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dc.contributor.advisorBolton, Ronald J.en_US
dc.creatorOttley, Adam Carlen_US
dc.date.accessioned2007-04-08T09:03:54Zen_US
dc.date.accessioned2013-01-04T04:28:26Z
dc.date.available2007-04-11T08:00:00Zen_US
dc.date.available2013-01-04T04:28:26Z
dc.date.created2007-04en_US
dc.date.issued2007-04-11en_US
dc.date.submittedApril 2007en_US
dc.identifier.urihttp://hdl.handle.net/10388/etd-04082007-090354en_US
dc.description.abstractCardiologists can gain useful insight into a patient's condition when they are able to correlate the patent's symptoms and activities. For this purpose, a Holter Monitor is often used - a portable electrocardiogram (ECG) recorder worn by the patient for a period of 24-72 hours. Preferably, the monitor is not cumbersome to the patient and thus it should be designed to be as small and light as possible; however, the storage requirements for such a long signal are very large and can significantly increase the recorder's size and cost, and so signal compression is often employed. At the same time, the decompressed signal must contain enough detail for the cardiologist to be able to identify irregularities. "Lossy" compressors may obscure such details, where a "lossless" compressor preserves the signal exactly as captured.The purpose of this thesis is to develop a platform upon which a Holter Monitor can be built, including a hardware-assisted lossless compression method in order to avoid the signal quality penalties of a lossy algorithm. The objective of this thesis is to develop and implement a low-complexity lossless ECG encoding algorithm capable of at least a 2:1 compression ratio in an embedded system for use in a Holter Monitor. Different lossless compression techniques were evaluated in terms of coding efficiency as well as suitability for ECG waveform application, random access within the signal and complexity of the decoding operation. For the reduction of the physical circuit size, a System On a Programmable Chip (SOPC) design was utilized. A coder based on a library of linear predictors and Rice coding was chosen and found to give a compression ratio of at least 2:1 and as high as 3:1 on real-world signals tested while having a low decoder complexity and fast random access to arbitrary parts of the signal. In the hardware-assisted implementation, the speed of encoding was a factor of between four and five faster than a software encoder running on the same CPU while allowing the CPU to perform other tasks during the encoding process.en_US
dc.language.isoen_USen_US
dc.subjectsignal compressionen_US
dc.subjectlosslessen_US
dc.subjectholter monitoringen_US
dc.subjectembedded systemen_US
dc.subjectsopcen_US
dc.subjectecgen_US
dc.subjectelectrocardiogramen_US
dc.titleECG compression for Holter monitoringen_US
thesis.degree.departmentElectrical Engineeringen_US
thesis.degree.disciplineElectrical Engineeringen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M.Sc.)en_US
dc.type.materialtexten_US
dc.type.genreThesisen_US
dc.contributor.committeeMemberDolovich, Allan T.en_US
dc.contributor.committeeMemberDinh, Anh vanen_US
dc.contributor.committeeMemberDaku, Brian L.en_US
dc.contributor.committeeMemberSaadat Mehr, Aryanen_US


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