Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/13289
Authors: Agarwal, Anil Kumar
Issue Date: 2008
Abstract: Spatial Temporal analysis of multichannel ECG begins from measurement of the 64-channel ECG signals from the healthy volunteers and the patients. These signals are stored in *bdf format by using active two interface developed by BioSemi. The stored data is in *bdf format which is not readable using Matlab. For this we used the GUI "MCECGreader" created by Antoun. First we import the data using MCECGreader and then apply the downsampling to all 64 channels. Downsampling makes the process faster and at same time reduce the size of the data. After this, we perform wavelet threshold based denoising and baseline wander removal using wavelets. Then, the Wilson Central Terminal is calculated and subtracted from each channel. This subtraction improves the CMRR of the ECG signals. After this, characteristic points of ECG signal are calculated by using Modulus Maxima Wavelet Transform approach. This is followed by QT beat extraction from each beat in every channel and then alignment (R-R alignment) of these beats is carried out by correlation technique. If some outliers are present in the extracted signal then those outliers are separated out by using Hotelling's T2 method. This process removes the unwanted beats present in the extracted beats. After this, the T waves are separated out from QT segment by cutting off QRS complex. This will give a matrix for each channel and this matrix called Temporal Matrix (TM). Now Principal Component Analysis (PCA) is applied to visualize morphological variations in temporal domain. Here we have got only temporal accuracy. So, to improve the spatial accuracy we arrange the temporal matrices of all channels side by side and get a new matrix called Spatial-Temporal Matrix (STM). Now again PCA is applied on STM and morphological variations are visualized in spatial-temporal domain. Here, to visualize the spatial temporal morphological variation of T wave we plotted the Body Surface Potential Maps for each sample. These plots are based on the potential values of the T wave at that particular sample. And finally we plotted the BSPM influenced by the principal components (PC 1, PC2 and PC3). These BSPM visualizations give important information's about the normal heart beat and its natural fluctuations. iv
Other Identifiers: M.Tech
Research Supervisor/ Guide: Kumar, Vinod
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' THESES (Electrical Engg)

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