| Summary: | 碩士 === 國立成功大學 === 電機工程學系碩博士班 === 101 === In order to develop a high accuracy and low complexity ECG feature extraction algorithm, using the concept of waveform similarity measurement among the ECG features and corresponding similar bases is the main strategy for accuracy improvement; considering the influences caused by baseline drift noise and solving them during the processing procedures of feature extraction to omit the step of “baseline drift removal” is the major tactic for complexity reduction. Based on these purposes, the primarily detecting methods including Gabor wavelet transform and matching process using half Gaussian model with various scales are proposed to develop an accurate ECG feature extraction systems without baseline drift removal. Finally, subjective and objective experimental results present that proposed baseline drift removal omission algorithm can not only extract the useful features correctly but also be applicable for widely types of ECG signals. Therefore, the proposed algorithm is suitable for not only traditional medical diagnosis but also more novel applications in real-time, health-cloud, health-care systems, etc. In addition, the proposed algorithm could also be implemented on various platforms such as software, hardware, or embedded systems, which may reduce the computing time or power consumption.
|
|---|
