EDR-Oriented Respiratory Rate Detection Based on Sequential Support Vector Machines

• Main Authors: Liao, Ying-Siou, 廖英秀
• Other Authors: Lee, Chen-Yi
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/49424963495449478289

碩士 === 國立交通大學 === 生醫工程研究所 === 102 === Respiratory rate, blood pressure, pulse and body temperature are the vital signs of human being. However, some studies have indicated that the record of these signs is poor even in the hospital. Of all four signs, respiratory rate is often not recorded, though the abnormal respiratory rate is reported as an important predictor of serious illnesses. Generally, techniques that record respiratory signal require cumbersome importable devices that may cause uncomfortable feelings and interfere with natural breathing. Some application based on the respiratory analysis may even fail such as sleep quality analysis and stress testing. Fortunately, thanks to the joint study of respiratory and electrocardiography, some studies have reported the possibility of indirect methods to extract the respiration information which is well-known as ECG-Derived Respiration (EDR). The existing EDR approaches are achieved by the DSP method. However, the ECG signal is easily influenced by the body motion and the individual health status. The DSP-based EDR is not capable to generate a general model for all cases and is often limited by the specific respiratory frequency. Therefore, we adopted several EDR algorithms and the wavelet transform for the feature extraction. The output features are introduced to machine learning (ML) algorithms: least absolute shrinkage and selection operator (LASSO) regression, support vector machines (SVM) for advance feature select and data analysis. The proposed sequential SVM comprises multiple classifiers and a region score. Since the models for classifying the respiratory rate from 14 to 20 breathes per minute (bpm) are hard to learn, we assign a weight to each model. By calculating the region score with the classification result and the corresponding weight, the respiratory rate is detected. The data source used for the feature selection and the model learning is obtained from Physionet Fantasia database. Besides, we also use the data collected by Si2-lab respiratory sensor studies and the heartwave ECG sensor of bOMDIC Inc. Both data source contains the continuous respiratory signal and the ECG signal. By using the presented EDR-oriented respiratory rate detection based on the sequential SVM, the average accuracy is 91.78%, and 77.45% of the data achieve the accuracy more than 90%. For the cases of high respiratory rate and low respiratory rate, our work performs better than DSP-based EDR, and achieves 100% accuracy. With miniaturized-sensors-integrated mobile devices, it enables the opportunities for ordinary daily healthcare applications, e.g. continuous cardiac signal monitoring of ECG. Accordingly, if the respiratory rate is correctly extracted from cardiac signal, the healthcare system can provide more information for the in-depth monitoring.