Raul Fernandez Rojas

Region of interest detection and evaluation in functional near infrared spectroscopy

This paper describes the use of a computational method based on an optical flow algorithm to detect regions of interest in near infrared (NIR) spectroscopy. The evaluation of such method is also presented. Visual inspection and cross correlation analysis of NIR cortical activation images were used to evaluate our method. The visual analysis exposed pain-related activations in the primary somatosensory cortex (S1) after stimulation which is consistent with similar studies, and the cross correlation results showed dominant channels on both cerebral hemispheres. Optical flow exhibited the nature of the dominant channel, the extent of the stimulation spatial distribution and the stimulation status. In addition, the directions of the optical flow vectors were linked to the stimulation perception of the participant. The two evaluation methods confirmed the success of our method in finding the region of interest in both hemispheres. The results of this real case study show that the computational method can successfully analyse and detect regions of interest and show temporal interactions between channels, and could be employed to investigate pain assessment in human subjects.

ANALYSIS OF PAIN HEMODYNAMIC RESPONSE USING NEAR -INFRARED SPECTROSCOPY (NIRS)

Despite recent advances in brain research, understanding the various signals for pain and pain intensities in the brain cortex is still a complex task due to temporal and spatial variations of brain haemodynamics. In this paper we have investigated pain based on cerebral hemodynamics via near-infrared spectroscopy (NIRS). This study presents a pain stimulation experiment that uses three acupuncture manipulation techniques to safely induce pain in healthy subjects. Acupuncture pain response was presented and Haemodynamic pain signal analysis showed the presence of dominant channels and their relationship among surrounding channels, which contribute the further pain research area.

Novel Information Processing for Image De-noising Based on Sparse Basis

Image de-noising is one of the important information processing technologies and a fundamental image processing step for improving the overall quality of medical images. Conventional de-noising methods, however, tend to over-suppress high-frequency details. To overcome this problem, in this paper we present a novel compressive sensing (CS) based noise removing algorithm using proposed sparse basis on CDF9/7 wavelet transform. The measurement matrix is applied to the transform coefficients of the noisy image for compressive sampling. The orthogonal matching pursuit (OMP) and Basis Pursuit (BP) are applied to reconstruct image from noisy sparse image. In the reconstruction process, the proposed threshold with Bayeshrink thresholding strategies is used. Experimental results demonstrate that the proposed method removes noise much better than existing state-of-the-art methods in the sense image quality evaluation indexes.

Evidentiary assessment for protecting WSNs from internal attacks in real-time

Abstract Wireless sensor networks (WSNs) are becoming a vital role in our current modern life for detecting and collecting data about a natural or built environment, including human body. One of the reasons is due to WSNs have very attractive advantages. But one of the problems is internal attacks that have gained prominence and posed most challenging threats to all WSNs. In this paper, we extend our discussion at the conference of the AISC 2016 to an effective algorithm to make an evaluation for detecting internal attack by evidentiary assessment for protecting a WSN from the internal attacks with multi-criteria in real-time. This protecting is based on the combination of the multiple pieces of evidences collected from the nodes suffering from an internal attacker in a network. A decision made is carefully discussed based on the Dempster–Shafer Theory (DST). One of the advantages of this proposed method is that it is not just making a performance in real-time but also it is effective due to it does not need the knowledge about the normal or malicious node in advance with very high average accuracy that is close to 100%.

Bilateral connectivity in the somatosensory region using near-infrared spectroscopy (NIRS) by wavelet coherence

Near-infrared spectroscopy (NIRS) has been used in medical imaging to obtain oxygenation and hemodynamic response in the cerebral cortex. This technique has been applied in cortical activation detection and functional connectivity in brain research. Despite some advances in functional connectivity, most of the studies have focused on the prefrontal cortex and little has been done to study the somatosensory region (S1). For that reason, the aim of our present study is to assess bilateral connectivity in the somatosensory region by using NIRS and noxious stimulation. Eleven healthy subjects were investigated using near-infrared spectroscopy during an acupuncture stimulation procedure to safely induce pain in subjects. A multiscale analysis based on wavelet transform coherence (WTC) was designed to assess the functional connectivity of corresponding channel pairs within the left and right s1 region. The cortical activation in the somatosensory region was higher after the acupuncture stimulation, which was consistent with similar studies. The coherence in time-frequency domain between homologous signals generated by contralateral channel pairs revealed two main periods (3.2 s and 12.8 s) with high coherence. Based on the WTC analysis, it was also found that the coherence increase in these periods was task-related. This study contributes to the research field to investigate cerebral hemodynamic response of pain perception using NIRS and demonstrates the use of wavelet transform as a method to investigate functional lateralization in the cerebral cortex.

