The middle-aged and older Chinese adults' health using actigraphy in Taiwan (MOCHA-T): protocol for a multidimensional dataset of health and lifestyle.

BACKGROUND AND OBJECTIVES: Older adults keep transforming with Baby Boomers and Gen Xers being the leading older population. Their lifestyle, however, is not well understood. The middle-aged and older Chinese adults' health using actigraphy in Taiwan (MOCHA-T) collected both objective and subjective data to depict the health and lifestyle of this population. The objectives, design, and measures of the MOCHA-T study are introduced, and the caveats and future directions related to the use of the data are presented. METHODS: People aged 50 and over were recruited from the community, with a subset of women aged 45-49 invited to supplement data on menopause and aging. Four instruments (i.e., self-reported questionnaires, diary, wrist actigraphy recorder, and GPS) were used to collect measures of sociodemographic, health, psychosocial, behavioral, temporal, and spatial data. RESULTS: A total of 242 participants who returned the informed consent and questionnaires were recruited in the MOCHA-T study. Among them, 94.6%, 95.0%, and 25.2% also completed the diary, actigraphy, and GPS data, respectively. There was almost no difference in sociodemographic characteristics between those with and without a completed diary, actigraphy, and GPS data, except for age group and educational level for those who returned completed actigraphy data. CONCLUSION: The MOCHA-T study is a multidimensional dataset that allows researchers to describe the health, behaviors, and lifestyle patterns, and their interactions with the environment of the newer generation of middle-aged and older adults in Taiwan. It can be compared with other countries with actigraphy and GPS-based lifestyle data of middle-aged and older adults in the future.

Collaborative Human-Computer Vision Operative Video Analysis Algorithm for Analyzing Surgical Fluency and Surgical Interruptions in Endonasal Endoscopic Pituitary Surgery: Cohort Study.

BACKGROUND: The endonasal endoscopic approach (EEA) is effective for pituitary adenoma resection. However, manual review of operative videos is time-consuming. The application of a computer vision (CV) algorithm could potentially reduce the time required for operative video review and facilitate the training of surgeons to overcome the learning curve of EEA. OBJECTIVE: This study aimed to evaluate the performance of a CV-based video analysis system, based on OpenCV algorithm, to detect surgical interruptions and analyze surgical fluency in EEA. The accuracy of the CV-based video analysis was investigated, and the time required for operative video review using CV-based analysis was compared to that of manual review. METHODS: The dominant color of each frame in the EEA video was determined using OpenCV. We developed an algorithm to identify events of surgical interruption if the alterations in the dominant color pixels reached certain thresholds. The thresholds were determined by training the current algorithm using EEA videos. The accuracy of the CV analysis was determined by manual review, and the time spent was reported. RESULTS: A total of 46 EEA operative videos were analyzed, with 93.6%, 95.1%, and 93.3% accuracies in the training, test 1, and test 2 data sets, respectively. Compared with manual review, CV-based analysis reduced the time required for operative video review by 86% (manual review: 166.8 and CV analysis: 22.6 minutes; P<.001). The application of a human-computer collaborative strategy increased the overall accuracy to 98.5%, with a 74% reduction in the review time (manual review: 166.8 and human-CV collaboration: 43.4 minutes; P<.001). Analysis of the different surgical phases showed that the sellar phase had the lowest frequency (nasal phase: 14.9, sphenoidal phase: 15.9, and sellar phase: 4.9 interruptions/10 minutes; P<.001) and duration (nasal phase: 67.4, sphenoidal phase: 77.9, and sellar phase: 31.1 seconds/10 minutes; P<.001) of surgical interruptions. A comparison of the early and late EEA videos showed that increased surgical experience was associated with a decreased number (early: 4.9 and late: 2.9 interruptions/10 minutes; P=.03) and duration (early: 41.1 and late: 19.8 seconds/10 minutes; P=.02) of surgical interruptions during the sellar phase. CONCLUSIONS: CV-based analysis had a 93% to 98% accuracy in detecting the number, frequency, and duration of surgical interruptions occurring during EEA. Moreover, CV-based analysis reduced the time required to analyze the surgical fluency in EEA videos compared to manual review. The application of CV can facilitate the training of surgeons to overcome the learning curve of endoscopic skull base surgery. TRIAL REGISTRATION: ClinicalTrials.gov NCT06156020; https://clinicaltrials.gov/study/NCT06156020.

