![]() ![]() Table 1 outlines several currently available options for biosignal generation and their relevant specifications. In this work we present a methodology for using readily available off-the-shelf audio hardware with a simple analog conditioning circuit to inexpensively recreate a wide range of biosignals for plug- and-play bench-top testing of biomedical devices and provide an open-source design for doing the same ( figure 1). Finally, a microSD card is used to store the playback data meaning that data files exceeding the storage size of the memory card cannot be recreated in full and changing or altering the signal requires re-loading card with the new dataset. Additionally, a custom Raspberry Pi application and FPGA bitstream require significant user know-how to make the device operational. However, significant custom circuitry makes it difficult to adapt this design for diverse laboratory needs or to replicate the device without access to circuit schematics, thus limiting its accessibility. Haci et al presented a custom built biosignal playback device that uses a RaspberryPi single board computer along with a field programmable gate array (FPGA), audio digital to analog converters (Analog Devices ADAU1966A), and custom circuitry, to provide 32 channels of high quality biosignal playback. However, general purpose lab equipment are optimized for generic signals with low channel counts and limited ability to reproduce μV scale amplitudes while commercial simulators are often built for modeling specific electrophysiological events with stereotyped characteristics and are limited to the specific subset of biosignals programmed into the device. īench-top signal generation is possible using a variety of tools ranging from laboratory function generators and arbitrary waveform generators to commercially available biosignal specific simulators or ‘patient simulators’. Ultimately, subjecting a closed-loop device or algorithm to bench-top testing wherein real-life signals can be presented in a repeatable and controlled manner is an indispensable part of the development life-cycle for any such tool. Fully computational methods wherein biological systems and device firmware are modeled together allow for rapid testing with programmatic control over all system parameters but are idealized and depend on the accuracy of the model to predict real-life behavior. Animal models are effective but are expensive and prohibit early innovation by preventing rapid iteration of new designs. ![]() However, the opportunity for using computer-in-the-loop techniques is limited to clinical trials where a device is already being implanted to meet a clinical need. The Medtronic Activa PC+S™ and Summit RC+S™ were designed with a hardware and software ecosystem to enable computer-in-the-loop development of algorithms on an already-implanted device, within clinician set safety limits. While on-the-fly re-programmability is a powerful capability, it is imperative that patient safety not be compromised for the improved flexibility of a therapeutic or research platform. Many of these devices are capable of updating their embedded algorithms in situ, allowing researchers to investigate and iterate over new processing techniques without the need for additional surgery. Ĭlosed-loop therapies are enabled by implantable medical devices, for example Medtronic’s Micra™ leadless pacemaker (Medtronic, Minneapolis, MN, USA), the Neuropace RNS™ (Responsive Neural Stimulator), and Medtronic’s Activa PC+S™ and Summit RC+S™ devices. Ongoing research aims to apply these same adaptive neuromodulation tools towards the treatment of mental disorders such as obsessive compulsive disorder and depression. More recently, local field potentials (LFPs) measured using deep brain electrodes and electrocorticograms (ECoG) have been used in adaptive deep brain stimulation (DBS) paradigms to treat epilepsy, Parkinson’s disease, and Tourette’s syndrome. Electrocardiograms (ECGs), tissue impedance measurements, and accelerometer based activity signals are used in implantable cardioverter-defibrillators (ICDs) and pacemakers to deliver life saving counter-shocks and rhythmic electrical stimulation to normalize dysrhythmias. Used as indicators for the function or dysfunction of biological systems, successful interpretation of such signals can be used in adaptive therapies to provide precisely timed, personalized treatments to patients. There is an ever growing need to access, process, and understand the complex electrophysiological signals produced by our bodies.
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