| Summary: | 碩士 === 中原大學 === 生物醫學工程研究所 === 103 === In recent years, most of the wearable devices have the capability of physiological signal measurement function. However ,these wearable devices do not include real time heart diseases analysis capability. This study used fabric electrodes and ADS1298 analog front end to capture 12-lead electrocardiogram (ECG) signals. The TMS320c5515 digital signal processor (DSP) was used to realize wavelet transform and to analyze ECG signals. Finally, the analyzed result was transmitted through cc2541 bluetooth low power energy (BLE) to the mobile device for display and storage.
In order to lower the hardware cost, a fixed-point processor was used as the computational core, and a interactive method was introduced that allow the user to target a particular cardiac disease of interest. The system then choose specific signal configuration from the 12 lead ECG for this selected cardiac disease to start analysis. By doing this, it can not only reduce the burden on the processor but also increase the accuracy and specificity of the disease analysis. In this, the analysis of the two most common heart diseases, atrial fibrillation (AF) and premature ventricular contractions (PVC) were realized.
QRS complex detection and disease classification were evaluated using MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database, in this study. The averaged QRS complex detection accuracies are 97.66±4.92% and 96.76±1.7% for MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database, respectively. On the other hand, the averaged classification accuracies are 92.22±8.98% and 83.65±4.47% for MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database, respectively. From the above demonstrate that the proposed algorithm can successfully identify these two cardiac diseases. The technology acceptance model questionnaire also used to investigate the usage of this device. The result shows that the averaged overall score are 3.93±0.77. In addition, the design of smart clothes deeply affects the algorithm performance and subject''s using situation. When fabric electrodes are moving or have poor contact, the quality of ECG signals are lower.
In conclusion, result of technology acceptance questionnaire shows that the smart clothes design score the lowest in all the questions. This means that the smart clothes design and measurement stability should be enhanced in the future. However, the overall acceptability of the proposed system was high.
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