Physiological Tremor Filtering Without Phase Distortion for Robotic Microsurgery

Abstract

All existing physiological tremor filtering algorithms, developed for robotic microsurgery, use non-linear phase pre-filters to isolate the tremor signal. Such filters cause phase distortion to the filtered tremor signal and limit the filtering accuracy. We revisited this long-standing problem to enable filtering of the physiological tremor without any phase distortion. We developed a combined estimation-prediction paradigm that offers zero-phase type filtering. The estimation is achieved with the mathematically modified recursive singular spectrum analysis algorithm and the prediction is delivered with the standard extreme learning machine. In addition, to limit the computational cost; we developed two moving window versions of this structure, appropriate for real-time implementation. The proposed paradigm preserved the natural phase of the filtered tremor. It achieved the key performance index of error limitation below 10µm, yielding the estimation accuracy larger than 70%, at a time delay of 36ms only. Both moving window versions of the proposed approach restricted the computational cost considerably; whilst offering the same performance. It is for the first time that effective estimation of the physiological tremor is achieved, without any pre-filtering and phase distortion. This proposed method is feasible for real-time implantation. Clinical translation of the proposed paradigm can significantly enhance the outcome in hand-held surgical robotics. Note to Practitioners-The imprecision caused by physiological hand tremor in microsurgeries has motivated researchers to innovate an efficient tremor compensating technique that can improve the surgical performance. Yet, all the existing tremor filtering algorithms, implemented in hand-held surgical instruments, use non-linear phase pre-filters to separate the tremor signal. The inherent phase distortion caused by such pre-filters restricts the filtering performance significantly and renders the existing methods inadequate for hand-held robotic surgery. Motivated by this, we proposed a novel estimator-predictor based framework, by adopting the modified recursive singular spectrum analysis estimator and the extreme learning machine predictor. The proposed framework filters the tremor signal accurately, without distorting it, but at a small fixed lag. In a set of rigorous testing performed by emulating a real-time processing, the proposed Manuscript received August 29, 2019.

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