Kumar Pramendra, Sharma Vijay Kumar
Department of Computer and Communication Engineering, SCIT, Manipal University Jaipur, India.
Healthc Technol Lett. 2020 Feb 18;7(1):18-24. doi: 10.1049/htl.2019.0096. eCollection 2020 Feb.
In this Letter, a robust technique is presented to detect and classify different electrocardiogram (ECG) noises including baseline wander (BW), muscle artefact (MA), power line interference (PLI) and additive white Gaussian noise (AWGN) based on signal decomposition on mixed codebooks. These codebooks employ temporal and spectral-bound waveforms which provide sparse representation of ECG signals and can extract ECG local waves as well as ECG noises including BW, PLI, MA and AWGN simultaneously. Further, different statistical approaches and temporal features are applied on decomposed signals for detecting the presence of the above mentioned noises. The accuracy and robustness of the proposed technique are evaluated using a large set of noise-free and noisy ECG signals taken from the Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH polysmnographic database and Fantasia database. It is shown from the results that the proposed technique achieves an average detection accuracy of above 99% in detecting all kinds of ECG noises. Furthermore, average results show that the technique can achieve an average sensitivity of 98.55%, positive productivity of 98.6% and classification accuracy of 97.19% for ECG signals taken from all three databases.
在本信函中，提出了一种稳健的技术，用于基于混合码本上的信号分解来检测和分类不同的心电图（ECG）噪声，包括基线漂移（BW）、肌肉伪迹（MA）、电力线干扰（PLI）和加性高斯白噪声（AWGN）。这些码本采用时间和频谱受限波形，能为ECG信号提供稀疏表示，同时提取ECG局部波以及包括BW、PLI、MA和AWGN在内的ECG噪声。此外，对分解后的信号应用不同的统计方法和时间特征来检测上述噪声的存在。使用从麻省理工学院 - 波士顿贝斯以色列医院（MIT - BIH）心律失常数据库、MIT - BIH多导睡眠图数据库和幻想曲数据库获取的大量无噪声和有噪声的ECG信号，对所提技术的准确性和稳健性进行了评估。结果表明，所提技术在检测各种ECG噪声时的平均检测准确率高于99%。此外，平均结果表明，对于从所有三个数据库获取的ECG信号，该技术的平均灵敏度可达98.55%，阳性预测值可达98.6%，分类准确率可达97.19%。
