BioelectromagnetismPrinciples and Applications of Bioelectric and Biomagnetic Fields

Malmivuo, Jaakko; Plonsey, Robert

doi:10.1093/acprof:oso/9780195058239.001.0001

Cited by 1,237 publications

(512 citation statements)

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“…Mean values have been put forth as 0.15 S/m for white matter, 0.45 S/m for gray matter and 0.17 S/m as a mean conductivity (Geddes and Baker, 1967;Malmivuo and Plonsey, 1995). Another common value for mean conductivity is 0.3 S/m (Ranck, 1963).…”

Section: Discussionmentioning

confidence: 99%

“…The length could be viewed as the distance from electrode contact to IPG, while the height reflects the diameter of the head and neck. Tissue conductivity (σ T ) was varied from 0.15-0.3 S/m, derived from previous experimental ranges (Geddes and Baker, 1967;Malmivuo and Plonsey, 1995;Ranck, 1963). We also explored the effects of electrode contact size.…”

Section: Impedance Modelmentioning

confidence: 99%

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Sources and effects of electrode impedance during deep brain stimulation

Butson

Maks

McIntyre

2006

Clinical Neurophysiology

318

281

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Objective-Clinical impedance measurements for deep brain stimulation (DBS) electrodes in human patients are normally in the range 500-1500 Ω. DBS devices utilize voltage-controlled stimulation; therefore, the current delivered to the tissue is inversely proportional to the impedance. The goals of this study were to evaluate the effects of various electrical properties of the tissue medium and electrode-tissue interface on the impedance and to determine the impact of clinically relevant impedance variability on the volume of tissue activated (VTA) during DBS.Methods-Axisymmetric finite-element models (FEM) of the DBS system were constructed with explicit representation of encapsulation layers around the electrode and implanted pulse generator. Impedance was calculated by dividing the stimulation voltage by the integrated current density along the active electrode contact. The models utilized a Fourier FEM solver that accounted for the capacitive components of the electrode-tissue interface during voltagecontrolled stimulation. The resulting time-and space-dependent voltage waveforms generated in the tissue medium were superimposed onto cable model axons to calculate the VTA.Results-The primary determinants of electrode impedance were the thickness and conductivity of the encapsulation layer around the electrode contact and the conductivity of the bulk tissue medium. The difference in the VTA between our low (790 Ω) and high (1244 Ω) impedance models with typical DBS settings (−3 V, 90 μs, 130 Hz pulse train) was 121 mm 3 , representing a 52% volume reduction.Conclusions-Electrode impedance has a substantial effect on the VTA and accurate representation of electrode impedance should be an explicit component of computational models of voltage-controlled DBS.Significance-Impedance is often used to identify broken leads (for values >2000 Ω) or short circuits in the hardware (for values <50 Ω); however, clinical impedance values also represent an important parameter in defining the spread of stimulation during DBS.

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Section: Discussionmentioning

confidence: 99%

Section: Impedance Modelmentioning

confidence: 99%

Sources and effects of electrode impedance during deep brain stimulation

Butson

Maks

McIntyre

2006

Clinical Neurophysiology

318

281

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“…The phantom contained 9.0% agarose and 0.07% sodium (wt/wt), which led to a conductivity around 0.33 S/m (9,10). The estimated conductivity was in the range of the published data for human brain tissues (0.15 S/m for white matter and 0.45 S/m for gray matter) (11,12). DBS electrodes (Medtronic 3387; Medtronic, Minneapolis, MN, USA) were inserted into the phantom and the tip was positioned near the center of the phantom.…”

Section: Methodsmentioning

confidence: 58%

False‐positive analysis of functional MRI during simulated deep brain stimulation: A phantom study

Liu

Chen

et al. 2008

Magnetic Resonance Imaging

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Purpose:To investigate the false-positive activations/deactivations in functional MRI (fMRI) of deep brain stimulation (DBS) using a phantom. Materials and Methods: fMRI experiments were performed on a 1.5T scanner using a single-shot gradientecho echo-planar imaging (GE-EPI) sequence (TR/TE/ FA ϭ 6000 msec/60 msec/90°) on an agar-gel phantom inserted with DBS electrodes. During the experimental blocks, two-second stimuli were delivered during the interscan waiting time (ISWT), which was adjusted by changing the number of slices acquired within the TR (3500 msec with 30 slices and 5160 msec with 10 slices). Data were analyzed using SPM2 software, and the falsepositive voxels were detected with five different P-value thresholds.Results: The number of false-positive voxels in experimental conditions had no significant differences from those in control conditions with either long or short ISWT, which increased with the P-value threshold from zero at P Ͻ 0.0001 to approximately 40 at P Ͻ 0.05. The pattern of increasing number of false-positive reactions along with P-value was similar between all conditions. Conclusion:False-positive findings from fMRI with similar experimental design can be well controlled with a statistical threshold of P Ͻ 0.001 or tighter. The short ISWT of 3500 msec did not increase false-positive reactions compared to the long ISWT of 5160 msec.

