Li Yanjun, Tang Xiaoying
Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China.
State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, 100094, China.
Australas Phys Eng Sci Med. 2017 Dec;40(4):895-907. doi: 10.1007/s13246-017-0594-7. Epub 2017 Oct 26.
Diagnosis of long-term electrocardiogram (ECG) calls for automatic and accurate methods of ECG signal quality estimation, not only to lighten the burden of the doctors but also to avoid misdiagnoses. In this paper, a novel waveform-based method of phase-space reconstruction for signal quality estimation on a single lead ECG was proposed by projecting the amplitude of the ECG and its first order difference into grid cells. The waveform of a single lead ECG was divided into non-overlapping episodes (T = 10, 20, 30 s), and the number of grids in both the width and the height of each map are in the range [20, 100] (N = N = 20, 30, 40, … 90, 100). The blank pane ratio (BPR) and the entropy were calculated from the distribution of ECG sampling points which were projected into the grid cells. Signal Quality Indices (SQI) bSQI and eSQI were calculated according to the BPR and the entropy, respectively. The MIT-BIH Noise Stress Test Database was used to test the performance of bSQI and eSQI on ECG signal quality estimation. The signal-to-noise ratio (SNR) during the noisy segments of the ECG records in the database is 24, 18, 12, 6, 0 and - 6 dB, respectively. For the SQI quantitative analysis, the records were divided into three groups: good quality group (24, 18 dB), moderate group (12, 6 dB) and bad quality group (0, - 6 dB). The classification among good quality group, moderate quality group and bad quality group were made by linear support-vector machine with the combination of the BPR, the entropy, the bSQI and the eSQI. The classification accuracy was 82.4% and the Cohen's Kappa coefficient was 0.74 on a scale of N = 40 and T = 20 s. In conclusion, the novel grid mapping offers an intuitive and simple approach to achieving signal quality estimation on a single lead ECG.
长期心电图（ECG）诊断需要自动且准确的心电图信号质量评估方法，这不仅能减轻医生的负担，还能避免误诊。本文提出了一种基于波形的相空间重构新方法，用于单导联心电图的信号质量评估，该方法通过将心电图的幅度及其一阶差分投影到网格单元中实现。单导联心电图的波形被划分为不重叠的片段（T = 10、20、30秒），每个映射图的宽度和高度上的网格数量在[20, 100]范围内（N = N = 20、30、40、… 90、100）。根据投影到网格单元中的心电图采样点分布计算空白面板比率（BPR）和熵。分别根据BPR和熵计算信号质量指标（SQI）bSQI和eSQI。使用麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）噪声应激测试数据库来测试bSQI和eSQI在心电图信号质量评估方面的性能。数据库中心电图记录的噪声段期间的信噪比（SNR）分别为24、18、12、6、0和 - 6 dB。对于SQI定量分析，记录被分为三组：高质量组（24、18 dB）、中等质量组（12、6 dB）和低质量组（0、 - 6 dB）。通过线性支持向量机结合BPR、熵、bSQI和eSQI对高质量组、中等质量组和低质量组进行分类。在N = 40且T = 20秒的规模下，分类准确率为82.4%，科恩卡帕系数为0.74。总之，这种新颖的网格映射为实现单导联心电图的信号质量评估提供了一种直观且简单的方法。