The Potential Neurological Impact of Intraoperative Hyponatremia Using Histidine-Tryptophan-Ketoglutarate Cardioplegia Infusion in Adult Cardiac Surgery.

Background and Objectives: The relationship between histidine-tryptophan-ketoglutarate (HTK)-induced hyponatremia and brain injury in adult cardiac surgery patients is unclear. This study analyzed postoperative neurological outcomes after intraoperative HTK cardioplegia infusion. Materials and Methods: A prospective cohort study was conducted on 60 adult patients who underwent cardiac surgery with cardiopulmonary bypass. Of these patients, 13 and 47 received HTK infusion and conventional hyperkalemic cardioplegia, respectively. The patients' baseline characteristics, intraoperative data, brain injury markers, Mini-Mental State Examination (MMSE) scores, and quantitative electroencephalography (qEEG) data were collected. Electrolyte changes during cardiopulmonary bypass, the degree of hyponatremia, and any associated brain insults were evaluated. Results: The HTK group presented with acute hyponatremia during cardiopulmonary bypass, which was intraoperatively corrected through ultrafiltration and normal saline administration. Postoperative sodium levels were higher in the HTK group than in the conventional cardioplegia group. The change in neuron-specific enolase levels after cardiopulmonary bypass was significantly higher in the HTK group (p = 0.043). The changes showed no significant differences using case-control matching. qEEG analysis revealed a significant increase in relative delta power in the HTK group on postoperative day (POD) 7 (p = 0.018); however, no significant changes were noted on POD 60. The MMSE scores were not significantly different between the two groups on POD 7 and POD 60. Conclusions: HTK-induced acute hyponatremia and rapid correction with normal saline during adult cardiac surgeries were associated with a potential short-term but not long-term neurological impact. Further studies are required to determine the necessity of correction for HTK-induced hyponatremia.

Cognitive Function Deterioration After Cardiopulmonary Bypass: Can Intraoperative Optimal Cerebral Regional Tissue Oxygen Saturation Predict Postoperative Cognitive Function?

OBJECTIVE: Cognitive impairment is a common neurologic complication after cardiac surgery with cardiopulmonary bypass (CPB). This study evaluated postoperative cognitive function to determine predictors of cognitive dysfunction, including intraoperative cerebral regional tissue oxygen saturation (rSO2). DESIGN: A prospective observational cohort study. SETTING: At a single academic tertiary-care center. PARTICIPANTS: A total of 60 adults undergoing cardiac surgery with CPB from January to August 2021. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: All patients underwent Mini-Mental State Examination (MMSE) and quantified electroencephalography (qEEG) 1 day before cardiac surgery, 7 days postoperatively (POD7), and POD60. Intraoperative cerebral rSO2 was monitored continuously. For MMSE, no significant decrease in MMSE score was found on POD7 versus preoperatively (p = 0.09), but POD60 scores showed significant improvement compared with both preoperative (p = 0.02) and POD7 scores (p < 0.001). On qEEG, relative theta power on POD7 was increased versus preoperatively (p < 0.001), but it was decreased on POD60 (POD7 versus POD60, p < 0.001), and was close to preoperative data (p > 0.99). Baseline rSO2 was an independent factor for postoperative MMSE. Both baseline and mean rSO2 showed a significant influence in postoperative relative theta activity, whereas mean rSO2 was the only predictor for the theta-gamma ratio (p = 0.04). CONCLUSIONS: The MMSE in patients undergoing CPB declined at POD7 and recovered by POD60. Lower baseline rSO2 indicated a higher potential for MMSE decline at POD60. Inferior intraoperative mean rSO2 was related to higher postoperative relative theta activity and theta-gamma ratio, implying subclinical or further cognitive impairment.

Home-Use and Real-Time Sleep-Staging System Based on Eye Masks and Mobile Devices with a Deep Learning Model.

