الفهرس 
Abstract
Classification of Compression TechniquesOnly 14 pages are availabe for public view
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Abstract
It is known that cardiac related health problems can occur at any age. There are evidences that the number of people in the world that facing with the heart attack problem had increased. World Health Organization (WHO) had revealed, it is estimated that each year more than 16 million people around the world died of cardiovascular disease where this figure represent 30 percent of global cause of death. In recent years, this problem has stimulated the development of systems for mobile monitoring of ECG which can provide investigation outside the medical facilities. One of the first such systems is the Holter monitor which can provide a constant recording of ECG data, but is limited to 24 or 48 hours of recording. Analysis of the ECG data is vital to these monitoring systems as they must be able to determine whether the signal is normal or not, and what should be transmitted for analysis by a trained cardiologist. A single ECG beat can be broken down into the segments that are evaluated during the signal analysis. Any abnormalities seen in the shape of the ECG waves, the value of the voltage, or incorrect lengths in the time domain of the individual waves indicate that a cardiac irregularity may have occurred and that the data surrounding this event should be saved for further analysis.
With the rapid advances in wireless communication, the transmission of biomedical signals through wireless network is not impossible anymore. Many researches have been done in the wireless biomedical sensor network to monitor the physiological signal. Furthermore, remote monitoring of patients’ physiological data has received increasing attention in recent years. The most important physiological signals are: the ElectroMyoGram (EMG) for monitoring muscle activity, Electro-EncephaloGram (EEG) for monitoring brain electrical activity and ElectroCardioGram (ECG) which can be used to monitor heart activity. Among these physiological signals, ECG signal is the most important to be measured. However, patients are usually required to lie down or sit in place with the ECG machine. Moreover, doctors that monitor the patient’s health through the investigation of ECG signal also need to be close to the machine. ECG machine that uses wireless sensor network as a medium of transmission should be developed. By this, we can monitor patient’ heart beat outside hospital environment such as at home. The wireless link is implemented in the ECG machine for patient’s mobility and to transmit the real time medical information.
Since human life and health are the issue, a high level of accuracy and reliability are essential. The closed space of hospital examination rooms and the presence of all manner of medical instrumentation pose additional problems to successful wireless communication due to multipath interference and mutual disturbances between transmissions. For this purpose the development of efficient compression techniques that is adapted with wireless transmission concepts is necessary. This is the main objective of this thesis.
Many algorithms for ECG compression have been proposed in the last thirty years. ECG compression algorithms are effective in reducing the signal samples redundancy. This allows signals archiving and communication systems to reduce the files’ sizes with their storage requirements while maintaining relevant diagnostic information. ECG compression techniques can be classified into lossless and loss techniques. Although lossy compression techniques yield high compression rates, the medical community has been reluctant to adopt these methods, largely for legal reasons, and has instead relied on lossless compression techniques that yield low compression rates. The true goal is to maximize compression while maintaining clinical relevance and balancing legal risk. In this thesis an approach to improve the performance of ECG signal compression while satisfying both the medical team who need to use it, and the legal team who need to defend the hospital against any malpractice resulting from misdiagnosis owing to faulty compression of ECG signals. A fundamental shift in the ECG compression approach came after the Discrete Wavelet Transform (DWT) became popular. DWT compression based techniques overcome the inefficiencies in the other ECG compression techniques.
This thesis presents a hybrid technique for the compression of ECG signals based on DWT and exploiting the correlation between signal samples. It incorporates DWT, Differential Pulse Code Modulation (DPCM), and run-length coding techniques for the compression of different parts of the signal; where lossless compression is adopted in clinically relevant parts and lossy compression is used in those parts that are not clinically relevant. The proposed algorithm begins by segmenting the ECG signal into its main components (P-waves, QRS-complexes, T-waves, U-waves and the isoelectric waves). The resulting waves are grouped into Region of Interest (RoI) and Non Region of Interest (NonRoI) parts. Consequently, lossless and lossy compression schemes are applied to the RoI and NonRoI parts respectively. Ideally, we would like to compress the signal losslessly, but in many applications this is not an option. Thus, given a fixed bit budget, it makes sense to spend more bits to represent those parts of the signal that belong to a specific RoI part and, thus, reconstruct them with higher fidelity, while allowing other parts to suffer larger distortion. For this purpose, the correlation between the successive samples of the RoI part is utilized by adopting DPCM approach. However the NonRoI part is compressed using DWT, thresholding and coding techniques. The wavelet transformation is used for concentrating the signal energy into a small number of transform coefficients. Compression is then achieved by selecting a subset of the most relevant coefficients which afterwards are efficiently coded. The performance of the proposed algorithm is tested in terms of the compression ratio and the PRD distortion metrics for the compression of records extracted from the Massachusetts Institute of Technology (MIT-BIH) database. The principal advantages of the proposed approach are: 1) the deployment of different compression schemes to compress different ECG parts to reduce the correlation between consecutive signal samples; and 2) getting high compression ratios with acceptable reconstruction signal quality compared to the recently published results.