Stage-independent, single lead EEG sleep spindle detection using the continuous wavelet transform and local weighted smoothing

Effective Diagnosis of Various Sleep Disorders by LEE Classifier: LightGBM-EOG-EEG

A novel method is proposed for diagnosing the sleep disorders of insomnia, narcolepsy, periodic leg movement syndrome, nocturnal frontal lobe epilepsy, rapid eye movement behavior disorder, and sleep-disordered breathing. We use the light gradient boosting decision tree model (LightGBM) for the classification of healthy controls and different sleep disorders. The proposed approach is evaluated on the publicly available CAP dataset of 108 subjects. The LightGBM classifier using only an electrooculogram (EOG) channel (L-EOG) achieves a seven-class classification accuracy of 83.3%. This performance improves to 93.3% with the LightGBM-EOG-EEG (LEE) classifier, which harnesses the combined strengths of EOG and electroencephalogram (EEG) channels. LEE classifier employs four binary classifiers that disambiguate the classes confused by the L-EOG classifier. An additional 1% improvement is achieved by applying a simple thresholding decision rule to the results of the LEE classifier, resulting in an overall accuracy of 94.4%, the best in the literature for the seven-class classification of sleep disorders.

Metabolic Signature of Insulin Resistance and Risk of Alzheimer's Disease

BACKGROUND

Substantial evidence supports the relationship between peripheral insulin resistance (IR) and the development of Alzheimer's disease (AD)-dementia. However, the mechanisms explaining these associations are only partly understood. We aimed to identify a metabolic signature of IR associated with the progression from mild cognitive impairment (MCI) to AD-dementia.

METHODS

This is a case-control study on 400 MCI subjects, free of type 2 diabetes, within the ACE cohort, including individuals ATN + and ATN-. After a median of 2.1 years of follow-up, 142 subjects converted to AD-dementia. IR was assessed using the homeostasis model assessment for insulin resistance (HOMA-IR). A targeted multiplatform approach profiled over 600 plasma metabolites. Elastic net penalized linear regression with 10-fold cross-validation was employed to select those metabolites associated with HOMA-IR. The prediction ability of the signature was assessed using support vector machine and performance metrics. The metabolic signature was associated with AD-dementia risk using a multivariable Cox regression model. Using counterfactual-based mediation analysis, we investigated the mediation role of the metabolic signature between HOMA-IR and AD-dementia. The metabolic pathways in which the metabolites were involved were identified using MetaboAnalyst.

RESULTS

The metabolic signature comprised 18 metabolites correlated with HOMA-IR. After adjustments by confounders, the signature was associated with increased AD-dementia risk (HR = 1.234; 95% CI = 1.019-1.494; p < .05). The metabolic signature mediated 35% of the total effect of HOMA-IR on AD-dementia risk. Significant metabolic pathways were related to glycerophospholipid and tyrosine metabolism.

CONCLUSIONS

We have identified a blood-based metabolic signature that reflects IR and may enhance our understanding of the biological mechanisms through which IR affects AD-dementia.

Flowering time responses to warming drive reproductive fitness in a changing Arctic

BACKGROUND AND AIMS

The Arctic is warming at an alarming rate, leading to earlier spring conditions and plant phenology. It is often unclear to what degree changes in reproductive fitness (flower, fruit and seed production) are a direct response to warming versus an indirect response through shifting phenology. The aim of this study was to quantify the relative importance of these direct and indirect pathways and project the net effects of warming on plant phenology and reproductive fitness under current and future climate scenarios.

METHODS

We used two long-term datasets on 12 tundra species in the Canadian Arctic as part of the International Tundra Experiment (ITEX). Phenology and reproductive fitness were recorded annually on tagged individual plants at both Daring Lake, Northwest Territories (64° 52' N, - 111° 35' W) and Alexandra Fiord, Nunavut (78° 49' N, - 75° 48' W). The plant species encompassed a wide taxonomic diversity across a range of plant functional types with circumpolar/boreal distributions. We used hierarchical Bayesian structural equation models to compare the direct and indirect effects of climate warming on phenology and reproductive fitness across species, sites and years.

KEY RESULTS

We found that warming, both experimental and ambient, drove earlier flowering across species, which led to higher numbers of flowers and fruits produced, reflecting directional phenotypic selection for earlier flowering phenology. Furthermore, this indirect effect of climate warming mediated through phenology was generally about two to three times stronger than the direct effect of climate on reproductive fitness. Under future climate predictions, individual plants showed a ~2- to 4.5-fold increase in their reproductive fitness (flower counts) with advanced flowering phenology.

CONCLUSIONS

Our results suggest that, on average, the benefits of early flowering, such as increased development time and subsequent enhanced reproductive fitness, might outweigh its risks. Overall, this work provides important insights into population-level consequences of phenological shifts in a warming Arctic over multi-decadal time scales.

The Ultra-Long-Term Sleep study: Design, rationale, data stability and user perspective

Sleep deprivation and poor sleep quality are significant societal challenges that negatively impact individuals' health. The interaction between subjective sleep quality, objective sleep measures, physical and cognitive performance, and their day-to-day variations remains poorly understood. Our year-long study of 20 healthy individuals, using subcutaneous electroencephalography, aimed to elucidate these interactions, assessing data stability and participant satisfaction, usability, well-being and adherence. In the study, 25 participants were fitted with a minimally invasive subcutaneous electroencephalography lead, with 20 completing the year of subcutaneous electroencephalography recording. Signal stability was measured using covariance of variation. Participant satisfaction, usability and well-being were measured with questionnaires: Perceived Ease of Use questionnaire, System Usability Scale, Headache questionnaire, Major Depression Inventory, World Health Organization 5-item Well-Being Index, and interviews. The subcutaneous electroencephalography signals remained stable for the entire year, with an average participant adherence rate of 91%. Participants rated their satisfaction with the subcutaneous electroencephalography device as easy to use with minimal or no discomfort. The System Usability Scale score was high at 86.3 ± 10.1, and interviews highlighted that participants understood how to use the subcutaneous electroencephalography device and described a period of acclimatization to sleeping with the device. This study provides compelling evidence for the feasibility of longitudinal sleep monitoring during everyday life utilizing subcutaneous electroencephalography in healthy subjects, showcasing excellent signal stability, adherence and user experience. The amassed subcutaneous electroencephalography data constitutes the largest dataset of its kind, and is poised to significantly advance our understanding of day-to-day variations in normal sleep and provide key insights into subjective and objective sleep quality.

