The selective de-identification of ECGs
| dc.contributor.author | Akhtar, Musamma, author | |
| dc.contributor.author | Simske, Steven, advisor | |
| dc.contributor.author | Wang, Zhijie, committee member | |
| dc.contributor.author | Vans, Marie, committee member | |
| dc.date.accessioned | 2023-01-21T01:24:22Z | |
| dc.date.available | 2023-01-21T01:24:22Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Biometrics are often used for immigration control, business applications, civil identity, and healthcare. Biometrics can also be used for authentication, monitoring (e.g., subtle changes in biometrics may have health implications), and personalized medical concerns. Increased use of biometrics creates identity vulnerability through the exposure of personal identifiable information (PII). Hence an increasing need to not only validate but secure a patient's biometric data and identity. The latter is achieved by anonymization, or de-identification, of the PII. Using Python in collaboration with the PTB-XL ECG database from Physionet, the goal of this thesis is to create "selective de-identification." When dealing with data and de-identification, clusters, or groupings, of data with similarity of content and location in feature space are created. Classes are groupings of data with content matching that of a class definition within a given tolerance and are assigned metadata. Clusters start without derived information, i.e., metadata, that is created by intelligent algorithms, and are thus considered unstructured. Clusters are then assigned to pre-defined classes based on the features they exhibit. The goal is to focus on features that identify pathology without compromising PII. Methods to classify different pathologies are explored, and the effect on PII classification is measured. The classification scheme with the highest "gain," or (improvement in pathology classification)/ (improvement in PII classification), is deemed the preferred approach. Importantly, the process outlined can be used in many other systems involving patient recordings and diagnostic-relevant data collection. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Akhtar_colostate_0053N_17568.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/235985 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | biometrics | |
| dc.subject | de-identification | |
| dc.subject | pathologies | |
| dc.subject | cyber security | |
| dc.subject | anonymization | |
| dc.subject | ECG | |
| dc.title | The selective de-identification of ECGs | |
| dc.type | Text | |
| dc.type | Image | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Biomedical Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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