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BackgroundThis study assessed the accuracy of AlwaysBP (test) in blood pressure (BP) measurement compared with the standard cuff-based manual method of brachial BP measurement (reference). AlwaysBP is a smartphone software that estimates systolic BP (SBP) and diastolic BP (DBP) based on the pulse transit time (PTT). The PTT was calculated from the finger photoplethysmogram using the camera and from seismocardiogram using the inertial measurement unit sensor of a commercially available smartphone.MethodsA total of 87 participants without known cardiovascular disease were enrolled in the study. The primary analysis aimed to evaluate the accuracy of the test method compared with the reference method in the entire study population. The secondary analysis was performed to confirm the stability of the test method for up to 4 weeks in 15 subjects. At enrollment, the gender, arm circumference, and BP distribution were considered according to the current guidelines.ResultsAmong the study subjects, 45 (51.7%) individuals were male and the average age was 35.6 ± 10.4 years. Hypertension was diagnosed in 14 subjects (16.1%) prior to the study. The mean test and reference SBP were 120.0 ± 18.8 and 118.7 ± 20.2 mmHg, respectively, and the difference was 1.2 ± 7.1 mmHg (r = 0.935, P < 0.001). The mean test and reference DBP were 80.1 ± 12.6 and 81.1 ± 14.4 mmHg, respectively, and the difference was −1.0 ± 6.0 mmHg (r = 0.911, P < 0.001). At the 4-week time point, the differences between SBP and DBP were 0.1 ± 8.8 and −2.4 ± 7.6 mmHg.ConclusionsThe AlwaysBP software exhibited acceptable accuracy regarding SBP and DBP measurement compared with the standard BP measurement method. This technology can be easily applied in everyday life and may improve the general population’s awareness of hypertension, thus helping to control it.

In this study, we examined the phase locking value (PLV) for seizure prediction, particularly, in the gamma frequency band. We prepared simulation data and 65 clinical cases of seizure. In addition, various filtering algorithms including bandpass filtering, empirical mode decomposition, multivariate empirical mode decomposition and noise-assisted multivariate empirical mode decomposition (NA-MEMD) were used to decompose spectral components from the data. Moreover, in the case of clinical data, the PLVs were used to classify between interictal and preictal stages using a support vector machine. The highest PLV was achieved with NA-MEMD with 0-dB white noise algorithm (0.9988), which exhibited statistically significant differences compared to other filtering algorithms. Moreover, the classification rate was the highest for the NA-MEMD with 0-dB algorithm (83.17%). In terms of frequency components, examining the gamma band resulted in the highest classification rates for all algorithms, compared to other frequency bands such as theta, alpha, and beta bands. We found that PLVs calculated with the NA-MEMD algorithm could be used as a potential biological marker for seizure prediction. Moreover, the gamma frequency band was useful for discriminating between interictal and preictal stages.

Objective Dexmedetomidine (DEX) has been widely used as a sedative, acting as an α2-adrenergic agonist on autoreceptors, presynaptic receptors and postsynaptic receptors without risk of respiratory depression. Although consciousness impairment is closely related to disturbances of brain function in different frequency bands, the time-varying DEX effects on cortical activity in specific frequency bands has not yet been studied. Methods We used electroencephalography (EEG) to analyse differences in cerebral cortex activity between the awake and sedated states, using electromagnetic tomography (standardized low resolution electromagnetic tomography (sLORETA)) to localize multiple channel scalp recordings of cerebral electric activity to specific brain regions. Results The results revealed increased activity in the cuneus at delta-band frequencies, and in the posterior cingulate cortex at theta frequencies, during awake and sedated states induced by DEX at specific frequency bands. Differences in standardized low resolution cingulate gyrus were found in beta1 frequencies (13–18 Hz), and in the cuneus at gamma frequencies. Conclusion Cerebral cortical activity was significantly altered in various brain areas during DEX sedation, including parts of the default mode network and common midline core in different frequency ranges. These alterations may elucidate the mechanisms underlying DEX sedation.