Biomedic Signal Processing and Analysis of N euroimaging from fNIRS for Human pain

One of major biomedical signals, pain, and its diagnosis has been critical but hard in clinical practice, in particularly for nonverbal patients. However, as we know that neuroimaging methods, such as functional near-infrared spectroscopy (fNIRS), have shown some great encouraging assessing neuronal function corresponding to nociception and pain. Specially some research results strongly suggest that neuroimaging, together with supports from machine learning, may be practically used to not only facilitate but also can predict different cognitive tasks over this challenge. The aim of this current research is to expand our previous studies by exploring the classification of fNIRS signals (oxyhaemoglobin) according to temperature level (we define cold and hot) and corresponding pain intensity (say low and high) by means of machine learning models. In order to find out the relations between temperatures and pain intensity, we defined and used the quantitative sensory testing to determine pain threshold and pain tolerance for the cold and heat in all eighteen-healthy people. The classification algorithm is based on a bag-of-words approach, a histogram representation was used in document classification based on the frequencies of extracted words and adapted for time series. Two machine learning algorithms were used separately, namely, K-nearest neighbor (K-NN) and support vector machines (SVM). A comparison between two sets of fNIRS channels was made in our classification task. The results showed that K-NN obtained slightly better results (92.1%) than SVM (91.3%) with all the 24 channels; however, the performances slightly dropped if using only channels from the region of interest with K-NN (91.5%) and SVM (90.8%). These research results encourage potential applications of fNIRS in the development of a physiologically based diagnosis of human pain, including in clinical parties.

fNIRS Approach to Pain Assessment for Non-verbal Patients

The absence of verbal communication in some patients (e.g., critically ill, suffering from advanced dementia) difficults their pain assessment due to the impossibility to self-report pain. Functional near-infrared spectroscopy (fNIRS) is a non-invasive technology that has showed promising results in assessing cortical activity in response to painful stimulation. In this study, we used fNIRS signals to predict the state of pain in humans using machine learning methods. Eighteen healthy subjects were stimulated using thermal stimuli with a thermode, while their cortical activity was recorded using fNIRS. Bag-of-words (BoW) model was used to represent each fNIRS time series. The effect of different step sizes, window lengths, and codebook sizes was investigated to improve computational cost and generalization. In addition, we explored the effect of choosing different features as neurological biomarkers in three different domains: time, frequency, and time-frequency (wavelet). Classification on the histogram representation was performed using K-nearest neighbours (K-NN). The performance is evaluated by using leave-one-out cross validation and with different nearest neighbours. The results showed that wavelet-based features produced the highest accuracy (\(88.33\%\)) to distinguish between heat and cold pain while discriminate between low and high pain. It is possible to use fNIRS to assess pain in response to four types of thermal pain. However, future research is needed for the assessment of pain in clinical settings.

A Computational Investigation of an Active Region in Brain Network Based on Stimulations with Near-Infrared Spectroscopy

Near-infrared spectroscopy (NIRS) has been widely used in medical imaging to observe oxygenation and hemodynamic responses in the cerebral cortex. In this paper, the major target is reporting our current study about the computational investigation of functional near infrared spectroscopy (fNIRS) in the somatosensory region with noxious stimulations. Based on signal processing technologies within communication network, the related technologies are applied, including cross correlation analysis, optic flow, and wavelet. The visual analysis exposed pain-related activations in the primary somatosensory cortex (S1) after stimulation which is consistent with similar studies, but the cross correlation results strongly evidenced dominant channels on both cerebral hemispheres. Our investigation also demonstrated that the spatial distribution of the cortical activity origin can be described by the hemodynamic responses in the cerebral cortex after evoked stimulation using near infrared spectroscopy. The current outcomes of this computational investigation explore that it is good potential to be employed to deal with pain assessment in human subjects.

Physiological fluctuations show frequency-specific networks in fNIRS signals during resting state

Physiological fluctuations are commonly present in functional studies of hemodynamic response such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS). However, the effects of these signals in neural mechanisms are not fully understood. Thus, the aim of this study is to propose that frequency-specific networks exist in the somatosensory region within the frequency range of physiological fluctuations. We used a wavelet coherence approach to identify functional connectivity between cortical regions. Based on the spectral response, four frequency bands were identified: cardiac (0.8–1.5 Hz), respiration (0.16–0.6 Hz), low frequency oscillations (LFO) (0.04–0.15 Hz), and very low frequency oscillations (VLFO) (0.0130.04 Hz). Eight cortical networks were revealed after ipsilateral and contralateral analysis to evaluate connectivity in each frequency band. The ANOVA analysis proved the adequacy of the connectivity map for all frequencies bands. Finally, these findings suggest possible frequency-specific organizations within the frequency bands of physiological fluctuations in the resting human brain.

Exploring the use of optical flow for the study of functional NIRS signals

Near infrared spectroscopy (NIRS) is an optical imaging technique that allows real-time measurements of Oxy and Deoxy-hemoglobin concentrations in human body tissue. In functional NIRS (fNIRS), this technique is used to study cortical activation in response to changes in neural activity. However, analysis of activation regions using NIRS is a challenging task in the field of medical image analysis and despite existing solutions, no homogeneous analysis method has yet been determined. For that reason, the aim of our present study is to report the use of an optical flow method for the analysis of cortical activation using near-infrared spectroscopy signals. We used real fNIRS data recorded from a noxious stimulation experiment as base of our implementation. To compute the optical flow algorithm, we first arrange NIRS signals (Oxy-hemoglobin) following our 24 channels (12 channels per hemisphere) head-probe configuration to create image-like samples. We then used two consecutive fNIRS samples per hemisphere as input frames for the optical flow algorithm, making one computation per hemisphere. The output from these two computations is the velocity field representing cortical activation from each hemisphere. The experimental results showed that the radial structure of flow vectors exhibited the origin of cortical activity, the development of stimulation as expansion or contraction of such flow vectors, and the flow of activation patterns may suggest prediction in cortical activity. The present study demonstrates that optical flow provides a power tool for the analysis of NIRS signals. Finally, we suggested a novel idea to identify pain status in nonverbal patients by using optical flow motion vectors; however, this idea will be study further in our future research.