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Objective Monitoring of the ultra-low frequency potentials, particularly cortical spreading depression (CSD), is excluded in epilepsy monitoring due to technical barriers imposed by the scalp ultra-low frequency electroencephalogram (EEG). As a result, clinical studies of CSD have been limited to invasive EEG. Therefore, the occurrence of CSD and its interaction with epileptiform field potentials (EFP) require investigation in epilepsy monitoring. Methods Using a novel AC/DC-EEG approach, the occurrence of DC potentials in patients with intractable epilepsy presenting different symptoms of aura was investigated during long-term video-EEG monitoring. Results Various forms of slow potentials, including simultaneous negative direct current (DC) potentials and prolonged EFP, propagated negative DC potentials, and non-propagated single negative DC potentials were recorded from the scalp of the epileptic patients. The propagated and single negative DC potentials preceded the prolonged EFP with a time lag and seizure appeared at the final shoulder of some instances of the propagated negative DC potentials. The slow potential deflections had a high amplitude and prolonged duration and propagated slowly through the brain. The high-frequency EEG was suppressed in the vicinity of the negative DC potential propagations. Conclusions The study is the first to report the recording of the propagated and single negative DC potentials with EFP at the scalp of patients with intractable epilepsy. The negative DC potentials preceded the prolonged EFP and may trigger seizures. The propagated and single negative DC potentials may be considered as CSD. Significance Recordings of CSD may serve as diagnostic and prognostic monitoring tools in epilepsy.

A newborn infant has an extraordinarily vulnerable and immature central nervous system, which is undergoing rapid structural and functional development. As these infants are pre-verbal and their neurological systems are immature, assessing accurately and treating effectively procedure-related pain is a significant challenge. The nociceptive signals caused by the pain are accompanied by changes in regional blood oxygenation and neuronal activity in the infant’s brain. In this study, we developed a dual-mode Near-Infrared Spectroscopy (NIRS) and electroencephalography (EEG) monitor that can measure regional brain oxygenation and neuronal activity concurrently (safe and non-invasive). The neuronal activity is measured by an innovative low-noise EEG amplifier in both conventional and ultra-low frequency bandwidths. This multimodal recording allows us to investigate the coupling of neuronal activity and the neurovascular system as never before. NIRS and EEG electrodes are miniaturized and unified in one sensor. This modification facilitates the use of a NIRS/EEG device for recording from neonatal subjects. Ten infants, born between 27-35 weeks gestational age, are being recruited from the NICU at BCWH. They are monitored during a single, routine blood draw required for clinical care. In this experiment, we investigate the change of cerebral hemodynamic across 3 phases of blood collection, baseline, heel lance, recovery. Variation of blood flow accompanied with the slow shift of EEG has been detected during the pain stimulus phase. Additionally, the increase of gamma-band correlated to a rise in blood flow is also observed

Electroencephalography (EEG) and cerebral near-infrared spectroscopy (NIRS) are both well-known monitoring methods to quantify cerebral neurophysiology and hemodynamics states of the brain. A stable regulatory system operates to guarantee sufficient spatial and temporal distribution of energy substrates for ongoing neuronal activity. Most EEG signals are associated with the neural activity of an enormous number of neurons that are interconnected and firing concurrently. The conventional EEG bandwidth is 0.16Hz to 70Hz. In this study, the EEG recording bandwidth is extended in low frequency (0.016Hz to 70Hz) by using a novel EEG amplifier. We aimed to investigate the low-frequency EEG and brain tissue deoxygenation by using novel multi-modal measurements. We used combined NIRS and EEG measurements for estimating the electrophysiological activity and hemodynamic changes in the adult human forehead during a hypoxic breathing condition. For the experiment, an altitude simulation kit was used to restrict the concentration of oxygen in the air that was inhaled by the subjects. The hypoxic breathing conditions led to variations in CO2 concentration (pCO2). Prolong (low-frequency) EEG signal shift, accompanied by an increase of deoxygenated hemoglobin during simulated hypoxic breathing were observed in this experiment.

Near-Infrared Spectroscopy (NIRS) is a non-invasive technique, extensively used to monitor the hemodynamic variations in cerebral neuronal tissues. For cerebral NIRS, the back-scattering probe is more prevailing, in which an incident beam is diffused, and only a slight fraction of the source optical energy reaches the light detectors. Multiplexing in the time domain is the conventional method used to distinguish the optical density of each NIR source at the receiver site. Even though time-multiplexing is straightforward and convenient, the ambient light can significantly contaminate the NIR beams during the sampling-path from the source to the detector. In this work, we present a novel method based on frequency division multiplexing (FDM) to overcome the interference of ambient light even without an external optical filter. The method proposes to modulate the NIR source intensities by using specific carrier frequencies distinct from the dominant frequency components of ambient light intensity. By modulating the intensity of each NIR source, and applying them at their specific frequency channels, the receiver is capable of distinguishing the received optical signals based on their frequency channel. Because the frequency channels are adjusted at distinct dominant frequency components of the ambient intensity, the latter ambient noise can be filtered out instantly. The method has been implemented by using electronic circuit design and evaluated both by numerical simulation and experimental measurements. The signal to noise ratio (SNR) has been improved at least by 45dB.

