Noninvasive Detection of Suction in an implantable Rotary Blood Pump Using Neural Networks

Granting those heart failure patients who are recipients of an implantable rotary blood pump (iRBP) greater functionality in daily activities is a key long-term strategy currently being pursued by many research groups. A reliable technique for noninvasive detection of the various pumping states, most notably that of ventricular collapse or suction, is an essential component of this strategy. Presented in this study is such a technique, whereby various indicators are derived from the noninvasive pump feedback signals, and a suitable computational methodology developed to classify the pumping states of interest. Clinical telemetry data from ten implant recipients was categorized (with the aid of trans-oesophageal echocardiography) into the normal and suction states. These data are used to develop a pumping state classifier based on an artificial neural network (ANN). Nine indices, derived from the noninvasive impeller speed signal, form the inputs to this ANN classifier. During validation, the resulting ANN classifier achieved a maximum sensitivity of 98.54% (609/618 samples of 5 s in length) and specificity of 99.26% (12,123/12,213 samples) for correct detection of the suction state. The ability to detect the suction state with such a high degree of accuracy provides a critical parameter both for control strategy development, and for clinical care of the implant recipient.