Modelling and detection of failure in medical electrodes

Marasović, Ivan; Milanović, Željka; Zulim, Ivan

doi:10.7305/automatika.2015.12.768

Cited by 1 publication

(2 citation statements)

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“…The developed computer model has been applied in [27]. It is known from the literature that the resistance fluctuations can be characterized as a superposition of the flicker and white noise [3,6]. Noise-to-noise and signal-to-noise ratios are determined using the average signal energy and average global entropy of the measured signal spectrograms.…”

Section: Criteria Application On Simulated Datamentioning

confidence: 99%

“…In many cases, failure of electronic devices can be caused by degradation (electromigration or oxidization) of metallic interconnects and solder joints which lose their conduction properties. Some known failure detection methods are based on the research of DC resistance fluctuations, material fatigue, corrosion, and mechanical stress [1][2][3]. Researching solely resistance fluctuations of the material has proven to be unreliable for determining failure appearance as shown in literature that researched resistance fluctuations using *Correspondence: ivan.marasovic@fesb.hr 2 University of Split Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia Full list of author information is available at the end of the article disordered networks such as large scale random resistor network (RRN) which exploits the fact that faulty electrode presents non-Gaussian behavior and valid does not, which has not been successfully applied as a reliable discrimination criteria [3].…”

Section: Introductionmentioning

confidence: 99%

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Spectrogram-based assessment of small SNR variations, with application to medical electrodes

Milanović

Saulig

Marasović

et al. 2019

EURASIP J. Adv. Signal Process.

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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.

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Section: Criteria Application On Simulated Datamentioning

confidence: 99%