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首页> 外文期刊>Science, Measurement & Technology, IET >Intelligent electrocardiogram pattern classification and recognition using low-cost cardio-care system
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Intelligent electrocardiogram pattern classification and recognition using low-cost cardio-care system

机译：使用低成本心脏护理系统的智能心电图模式分类和识别

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摘要

Electrocardiogram (ECG) contains detailed information regarding incidental abnormality of a subject. Manual analysis of a long time ECG record is a lengthy process. Computerised ECG analysis supports clinicians in decision making. While designing a low-cost diagnostic support system, constraints on the system resources limit the processing speed, eventually affecting the reliability. To resolve these issues, three key factors have been addressed in this study: the feature extraction method, total number of features and the database used. For feature extraction, `polar Teager energy' algorithm has been developed, yielding nearly 70% saving in processing time as compared to other well-known methods. Using features with linear relationship leads to reduction in feature vector dimension, without compromising its classification performance. Therefore the linear relationship between two ECG features, namely `informational entropy'() and `mean Teager energy' has been revealed. These features are utilised for ECG beat classification using `fuzzy C-means clustering' algorithm. The algorithm is evaluated using the MIT-BIH database and then tested by ECG measured with the cardio-care unit. The QRS detection performance of the proposed method is very good, with 0.27% detection error rate. For classification of ECG beats, average sensitivity and positive prediction rate achieved are 98.93% each.
机译：心电图（ECG）包含有关对象的偶然异常的详细信息。手动分析长时间的心电图记录是一个漫长的过程。计算机化的心电图分析可支持临床医生进行决策。在设计低成本诊断支持系统时，对系统资源的限制限制了处理速度，最终影响了可靠性。为了解决这些问题，本研究解决了三个关键因素：特征提取方法，特征总数和使用的数据库。对于特征提取，已开发了“极地Teager能量”算法，与其他众所周知的方法相比，可节省近70％的处理时间。使用具有线性关系的特征会导致特征向量维数的减小，而不会影响其分类性能。因此，揭示了两个心电图特征之间的线性关系，即“信息熵”（）和“平均提格能量”。这些功能可通过“模糊C均值聚类”算法用于ECG搏动分类。该算法使用MIT-BIH数据库进行评估，然后通过用心脏监护仪测量的ECG进行测试。所提方法的QRS检测性能非常好，检测错误率为0.27％。对于ECG搏动的分类，平均灵敏度和阳性预测率均为98.93％。

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