PURPOSE: Sleep is an important human activity. Comfortable sensing and accurate analysis in sleep monitoring is beneficial to many healthcare and medical applications. From 2020, owing to the COVID­19 pandemic that spreads between people when they come into close physical contact with one another, the willingness to go to hospital for receiving care has reduced; care-at-home is the trend in modern healthcare. Therefore, a home-use and real-time sleep-staging system is developed in this paper. METHODS: We developed and implemented a real-time sleep staging system that integrates a wearable eye mask for high-quality electroencephalogram/electrooculogram measurement and a mobile device with MobileNETV2 deep learning model for sleep-stage identification. In the experiments, 25 all-night recordings were acquired, 17 of which were used for training, and the remaining eight were used for testing. RESULTS: The averaged scoring agreements for the wake, light sleep, deep sleep, and rapid eye movement stages were 85.20%, 87.17%, 82.87%, and 89.30%, respectively, for our system compared with the manual scoring of PSG recordings. In addition, the mean absolute errors of four objective sleep measurements, including sleep efficiency, total sleep time, sleep onset time, and wake after sleep onset time were 1.68%, 7.56 min, 5.50 min, and 3.94 min, respectively. No significant differences were observed between the proposed system and manual PSG scoring in terms of the percentage of each stage and the objective sleep measurements. CONCLUSION: These experimental results demonstrate that our system provides high scoring agreements in sleep staging and unbiased sleep measurements owing to the use of EEG and EOG signals and powerful mobile computing based on deep learning networks. These results also suggest that our system is applicable for home-use real-time sleep monitoring.

Development of a rule-based automatic five-sleep-stage scoring method for rats.

BACKGROUND: Sleep problem or disturbance often exists in pain or neurological/psychiatric diseases. However, sleep scoring is a time-consuming tedious labor. Very few studies discuss the 5-stage (wake/NREM1/NREM2/transition sleep/REM) automatic fine analysis of wake-sleep stages in rodent models. The present study aimed to develop and validate an automatic rule-based classification of 5-stage wake-sleep pattern in acid-induced widespread hyperalgesia model of the rat. RESULTS: The overall agreement between two experts' consensus and automatic scoring in the 5-stage and 3-stage analyses were 92.32% (κ = 0.88) and 94.97% (κ = 0.91), respectively. Standard deviation of the accuracy among all rats was only 2.93%. Both frontal-occipital EEG and parietal EEG data showed comparable accuracies. The results demonstrated the performance of the proposed method with high accuracy and reliability. Subtle changes exhibited in the 5-stage wake-sleep analysis but not in the 3-stage analysis during hyperalgesia development of the acid-induced pain model. Compared with existing methods, our method can automatically classify vigilance states into 5-stage or 3-stage wake-sleep pattern with a promising high agreement with sleep experts. CONCLUSIONS: In this study, we have performed and validated a reliable automated sleep scoring system in rats. The classification algorithm is less computation power, a high robustness, and consistency of results. The algorithm can be implanted into a versatile wireless portable monitoring system for real-time analysis in the future.

Portable wireless neurofeedback system of EEG alpha rhythm enhances memory.

BACKGROUND: Effect of neurofeedback training (NFT) on enhancement of cognitive function or amelioration of clinical symptoms is inconclusive. The trainability of brain rhythm using a neurofeedback system is uncertainty because various experimental designs are used in previous studies. The current study aimed to develop a portable wireless NFT system for alpha rhythm and to validate effect of the NFT system on memory with a sham-controlled group. METHODS: The proposed system contained an EEG signal analysis device and a smartphone with wireless Bluetooth low-energy technology. Instantaneous 1-s EEG power and contiguous 5-min EEG power throughout the training were developed as feedback information. The training performance and its progression were kept to boost usability of our device. Participants were blinded and randomly assigned into either the control group receiving random 4-Hz power or Alpha group receiving 8-12-Hz power. Working memory and episodic memory were assessed by the backward digital span task and word-pair task, respectively. RESULTS: The portable neurofeedback system had advantages of a tiny size and long-term recording and demonstrated trainability of alpha rhythm in terms of significant increase of power and duration of 8-12 Hz. Moreover, accuracies of the backward digital span task and word-pair task showed significant enhancement in the Alpha group after training compared to the control group. CONCLUSIONS: Our tiny portable device demonstrated success trainability of alpha rhythm and enhanced two kinds of memories. The present study suggest that the portable neurofeedback system provides an alternative intervention for memory enhancement.