Semi automatic quantification of REM sleep without atonia in natural sleep environment

Polysomnography, the gold standard diagnostic tool in sleep medicine, is performed in an artificial environment. This might alter sleep and may not accurately reflect typical sleep patterns. While macro-structures are sensitive to environmental effects, micro-structures remain more stable. In this study we applied semi-automated algorithms to capture REM sleep without atonia (RSWA) and sleep spindles, comparing lab and home measurements. We analyzed 107 full-night recordings from 55 subjects: 24 healthy adults, 28 Parkinson's disease patients (15 RBD), and three with isolated Rem sleep behavior disorder (RBD). Sessions were manually scored. An automatic algorithm for quantifying RSWA was developed and tested against manual scoring. RSWAi showed a 60% correlation between home and lab. RBD detection achieved 83% sensitivity, 79% specificity, and 81% balanced accuracy. The algorithm accurately quantified RSWA, enabling the detection of RBD patients. These findings could facilitate more accessible sleep testing, and provide a possible alternative for screening RBD.

A personalized semi-automatic sleep spindle detection (PSASD) framework

BACKGROUND

Sleep spindles are distinct electroencephalogram (EEG) patterns of brain activity that have been posited to play a critical role in development, learning, and neurological disorders. Manual scoring for sleep spindles is labor-intensive and tedious but could supplement automated algorithms to resolve challenges posed with either approaches alone.

NEW METHODS

A Personalized Semi-Automatic Sleep Spindle Detection (PSASD) framework was developed to combine the strength of automated detection algorithms and visual expertise of human scorers. The underlying model in the PSASD framework assumes a generative model for EEG sleep spindles as oscillatory components, optimized to EEG amplitude, with remaining signals distributed into transient and low-frequency components.

RESULTS

A single graphical user interface (GUI) allows both manual scoring of sleep spindles (model training data) and verification of automatically detected spindles. A grid search approach allows optimization of parameters to balance tradeoffs between precision and recall measures.

COMPARISON WITH EXISTING METHODS

PSASD outperformed DETOKS in F1-score by 19% and 4% on the DREAMS and P-DROWS-E datasets, respectively. It also outperformed YASA in F1-score by 25% in the P-DROWS-E dataset. Further benchmarking analysis showed that PSASD outperformed four additional widely used sleep spindle detectors in F1-score in the P-DROWS-E dataset. Titration analysis revealed that four 30-second epochs are sufficient to fine-tune the model parameters of PSASD. Associations of frequency, duration, and amplitude of detected sleep spindles matched those previously reported with automated approaches.

CONCLUSIONS

Overall, PSASD improves detection of sleep spindles in EEG data acquired from both younger healthy and older adult patient populations.

Effective diagnosis of sleep disorders using EEG and EOG signals

This work focuses on the diagnosis of various sleep disorders such as insomnia, narcolepsy, periodic leg movement, nocturnal frontal lobe epilepsy, bruxism, REM behavior disorder, and sleep-disordered breathing. We utilize SVM for classifying each of the sleep disorders from healthy controls. The proposed approach is evaluated on the publicly available CAP dataset comprising 108 overnight recordings from healthy controls and patients with sleep disorders. A single feature called gridded distribution entropy derived from Poincaré plots of EEG signal provides 100% accuracy in distinguishing healthy controls from each pathology, except insomnia and PLM. With the EOG channel, we are able to classify these two groups as well with 100% accuracy, demonstrating the effectiveness of EOG in disambiguating insomnia and PLM from controls.Clinical relevance- Diagnosis of sleep disorders is important to facilitate appropriate treatment. It is challenging due to the diverse nature and inter-subject variation of the physiological symptoms. Automated sleep disorder detection can improve cost efficiency and reduce variability.

Modality-Specific Feature Selection, Data Augmentation and Temporal Context for Improved Performance in Sleep Staging

This work attempts to design an effective sleep staging system, making the best use of the available signals, strategies, and features in the literature. It must not only perform well on different datasets comprising healthy and clinical populations but also achieve good accuracy in cross-dataset experiments. Toward this end, we propose a model comprising multiple binary classifiers in a hierarchical fashion, where, at each level, one or more of EEG, EOG, and EMG are selected to best differentiate between two sleep stages. The best set of 100 features is chosen out of all the features derived from selected signals. The class imbalance in data is addressed by random undersampling and boosting techniques with decision trees as weak learners. Temporal context and data augmentation are used to improve the performance. We also evaluate the performance of our model by training and testing on different datasets. We compare the results of five approaches: using only EEG, EEG+EOG, EEG+EMG+EOG, EEG+EMG, and selective modality with a specific combination of EEG, EMG, and/or EOG at each level. The best results are obtained by considering features from EEG+EMG+EOG at each hierarchical level. The proposed model achieves average accuracies of 83.1%, 90.0%, 84.4%, 82.1%, 81.5%, 79.9%, and 73.7% on Sleep-EDF, Exp Sleep-EDF, ISRUC-S1, S2 and S3, DRMS-SUB, and DRMS-PAT datasets, respectively. For all the datasets except DRMS-SUB, the proposed method outperforms all the state-of-the-art approaches. Cross-dataset performance exceeds 80% for all datasets except DRMS-PAT; independent of whether the test data is from normal subjects or patients.

Automatic sleep spindles identification and classification with multitapers and convolution

Sleep spindles are isolated transient surges of oscillatory neural activity present during sleep stages 2 and 3 in the nonrapid eye movement (NREM). They can indicate the mechanisms of memory consolidation and plasticity in the brain. Spindles can be identified across cortical areas and classified as either slow or fast. There are spindle transients across different frequencies and power, yet most of their functions remain a mystery. Using several electroencephalogram (EEG) databases, this study presents a new method, called the "spindles across multiple channels" (SAMC) method, for identifying and categorizing sleep spindles in EEGs during the NREM sleep. The SAMC method uses a multitapers and convolution (MT&C) approach to extract the spectral estimation of different frequencies present in sleep EEGs and graphically identify spindles across multiple channels. The characteristics of spindles, such as duration, power, and event areas, are also extracted by the SAMC method. Comparison with other state-of-the-art spindle identification methods demonstrated the superiority of the proposed method with an agreement rate, average positive predictive value, and sensitivity of over 90% for spindle classification across the three databases used in this paper. The computing cost was found to be, on average, 0.004 seconds per epoch. The proposed method can potentially improve the understanding of the behavior of spindles across the scalp and accurately identify and categories sleep spindles.

