An Integrated Biosensor System With a High-Density Microelectrode Array for Real-Time Electrochemical Imaging

Physiological events related to oxygen concentration gradients provide valuable information to determine the state of metabolizing biological cells. The existing oxygen sensing methods (i.e., optical photoluminescence, magnetic resonance, and scanning electrochemical) are well-established and optimized for existing in vitro analyses. However, such methods also present various limitations in resolution, real-time sensing performance, complexity, and costs. An electrochemical imaging system with an integrated microelectrode array (MEA) would offer attractive means of measuring oxygen consumption rate (OCR) based on the cell’s two-dimensional (2D) oxygen concentration gradient. This paper presents an application of an electrochemical sensor platform with a custom-designed complementary-metal-oxide-semiconductor (CMOS)-based microchip and its Pt-coated surface MEA. The high-density MEA provides 16,064 individual electrochemical pixels that cover a 3.6 mm × 3.6 mm area. Utilizing the three-electrode configuration, the system is capable of imaging low oxygen concentration (18.3 µM, 0.58 mg/L, or 13.8 mmHg) at 27.5 µm spatial resolution and up to 4 Hz temporal resolution. In vitro oxygen imaging experiments were performed to analyze bovine cumulus-oocytes-complexes cells OCR and oxygen flux density. The integration of a microfluidic system allows proper bio-sample handling and delivery to the MEA surface for imaging. Finally, the imaging results are processed and presented as two-dimensional (2D) heatmaps, representing the dissolved oxygen concentration in the immediate proximity of the MEA. This paper provides the results of real-time 2D imaging of OCR of live cells/tissues to gain spatial and temporal dynamics of target cell metabolism.

Download Full-text

Spatiotemporal norepinephrine mapping using a high-density CMOS microelectrode array

Lab on a Chip ◽

10.1039/c5lc00778j ◽

2015 ◽

Vol 15 (20) ◽

pp. 4075-4082 ◽

Cited By ~ 20

Author(s):

John B. Wydallis ◽

Rachel M. Feeny ◽

William Wilson ◽

Tucker Kern ◽

Tom Chen ◽

...

Keyword(s):

Microelectrode Array ◽

High Density ◽

Spatiotemporal Resolution ◽

High Spatiotemporal Resolution ◽

Electrochemical Imaging

Electrochemical imaging with high spatiotemporal resolution of dynamic norepinephrine distributions is achieved using microfluidics and a high-density CMOS platinum microelectrode array with an on-board potentiostat.

Download Full-text

Real-Time Lossless Compression for Ultra-High-Density Synchrophasor and Point on Wave Data

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2021.3057034 ◽

2021 ◽

pp. 1-1

Author(s):

Chang Chen ◽

Weikang Wang ◽

Yin He ◽

Lingwei Zhan ◽

Yilu Liu

Keyword(s):

Real Time ◽

Lossless Compression ◽

High Density ◽

Wave Data

Download Full-text

Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

Scientific Reports ◽

10.1038/s41598-021-90688-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Simon Tam ◽

Mounir Boukadoum ◽

Alexandre Campeau-Lecours ◽

Benoit Gosselin

Keyword(s):

Real Time ◽

Transfer Learning ◽

Control Strategy ◽

High Density ◽

Fine Tuning ◽

Mode Switching ◽

Correct Prediction ◽

Learning Approach ◽

Predictive Values ◽

Hand Prosthesis

AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

Download Full-text

A novel microfluidic microelectrode chip for a significantly enhanced monitoring of NPY-receptor activation in live mode

Lab on a Chip ◽

10.1039/c7lc00754j ◽

2017 ◽

Vol 17 (24) ◽

pp. 4294-4302 ◽

Cited By ~ 7

Author(s):

Franziska D. Zitzmann ◽

Heinz-Georg Jahnke ◽

Felix Nitschke ◽

Annette G. Beck-Sickinger ◽

Bernd Abel ◽

...

Keyword(s):

Real Time ◽

Microfluidic Chip ◽

Microelectrode Array ◽

Fem Simulation ◽

Receptor Activation ◽

Living Cells ◽

Real Time Monitoring ◽

Non Invasive ◽

Npy Receptor ◽

Simulation Based

We present a FEM simulation based step-by-step development of a microelectrode array integrated into a microfluidic chip for the non-invasive real-time monitoring of living cells.

Download Full-text