Polysomnography scoring-related training and quantitative assessment for improving interscorer agreement.

STUDY OBJECTIVES: To efficiently improve the scoring competency of scorers with varying levels of experience across regions in Taiwan, we developed a training program with a cloud-based polysomnography scoring platform to evaluate and improve interscorer agreement. METHODS: A total of 70 scorers from 34 sleep centers in Taiwan (job tenure: 0.5-39.0 years) completed a scoring test. All scorers scored a 742-epoch (30 s/epoch) overnight polysomnography recording of a patient with a moderate apnea-hypopnea index. Subsequently, 8 scoring experts delivered 8 interactive online lectures (each lasting 30 minutes). The training program included identifying scoring weaknesses, highlighting the latest scoring rules, and providing physicians' perspectives. Afterward, the scorers completed the second scoring test on the same participant. Changes in agreement from the first to second scoring test were identified. Sleep staging, sleep parameters, and respiratory events were considered for evaluating scoring agreement. RESULTS: The scorers' agreement in overall sleep stage scoring significantly increased from 74.6 to 82.3% (median score). The proportion of scorers with an agreement of ≥ 80% increased from 20.0% (14/70) to 58.6% (41/70) after the online training program. In addition, the scorers' agreement in overall respiratory-event scoring increased to 88.8% (median score) after training. The scorers with a job tenure of 2.0-4.9 years exhibited the highest level of improvement in overall sleep staging (their median agreement increased from 72.8 to 84.9%; P < .001). CONCLUSIONS: Our interactive online training program efficiently targeted the scorers' scoring weaknesses identified in the first scoring test, leading to substantial improvements in scoring proficiency. CITATION: Liao Y-S, Wu M-C, Li C-X, Lin W-K, Lin C-Y, Liang S-F. Polysomnography scoring-related training and quantitative assessment for improving interscorer agreement. J Clin Sleep Med. 2024;20(2):271-278.

Functional abnormalities in the cortical processing of sound complexity and musical consonance in schizophrenia: evidence from an evoked potential study.

BACKGROUND: Previous studies have demonstrated functional and structural temporal lobe abnormalities located close to the auditory cortical regions in schizophrenia. The goal of this study was to determine whether functional abnormalities exist in the cortical processing of musical sound in schizophrenia. METHODS: Twelve schizophrenic patients and twelve age- and sex-matched healthy controls were recruited, and participants listened to a random sequence of two kinds of sonic entities, intervals (tritones and perfect fifths) and chords (atonal chords, diminished chords, and major triads), of varying degrees of complexity and consonance. The perception of musical sound was investigated by the auditory evoked potentials technique. RESULTS: Our results showed that schizophrenic patients exhibited significant reductions in the amplitudes of the N1 and P2 components elicited by musical stimuli, to which consonant sounds contributed more significantly than dissonant sounds. Schizophrenic patients could not perceive the dissimilarity between interval and chord stimuli based on the evoked potentials responses as compared with the healthy controls. CONCLUSION: This study provided electrophysiological evidence of functional abnormalities in the cortical processing of sound complexity and music consonance in schizophrenia. The preliminary findings warrant further investigations for the underlying mechanisms.

Seed correlation analysis based on brain region activation for ADHD diagnosis in a large-scale resting state data set.

