This paper studies the acoustic signals of left ventricular assist devices (LVADs) as it relates to machine health. Current LVAD condition monitoring requires examination from trained medical professionals, and is both inefficient and roughly-prognostic. To better quantify a patient's condition, the diagnostic method must be robust, non-invasive, and simple to apply. The concept behind this work is to determine an identifying pattern between the specific acoustics produced by an LVAD with the related overall health of the patient. Due to the cycle-to-cycle variance of heart sounds, the continuous wavelet transform (CWT) is applied to the objective audio signal so that a high resolution spectra is obtained. From this, region specific image features are developed and subsequently used in a support vector machine (SVM) algorithm to classify between health conditions. The preliminary goal is to develop an accurate and non-invasive diagnostic method for determining patient health that can be applied for any LVAD variant. This process is validated through in vitro testing using a DC motor as an LVAD proxy.
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