Electrocardiogram attribute extraction using big data analytics /
Massive quantities of data are transmitted and received on a daily basis, which means that the era of Big Data has begun. Big Data Analytics have been highly successful in dealing with this immensely large volume of data. Unfortunately, the different attributes studied in the various presentational...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
Kuala Lumpur :
Kulliyyah of Information and Communication Technology, International Islamic University Malaysia,
2018
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| Subjects: | |
| Online Access: | Click here to view 1st 24 pages of the thesis. Members can view fulltext at the specified PCs in the library. |
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| Summary: | Massive quantities of data are transmitted and received on a daily basis, which means that the era of Big Data has begun. Big Data Analytics have been highly successful in dealing with this immensely large volume of data. Unfortunately, the different attributes studied in the various presentational analytical schemes possess certain drawbacks. For instance, Electrocardiogram (ECG) data require that each pattern of an ECG recording obtained from different people at different sessions be evaluated. This problem applies to the entire Big Data Analytics cycle, especially at the point of decisions making. In order to address this issue, the present study proposes a new technique capable of parsing and tokenizing text data in Hadoop to evaluate the attributes of the ECG data in relation to the patients health. An algorithm is developed for the map reduce cycle of Hadoop to parse the Electrocardiogram data in text format before tokenizing them in the map phase. The required attributes are pulled to the Hadoop context to be further processed in the reduce phase to obtain an aggregation of the results for the patient. Evaluating this aggregation of data can assist medical professionals by supplying them with another beneficial health indicator. This information is likely to impact the design of future heart health indicators using machine learning so that individuals are able to obtain metrics on the health of their heart as part of preventive medicine. |
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| Physical Description: | xii, 67 leaves : colour illustrations ; 30cm. |
| Bibliography: | Includes bibliographical references (leaves 62-67). |