Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/187287
Title: Signal processing methods for robust heart rate estimation from multimodal Physiological signals
Researcher: Rankawat, Shalini A.
Guide(s): Rahul Dubey
Keywords: Physiology
Cardiac Arrhythmia
Electrocardiography
Types of ECG Noise
Non-cardiovascular signals
Data acquisition system
University: Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT)
Completed Date: 
Abstract: quotCardiovascular diseases are the major cause of world wide mortality. Heart rate (HR) and heart rate variability (HRV) are important health parameters to monitor functioning of heart of cardiac patients. Multimodal physiological signals namely; Electrocardiogram, Arterial Blood newlinePressure, Photoplethysmogram, Electroencephalogram, Electrooculogram, Electromyogram etc. are recorded in ICU for close monitoring of vital health parameters of critically ill patients. newline newlineHowever, Electrocardiogram (ECG), that provides direct measure of heart rate, is often corrupted by noise or is missing and the heart rate estimated from such signals would be erroneous. Thus, there is a need for development of methods for robust heart rate estimation especially when ECG is either noisy or missing. This thesis investigates the development newlineof appropriate signal processing techniques for robust heart rate estimation from fusion of cardiovascular signals with non-cardiovascular (NC) signals that are not related to cardiac activities newlinebut contain some markers of heart beats. The signals used in proposed study are ECG, Arterial Blood Pressure (ABP), Electroencephalogram (EEG), Electro-oculogram (EOG) and Electromyogram (EMG). newline newlineA novel slope sum function and Teager-Kaiser Energy (SSF-TKE) method is developed for ECG artifacts detection in NC signals. It requires neither additional ECG channel nor a priori user input. Results from evaluation on standard databases have shown that SSF-TKE method is a highly effective technique for R-peak artifacts detection in non-cardiovascular signals contaminated with ECG artifacts. newline newlineThe use of SSF-TKE method is then explored in R-peak detection in ECG signal. SSFTKE is a simple method for R-peak detection that does not consider detail morphology of ECG, except steep slopes, amplitude and periodicity of QRS complex. This method has achieved excellent R-peak detection performance across a number of standard databases with newlinevariety of signal morphology.
Pagination: xvii, 205 p.
URI: http://hdl.handle.net/10603/187287
Appears in Departments:Department of Information and Communication Technology

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01_title.pdfAttached File241.91 kBAdobe PDFView/Open
02_declaration and certificate.pdf49.6 kBAdobe PDFView/Open
03_acknowledgements.pdf52.3 kBAdobe PDFView/Open
04_abstract.pdf53.23 kBAdobe PDFView/Open
05_contents.pdf71.13 kBAdobe PDFView/Open
06_list of figures.pdf67.34 kBAdobe PDFView/Open
07_list of tables.pdf61.73 kBAdobe PDFView/Open
08_chapter 1.pdf583.41 kBAdobe PDFView/Open
09_chapter 2.pdf1.55 MBAdobe PDFView/Open
10_chapter 3.pdf623.16 kBAdobe PDFView/Open
11_chapter 4.pdf614.03 kBAdobe PDFView/Open
12_chapter 5.pdf259.6 kBAdobe PDFView/Open
13_chapter 6.pdf1.27 MBAdobe PDFView/Open
14_chapter 7.pdf1.13 MBAdobe PDFView/Open
15_chapter 8.pdf54.87 kBAdobe PDFView/Open
16_reference.pdf137.06 kBAdobe PDFView/Open
17_appendix.pdf317.49 kBAdobe PDFView/Open


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