A comparative analysis of sleep spindle characteristics of sleep-disordered patients and normal subjects

Spindles differ in density, amplitude, and frequency, and these variations reflect different physiological processes. Sleep disorders are characterized by difficulty in falling asleep and maintaining sleep. In this study, we proposed a new spindle wave detection algorithm, which was more effective compared with traditional detection algorithms such as wavelet algorithm. Besides, we recorded EEG data from 20 subjects with sleep disorders and 10 normal subjects, and then we compared the spindle characteristics of sleep-disordered subjects and normal subjects (those without any sleep disorder) to assess the spindle activity during human sleep. Specifically, we scored 30 subjects on the Pittsburgh Sleep Quality Index and then analyzed the association between their sleep quality scores and spindle characteristics, reflecting the effect of sleep disorders on spindle characteristics. We found a significant correlation between the sleep quality score and spindle density (p = 1.84 × 10−8, p-value &lt;0.05 was considered statistically significant.). We, therefore, concluded that the higher the spindle density, the better the sleep quality. The correlation analysis between the sleep quality score and mean frequency of spindles yielded a p-value of 0.667, suggesting that the spindle frequency and sleep quality score were not significantly correlated. The p-value between the sleep quality score and spindle amplitude was 1.33 × 10−4, indicating that the mean amplitude of the spindle decreases as the score increases, and the mean spindle amplitude is generally slightly higher in the normal population than in the sleep-disordered population. The normal and sleep-disordered groups did not show obvious differences in the number of spindles between symmetric channels C3/C4 and F3/F4. The difference in the density and amplitude of the spindles proposed in this paper can be a reference characteristic for the diagnosis of sleep disorders and provide valuable objective evidence for clinical diagnosis. In summary, our proposed detection method can effectively improve the accuracy of sleep spindle wave detection with stable performance. Meanwhile, our study shows that the spindle density, frequency and amplitude are different between the sleep-disordered and normal populations.

Hierarchical fusion detection algorithm for sleep spindle detection

Background

Sleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person’s learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The “gold standard” of spindle detection is based on expert experience; however, the detection cost is high, and the detection time is long. Additionally, the accuracy of detection is influenced by subjectivity.

Methods

To improve detection accuracy and speed, reduce the cost, and improve efficiency, this paper proposes a layered spindle detection algorithm. The first layer used the Morlet wavelet and RMS method to detect spindles, and the second layer employed an improved k-means algorithm to improve spindle detection efficiency. The fusion algorithm was compared with other spindle detection algorithms to prove its effectiveness.

Results

The hierarchical fusion spindle detection algorithm showed good performance stability, and the fluctuation range of detection accuracy was minimal. The average value of precision was 91.6%, at least five percentage points higher than other methods. The average value of recall could reach 89.1%, and the average value of specificity was close to 95%. The mean values of accuracy and F1-score in the subject sample data were 90.4 and 90.3%, respectively. Compared with other methods, the method proposed in this paper achieved significant improvement in terms of precision, recall, specificity, accuracy, and F1-score.

Conclusion

A spindle detection method with high steady-state accuracy and fast detection speed is proposed, which combines the Morlet wavelet with window RMS and an improved k-means algorithm. This method provides a powerful tool for the automatic detection of spindles and improves the efficiency of spindle detection. Through simulation experiments, the sampled data were analyzed and verified to prove the feasibility and effectiveness of this method.

Do naps benefit novel word learning? Developmental differences and white matter correlates

Memory representations of newly learned words undergo changes during nocturnal sleep, as evidenced by improvements in explicit recall and lexical integration (i.e., after sleep, novel words compete with existing words during online word recognition). Some studies have revealed larger sleep-benefits in children relative to adults. However, whether daytime naps play a similar facilitatory role is unclear. We investigated the effect of a daytime nap (relative to wake) on explicit memory (recall/recognition) and lexical integration (lexical competition) of newly learned novel words in young adults and children aged 10-12 years, also exploring white matter correlates of the pre- and post-nap effects of word learning in the child group with diffusion weighted MRI. In both age groups, a nap maintained explicit memory of novel words and wake led to forgetting. However, there was an age group interaction when comparing change in recall over the nap: children showed a slight improvement whereas adults showed a slight decline. There was no evidence of lexical integration at any point. Although children spent proportionally more time in slow-wave sleep (SWS) than adults, neither SWS nor spindle parameters correlated with over-nap changes in word learning. For children, increased fractional anisotropy (FA) in the uncinate fasciculus and arcuate fasciculus were associated with the recognition of novel words immediately after learning, and FA in the right arcuate fasciculus was further associated with changes in recall of novel words over a nap, supporting the importance of these tracts in the word learning and consolidation process. These findings point to a protective role of naps in word learning (at least under the present conditions), and emphasize the need to better understand both the active and passive roles that sleep plays in supporting vocabulary consolidation over development.

Deep-spindle: An automated sleep spindle detection system for analysis of infant sleep spindles

BACKGROUND

Sleep spindles are an indicator of the development and integrity of the central nervous system in infants. Identifying sleep spindles manually in EEG is time-consuming and typically requires experienced experts. Automated detection of sleep spindles would greatly facilitate this analysis. Deep learning methods have been widely used recently in EEG analysis.

METHOD

We have developed a deep learning-based automated sleep spindle detection system, Deep-spindle, which employs a convolutional neural network (CNN) combined with a bidirectional Long Short-Term Memory (LSTM) network, which could assist in the analysis of infant sleep spindles. Deep-spindle was trained on the EEGs of ex-term infants to estimate the number and duration of sleep spindles. The ex-term EEG on channel F4-C4 was split into training (N=81) and validation (N=30) sets. An additional 30 ex-term EEG and 54 ex-preterm infant EEGs (channel F4-C4 and F3-C3) were used as an independent test set.

