A signal quality assessment-based ECG waveform delineation method used for wearable monitoring systems
A signal quality assessment-based ECG waveform delineation method used for wearable monitoring systems
复制标题
一种用于可穿戴监护系统的基于信号质量评估的心电图波形描绘方法
DOI：
10.1007/s11517-021-02425-8
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Yongqin Li
中科院分区：
文献类型：
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作者：
Jialing Xie;Li Peng;Liang Wei;Yushun Gong;Feng Zuo;Juan Wang;Changlin Yin;Yongqin Li
通过对高危人群的持续监测，识别短暂性和非持续性的异常心电图波形，对心血管疾病的诊断、治疗和预防具有重要意义。近年来，织物电极因其无刺激性和比传统AgCl电极更好的舒适性而被广泛应用于可穿戴设备中。然而，织物电极与皮肤之间的相对运动产生的运动噪声影响了心电图的质量，降低了诊断的准确性。因此，描述从具有不同噪音水平的可穿戴设备记录的ECG波形仍然具有挑战性。在本研究中，开发了一种基于信号质量评估（SQA）的可穿戴系统心电波形描绘方法。首先用带通滤波器对心电信号进行预处理。利用预处理后的心电信号计算多尺度非线性幅度统计分布（adSQI1、adSQI2）、T波相关能量比例（ptSQI）、R波和T波计算心率（分别为rHR和tHR）等5个指标。通过使用神经网络将这些指标结合起来，将信号分为良好、可接受和不可接受的心电图。随后，根据SQA结果识别R波或/和T波进行相应的特征解释。将29名志愿者在不同活动状态下的胸带记录的心电图分为4-s段。共使用7133个人工标记的片段来推导（4985个）和验证（2148个）算法。良好、可接受和不可接受心电图的adSQI1、adSQI2、tHR和rHR特征有显著差异。良好心电图的ptSQI值明显高于可接受和不可接受的心电图。采用该方法对不同质量水平的心电段进行分类，准确率达96.74%。对于可接受和/或良好的片段，R波和T波的识别准确率分别为99.95%和99.57%。基于sqa的心电波形描绘方法可以进行可靠的分析，具有应用于可穿戴心电系统的潜力，可用于心血管疾病的早期诊断和预防。图形抽象
Identifying transient and nonpersistent abnormal electrocardiogram (ECG) waveforms by continuously monitoring the high-risk populations is of great importance for the diagnosis, treatment, and prevention of cardiovascular diseases. In recent years, fabric electrodes have been widely used in wearable devices because of their non-irritating properties and better comfort than traditional AgCl electrodes. However, the motion noise caused by the relative movement between the fabric electrodes and skin affects the quality of ECGs and reduces the accuracy of diagnosis. Therefore, delineating the ECG waveforms that are recorded from wearable devices with varying levels of noise is still challenging. In this study, a signal quality assessment (SQA)–based ECG waveform delineation method that is used for wearable systems was developed. The ECG signal was first preprocessed by a bandpass filter. Five indices, including the multiscale nonlinear amplitude statistical distribution (adSQI1, adSQI2), the proportion of energy-related to T wave (ptSQI), and heart rates computed from R waves and T waves (rHR and tHR, respectively), were then calculated from the preprocessed ECG signal. The signals were classified as good, acceptable, and unacceptable ECGs by combining these indices through the use of a neural network. Subsequently, the R waves or/and T waves were identified for the corresponding feature interpretations based on the SQA results. ECGs that were recorded from the chest belts from 29 volunteers at different activity statuses were divided into 4-s segments. A total of 7133 manually labeled segments were used to derive (4985 segments) and validate (2148 segments) the algorithm. The adSQI1, adSQI2, tHR, and rHR characteristics were significantly different among the good, acceptable, and unacceptable ECGs. The ptSQI value was considerably higher in the good ECGs than in the acceptable and unacceptable ECGs. The ECG segments of different quality levels were classified with an accuracy of 96.74% by using the proposed SQA method. The R waves and T waves were identified with accuracies of 99.95% and 99.57%, respectively, for segments that were classified as acceptable and/or good. The SQA-based ECG waveform delineation method can perform reliable analysis and has the potential to be applied in wearable ECG systems for the early diagnosis and prevention of cardiovascular diseases.Graphical abstract