Blood Pressure Measurement Based on the Camera and Inertial Measurement Unit of a Smartphone: Instrument Validation Study.

Background: Even though several mobile apps that can measure blood pressure have been developed, the data about the accuracy of these apps are limited. Objective: We assessed the accuracy of AlwaysBP (test) in blood pressure measurement compared with the standard, cuff-based, manual method of brachial blood pressure measurement (reference). Methods: AlwaysBP is a smartphone software that estimates systolic blood pressure (SBP) and diastolic blood pressure (DBP) based on pulse transit time (PTT). PTT was calculated with a finger photoplethysmogram and seismocardiogram using, respectively, the camera and inertial measurement unit sensor of a commercially available smartphone. After calculating PTT, SBP and DBP were estimated via the Bramwell-Hill and Moens-Korteweg equations. A calibration process was carried out 3 times for each participant to determine the input parameters of the equations. This study was conducted from March to August 2021 at Chungnam National University Sejong Hospital with 87 participants aged between 19 and 70 years who met specific conditions. The primary analysis aimed to evaluate the accuracy of the test method compared with the reference method for the entire study population. The secondary analysis was performed to confirm the stability of the test method for up to 4 weeks in 15 participants. At enrollment, gender, arm circumference, and blood pressure distribution were considered according to current guidelines. Results: Among the 87 study participants, 45 (52%) individuals were male, and the average age was 35.6 (SD 10.4) years. Hypertension was diagnosed in 14 (16%) participants before this study. The mean test and reference SBPs were 120.0 (SD 18.8) and 118.7 (SD 20.2) mm Hg, respectively (difference: mean 1.2, SD 7.1 mm Hg). The absolute differences between the test and reference SBPs were <5, <10, and <15 mm Hg in 57.5% (150/261), 84.3% (220/261 ), and 94.6% (247/261) of measurements. The mean test and reference DBPs were 80.1 (SD 12.6) and 81.1 (SD 14.4) mm Hg, respectively (difference: mean -1.0, SD 6.0 mm Hg). The absolute differences between the test and reference DBPs were <5, <10, and <15 mm Hg in 75.5% (197/261), 93.9% (245/261), and 97.3% (254/261) of measurements, respectively. The secondary analysis showed that after 4 weeks, the differences between SBP and DBP were 0.1 (SD 8.8) and -2.4 (SD 7.6) mm Hg, respectively. Conclusions: AlwaysBP exhibited acceptable accuracy in SBP and DBP measurement compared with the standard measurement method, according to the Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization protocol criteria. However, further validation studies with a specific validation protocol designed for cuffless blood pressure measuring devices are required to assess clinical accuracy. This technology can be easily applied in everyday life and may improve the general population's awareness of hypertension, thus helping to control it.

Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural Networks.

Hypertension is a chronic disease that kills 7.6 million people worldwide annually. A continuous blood pressure monitoring system is required to accurately diagnose hypertension. Here, a chair-shaped ballistocardiogram (BCG)-based blood pressure estimation system was developed with no sensors attached to users. Two experimental sessions were conducted with 30 subjects. In the first session, two-channel BCG and blood pressure data were recorded for each subject. In the second session, the two-channel BCG and blood pressure data were recorded after running on a treadmill and then resting on the newly developed system. The empirical mode decomposition algorithm was used to remove noise in the two-channel BCG, and the instantaneous phase was calculated by applying a Hilbert transform to the first intrinsic mode functions. After training a convolutional neural network regression model that predicts the systolic and diastolic blood pressures (SBP and DBP) from the two-channel BCG phase, the results of the first session (rest) and second session (recovery) were compared. The results confirmed that the proposed model accurately estimates the rapidly rising blood pressure in the recovery state. Results from the rest sessions satisfied the Association for the Advancement of Medical Instrumentation (AAMI) international standards. The standard deviation of the SBP results in the recovery session exceeded 0.7.

The Effects of Transcranial Direct Current Stimulation on the Cognitive and Behavioral Changes After Electrode Implantation Surgery in Rats.