Background: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder of multifactorial pathogenesis, which is often accompanied by dysfunction in several brain functional connectivity. Resting-state functional MRI have been used in ADHD, and they have been proposed as a possible biomarker of diagnosis information. This study's primary aim was to offer an effective seed-correlation analysis procedure to investigate the possible biomarker within resting state brain networks as diagnosis information. Method: Resting-state functional magnetic resonance imaging (rs-fMRI) data of 149 childhood ADHD were analyzed. In this study, we proposed a two-step hierarchical analysis method to extract functional connectivity features and evaluation by linear classifiers and random sampling validation. Result: The data-driven method-ReHo provides four brain regions (mPFC, temporal pole, motor area, and putamen) with regional homogeneity differences as second-level seeds for analyzing functional connectivity differences between distant brain regions. The procedure reduces the difficulty of seed selection (location, shape, and size) in estimations of brain interconnections, improving the search for an effective seed; The features proposed in our study achieved a success rate of 83.24% in identifying ADHD patients through random sampling (saving 25% as the test set, while the remaining data was the training set) validation (using a simple linear classifier), surpassing the use of traditional seeds. Conclusion: This preliminary study examines the feasibility of diagnosing ADHD by analyzing the resting-state fMRI data from the ADHD-200 NYU dataset. The data-driven model provides a precise way to find reliable seeds. Data-driven models offer precise methods for finding reliable seeds and are feasible across different datasets. Moreover, this phenomenon may reveal that using a data-driven approach to build a model specific to a single data set may be better than combining several data and creating a general model.

A transition-constrained discrete hidden Markov model for automatic sleep staging.

BACKGROUND: Approximately one-third of the human lifespan is spent sleeping. To diagnose sleep problems, all-night polysomnographic (PSG) recordings including electroencephalograms (EEGs), electrooculograms (EOGs) and electromyograms (EMGs), are usually acquired from the patient and scored by a well-trained expert according to Rechtschaffen & Kales (R&K) rules. Visual sleep scoring is a time-consuming and subjective process. Therefore, the development of an automatic sleep scoring method is desirable. METHOD: The EEG, EOG and EMG signals from twenty subjects were measured. In addition to selecting sleep characteristics based on the 1968 R&K rules, features utilized in other research were collected. Thirteen features were utilized including temporal and spectrum analyses of the EEG, EOG and EMG signals, and a total of 158 hours of sleep data were recorded. Ten subjects were used to train the Discrete Hidden Markov Model (DHMM), and the remaining ten were tested by the trained DHMM for recognition. Furthermore, the 2-fold cross validation was performed during this experiment. RESULTS: Overall agreement between the expert and the results presented is 85.29%. With the exception of S1, the sensitivities of each stage were more than 81%. The most accurate stage was SWS (94.9%), and the least-accurately classified stage was S1 (<34%). In the majority of cases, S1 was classified as Wake (21%), S2 (33%) or REM sleep (12%), consistent with previous studies. However, the total time of S1 in the 20 all-night sleep recordings was less than 4%. CONCLUSION: The results of the experiments demonstrate that the proposed method significantly enhances the recognition rate when compared with prior studies.

Directions of Deep Brain Stimulation for Epilepsy and Parkinson's Disease.

BACKGROUND: Deep brain stimulation (DBS) is an effective treatment for movement disorders and neurological/psychiatric disorders. DBS has been approved for the control of Parkinson disease (PD) and epilepsy. OBJECTIVES: A systematic review and possible future direction of DBS system studies is performed in the open loop and closed-loop configuration on PD and epilepsy. METHODS: We searched Google Scholar database for DBS system and development. DBS search results were categorized into clinical device and research system from the open-loop and closed-loop perspectives. RESULTS: We performed literature review for DBS on PD and epilepsy in terms of system development by the open loop and closed-loop configuration. This study described development and trends for DBS in terms of electrode, recording, stimulation, and signal processing. The closed-loop DBS system raised a more attention in recent researches. CONCLUSION: We overviewed development and progress of DBS. Our results suggest that the closed-loop DBS is important for PD and epilepsy.

A novel application of the S-transform in removing powerline interference from biomedical signals.

Powerline interference always disturbs recordings of biomedical signals. Numerous methods have been developed to reduce powerline interference. However, most of these techniques not only reduce the interference but also attenuate the 60 Hz power of the biomedical signals themselves. In the present study, we applied the S-transform, which provides an absolute phase of each frequency in a multi-resolution time-frequency analysis, to reduce 60 Hz interference. According to results from an electrocardiogram (ECG) to which a simulated 60 Hz noise was added, the S-transform de-noising process restored a power spectrum identical to that of the original ECG coincident with a significant reduction in the 60 Hz interference. Moreover, the S-transform de-noised the signal in an intensity-independent manner when reducing the 60 Hz interference. In both a real ECG signal from the MIT database and natural brain activity contaminated with 60 Hz interference, the S-transform also displayed superior merit to a notch filter in the aspect of reducing noise and preserving the signal. Based on these data, a novel application of the S-transform for removing powerline interference is established.

Development of a human-computer collaborative sleep scoring system for polysomnography recordings.

The overnight polysomnographic (PSG) recordings of patients were scored by an expert to diagnose sleep disorders. Visual sleep scoring is a time-consuming and subjective process. Automatic sleep staging methods can help; however, the mechanism and reliability of these methods are not fully understood. Therefore, experts often need to rescore the recordings to obtain reliable results. Here, we propose a human-computer collaborative sleep scoring system. It is a rule-based automatic sleep scoring method that follows the American Academy of Sleep Medicine (AASM) guidelines to perform an initial scoring. Then, the reliability level of each epoch is analyzed based on physiological patterns during sleep and the characteristics of various stage changes. Finally, experts would only need to rescore epochs with a low-reliability level. The experimental results show that the average agreement rate between our system and fully manual scorings can reach 90.42% with a kappa coefficient of 0.85. Over 50% of the manual scoring time can be reduced. Due to the demonstrated robustness and applicability, the proposed approach can be integrated with various PSG systems or automatic sleep scoring methods for sleep monitoring in clinical or homecare applications in the future.

Automated recognition system to classify subcellular protein localizations in images of different cell lines acquired by different imaging systems.

Systemic analysis of subcellular protein localization (location proteomics) provides clues for understanding gene functions and physiological condition of the cells. However, recognition of cell images of subcellular structures highly depends on experience and becomes the rate-limiting step when classifying subcellular protein localization. Several research groups have extracted specific numerical features for the recognition of subcellular protein localization, but these recognition systems are restricted to images of single particular cell line acquired by one specific imaging system and not applied to recognize a range of cell image sources. In this study, we establish a single system for automated subcellular structure recognition to identify cell images from various sources. Two different sources of cell images, 317 Vero (http://gfp-cdna.embl.de) and 875 CHO cell images of subcellular structures, were used to train and test the system. When the system was trained by a single source of images, the recognition rate is high and specific to the trained source. The system trained by the CHO cell images gave high average recognition accuracy for CHO cells of 96%, but this was reduced to 46% with Vero images. When we trained the system using a mixture of CHO and Vero cell images, an average accuracy of recognition reached 86.6% for both CHO and Vero cell images. The system can reject images with low confidence and identify the cell images correctly recognized to avoid manual reconfirmation. In summary, we have established a single system that can recognize subcellular protein localizations from two different sources for location-proteomic studies. studies.

Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning.

Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.

Spike densities of the amygdala and neocortex reflect progression of kindled motor seizures.

Amygdala kindling is a common temporal lobe-like seizure model. In the present study, temporal and spectral analyses of the ictal period were investigated throughout amygdala kindling in response to different behavioral seizures. Right-side amygdala was kindled to induce epileptiform afterdischarges (ADs). ADs of both the frontal cortex and amygdala were analyzed. Powers of the low (0-9 Hz)- and high (12-30 Hz)-frequency bands in response to different behavioral seizures were calculated. Densities of upward and downward peaks of spikes, which reflected information of spike count and spike pattern, throughout kindle-induced ADs were calculated. Progression was seen in the temporal and spectral characteristics of amygdala-kindled ADs in response to behaviors. Numbers of significant differences of all 1-s AD segments between two Racine's seizure stages were significantly higher in upward and downward indexes of the temporal spike than those using the spectral method in both the amygdala and neocortex. Ability for distinguishing seizure stages was significantly higher in temporal spike density of amygdala ADs compared to those of frontal ADs. Our results showed that amygdala kindling caused spectrotemporal changes of activities in the amygdala and frontal cortex. The density of spike-related peaks had better distinguishability in response to behavioral seizures, particularly in a seizure zone of amygdala. The present study provides a new temporal index of spike's peak density to understand progression of motor seizures in the kindling process.

