A Study of Heart Rate Variability Analysis Algorithm

• Main Authors: Yi-Ming Hsieh, 謝易?
• Other Authors: 張朝陽
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/7vssy4

碩士 === 國立虎尾科技大學 === 資訊工程系碩士班 === 104 === Along with the advancement of medical technology in recent years, it prolongs the human life and increase the elderly population. However, a lower birth rate causes the aging of the population. Because of the changes in human diet and the pressures of life. Some diseases when the human age increases. Therefore, the long-term treatment becomes an important issue. Among the diseases, the cardiovascular disease is a major of them. To reduce the disease onset, several instruments are commonly used to obtain the electrocardiogram (ECG) signal of the patient. Additionally, it can distinguish many indications of the illness by analysis the electrocardiogram signal. The analyzed information can be uploaded to the cloud systems to provide the inquiry for the users. Furthermore, medical professionals can employ the information to diagnose the symptoms or to call for help immediately. In order to analyze the symptom of disease, we propose a simple and instantaneous detection algorithm. The proposed method includes three stages. The first stage is the signal preprocessing. We use the filter to remove the noise interference and obtain the feature waveform by waveform transformation. It can enhance the QRS complexes and depress the unnecessary waveform. The second stage is to detect the R-peak location. It is a crucial stage in the proposed method. The detection method is influenced by irregular morphology and noise. It may cause the false detection. Therefore, we add the noise judgment into the peak detection stage to reduce the false detection. The third stage is the arrhythmia analysis. This stage operation is based on the R-peak. It contains the interval of each R-peak and the values of the waveform feature. We judge the arrhythmia by using the interval and eigenvalues and detect some symptoms in the database. The detection electrocardiogram data are from MIT-BIH arrhythmia database. It includes the 48 half-hour of two channel records. The proposed method of R-peak detection achieves average detection accuracy of 99.69 percent, sensitivity of 99.78 percent, and positive predictivity of 99.91 percent. The proposed method of symptom detection achieves overall detection accuracy of 64.89 percent. Experimental result show that the waveform morphological analysis is easily affected by noise and sudden change.