| dc.description.abstract | Cardiovascular disease is the No. 1 cause of death worldwide, resulting in middle-income developing countries a great burden. The mobile phone technology advances recently, it is more feasible solution is to use their phones to record, re-transmission and provide who have expertise in diagnosis. But collection staff encountered a bottleneck in lack of experience of, resulting not in accordance with standard procedures of the collected data will be unusable.
PhysioNet provides ECG data every year, and contested to promote together scholars in various fields to solve the problem of interpretation
of ECG machines. Taking variety of analyzed in frequency domain, and data processing, there is no guarantee of information distortion and inefficient. So we take the time series model - HMM, and with minimal processing of data for analysis.
This paper training the HMM using ECG, ECG data for the original sequence of machine learning, hoping to interpret the measured period of 10 seconds of 12 lead ECG 5000 value, whether the amount was in accordance with standard procedures. Accuracy when used alone as really limited (80.7%), this paper again merge HMM combining Adaboost classifier decision theory between the views, the best answer, successful interpretation of the success rate will suddenly increase a lot (88.1%).
To improve the detection accuracy and efficiency from two-pronged approach. We wish to face other issues and get a proper combination.
| en_US |