Browsing by Subject "Electrocardiography--Data processing."

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    Multilead ECG data compression by multirate signal processing and transform domain coding techniques
    (1991) Aydın, M. Cengiz
    This thesis presents two data compression methods for digital electrocardiogram (EGG) signals. The first method is proposed for the compression of single lead EGG data. Various coding technicgies such as Sub-Band Transform and Variable Length Godings are used to realize that purpose. Data size reduction which is a problem in EGG databases is chosen as an application and successful results are achieved during simulations. The second algorithm is proposed for the multilead EGG data compression. A linear transform is applied to samples of the eight standard EGG lead signals. The resulting transform domain signals are compressed using various coding methods, including multi-rate signal processing and transform domain techniques. Higher compression ratios are achieved compared to the single channel compression techniques.
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    Online ECG signal orthogonalization based on singular value decomposition
    (1996) Acar, Burak
    Electrocardiogram (ECG) is the measurement of potential differences occurring on the body due to the currents that flow on the heart during diastole and systole. Cardiac abnormalities cause uncommon current flows, leading to strange waveform morphologies in the recorded ECG. Since some abnormalities become visible in ECG only during activity, exercise ECG tests are conducted. The sources of noise during an exercise test are electro myogram (EMG) due to increased muscle activity and baseline wander (BW) due to mechanical motion. Frequency band filtering, used to eliminate noise, is not an efficient method for filtering noise because usually frequency spectra of the interference and the ECG overlap. Rather, a fast morphological filter is required. This thesis is focused on an online filtering approach which separates noise and ECG signals without changing the morphology. The redundancy present in standard 12 lead ECG records is made operational by a Singular Value Decomposition based orthogonalization of the input signals. ECG is represented in a minimum dimensional space whose orthogonal complement takes on noise. The signals in this low dimensional space are used to reconstruct the input signals without noise. Noise elimination also improves data compression. A comparative study of the ST analysis of original and reconstructed signals is presented at the end.