation with a jaccard index of J, and a very good performance of feature summarization with ajaccard index of 0.98.
Metrics
3 Record Views
Details
- Title
- Spiral Wave Clustering using Normalized Compression Distance
- Creators
- Celal Alagoz - Drexel UniversityAndrew R. Cohen - Drexel UniversityAllon Guez - Drexel UniversityJohn Bullinga - Penn Presbyterian Med Ctr, Penn Cardiol, Philadelphia, PA USA
- Contributors
- A Murray (Editor)
- Publication Details
- 2014 COMPUTING IN CARDIOLOGY CONFERENCE (CINC), VOL 41, Vol.41(January)
- Series
- Computing in Cardiology Conference
- Publisher
- IEEE
- Number of pages
- 4
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Identifiers
- 991019170594104721
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Cardiac & Cardiovascular Systems
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology