Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
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Sheng Wu Yi Xue Gong Cheng Xue Za Zhi · Oct 2013
Review[Research progress of adventitious respiratory sound signal processing].
Adventitious respiratory sound signal processing has been an important researching topic in the field of computerized respiratory sound analysis system. In recent years, new progress has been achieved in adventitious respiratory sound signal analysis due to the applications of techniques of non-stationary random signal processing. Algorithm progress of adventitious respiratory sound detections is discussed in detail in this paper. Then the state of art of adventitious respiratory sound analysis is reviewed, and development directions of next phase are pointed out.
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Sheng Wu Yi Xue Gong Cheng Xue Za Zhi · Oct 2013
[Manual chest compression depth estimation based on integration reset mechanism].
To realize the measurement of the chest compression depth during the administration of manual cardiopulmonary resuscitation, two 3-axis digital accelerometers were applied for chest compression acceleration and environment acceleration acquisition, with one placed in the chest compression sensor pad, and the other placed in the back sensor pad. Then double integration was made for the acceleration-to-depth conversion with both of the accelerations after preprocessing. ⋯ The final performance of the compression depth estimation is within +/- 0.6 cm with 95% confidence of a total of 283 compressions. Accurate and real-time estimation of chest compression depth greatly facilitates the control of compression depth for the lifesaver during manual cardiopulmonary resuscitation.
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Sheng Wu Yi Xue Gong Cheng Xue Za Zhi · Oct 2013
[An algorithm based on ECG signal for sleep apnea syndrome detection].
The diagnosis of sleep apnea syndrome (SAS) has a significant importance in clinic for preventing diseases of hypertention, coronary heart disease, arrhythmia and cerebrovascular disorder, etc. This study presents a novel method for SAS detection based on single-channel electrocardiogram (ECG) signal. ⋯ After that support vector machine (SVM) was used to classify the signals as "apnea" or "normal". Finally, the performance of the method was evaluated by the MIT-BIH Apnea-ECG database, and an accuracy of 95% in train sets and an accuracy of 88% in test sets were achieved.