本文選題：宮縮狀態(tài) + 基線估計�。� 參考：《生物醫(yī)學(xué)工程學(xué)雜志》2017年05期

【摘要】：宮縮狀態(tài)實時識別在分娩鎮(zhèn)痛中具有重要意義,但相關(guān)傳統(tǒng)算法和系統(tǒng)無法滿足實時識別宮縮狀態(tài)的要求。針對上述情況,本文設(shè)計了一套宮縮狀態(tài)實時分析算法。該算法包括宮縮信號預(yù)處理、基于直方圖和線性迭代的宮縮基線估計以及一種基于有限狀態(tài)機原理的實時識別算法,可根據(jù)前一點的宮縮狀態(tài)以及一系列狀態(tài)轉(zhuǎn)換條件來識別當(dāng)前的宮縮狀態(tài),并且設(shè)置緩沖機制來避免不真實的狀態(tài)轉(zhuǎn)換。為了評估該算法的性能表現(xiàn),本文將其與現(xiàn)有的一種電子胎兒監(jiān)護儀的宮縮分析算法進行比較。實驗結(jié)果表明,本文算法能夠在宮縮信號實時監(jiān)測的同時對宮縮狀態(tài)進行實時分析,算法敏感度為0.939 9,陽性預(yù)測值為0.869 3,具有較高的準(zhǔn)確度,可達到臨床監(jiān)測的要求。
[Abstract]:The real-time recognition of uterine contractions is of great significance in labor analgesia, but the traditional algorithms and systems can not meet the requirements of real-time recognition of uterine contractions. In view of the above situation, this paper designs a set of real-time analysis algorithm of uterine contraction state. The algorithm includes preprocessing of uterine contraction signal, histogram and linear iterative baseline estimation of uterine contraction, and a real-time recognition algorithm based on the principle of finite state machine. The current state of uterine contraction can be identified according to the contractive state of the former point and a series of state transition conditions, and a buffer mechanism is set to avoid the untrue state transition. In order to evaluate the performance of the algorithm, this paper compares it with an existing algorithm for uterine contraction analysis of an electronic fetal monitor. The experimental results show that the algorithm can analyze the state of uterine contraction at the same time as the real-time monitoring of uterine contraction signal. The sensitivity of the algorithm is 0.939 9 and the positive predictive value is 0.869 3. The algorithm has a high accuracy and can meet the requirements of clinical monitoring.
【作者單位】： 暨南大學(xué)信息科學(xué)技術(shù)學(xué)院電子工程系;
【基金】：國家國際科技合作專項資助項目(2015DFI12970) 粵港共性技術(shù)招標(biāo)資助項目(2013B010136002) 廣東省科技計劃應(yīng)用型科技研發(fā)專項資助項目(2015B020233010) 廣東省科技計劃重點資助項目(2015B020214004)
【分類號】：R714;TN911.7
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本文編號：1806773