In this paper, the problem of detection of small signal-to-noise ratio (SNR) variations in noisy signals is addressed in order to provide an efficient and fast method for detection of faulty electroencephalogram (EEG) electrodes which can improve the interpretation of medical data. The method for slight SNR variation assessment, based on the estimation of the longest useful information cluster, is proposed as an alternative to commonly used estimators such as signal energy spectral density, spectral peaks, and spectrogram entropy, which exhibited limited reliability for the considered task. The method proposed in this paper is validated on real signals, which are resistance fluctuations of the EEG Corkscrew electrode solder connection, in which failure is typically manifested as a lower signal-to-noise ratio in the output signal, when compared to the valid electrode. In order to obtain a reliable criterion for the distinction of signals with slight SNR variations, a time-frequency method that relies on observation of the longest useful information cluster of data preserved after the K-means-based denoising application has been introduced. Based on the measurement of the longest existing stationary component, an expert system has been developed, which provides reliable failure detection method with detection accuracy of up to 97.6%. Results on real and simulated data show that the proposed method can be adopted as a computer-aided decision system in a wide range of applications requiring high sensitivity to slight variations of SNRs.

中文翻译：

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基于频谱图的小SNR变化评估，应用于医疗电极

在本文中，解决了在噪声信号中检测小的信噪比（SNR）变化的问题，以便提供一种有效且快速的方法来检测有缺陷的脑电图（EEG）电极，从而可以改善医学解释数据。提出了基于最长的有用信息簇的估计进行轻微SNR变化评估的方法，以替代常用的估计器，例如信号能量频谱密度，频谱峰值和频谱图熵，这些方法在考虑的任务上显示出有限的可靠性。本文提出的方法在真实信号上得到了验证，真实信号是EEG开瓶器电极焊料连接的电阻波动，其中故障通常表现为输出信号中较低的信噪比，与有效电极相比。为了获得区分信噪比变化很小的信号的可靠标准，已引入了一种时频方法，该方法依赖于对基于K均值的去噪应用程序后保留的数据的最长有用信息簇的观察。基于对现有最长静止部件的测量，开发了一个专家系统，该系统提供了可靠的故障检测方法，检测精度高达97.6。％。实际数据和模拟数据的结果表明，该方法可被用作需要对SNR的微小变化具有高灵敏度的广泛应用中的计算机辅助决策系统。