RESULT

Deep-spindle detected the number of sleep spindles with 91.9% to 96.5% sensitivity and 95.3% to 96.7% specificity, and estimated sleep spindle duration with a percent error of 13.1% to 19.1% in the independent test set. For each detected spindle event, the user is presented with amplitude, power spectral density and the spectrogram of the corresponding spindle EEG, and the probability of the event being a sleep spindle event, providing the user with insight into why the event is predicted as a sleep spindle to provide confidence in the predictions.

CONCLUSION

The Deep-spindle system can reduce physicians' workload, demonstrating the potential to assist physicians in the automated analysis of sleep spindles in infants.

Wavelet-Based Biphase Analysis of Brain Rhythms in Automated Wake-Sleep Classification

Many studies in the field of sleep have focused on connectivity and coherence. Still, the nonstationary nature of electroencephalography (EEG) makes many of the previous methods unsuitable for automatic sleep detection. Time-frequency representations and high-order spectra are applied to nonstationary signal analysis and nonlinearity investigation, respectively. Therefore, combining wavelet and bispectrum, wavelet-based bi-phase (Wbiph) was proposed and used as a novel feature for sleep-wake classification. The results of the statistical analysis with emphasis on the importance of the gamma rhythm in sleep detection show that the Wbiph is more potent than coherence in the wake-sleep classification. The Wbiph has not been used in sleep studies before. However, the results and inherent advantages, such as the use of wavelet and bispectrum in its definition, suggest it as an excellent alternative to coherence. In the next part of this paper, a convolutional neural network (CNN) classifier was applied for the sleep-wake classification by Wbiph. The classification accuracy was 97.17% in nonLOSO and 95.48% in LOSO cross-validation, which is the best among previous studies on sleep-wake classification.

SpindleU-Net: An Adaptive U-Net Framework for Sleep Spindle Detection in Single-Channel EEG

The sleep spindles in EEG have become one type of biomarker used to assess cognitive abilities and related disorders, and thus their detection is crucial for clinical research. This task, traditionally performed by sleep experts, is time-consuming. Many methods have been proposed to automate this process, yet an increase in performance is still expected. Inspired by the application in image segmentation, we propose a point-wise spindle detection method based on the U-Net framework with an attention module (SpindleU-Net). It maps the sequences of arbitrary-length EEG inputs to those of dense labels of spindle or non-spindle on freely chosen intervals. The attention module that focuses on the salient spindle region allows better performance, and a task-specific loss function is defined to alleviate the problem of imbalanced classification. As a deep learning method, SpindleU-Net outperforms state-of-the-art methods on the widely used benchmark dataset of MASS as well as the DREAMS dataset with a small number of samples. On MASS dataset it achieves average F1 scores of 0.854 and 0.803 according to its consistency with the annotations by two sleep experts respectively. On DREAMS dataset, it shows the average F1 score of 0.739. Its cross-dataset performance is also better compared to other methods, showing the good generalization ability for cross-dataset applications.

Spindle-AI: Sleep spindle number and duration estimation in infant EEG

OBJECTIVE

Sleep spindle features show developmental changes during infancy and have the potential to provide an early biomarker for abnormal brain maturation. Manual identification of sleep spindles in the electroencephalogram (EEG) is time-consuming and typically requires highly-trained experts. Automated detection of sleep spindles would greatly facilitate this analysis. Research on the automatic detection of sleep spindles in infant EEG has been limited to-date.

METHODS

We present a random forest-based sleep spindle detection method (Spindle-AI) to estimate the number and duration of sleep spindles in EEG collected from 141 ex-term born infants, recorded at 4 months of age. The signal on channel F4-C4 was split into a training set (81 ex-term) and a validation set (30 ex-term). An additional 30 ex-term infant EEGs (channel F4-C4 and channel F3-C3) were used as an independent test set. Fourteen features were selected for input into a random forest algorithm to estimate the number and duration of spindles and the results were compared against sleep spindles annotated by an experienced clinical physiologist.

RESULTS

The prediction of the number of sleep spindles in the independent test set demonstrated 93.3% to 93.9% sensitivity, 90.7% to 91.5% specificity, and 89.2% to 90.1% precision. The duration estimation of sleep spindle events in the independent test set showed a percent error of 5.7% to 7.4%.

CONCLUSION AND SIGNIFICANCE

Spindle-AI has been implemented as a web server that has the potential to assist clinicians in the fast and accurate monitoring of sleep spindles in infant EEGs.

The effect of zolpidem on memory consolidation over a night of sleep

STUDY OBJECTIVES

Nonrapid eye movement sleep boosts hippocampus-dependent, long-term memory formation more so than wake. Studies have pointed to several electrophysiological events that likely play a role in this process, including thalamocortical sleep spindles (12-15 Hz). However, interventional studies that directly probe the causal role of spindles in consolidation are scarce. Previous studies have used zolpidem, a GABA-A agonist, to increase sleep spindles during a daytime nap and promote hippocampal-dependent episodic memory. The current study investigated the effect of zolpidem on nighttime sleep and overnight improvement of episodic memories.

METHODS

We used a double-blind, placebo-controlled within-subject design to test the a priori hypothesis that zolpidem would lead to increased memory performance on a word-paired associates task by boosting spindle activity. We also explored the impact of zolpidem across a range of other spectral sleep features, including slow oscillations (0-1 Hz), delta (1-4 Hz), theta (4-8 Hz), sigma (12-15 Hz), as well as spindle-SO coupling.

RESULTS

We showed greater memory improvement after a night of sleep with zolpidem, compared to placebo, replicating a prior nap study. Additionally, zolpidem increased sigma power, decreased theta and delta power, and altered the phase angle of spindle-SO coupling, compared to placebo. Spindle density, theta power, and spindle-SO coupling were associated with next-day memory performance.

CONCLUSIONS

These results are consistent with the hypothesis that sleep, specifically the timing and amount of sleep spindles, plays a causal role in the long-term formation of episodic memories. Furthermore, our results emphasize the role of nonrapid eye movement theta activity in human memory consolidation.