Anesthesia is thought to be mediated by inhibiting the integration of information between different areas of the brain. Long-range thalamo-cortical interaction plays a critical role in inducing anesthesia-related unconsciousness. However, it remains unclear how this interaction change according to anesthetic depth. In this study, we aimed to investigate how different levels of anesthesia affect thalamo-frontal interactions. Prior to the experiment, electrodes were implanted to record local field potentials (LFPs). Isoflurane (ISO) was administered and LFPs were measured in rats from four different brain areas (left frontal, right frontal, left thalamus and right thalamus) at four different anesthesia levels: awake, deep (ISO 2.5 vol%), light (ISO 1 vol%) and recovery. Spectral granger causality (Spectral-GC) were calculated at the measured areas in accordance with anesthetic levels. Anesthesia led to a decrease in connectivity in the thalamo-frontal direction and an increase in connectivity in the frontal-thalamic direction. The changes in thalamo-frontal functional connectivity were prominent during deep anesthesia at high frequency bands. The connection strengths between the thalamus and the frontal area changed depending on the depth of anesthesia. The relationships between anesthetic levels and thalamo-frontal activity may shed light on the neural mechanism by which different levels of anesthesia act.

Hypertension is a well-known chronic disease that causes complications such as cardiovascular diseases or stroke, and thus needs to be continuously managed by using a simple system for measuring blood pressure. The existing method for measuring blood pressure uses a wrapping cuff, which makes measuring difficult for patients. To address this problem, cuffless blood pressure measurement methods that detect the peak pressure via signals measured using photoplethysmogram (PPG) and electrocardiogram (ECG) sensors and use it to calculate the pulse transit time (PTT) or pulse wave velocity (PWV) have been studied. However, a drawback of these methods is that a user must be able to recognize and establish contact with the sensor. Furthermore, the peak of the PPG or ECG cannot be detected if the signal quality drops, leading to a decrease in accuracy. In this study, a chair-type system that can monitor blood pressure using polyvinylidene fluoride (PVDF) films in a nonintrusive manner to users was developed. The proposed method also uses instantaneous phase difference (IPD) instead of PTT as the feature value for estimating blood pressure. Experiments were conducted using a blood pressure estimation model created via an artificial neural network (ANN), which showed that IPD could estimate more accurate readings of blood pressure compared to PTT, thus demonstrating the possibility of a nonintrusive blood pressure monitoring system.

Objective: In this study, electroencephalography data of imagined words were classified using four different feature extraction approaches. Eight subjects were recruited for the recording of imagination with five different words, namely; “go,” “back,” “left,” “right,” and “stop.” Methods: One hundred trials for each word were recorded for both imagination and perception, although this study utilized only imagination data. Two different connectivity methods were applied, namely; a covariance-based and a maximum linear cross-correlation-based connectivity measure. These connectivity measures were further computed to extract the phase-only data as an additional method of feature extraction. In addition, four different channel selections were used. The final connectivity matrix from each of the four methods was vectorized and used as the feature vector for the classifier. To classify EEG data, a sigmoid activation function-based linear extreme learning machine was used. Result and Significance: We achieved a maximum classification rate of 40.30% ( p $ 0.007) and 87.90% ( p $ 0.003) in multiclass (five classes) and binary settings, respectively. Thus, our results suggested that EEG responses to imagined speech could be successfully classified using an extreme learning machine. Conclusion: This study involving the classification of imagined words can be a milestone contribution toward the development of practical brain–computer interface systems using silent speech.

Current electroencephalogram (EEG) based-consciousness monitoring technique is vulnerable to specific clinical conditions (eg, epilepsy and dementia). However, hemodynamics is the most fundamental and well-preserved parameter to evaluate, even under severe clinical situations. In this study, we applied near-infrared spectroscopy (NIRS) system to monitor hemodynamic change during ketamine-induced anesthesia to find its correlation with the level of consciousness. Oxy-hemoglobin (OHb) and deoxy-hemoglobin concentration levels were continuously acquired throughout the experiment, and the reflectance ratio between 730 and 850 nm was calculated to quantify the hemodynamic changes. The results showed double peaks of OHb concentration change during ketamine anesthesia, which seems to be closely related to the consciousness state of the rat. This finding suggests the possibility of NIRS based-hemodynamic monitoring as a supplementary parameter for consciousness monitoring, compensating drawbacks of EEG signal based monitoring.

Objective: Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Approach: Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. Main results: HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Significance: Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

Delirium is an important syndrome in intensive care unit (ICU) patients, however, its characteristics are still unclear. Many evidences showed that this syndrome can be related to the autonomic instability. In this study, we aimed to investigate the possible alterations of autonomic nervous system (ANS) in delirium patients in ICU. Electrocardiography (ECG) of every ICU patient was measured during routine daily ICU care, and the data were gathered to evaluate the heart rate variability (HRV). HRV of total 60 patients were analyzed in time, frequency and non-linear domains. As a result, we found that heart rates of delirium patients were more variable and irregular than non-delirium patients. These findings may facilitate early detection and prevention of delirium in ICU.

