A DSP for Sensing the Bladder Volume Through Afferent Neural Pathways

In this paper, we present a digital signal processor (DSP) capable of monitoring the urinary bladder volume through afferent neural pathways. The DSP carries out real-time detection and can discriminate extracellular action potentials, also known as on-the-fly spike sorting. Next, the DSP performs a...

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Bibliographic Details
Published in:IEEE transactions on biomedical circuits and systems 2014-08, Vol.8 (4), p.552-564
Main Authors: Mendez, Arnaldo, Belghith, Abrar, Sawan, Mohamad
Format: Article
Language:English
Subjects:
Biomedical signal processing
biomedical transducers
Bladder
bladder volume
Decoding
Digital signal processing
Digital signal processors
Equipment Design
Humans
Neural Pathways
neural prosthesis
Neurons, Afferent - physiology
Noise measurement
Real-time systems
Signal Processing, Computer-Assisted
Signal-To-Noise Ratio
Sorting
spike sorting
Training
Urinary Bladder - physiology
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Summary:In this paper, we present a digital signal processor (DSP) capable of monitoring the urinary bladder volume through afferent neural pathways. The DSP carries out real-time detection and can discriminate extracellular action potentials, also known as on-the-fly spike sorting. Next, the DSP performs a decoding method to estimate either three qualitative levels of fullness or the bladder volume value, depending on the selected output mode. The proposed DSP was tested using both realistic synthetic signals with a known ground-truth, and real signals from bladder afferent nerves recorded during acute experiments with animal models. The spike sorting processing circuit yielded an average accuracy of 92% using signals with highly correlated spike waveforms and low signal-to-noise ratios. The volume estimation circuits, tested with real signals, reproduced accuracies achieved by offline simulations in Matlab, i.e., 94% and 97% for quantitative and qualitative estimations, respectively. To assess feasibility, the DSP was deployed in the Actel FPGA Igloo AGL1000V2, which showed a power consumption of 0.5 mW and a latency of 2.1 ms at a 333 kHz core frequency. These performance results demonstrate that an implantable bladder sensor that perform the detection, discrimination and decoding of afferent neural activity is feasible.
ISSN:1932-4545
1940-9990
DOI:10.1109/TBCAS.2013.2282087