Dataset Information

A Time-Domain Analog Spatial Compressed Sensing Encoder for Multi-Channel Neural Recording.

ABSTRACT: A time-domain analog spatial compressed sensing encoder for neural recording applications is proposed. Owing to the advantage of MEMS technologies, the number of channels on a silicon neural probe array has doubled in 7.4 years, and therefore, a greater number of recording channels and higher density of front-end circuitry is required. Since neural signals such as action potential (AP) have wider signal bandwidth than that of an image sensor, a data compression technique is essentially required for arrayed neural recording systems. In this paper, compressed sensing (CS) is employed for data reduction, and a novel time-domain analog CS encoder is proposed. A simpler and lower power circuit than conventional analog or digital CS encoders can be realized by using the proposed CS encoder. A prototype of the proposed encoder was fabricated in a 180 nm 1P6M CMOS process, and it achieved an active area of 0.0342 mm 2 / ch . and an energy efficiency of 25.0 pJ / ch . · conv .

SUBMITTER: Okazawa T

PROVIDER: S-EPMC5795473 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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A Time-Domain Analog Spatial Compressed Sensing Encoder for Multi-Channel Neural Recording.

Okazawa Takayuki T Akita Ippei I

Sensors (Basel, Switzerland) 20180111 1

A time-domain analog spatial compressed sensing encoder for neural recording applications is proposed. Owing to the advantage of MEMS technologies, the number of channels on a silicon neural probe array has doubled in 7.4 years, and therefore, a greater number of recording channels and higher density of front-end circuitry is required. Since neural signals such as action potential (AP) have wider signal bandwidth than that of an image sensor, a data compression technique is essentially required ...[more]

PMID: 29324675

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Project description:Semiconductor nanowires and other semiconducting nanoscale materials configured as field-effect transistors have been studied extensively as biological/chemical (bio/chem) sensors. These nanomaterials have demonstrated high-sensitivity from one- and two-dimensional sensors, although the realization of the ultimate pointlike detector has not been achieved. In this regard, nanoscale p-n diodes are attractive since the device element is naturally localized near the junction, and while nanowire p-n diodes have been widely studied as photovoltaic devices, their applications as bio/chem sensors have not been explored. Here we demonstrate that p-n diode devices can serve as a new and powerful family of highly localized biosensor probes. Designed nanoscale axial p-n junctions were synthetically introduced at the joints of kinked silicon nanowires. Scanning electron microscopy images showed that the kinked nanowire structures were achieved, and electrical transport measurements exhibited rectifying behavior with well-defined turn-on in forward bias as expected for a p-n diode. In addition, scanning gate microscopy demonstrated that the most sensitive region of these nanowires was localized near the kinked region at the p-n junction. High spatial resolution sensing using these p-n diode probes was carried out in aqueous solution using fluorescent charged polystyrene nanobeads. Multiplexed electrical measurements show well-defined single-nanoparticle detection, and experiments with simultaneous confocal imaging correlate directly the motion of the nanobeads with the electrical signals recorded from the p-n devices. In addition, kinked p-n junction nanowires configured as three-dimensional probes demonstrate the capability of intracellular recording of action potentials from electrogenic cells. These p-n junction kinked nanowire devices, which represent a new way of constructing nanoscale probes with highly localized sensing regions, provide substantial opportunity in areas ranging from bio/chem sensing and nanoscale photon detection to three-dimensional recording from within living cells and tissue.

Project description:BACKGROUND:Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symptomatic experiences and functional impairments vacillate over the course of illness. Developing unobtrusive remote monitoring systems to detect early warning signs of impending symptomatic relapses would allow clinicians to intervene before the patient's condition worsens. OBJECTIVE:In this study, we aim to create the first models, exclusively using passive sensing data from a smartphone, to predict behavioral anomalies that could indicate early warning signs of a psychotic relapse. METHODS:Data used to train and test the models were collected during the CrossCheck study. Hourly features derived from smartphone passive sensing data were extracted from 60 patients with SSDs (42 nonrelapse and 18 relapse >1 time throughout the study) and used to train models and test performance. We trained 2 types of encoder-decoder neural network models and a clustering-based local outlier factor model to predict behavioral anomalies that occurred within the 30-day period before a participant's date of relapse (the near relapse period). Models were trained to recreate participant behavior on days of relative health (DRH, outside of the near relapse period), following which a threshold to the recreation error was applied to predict anomalies. The neural network model architecture and the percentage of relapse participant data used to train all models were varied. RESULTS:A total of 20,137 days of collected data were analyzed, with 726 days of data (0.037%) within any 30-day near relapse period. The best performing model used a fully connected neural network autoencoder architecture and achieved a median sensitivity of 0.25 (IQR 0.15-1.00) and specificity of 0.88 (IQR 0.14-0.96; a median 108% increase in behavioral anomalies near relapse). We conducted a post hoc analysis using the best performing model to identify behavioral features that had a medium-to-large effect (Cohen d>0.5) in distinguishing anomalies near relapse from DRH among 4 participants who relapsed multiple times throughout the study. Qualitative validation using clinical notes collected during the original CrossCheck study showed that the identified features from our analysis were presented to clinicians during relapse events. CONCLUSIONS:Our proposed method predicted a higher rate of anomalies in patients with SSDs within the 30-day near relapse period and can be used to uncover individual-level behaviors that change before relapse. This approach will enable technologists and clinicians to build unobtrusive digital mental health tools that can predict incipient relapse in SSDs.

Dataset Information

A Time-Domain Analog Spatial Compressed Sensing Encoder for Multi-Channel Neural Recording.

Publications

A Time-Domain Analog Spatial Compressed Sensing Encoder for Multi-Channel Neural Recording.

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