Multimodal features of structural and functional magnetic resonance imaging (MRI) of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE) and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001) accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function) support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.

In this paper, we propose a system for inferring the pinch-to-zoom gesture using surface EMG (Electromyography) signals in real time. Pinch-to-zoom, which is a common gesture in smart devices such as an iPhone or an Android phone, is used to control the size of images or web pages according to the distance between the thumb and index finger. To infer the finger motion, we recorded EMG signals obtained from the first dorsal interosseous muscle, which is highly related to the pinch-to-zoom gesture, and used a support vector machine for classification between four finger motion distances. The powers which are estimated by Welch’s method were used as feature vectors. In order to solve the multiclass classification problem, we applied a one-versus-one strategy, since a support vector machine is basically a binary classifier. As a result, our system yields 93.38% classification accuracy averaged over six subjects. The classification accuracy was estimated using 10-fold cross validation. Through our system, we expect to not only develop practical prosthetic devices but to also construct a novel user experience (UX) for smart devices.

The heart rate (HR) is one of the important indicators for observing the patient condition. Therefore, many estimation techniques for acquiring heart rate have been developed. The photoplethysmography (PPG) and electrocardiography (ECG) are the common measurement techniques for estimating the heart rate. However, they should contact on the human skin in order to estimate the accurate heart rate. In this paper, we propose a non-contact robust heart rate measurement method using a webcam device. In addition, we evaluated the performance of the proposed algorithm in comparison with other algorithms.

The thalamus is thought to relay peripheral sensory information to the somatosensory cortex in the parietal lobe. Long-range thalamo-parietal interactions play an important role in inducing the effect of anesthetic. However, whether these interaction changes vary with different levels of anesthesia is not known. In the present study, we investigated the influence of different levels of isoflurane-induced anesthesia on the functional connectivity between the thalamus and the parietal region. Microelectrodes were implanted in rats to record local field potentials (LFPs). The rats underwent different levels of isoflurane anesthesia [deep anesthesia: isoflurane (ISO) 2.5 vol%, light anesthesia (ISO 1 vol%), awake, and recovery state] and LFPs were recorded from four different brain areas (left parietal, right parietal, left thalamus, and right thalamus). Partial directed coherence (PDC) was calculated for these areas. With increasing depth of anesthesia, the PDC in the thalamus-to-parietal direction was significantly increased mainly in the high frequency ranges; however, in the parietal-to-thalamus direction, the increase was mainly in the low frequency band. For both directions, the PDC changes were prominent in the alpha frequency band. Functional interactions between the thalamus and parietal area are augmented proportionally to the anesthesia level. This relationship may pave the way for better understanding of the neural processing of sensory inputs from the periphery under different levels of anesthesia.

We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis.

We aimed to investigate experimentally how anesthetic levels affect cerebral metabolism measured by near-infrared spectroscopy (NIRS) and to identify a robust marker among NIRS parameters to discriminate various stages of anesthetic depth in rats under isoflurane anesthesia. In order to record the hemodynamic changes and local field potential (LFP) in the brain, fiber-optic cannulae and custom-made microelectrodes were implanted in the frontal cortex of the skull. The NIRS and LFP signals were continuously monitored before, during and after isoflurane anesthesia. As isoflurane concentration is reduced, the level of oxyhemoglobin and total hemoglobin concentrations of the frontal cortex decreased gradually, while deoxyhemoglobin increased. The reflectance ratio between 730nm and 850nm and burst suppression ratio (BSR) correspond similarly with the change of oxyhemoglobin during the variation of isoflurane concentration. These results suggest that NIRS signals in addition to EEG may provide a possibility of developing a new anesthetic depth index. (C) 2016 Optical Society of America

Today, the developing signal processing technology leads to the medical discoveries as well as the fast and elaborate communication. Especially, neurological findings on brain connectivity are one of the interesting issues. However, in spite of the effort to study the brain connectivity by the neuroscientists and engineers, a large part of brain functions are unknown. Among them, the consciousness mechanism in the anesthesia is one hot topic in the medical fields. Although the many researchers investigate the mechanism of loss of consciousness (LOC), the feasible cause for that phenomenon is unknown. In order to evaluate the state of unconsciousness by the anesthesia, the analysis of the brain connectivity is needed. The Granger causality (GC) is one of the algorithms for extracting brain connectivity using electroencephalogram (EEG). It indicates the information flows between two different brain regions whether they are connected physically or not. In this paper, we investigate brain connectivity which is based on the GC between the brain regions of rats depending on concentration levels of isoflurane that is widely used for inhalation anesthetic in human medicine.