Postoperative delirium can lead to increased morbidity and mortality, and may even be a potentially life-threatening clinical syndrome. However, the neural mechanism underlying this condition has not been fully understood and there is little knowledge regarding potential preventive strategies. To date, investigation of transcranial direct current stimulation (tDCS) for the relief of symptoms caused by neuropsychiatric disorders and the enhancement of cognitive performance has led to promising results. In this study, we demonstrated that tDCS has a possible effect on the fast recovery from delirium in rats after microelectrode implant surgery, as demonstrated by postoperative behavior and neurophysiology compared with sham stimulation. This is the first study to describe the possible effects of tDCS for the fast recovery from delirium based on the study of both electroencephalography and behavioral changes. Postoperative rats showed decreased attention, which is the core symptom of delirium. However, anodal tDCS over the right frontal area immediately after surgery exhibited positive effects on acute attentional deficit. It was found that relative power of theta was lower in the tDCS group than in the sham group after surgery, suggesting that the decrease might be the underlying reason for the positive effects of tDCS. Connectivity analysis revealed that tDCS could modulate effective connectivity and synchronization of brain activity among different brain areas, including the frontal cortex, parietal cortex, and thalamus. It was concluded that anodal tDCS on the right frontal regions may have the potential to help patients recover quickly from delirium.

Effects of virtual reality immersive training with computerized cognitive training on cognitive function and activities of daily living performance in patients with acute stage stroke: A preliminary randomized controlled trial.

OBJECTIVE: The purpose of this study was to investigate the impact of virtual reality immersive training with computerized cognitive training on the cognitive function and activity of daily living in patients with acute stroke. METHOD: We included 42 patients with acute stage stroke from C hospital in Sungnam from May, 2017 to September, 2017. The patients were randomly selected and divided into the experimental (n = 21) and control (n = 21) group. The experimental group performed virtual reality training, including Head Mount Display with computerized cognitive therapy, and the control group performed computerized cognitive therapy. Both groups trained for 30 minutes a day 5 times a week; the intervention lasted 4 weeks. To evaluate the improvement in each group, pre-post-test evaluation was conducted using the Loewenstein Occupational Therapy Cognitive Assessment and Computerized Neurocognitive Function Test for cognitive function, and Functional Independent Measure for activities of daily living. RESULTS: Attention and memory in cognitive function and activity of daily living performance were improved in the both groups. CONCLUSION: Virtual reality immersive training might be an affordable approach for cognitive function and activity of daily living performance recovery for patients with acute stroke.

A Chair-Based Unconstrained/Nonintrusive Cuffless Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram.

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.

Multiclass Classification of Word Imagination Speech with Hybrid Connectivity Features.

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'.

The changes of cerebral hemodynamics during ketamine induced anesthesia in a rat model.

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.

Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

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.

Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine.

Virtual reality (VR) is a computer technique that creates an artificial environment composed of realistic images, sounds, and other sensations. Many researchers have used VR devices to generate various stimuli, and have utilized them to perform experiments or to provide treatment. In this study, the participants performed mental tasks using a VR device while physiological signals were measured: a photoplethysmogram (PPG), electrodermal activity (EDA), and skin temperature (SKT). In general, stress is an important factor that can influence the autonomic nervous system (ANS). Heart-rate variability (HRV) is known to be related to ANS activity, so we used an HRV derived from the PPG peak interval. In addition, the peak characteristics of the skin conductance (SC) from EDA and SKT variation can also reflect ANS activity; we utilized them as well. Then, we applied a kernel-based extreme-learning machine (K-ELM) to correctly classify the stress levels induced by the VR task to reflect five different levels of stress situations: baseline, mild stress, moderate stress, severe stress, and recovery. Twelve healthy subjects voluntarily participated in the study. Three physiological signals were measured in stress environment generated by VR device. As a result, the average classification accuracy was over 95% using K-ELM and the integrated feature (IT = HRV + SC + SKT). In addition, the proposed algorithm can embed a microcontroller chip since K-ELM algorithm have very short computation time. Therefore, a compact wearable device classifying stress levels using physiological signals can be developed.

Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine.

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.

Changes in brain activation during sedation induced by dexmedetomidine.

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.

Differential modulation of thalamo-parietal interactions by varying depths of isoflurane anesthesia.

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.

EEG-Based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition.

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.

Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis.

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.

Monitoring cerebral oxygenation and local field potential with a variation of isoflurane concentration in a rat model.

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.

Changes in thalamo-frontal interaction under different levels of anesthesia in rats.

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 2.5vol%), light (ISO 1vol%) 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.

A real-time pinch-to-zoom motion detection by means of a surface EMG-based human-computer interface.

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.