Spectral and temporal characterization of sleep spindles-methodological implications

Objective. Nested into slow oscillations (SOs) and modulated by their up-states, spindles are electrophysiological hallmarks of N2 sleep stage that present a complex hierarchical architecture. However, most studies have only described spindles in basic statistical terms, which were limited to the spindle itself without analyzing the characteristics of the pre-spindle moments in which the SOs are originated. The aim of this study was twofold: (a) to apply spectral and temporal measures to the pre-spindle and spindle periods, as well as analyze the correlation between them, and (b) to evaluate the potential of these spectral and temporal measures in future automatic detection algorithms.Approach. An automatic spindle detection algorithm was applied to the overnight electroencephalographic recordings of 26 subjects. Ten complementary features (five spectral and five temporal parameters) were computed in the pre-spindle and spindle periods after their segmentation. These features were computed independently in each period and in a time-resolved way (sliding window). After the statistical comparison of both periods, a correlation analysis was used to assess their interrelationships. Finally, a receiver operating-characteristic (ROC) analysis along with a bootstrap procedure was conducted to further evaluate the degree of separability between the pre-spindle and spindle periods.Main results. The results show important time-varying changes in spectral and temporal parameters. The features calculated in pre-spindle and spindle periods are strongly and significantly correlated, demonstrating the association between the pre-spindle characteristics and the subsequent spindle. The ROC analysis exposes that the typical feature used in automatic spindle detectors, i.e. the power in the sigma band, is outperformed by other features, such as the spectral entropy in this frequency range.Significance. The novel features applied here demonstrate their utility as predictors of spindles that could be incorporated into novel algorithms of automatic spindle detectors, in which the analysis of the pre-spindle period becomes relevant for improving their performance. From the clinical point of view, these features may serve as novel precision therapeutic targets to enhance spindle production with the aim of improving memory, cognition, and sleep quality in healthy and clinical populations. The results evidence the need for characterizing spindles in terms beyond power and the spindle period itself to more dynamic measures and the pre-spindle period. Physiologically, these findings suggest that spindles are more than simple oscillations, but nonstable oscillatory bursts embedded in the complex pre-spindle dynamics.

Protocol for the Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography (P-DROWS-E) study: a prospective observational study of delirium in elderly cardiac surgical patients

INTRODUCTION

Delirium is a potentially preventable disorder characterised by acute disturbances in attention and cognition with fluctuating severity. Postoperative delirium is associated with prolonged intensive care unit and hospital stay, cognitive decline and mortality. The development of biomarkers for tracking delirium could potentially aid in the early detection, mitigation and assessment of response to interventions. Because sleep disruption has been posited as a contributor to the development of this syndrome, expression of abnormal electroencephalography (EEG) patterns during sleep and wakefulness may be informative. Here we hypothesise that abnormal EEG patterns of sleep and wakefulness may serve as predictive and diagnostic markers for postoperative delirium. Such abnormal EEG patterns would mechanistically link disrupted thalamocortical connectivity to this important clinical syndrome.

METHODS AND ANALYSIS

P-DROWS-E (Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography) is a 220-patient prospective observational study. Patient eligibility criteria include those who are English-speaking, age 60 years or older and undergoing elective cardiac surgery requiring cardiopulmonary bypass. EEG acquisition will occur 1-2 nights preoperatively, intraoperatively, and up to 7 days postoperatively. Concurrent with EEG recordings, two times per day postoperative Confusion Assessment Method (CAM) evaluations will quantify the presence and severity of delirium. EEG slow wave activity, sleep spindle density and peak frequency of the posterior dominant rhythm will be quantified. Linear mixed-effects models will be used to evaluate the relationships between delirium severity/duration and EEG measures as a function of time.

ETHICS AND DISSEMINATION

P-DROWS-E is approved by the ethics board at Washington University in St. Louis. Recruitment began in October 2018. Dissemination plans include presentations at scientific conferences, scientific publications and mass media.

TRIAL REGISTRATION NUMBER

NCT03291626.

Random Forest-based Algorithm for Sleep Spindle Detection in Infant EEG

Sleep spindles are associated with normal brain development, memory consolidation and infant sleep-dependent brain plasticity and can be used by clinicians in the assessment of brain development in infants. Sleep spindles can be detected in EEG, however, identifying sleep spindles in EEG recordings manually is very time-consuming and typically requires highly trained experts. Research on the automatic detection of sleep spindles in infant EEGs has been limited to-date. In this study, we present a novel supervised machine learning-based algorithm to detect sleep spindles in infant EEG recordings. EEGs collected from 141 ex-term born infants and 6 ex-preterm born infants, recorded at 4 months of age (adjusted), were used to train and test the algorithm. Sleep spindles were annotated by experienced clinical physiologists as the gold standard. The dataset was split into training (81 ex-term), validation (30 ex-term), and testing (30 ex-term + 6 ex-preterm) set. 15 features were selected for input into a random forest algorithm. Sleep spindles were detected in the ex-term infant EEG test set with 92.1% sensitivity and 95.2% specificity. For ex-preterm born infants, the sensitivity and specificity were 80.3% and 91.8% respectively. The proposed algorithm has the potential to assist researchers and clinicians in the automated analysis of sleep spindles in infant EEG.

Objective Characterization of Activity, Sleep, and Circadian Rhythm Patterns Using a Wrist-Worn Actigraphy Sensor: Insights Into Posttraumatic Stress Disorder

BACKGROUND

Wearables have been gaining increasing momentum and have enormous potential to provide insights into daily life behaviors and longitudinal health monitoring. However, to date, there is still a lack of principled algorithmic framework to facilitate the analysis of actigraphy and objectively characterize day-by-day data patterns, particularly in cohorts with sleep problems.

OBJECTIVE

This study aimed to propose a principled algorithmic framework for the assessment of activity, sleep, and circadian rhythm patterns in people with posttraumatic stress disorder (PTSD), a mental disorder with long-lasting distressing symptoms such as intrusive memories, avoidance behaviors, and sleep disturbance. In clinical practice, these symptoms are typically assessed using retrospective self-reports that are prone to recall bias. The aim of this study was to develop objective measures from patients' everyday lives, which could potentially considerably enhance the understanding of symptoms, behaviors, and treatment effects.

