Renyi Entropy Based Failure Detection of Medical Electrodes

Medical electrodes used for measuring low amplitude signals, such as EEG electrodes, have to be robust and guarantee a high level of reliability. Corkscrew electrodes, considered in this paper, can become faulty due to cold solder that can appear immediately after the manufacturing process or due to mechanical stress after a few months of use. This problem is hard to detect and is usually manifested as noisy output signal. Commonly used method for monitoring the reliability of materials or circuit interconnects is the resistance measurement. Although very easy to implement, this method does not provide a reliable failure detection. Motivated by these facts, in this paper we propose a computer model based on resistance measurements, for predicting and detecting failure in EEG electrodes supported by laboratory measurements. Level and type of noise is obtained from the comparison of resistance fluctuations of the electrodes tip recorded under stress, and simulated signals. Time-frequency analysis has been applied to real and simulated reference and faulty electrode signals and results compared in order to establish a failure detection measure. Since the energy spectrum of the signal is shown to be an unreliable indicator of the failure appearance, the Renyi entropy is used to determine the difference between reference and faulty electrodes. This measure is applied to measured and simulated spectrograms, denoised using the K-means algorithm. It is shown that the difference between global entropies of the reference and faulty electrode spectrograms is significant when K-means based denoising is applied, thus providing a method for reliable failure detection.