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“…Three limb leads are commonly used to construct an Einthoven's triangle ( Figure 1). 13 An ECG wave can be recorded by placing leads at electrically equidistant points on the body from the heart, thus maximizing the potential difference between leads. 14 Lead I is configured as the positive electrode on the left arm (L), the negative electrode on the right arm (R); Lead II is configured as the positive electrode on the left leg/foot (F), the negative electrode on the right arm (R); Lead III sets the positive electrode on the left leg/foot (F), the negative electrode on the left arm (L).…”

Section: Obtaining An Real Ecg Signalmentioning

confidence: 99%

Design of an ECG Sensor Circuitry for Cardiovascular Disease Diagnosis

DU¹

2017

IJBSBE

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ECG biosignal conditioning (BC) is critical because it directly affects measurement accuracy, reliability, and repeatability. It also presents a great challenge due to the small amplitude of ECG raw signals and their ease of corruption with noise and other disturbances. This paper describes how an ECG biosignal conditioning circuitry was designed, built, and tested. The circuit consists of an instrument amplifier (AD8220), a 1st-order active high-pass filter (containing a MCP6271 op-amp and a RC filter in Sallen-Key configuration), a 5th-order active Bessel low-pass filter (consisting of a 1st-order LPF and two 2nd-order Sallen-Key filters), and a Twin-T active notch filter (combining two "T" shape RC filters with a MCP6271 op-amp). A right leg drive circuit was added in order to cancel the common-mode signal between the left and right arm electrodes. A power supply circuit provides a ±5V DC source for the system using two 9V batteries and two voltage regulators (NTE977 regulates +9V power supply to +5V; NTE1917 regulates -9V supply to -5V). Data acquisition and sampling were performed using a USB6009 module with a built-in A/D converter. Testing of a real electrocardiogram from a human subject was performed on the designed BC circuit, which has indicated that the developed BC circuitry can preserve useful ECG information while removing unwanted noise and interference components. with a cutoff frequency of 0.05Hz, and a low-pass filter with a cutoff frequency of nearly 100Hz. The sampling rate in the ADC (analog to digital conversion) for the ECG signal is 500Hz. Fulford-Jones et al. 4 designed a portable, low-power ECG system. 4 An operational amplifier (op-amp) embedded in a single INA321 chip was used due to its low noise and low power consumption. This op-am has a CMRR of 94dB. A high-pass feedback filter corrects any DC shift occurring over time. Their ADC sampling rate is 120Hz. Matviyenko 5 used a CY8C27443 microcontroller for ECG signal acquisition and processing. The IA embedded in the controller has a CMRR of 60dB. According to the author, this low CMRR was acceptable due to the unique design which placed a differential low-pass filter before the IA to reduce radio frequency interference (RFI), because RFI error cannot be filtered out after the ECG signal has been rectified by the IA. A 2kHz cutoff frequency high-pass filter was placed at the output of the IA. A buffer amplifier and an inverting amplifier were also used to cancel the RFI interference. The ADC sampling rate is 240Hz. Two industry leaders, Texas Instruments (TI) 6 and Analog Devices Inc. (AD), 7 also developed the ECG conditioning circuits. TI's circuitry features an INA321 IA with several unique features: a power down mode that shuts down the circuit when the supplied current is less than 1 mA (for power saving), embedded op-amps in the microcontroller, a feedback loop that maintains a constant DC level, and a 512Hz sampling rate. Further filtering was digitally implemented to remove power line noise and to provide a pass band o...

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BioelectromagnetismPrinciples and Applications of Bioelectric and Biomagnetic Fields

Cited by 1,237 publications

References 0 publications

Sources and effects of electrode impedance during deep brain stimulation

Sources and effects of electrode impedance during deep brain stimulation

False‐positive analysis of functional MRI during simulated deep brain stimulation: A phantom study

Design of an ECG Sensor Circuitry for Cardiovascular Disease Diagnosis

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