METHODS

Using a wrist-worn sensor, we recorded actigraphy, light, and temperature data over 7 consecutive days from three groups: 42 people diagnosed with PTSD, 43 traumatized controls, and 30 nontraumatized controls. The participants also completed a daily sleep diary over 7 days and the standardized Pittsburgh Sleep Quality Index questionnaire. We developed a novel approach to automatically determine sleep onset and offset, which can also capture awakenings that are crucial for assessing sleep quality. Moreover, we introduced a new intuitive methodology facilitating actigraphy exploration and characterize day-by-day data across 49 activity, sleep, and circadian rhythm patterns.

RESULTS

We demonstrate that the new sleep detection algorithm closely matches the sleep onset and offset against the participants' sleep diaries consistently outperforming an existing open-access widely used approach. Participants with PTSD exhibited considerably more fragmented sleep patterns (as indicated by greater nocturnal activity, including awakenings) and greater intraday variability compared with traumatized and nontraumatized control groups, showing statistically significant (P<.05) and strong associations (|R|>0.3).

CONCLUSIONS

This study lays the foundation for objective assessment of activity, sleep, and circadian rhythm patterns using passively collected data from a wrist-worn sensor, facilitating large community studies to monitor longitudinally healthy and pathological cohorts under free-living conditions. These findings may be useful in clinical PTSD assessment and could inform therapy and monitoring of treatment effects.

Validation of an automated sleep spindle detection method for mouse electroencephalography

Study Objectives

Sleep spindles are abnormal in several neuropsychiatric conditions and have been implicated in associated cognitive symptoms. Accordingly, there is growing interest in elucidating the pathophysiology behind spindle abnormalities using rodent models of such disorders. However, whether sleep spindles can reliably be detected in mouse electroencephalography (EEG) is controversial necessitating careful validation of spindle detection and analysis techniques.

Methods

Manual spindle detection procedures were developed and optimized to generate an algorithm for automated detection of events from mouse cortical EEG. Accuracy and external validity of this algorithm were then assayed via comparison to sigma band (10-15 Hz) power analysis, a proxy for sleep spindles, and pharmacological manipulations.

Results

We found manual spindle identification in raw mouse EEG unreliable, leading to low agreement between human scorers as determined by F1-score (0.26 ± 0.07). Thus, we concluded it is not possible to reliably score mouse spindles manually using unprocessed EEG data. Manual scoring from processed EEG data (filtered, cubed root-mean-squared), enabled reliable detection between human scorers, and between human scorers and algorithm (F1-score > 0.95). Algorithmically detected spindles correlated with changes in sigma-power and were altered by the following conditions: sleep-wake state changes, transitions between NREM and REM sleep, and application of the hypnotic drug zolpidem (10 mg/kg, intraperitoneal).

Conclusions

Here we describe and validate an automated paradigm for rapid and reliable detection of spindles from mouse EEG recordings. This technique provides a powerful tool to facilitate investigations of the mechanisms of spindle generation, as well as spindle alterations evident in mouse models of neuropsychiatric disorders.

Sleep Spindle Detection Using RUSBoost and Synchrosqueezed Wavelet Transform

Sleep spindles are important electroencephalographic (EEG) waveforms in sleep medicine; however, it is burdensome even for experts to detect spindles, so automatic spindle detection methodologies have been investigated. Conventional methods utilize waveforms template matching or machine learning for detecting spindles. In the former approach, it is necessary to tune thresholds for individual adaptation, while the latter approach has the problem of imbalanced data because the amount of sleep spindles is small compared with the entire EEG data. The present work proposes a sleep spindle detection method that combines wavelet synchrosqueezed transform (SST) and random under-sampling boosting (RUSBoost). SST is a time-frequency analysis method suitable for extracting features of spindle waveforms. RUSBoost is a framework for coping with the imbalanced data problem. The proposed SST-RUS can deal with the imbalanced data in spindle detection and does not require threshold tuning because RUSBoost uses majority voting of weak classifiers for discrimination. The performance of SST-RUS was validated using an open-access database called the Montreal archives of sleep studies cohort 1 (MASS-C1), which showed an F-measure of 0.70 with a sensitivity of 76.9% and a positive predictive value of 61.2%. The proposed method can reduce the burden of PSG scoring.

Automatic scoring of non-apnoea arousals using hand-crafted features from the polysomnogram

OBJECTIVE

We present a system for automated annotation of non-apnoea arousals using twelve signals from the polysomnogram (PSG) including airflow, six signals of electroencephalogram, the electrooculogram, chin electromyogram, oximetry signal, and chest and abdominal respiratory effort signals.

APPROACH

Fifty-nine time- and frequency-domain features were extracted from the twelve signals using 15 s epochs. Features from an epoch were combined with features from adjacent epochs and then processed with a bank of feed-forward networks that provided a probability estimate of the occurrence of a non-apnoea arousal event in every epoch. Data from the 2018 PhysioNet/Computing in Cardiology Challenge was used to develop and test the system. Ten-fold cross validation on the 994 PSGs of training data was used to compare the performance of different network configurations.

MAIN RESULTS

Our highest performing configuration utilised a bank of 30 feed-forward neural networks. Each network processed  ±4 epochs of features and each used a single hidden layer of 20 units. The performance of this configuration was evaluated on the independent test set of 989 PSGs and achieved an area under the receiver operator curve of 0.848 and an area under the precision-recall curve of 0.325 for correctly discriminating non-apnoea arousals from non-arousals samples.

SIGNIFICANCE

The classification performance results of our system demonstrate that automated annotation of non-apnoea arousals can be achieved with a high degree of reliability.

Sleep Spindles: Mechanisms and Functions

Sleep spindles are burstlike signals in the electroencephalogram (EEG) of the sleeping mammalian brain and electrical surface correlates of neuronal oscillations in thalamus. As one of the most inheritable sleep EEG signatures, sleep spindles probably reflect the strength and malleability of thalamocortical circuits that underlie individual cognitive profiles. We review the characteristics, organization, regulation, and origins of sleep spindles and their implication in non-rapid-eye-movement sleep (NREMS) and its functions, focusing on human and rodent. Spatially, sleep spindle-related neuronal activity appears on scales ranging from small thalamic circuits to functional cortical areas, and generates a cortical state favoring intracortical plasticity while limiting cortical output. Temporally, sleep spindles are discrete events, part of a continuous power band, and elements grouped on an infraslow time scale over which NREMS alternates between continuity and fragility. We synthesize diverse and seemingly unlinked functions of sleep spindles for sleep architecture, sensory processing, synaptic plasticity, memory formation, and cognitive abilities into a unifying sleep spindle concept, according to which sleep spindles 1) generate neural conditions of large-scale functional connectivity and plasticity that outlast their appearance as discrete EEG events, 2) appear preferentially in thalamic circuits engaged in learning and attention-based experience during wakefulness, and 3) enable a selective reactivation and routing of wake-instated neuronal traces between brain areas such as hippocampus and cortex. Their fine spatiotemporal organization reflects NREMS as a physiological state coordinated over brain and body and may indicate, if not anticipate and ultimately differentiate, pathologies in sleep and neurodevelopmental, -degenerative, and -psychiatric conditions.

