Investigation of highly scattering media using near-infrared continuous wave tunable semiconductor laser

Author(s):  
Udo Haberland ◽  
Walter Ruetten ◽  
Vladimir Blazek ◽  
Hans J. Schmitt
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Cheng Chen ◽  
Yizhen Wen ◽  
Shaoyang Cui ◽  
Xiangao Qi ◽  
Zhenhong Liu ◽  
...  

This paper presents a multichannel functional continuous-wave near-infrared spectroscopy (fNIRS) system, which collects data under a dual-level light intensity mode to optimize SNR for channels with multiple source-detector separations. This system is applied to classify different cortical activation states of the prefrontal cortex (PFC). Mental arithmetic, digit span, semantic task, and rest state were selected as four mental tasks. A deep forest algorithm is employed to achieve high classification accuracy. By employing multigrained scanning to fNIRS data, this system can extract the structural features and result in higher performance. The proposed system with proper optimization can achieve 86.9% accuracy on the self-built dataset, which is the highest result compared to the existing systems.


2012 ◽  
Vol 1 (4) ◽  
Author(s):  
Nelson Marquina ◽  
Roger Dumoulin-White ◽  
Arkady Mandel ◽  
Lothar Lilge

AbstractA randomized placebo-controlled clinical trial to evaluate an adjunctive treatment modality for pain associated with knee disorders was conducted utilizing a therapeutic laser system (low energy, non-surgical).The therapeutic laser system utilized a dual wavelength, multiple diode laser cluster probe with five super-pulsed 905 nm near-infrared (NIR) laser diodes, each emitting at 40 mW average power and four continuous wave 660 nm visible (VIS) red laser diodes, each emitting at 25 mW. It was used as an adjunctive modality providing 12 treatments, three times a week to a homogeneous patient population (n=126), in combination with standardized chiropractic techniques, to evaluate effectiveness on subjects presenting with osteoarthritis and knee pain. The primary endpoint was measured by the visual analog scale (VAS) to assess pain levels on a scale of 0–10. The success criteria for an individual patient in this study were identified as an improvement of 30% or more in the VAS from baseline to 12th treatment and/or an improvement of 20% or more in the VAS from baseline to 30-day follow-up evaluation.The data obtained in the study demonstrated that the present therapeutic laser system provided significant pain relief and osteoarthritic improvements in all primary evaluation criteria, with a statistical and clinical significance of


Small ◽  
2016 ◽  
Vol 12 (13) ◽  
pp. 1732-1743 ◽  
Author(s):  
Akshaya Bansal ◽  
Haichun Liu ◽  
Muthu Kumara Gnanasammandhan Jayakumar ◽  
Stefan Andersson-Engels ◽  
Yong Zhang

2012 ◽  
Vol 37 (24) ◽  
pp. 5049 ◽  
Author(s):  
Kavita Devi ◽  
S. Chaitanya Kumar ◽  
M. Ebrahim-Zadeh

2017 ◽  
Vol 192 ◽  
pp. 963-968 ◽  
Author(s):  
K.C. Camargo ◽  
R.R. Pereira ◽  
L.F. dos Santos ◽  
S.R. de Oliveira ◽  
R.R. Gonçalves ◽  
...  

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Noman Naseer ◽  
Nauman Khalid Qureshi ◽  
Farzan Majeed Noori ◽  
Keum-Shik Hong

We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA),k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that thepvalues were statistically significant relative to all of the other classifiers (p< 0.005) using HbO signals.


1985 ◽  
Vol 57 (7) ◽  
pp. 1219-1223 ◽  
Author(s):  
Kazuhiko. Nakanishi ◽  
Totaro. Imasaka ◽  
Nobuhiko. Ishibashi

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