According to the WHO, 15,000 children under five years are dying every day from preventable causes with 80% of these children being born in low-income countries. Portable optical medical diagnostic devices can help physicians, nurses and untrained health workers to objectively identify children who are at a higher risk of dying. In the last 2 years, we collected the oxygenation values of the brachioradialis muscle, using a commercial Near Infrared Spectroscopy (NIRS) device, in 200 children under 5 years admitted in two hospitals in Uganda. Data revealed that the tissue oxygen saturation decrease during a vascular occlusion predicts children at higher risk better than other vital signs (SpO2, respiration rate, heart rate and temperature). Based on these results, we designed a low cost Continuous Wave Spatially Resolved NIRS device controlled by a smartphone in order to extend our study to a larger population and confirm our observation. The total cost of this device (excluding the smartphone) is less than $100. The preliminary tests suggest a significant potential of our low cost mobile NIRS device and oxygenation values closely matching those reported by the best device on the market.

Spreading depression (SD), a slow propagating depolarization wave, plays an important role in pathophysiology of different neurological disorders. Yet, research into SD-related disorders has been hampered by the lack of non-invasive recording techniques of SD. Here we compared the manifestations of SD in continuous non-invasive electroencephalogram (EEG) recordings to invasive electrocorticographic (ECoG) recordings in order to obtain further insights into generator structures and electrogenic mechanisms of surface recording of SD. SD was induced by KCl application and simultaneous SD recordings were performed by scalp EEG as well as ECoG electrodes of somatosensory neocortex of rats using a novel homemade EEG amplifier, AgCl recording electrodes, and high chloride conductive gel. Different methods were used to analyze the data; including the spectrogram, bi-spectrogram, pattern distribution, relative spectrum power, and multivariable Gaussian fit analysis. The negative direct current (DC) shifts recorded by scalp electrodes exhibited a high homogeneity to those recorded by ECoG electrodes. Furthermore, this novel method of recording and analysis was able to separate SD recorded by scalp electrodes from non-neuronal DC shifts induced by other potential generators, such as the skin, muscles, arteries, dura, etc. These data suggest a novel application for continuous non-invasive monitoring of DC potential changes, such as SD. Non-invasive monitoring of SD would allow early intervention and improve outcome in SD-related neurological disorders.

Decomposing the EMG signal and determining fiber’s motor units is a challenging work in this area. The high amounts of resident noise and artifacts distort the signal. But, the decomposition gets more complicated, when we have motor unit action potential MUAP, interference during one channel recording. Considering this distortion, recognition and separation of motor units from each other, and determining the degree of their membership to each fiber gives valuable information. In this paper. First, motor units are determined and separated according to a set of filters similar to Gabor filters, then by extracting some different time-frequency and morphology features, the feature space will be determined. In the next step, the number of clusters which are the number of fibers will be determined. The clustering method used for this purpose is FCM clustering method. One of the innovations of the proposed method in this study is using an algorithm which improves the accuracy of the decomposition. This algorithm employs the membership information of each motor unit in fuzzy clustering along with the feature selection using mutual information of each motor unit. The results indicate 7.3% improvement while decreasing computational costs.

We present an automated method for decomposing EMG signals into their components, motor-unit action-potential (MUAP) trains based on short time Fourier transform STFT and wavelet transform. Since the number of MUAP classes composing the EMG signal, the number of MUAP's per class, their firing pattern, and the expected shape of the MUAP waveforms are unknown, the decomposition of real EMG signals into their constituent MUAP's and their classification into groups of similar shapes is a typical case of an unsupervised learning pattern recognition problem. The method is able to handle single-or multi-channel signals, recorded by concentric needle electrodes during low and moderate levels of muscular contraction. The method uses empirical features in STFT transform, shape and template of MU and CWT in order to decompose the signal to its original MUAP. Also the discrete wavelet transform has been acquired in early steps in order to eliminate the level of low amplitude noise in signal. We compare the output of the automated algorithm with manual decomposition and results seems quiet acceptable. The average success rate for the FCM with wavelet coefficients as features was 91.01 %.