Atypicalities in sleep and semantic consolidation in autism

Sleep is known to support the neocortical consolidation of declarative memory, including the acquisition of new language. Autism spectrum disorder (ASD) is often characterized by both sleep and language learning difficulties, but few studies have explored a potential connection between the two. Here, 54 children with and without ASD (matched on age, nonverbal ability and vocabulary) were taught nine rare animal names (e.g., pipa). Memory was assessed via definitions, naming and speeded semantic decision tasks immediately after learning (pre‐sleep), the next day (post‐sleep, with a night of polysomnography between pre‐ and post‐sleep tests) and roughly 1 month later (follow‐up). Both groups showed comparable performance at pre‐test and similar levels of overnight change on all tasks; but at follow‐up children with ASD showed significantly greater forgetting of the unique features of the new animals (e.g., pipa is a flat frog). Children with ASD had significantly lower central non‐rapid eye movement (NREM) sigma power. Associations between spindle properties and overnight changes in speeded semantic decisions differed by group. For the TD group, spindle duration predicted overnight changes in responses to novel animals but not familiar animals, reinforcing a role for sleep in the stabilization of new semantic knowledge. For the ASD group, sigma power and spindle duration were associated with improvements in responses to novel and particularly familiar animals, perhaps reflecting more general sleep‐associated improvements in task performance. Plausibly, microstructural sleep atypicalities in children with ASD and differences in how information is prioritized for consolidation may lead to cumulative consolidation difficulties, compromising the quality of newly formed semantic representations in long‐term memory.

Ground truth construction and parameter tuning for the detection of sleep spindle timing in rodents

BACKGROUND

The precise detection of cortical sleep spindles is critical to basic research on memory consolidation in rodents. Previous research using automatic spindle detection algorithms often lacks systematic parameter variations and validations.

NEW METHOD

We present a method to systematically tune and validate algorithm parameters in automatic spindle detection algorithms using a moderate number of human raters.

RESULTS

Comparing a Hilbert transform-based algorithm to a ground truth constructed by six human raters, this method produced a parameter set yielding an F1 score of 0.82 at 10 ms resolution. The algorithm performance fell within the range of human agreement with the ground truth. Both human and algorithm failures arose largely from disagreement in spindle boundaries rather than spindle occurrence. With no additional tuning, the algorithm performed similarly in recordings from different days or rats.

COMPARISON WITH EXISTING METHODS

Most spindle detection algorithms do not perform systematic parameter variations and validation using a ground truth. To our knowledge, our study is the first in which rodent spindle data is scored by humans, and in which an automatic spindle detection algorithm is evaluated with respect to this ground truth. The rodent data from this study make it possible to compare our algorithm with others previously tested on human data.

CONCLUSIONS

We present a general ground truth based approach for the tuning and validation of spindle extraction algorithms and suggest that algorithms aimed at extracting precise spindle timing in rats should use a systematic approach for parameter tuning.

Spindler: a framework for parametric analysis and detection of spindles in EEG with application to sleep spindles

OBJECTIVE

EEG spindles, narrow-band oscillatory signal bursts, are widely-studied biomarkers of subject state and neurological function. Most existing methods for spindle detection select algorithm parameters by optimizing agreement with expert labels. We propose a new framework for selecting algorithm parameters based on stability of spindle properties and elucidate the dependence of these properties on parameter selection for several algorithms.

APPROACH

To demonstrate this approach we developed a new algorithm (Spindler) that decomposes the signal using matching pursuit with Gabor atoms and computes the spindles for each point in a fine grid of parameter values. After computing characteristic surfaces as a function of parameters, Spindler selects algorithm parameters based on the stability of characteristic surface geometry.

MAIN RESULTS

Spindler performs well relative to several common supervised and unsupervised EEG sleep spindle detection methods. Spindler is available as an open-source MATLAB toolbox (https://github.com/VisLab/EEG-Spindles). In addition to Spindler, the toolbox provides implementations of several other spindle detection algorithms as well as standardized methods for matching ground truth to predictions and a framework for understanding algorithm parameter surfaces.

SIGNIFICANCE

This work demonstrates that parameter selection based on physical constraints rather than labelled data can provide effective, fully-automated, unsupervised spindle detection. This work also exposes the dangers of applying cross-validation without considering the dependence of spindle properties on parameters. Parameters selected to optimize one performance metric or matching method are not optimized for others. Furthermore, elucidation of the stability of predicted indicators with respect to algorithm parameter selection is critical to practical application of these algorithms.

Tracking wakefulness as it fades: Micro-measures of alertness

A major problem in psychology and physiology experiments is drowsiness: around a third of participants show decreased wakefulness despite being instructed to stay alert. In some non-visual experiments participants keep their eyes closed throughout the task, thus promoting the occurrence of such periods of varying alertness. These wakefulness changes contribute to systematic noise in data and measures of interest. To account for this omnipresent problem in data acquisition we defined criteria and code to allow researchers to detect and control for varying alertness in electroencephalography (EEG) experiments under eyes-closed settings. We first revise a visual-scoring method developed for detection and characterization of the sleep-onset process, and adapt the same for detection of alertness levels. Furthermore, we show the major issues preventing the practical use of this method, and overcome these issues by developing an automated method (micro-measures algorithm) based on frequency and sleep graphoelements, which are capable of detecting micro variations in alertness. The validity of the micro-measures algorithm was verified by training and testing using a dataset where participants are known to fall asleep. In addition, we tested generalisability by independent validation on another dataset. The methods developed constitute a unique tool to assess micro variations in levels of alertness and control trial-by-trial retrospectively or prospectively in every experiment performed with EEG in cognitive neuroscience under eyes-closed settings.

