Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2)
83 p.
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| Other Authors: | |
| Format: | Thesis |
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2010
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| Online Access: | http://hdl.handle.net/10356/36087 |
| _version_ | 1826128171777392640 |
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| author | Sattu Sreenu Babu. |
| author2 | Soh Cheong Boon |
| author_facet | Soh Cheong Boon Sattu Sreenu Babu. |
| author_sort | Sattu Sreenu Babu. |
| collection | NTU |
| description | 83 p. |
| first_indexed | 2024-10-01T07:20:34Z |
| format | Thesis |
| id | ntu-10356/36087 |
| institution | Nanyang Technological University |
| last_indexed | 2024-10-01T07:20:34Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/360872023-03-11T17:07:10Z Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2) Sattu Sreenu Babu. Soh Cheong Boon School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering::Assistive technology 83 p. Cardiac diseases are by far the most common reason for death in developed countries, which leads to the need of permanent surveillance of cardiac risk patients. The ECQ which provides the key information about the electrical activity of the heart is the most important biosignal used by cardiologists for diagnostic purposes. Modem medicine is dependent on the monitoring of patients and their conditions and illnesses. In preventive control of cardiovascular diseases, cardiac patient monitoring systems play a vital role by providing early detection and constant monitoring. To aid this, telemedicine systems and efficient protocols are developed to help cardiac patients with the transition from hospital to home-based cardiac outpatient programs, with improved continuity of care. Conventional methods of monitoring and diagnosing arrhythmia rely on detecting the presence of particular signal features by a human observer. (Due to the large number of patients in intensive care units and the need for continuous observation of such conditions, several techniques for automated arrhythmia detection have been developed in the past ten years to attempt to solve this problem. Many algorithms have been expensively used for ECQ classification such as Hidden Markov models, Neural Networks and Support Vector Machines etc. Master of Science (Biomedical Engineering) 2010-04-23T02:27:26Z 2010-04-23T02:27:26Z 2006 2006 Thesis http://hdl.handle.net/10356/36087 application/pdf |
| spellingShingle | DRNTU::Engineering::Mechanical engineering::Assistive technology Sattu Sreenu Babu. Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2) |
| title | Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2) |
| title_full | Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2) |
| title_fullStr | Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2) |
| title_full_unstemmed | Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2) |
| title_short | Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2) |
| title_sort | extreme learning machine based ecg analyzer for distributed diagnosis and home health care d2h2 |
| topic | DRNTU::Engineering::Mechanical engineering::Assistive technology |
| url | http://hdl.handle.net/10356/36087 |
| work_keys_str_mv | AT sattusreenubabu extremelearningmachinebasedecganalyzerfordistributeddiagnosisandhomehealthcared2h2 |