Human Blood Cell Sensing with Platinum Black Electroplated Impedance Sensor

AC impedance sensing is an important method for biological cell analysis in flow cytometry. For micro impedance cell sensors, downsizing electrodes increases the double layer impedance of the metal-electrolyte interface, thus leaves no sensing zone in frequency domain and reduces the sensitivity significantly. We proposed using platinum black electroplated electrodes to solve the problem. In this paper, using this technique we demonstrated human blood cell sensing with high signal to noise ratio

Resonance Induced Impedance Sensing of Human Blood Cells

A challenging problem in AC impedance sensing of particles (e.g., blood cells in plasma) with micro electrodes is that with the shrinking of electrode surface area the electrode double layer capacitance decreases. Combined with the parallel stray capacitance, the system impedance is dominated by these capacitive components. Hence the sensitivity for particle sensing decreases. In this paper, we propose a new approach to solve the problem. The idea is to use resonant sensing by connecting an external parallel inductor to the system. At the resonant frequency, the capacitive components in the system were nullified by the inductor, leaving the electrolyte and particle impedance to be a major component in the system impedance. We then successfully demonstrated this idea by sensing 5 mum polystyrene beads. More important, this technique was extended to sensing blood cells in diluted human whole blood and leukocyte rich plasma. The measured signal pulse height histogram matched well with known volume distribution of erythrocytes and leukocytes

A parylene-based microelectrode array implant for spinal cord stimulation in rats

The design and fabrication of an epidural spinal cord implant using a parylene-based microelectrode array is presented. Rats with hindlimb paralysis from a complete spinal cord transection were implanted with the device and studied for up to eight weeks, where we have demonstrated recovery of hindlimb stepping functionality through pulsed stimulation. The microelectrode array allows for a high degree of freedom and specificity in selecting the site of stimulation compared to wire-based implants, and triggers varied biological responses that can lead to an increased understanding of the spinal cord and locomotion recovery for victims of spinal cord injury

An Active Learning Algorithm for Control of Epidural Electrostimulation

Epidural electrostimulation has shown promise for spinal cord injury therapy. However, finding effective stimuli on the multi-electrode stimulating arrays employed requires a laborious manual search of a vast space for each patient. Widespread clinical application of these techniques would be greatly facilitated by an autonomous, algorithmic system which choses stimuli to simultaneously deliver effective therapy and explore this space. We propose a method based on GP-BUCB, a Gaussian process bandit algorithm. In n = 4 spinally transected rats, we implant epidural electrode arrays and examine the algorithm’s performance in selecting bipolar stimuli to elicit specified muscle responses. These responses are compared with temporally interleaved intra-animal stimulus selections by a human expert. GP-BUCB successfully controlled the spinal electrostimulation preparation in 37 testing sessions, selecting 670 stimuli. These sessions included sustained autonomous operations (ten-session duration). Delivered performance with respect to the specified metric was as good as or better than that of the human expert. Despite receiving no information as to anatomically likely locations of effective stimuli, GP-BUCB also consistently discovered such a pattern. Further, GP-BUCB was able to extrapolate from previous sessions’ results to make predictions about performance in new testing sessions, while remaining sufficiently flexible to capture temporal variability. These results provide validation for applying automated stimulus selection methods to the problem of spinal cord injury therapy

Resonance impedance sensing of human blood cells

A challenging problem in alternating current (AC) impedance sensing of particles (e.g., blood cells in plasma) with micro electrodes is that with the shrinking of electrode surface area the electrode double layer capacitance decreases. This double-layer capacitor dominates the system impedance in lowfrequency range, while the parallel stray capacitor dominates the system impedance in high frequency range. Hence the sensitivity for particle sensing for micro impedance sensors decreases over a wide frequency range. In this paper, we propose an approach to solve the problem. The idea is to use resonant sensing by connecting an external parallel inductor to the system. At the resonant frequency, the capacitive components in the system are nullified by the inductor, leaving the channel impedance (including the particle impedance) to be a major component in the system impedance. We then successfully demonstrate this idea by sensing 5 µm polystyrene beads. More important, this technique is extended to sensing blood cells in diluted human whole blood and leukocyte-rich plasma. The sensitivity can be improved by two orders of magnitude over more than three decades in frequency domain. The measured signal peak height histogram at low frequency matches well with known volume distribution of erythrocytes and leukocytes

An Active Learning Algorithm for Control of Epidural Electrostimulation

Epidural electrostimulation has shown promise for spinal cord injury therapy. However, finding effective stimuli on the multi-electrode stimulating arrays employed requires a laborious manual search of a vast space for each patient. Widespread clinical application of these techniques would be greatly facilitated by an autonomous, algorithmic system which choses stimuli to simultaneously deliver effective therapy and explore this space. We propose a method based on GP-BUCB, a Gaussian process bandit algorithm. In n = 4 spinally transected rats, we implant epidural electrode arrays and examine the algorithm's performance in selecting bipolar stimuli to elicit specified muscle responses. These responses are compared with temporally interleaved intra-animal stimulus selections by a human expert. GP-BUCB successfully controlled the spinal electrostimulation preparation in 37 testing sessions, selecting 670 stimuli. These sessions included sustained autonomous operations (ten-session duration). Delivered performance with respect to the specified metric was as good as or better than that of the human expert. Despite receiving no information as to anatomically likely locations of effective stimuli, GP-BUCB also consistently discovered such a pattern. Further, GP-BUCB was able to extrapolate from previous sessions' results to make predictions about performance in new testing sessions, while remaining sufficiently flexible to capture temporal variability. These results provide validation for applying automated stimulus selection methods to the problem of spinal cord injury therapy