EEG-based assessment of driver cognitive responses in a dynamic virtual-reality driving environment.

Accidents caused by errors and failures in human performance among traffic fatalities have a high death rate and become an important issue in public security. They are mainly caused by the failures of the drivers to perceive the changes of the traffic lights or the unexpected conditions happening accidentally on the roads. In this paper, we devised a quantitative analysis for assessing driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-light experiments in a virtual-reality (VR) dynamic driving environment. The VR technique allows subjects to interact directly with the moving virtual environment instead of monotonic auditory and visual stimuli, thereby provides interactive and realistic tasks without the risk of operating on an actual machine. Independent component analysis (ICA) is used to separate and extract noise-free ERP signals from the multi-channel EEG signals. A temporal filter is used to solve the time-alignment problem of ERP features and principle component analysis (PCA) is used to reduce feature dimensions. The dimension-reduced features are then input to a self-constructing neural fuzzy inference network (SONFIN) to recognize different brain potentials stimulated by red/green/yellow traffic events, the accuracy can be reached 87% in average eight subjects in this visual-stimuli ERP experiment. It demonstrates the feasibility of detecting and analyzing multiple streams of ERP signals that represent operators' cognitive states and responses to task events.

Development and Evaluation of a Wearable Device for Sleep Quality Assessment.

OBJECTIVE: In this study, a wearable actigraphy recording device with low sampling rate (1 Hz) for power saving and data reduction and a high accuracy wake-sleep scoring method for the assessment of sleep were developed. METHODS: The developed actigraphy recorder was successfully applied to overnight recordings of 81 subjects with simultaneous polysomnography (PSG) measurements. The total length of recording reached 639.8 h. A wake-sleep scoring method based on the concept of movement density evaluation and adaptive windowing was proposed. Data from subjects with good (N = 43) and poor (N = 16) sleep efficiency (SE) in the range of 52.7-97.42% were used for testing. The Bland-Altman technique was used to evaluate the concordance of various sleep measurements between the manual PSG scoring and the proposed actigraphy method. RESULTS: For wake-sleep staging, the average accuracy, sensitivity, specificity, and kappa coefficient of the proposed system were 92.16%, 95.02%, 71.30%, and 0.64, respectively. For the assessment of SE, the accuracy of classifying the subject with good or poor SE reached 91.53%. The mean biases of SE, sleep onset time, wake after sleep onset, and total sleep time were -0.95%, 0.74 min, 2.84 min, and -4.3 min, respectively. CONCLUSION: These experimental results demonstrate the robustness and reliability of our method using limited activity information to estimate wake-sleep stages during overnight recordings. SIGNIFICANCE: The results suggest that the proposed wearable actigraphy system is practical for the in-home screening of objective sleep measurements and objective evaluation of sleep improvement after treatment.

Rapid Amygdala Kindling Causes Motor Seizure and Comorbidity of Anxiety- and Depression-Like Behaviors in Rats.

Amygdala kindling is a model of temporal lobe epilepsy (TLE) with convulsion. The rapid amygdala kindling has an advantage on quick development of motor seizures and for antiepileptic drugs screening. The rapid amygdala kindling causes epileptogenesis accompanied by an anxiolytic response in early isolation of rat pups or depressive behavior in immature rats. However, the effect of rapid amygdala kindling on comorbidity of anxiety- and depression-like behaviors is unexplored in adult rats with normal breeding. In the present study, 40 amygdala stimulations given within 2 days were applied in adult Wistar rats. Afterdischarge (AD) and seizure stage were recorded throughout the amygdala kindling. Anxiety-like behaviors were evaluated by the elevated plus maze (EPM) test and open field (OF) test, whereas depression-like behaviors were assessed by the forced swim (FS) and sucrose consumption (SC) tests. A tonic-clonic convulsion was provoked in the kindle group. Rapid amygdala kindling resulted in a significantly lower frequency entering an open area of either open arms of the EPM or the central zone of an OF, lower sucrose intake, and longer immobility of the FS test in the kindle group. Our results suggest that rapid amygdala kindling elicited severe motor seizures comorbid with anxiety- and depression-like behaviors.