电子与信息学报
電子與信息學報
전자여신식학보
JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY
2015年
4期
824-829
,共6页
自动识别%胸阻抗%K均值%密度加权%偏好信息
自動識彆%胸阻抗%K均值%密度加權%偏好信息
자동식별%흉조항%K균치%밀도가권%편호신식
Automatic detection%TransThoracicImpedance (TTI)%K-means algorithm%Density weighted%Preference information
为了自动识别胸阻抗(TransThoracic Impedance, TTI)信号中的按压和通气波形，完成相关重要参数的计算，从而实现对心肺复苏质量的监测评估，该文提出一种基于密度加权与偏好信息的胸阻抗信号自动检测算法。该方法针对实验采集的猪的电诱导心脏骤停模型 TTI 信号，通过预处理和多分辨率窗口搜索法完成潜在按压和通气波形的标记；接着，提取每个标记波形的宽度、幅值以及相邻波形特征差作为特征，并按标记波形宽度对信号进行分段；之后，再对信号进行小波分解，提取其小波系数每段的能量与原始波形幅值之比作为特征；最后采用基于密度加权与偏好信息的K均值聚类分析法对标记的波形进行分类识别。实验结果表明，该算法对TTI信号中按压波形和波形分析识别的正确率和敏感度均较高，鲁棒性好，且运行时间(0.43 s±0.07 s)满足实时性要求。
為瞭自動識彆胸阻抗(TransThoracic Impedance, TTI)信號中的按壓和通氣波形，完成相關重要參數的計算，從而實現對心肺複囌質量的鑑測評估，該文提齣一種基于密度加權與偏好信息的胸阻抗信號自動檢測算法。該方法針對實驗採集的豬的電誘導心髒驟停模型 TTI 信號，通過預處理和多分辨率窗口搜索法完成潛在按壓和通氣波形的標記；接著，提取每箇標記波形的寬度、幅值以及相鄰波形特徵差作為特徵，併按標記波形寬度對信號進行分段；之後，再對信號進行小波分解，提取其小波繫數每段的能量與原始波形幅值之比作為特徵；最後採用基于密度加權與偏好信息的K均值聚類分析法對標記的波形進行分類識彆。實驗結果錶明，該算法對TTI信號中按壓波形和波形分析識彆的正確率和敏感度均較高，魯棒性好，且運行時間(0.43 s±0.07 s)滿足實時性要求。
위료자동식별흉조항(TransThoracic Impedance, TTI)신호중적안압화통기파형，완성상관중요삼수적계산，종이실현대심폐복소질량적감측평고，해문제출일충기우밀도가권여편호신식적흉조항신호자동검측산법。해방법침대실험채집적저적전유도심장취정모형 TTI 신호，통과예처리화다분변솔창구수색법완성잠재안압화통기파형적표기；접착，제취매개표기파형적관도、폭치이급상린파형특정차작위특정，병안표기파형관도대신호진행분단；지후，재대신호진행소파분해，제취기소파계수매단적능량여원시파형폭치지비작위특정；최후채용기우밀도가권여편호신식적K균치취류분석법대표기적파형진행분류식별。실험결과표명，해산법대TTI신호중안압파형화파형분석식별적정학솔화민감도균교고，로봉성호，차운행시간(0.43 s±0.07 s)만족실시성요구。
In order to recognize automatically the compression and ventilation waveforms of the TransThoracic Impedance (TTI) signal, and obtain the important parameters, for evaluating the CardioPulmonary Resuscitation (CPR) quality, this paper proposes an automatic detection algorithm for TTI signal based on density weighting and preference information. The TTI signals that come from the pig model based on electrically induced cardiac arrest are preprocessed, and the potential compression and ventilation waveforms are marked by using the searching algorithm of multiresolution window after the pretreatment. After that, the width, amplitude and the difference between the adjacent waveforms of the marked waveforms are selected as the features and the signal is divided into several sections according to the width of marked waveforms. Then the original signal is decomposed by wavelet transform. The ratio of the power of each section to the amplitude of the original one is taken as one feature. Finally, k-means clustering algorithm based on density weighting and preference information is used to recognize and classify the compression and ventilation of the marked waveforms. The experimental results show the accuracy and sensitivity of the recognition are high, the robustness is good and the running time (0.43±0.07 s) can meet the requirement of clinical application.