SpindleSphere: A Web-based Platform for Large-scale Sleep Spindle Analysis and Visualization

Sleep spindles are a hallmark of stage 2 non-REM sleep that contain information about heritable traits that play an important role in neurological diseases. One of the key challenges in leveraging spindles for clinical research is the lack of a data processing pipeline and web-based, platform for managing and visualizing spindle-specific data at scale. We propose SpindleSphere, a scalable integrated data management and visualization platform for spindle research. SpindleSphere has several features: (1) standardized, metadata-based, search and query of annotated polysomnography (PSG), the gold, standard for sleep diagnosis: (2) event-specific signal exporting: (3) interface for interactive waveform visualization: (4) multi-scale spindle rendering: and (5) parallel algorithm in MapReduce for detection of spindle segments. SpindleSphere provides real-time visualization of multi-modal signals from National Sleep Research Resource (NSRR) for spindle characterization. Preliminary evaluation of SpindleSphere was performed on the NSRR (130 GB of PSG data from 300 subjects).

Identifying sleep spindles with multichannel EEG and classification optimization

Researchers classify critical neural events during sleep called spindles that are related to memory consolidation using the method of scalp electroencephalography (EEG). Manual classification is time consuming and is susceptible to low inter-rater agreement. This could be improved using an automated approach. This study presents an optimized filter based and thresholding (FBT) model to set up a baseline for comparison to evaluate machine learning models using naïve features, such as raw signals, peak frequency, and dominant power. The FBT model allows us to formally define sleep spindles using signal processing but may miss examples most human scorers would agree are spindles. Machine learning methods in theory should be able to approach performance of human raters but they require a large quantity of scored data, proper feature representation, intensive feature engineering, and model selection. We evaluate both the FBT model and machine learning models with naïve features. We show that the machine learning models derived from the FBT model improve classification performance. An automated approach designed for the current data was applied to the DREAMS dataset [1]. With one of the expert's annotation as a gold standard, our pipeline yields an excellent sensitivity that is close to a second expert's scores and with the advantage that it can classify spindles based on multiple channels if more channels are available. More importantly, our pipeline could be modified as a guide to aid manual annotation of sleep spindles based on multiple channels quickly (6-10 s for processing a 40-min EEG recording), making spindle detection faster and more objective.

Multichannel sleep spindle detection using sparse low-rank optimization

BACKGROUND

Automated single-channel spindle detectors, for human sleep EEG, are blind to the presence of spindles in other recorded channels unlike visual annotation by a human expert.

NEW METHOD

We propose a multichannel spindle detection method that aims to detect global and local spindle activity in human sleep EEG. Using a non-linear signal model, which assumes the input EEG to be the sum of a transient and an oscillatory component, we propose a multichannel transient separation algorithm. Consecutive overlapping blocks of the multichannel oscillatory component are assumed to be low-rank whereas the transient component is assumed to be piecewise constant with a zero baseline. The estimated oscillatory component is used in conjunction with a bandpass filter and the Teager operator for detecting sleep spindles.

RESULTS AND COMPARISON WITH OTHER METHODS

The proposed method is applied to two publicly available databases and compared with 7 existing single-channel automated detectors. F₁ scores for the proposed spindle detection method averaged 0.66 (0.02) and 0.62 (0.06) for the two databases, respectively. For an overnight 6 channel EEG signal, the proposed algorithm takes about 4min to detect sleep spindles simultaneously across all channels with a single setting of corresponding algorithmic parameters.

CONCLUSIONS

The proposed method attempts to mimic and utilize, for better spindle detection, a particular human expert behavior where the decision to mark a spindle event may be subconsciously influenced by the presence of a spindle in EEG channels other than the central channel visible on a digital screen.

Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms

Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz) measured by electroencephalography (EEG) mostly during non-rapid eye movement (NREM) stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1) the lack of common benchmark databases, and (2) the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA), the Strength Pareto Evolutionary Algorithm (SPEA2), to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT), and two Hilbert-Huang transform (HHT) based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F₁-scores of 0.726–0.737.

Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS)

Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders. Therefore, procedures for automatic detection of spindles and K-complexes could provide valuable assistance to researchers and clinicians in the field. Recently, we proposed a framework for joint spindle and K-complex detection (Lajnef et al., 2015a) based on a Tunable Q-factor Wavelet Transform (TQWT; Selesnick, 2011a) and morphological component analysis (MCA). Using a wide range of performance metrics, the present article provides critical validation and benchmarking of the proposed approach by applying it to open-access EEG data from the Montreal Archive of Sleep Studies (MASS; O'Reilly et al., 2014). Importantly, the obtained scores were compared to alternative methods that were previously tested on the same database. With respect to spindle detection, our method achieved higher performance than most of the alternative methods. This was corroborated with statistic tests that took into account both sensitivity and precision (i.e., Matthew's coefficient of correlation (MCC), F1, Cohen κ). Our proposed method has been made available to the community via an open-source tool named Spinky (for spindle and K-complex detection). Thanks to a GUI implementation and access to Matlab and Python resources, Spinky is expected to contribute to an open-science approach that will enhance replicability and reliable comparisons of classifier performances for the detection of sleep EEG microstructure in both healthy and patient populations.

Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare

Physiological data from wearable sensors and smartphone are accumulating rapidly, and this provides us the chance to collect dynamic and personalized information as phenotype to be integrated to genotype for the holistic understanding of complex diseases. This integration can be applied to early prediction and prevention of disease, therefore promoting the shifting of disease care tradition to the healthcare paradigm. In this chapter, we summarize the physiological signals which can be detected by wearable sensors, the sharing of the physiological big data, and the mining methods for the discovery of disease-associated patterns for personalized diagnosis and treatment. We discuss the challenges of physiological informatics about the storage, the standardization, the analyses, and the applications of the physiological data from the wearable sensors and smartphone. At last, we present our perspectives on the models for disentangling the complex relationship between early disease prediction and the mining of physiological phenotype data.

Automatic sleep spindle detection: benchmarking with fine temporal resolution using open science tools

Sleep spindle properties index cognitive faculties such as memory consolidation and diseases such as major depression. For this reason, scoring sleep spindle properties in polysomnographic recordings has become an important activity in both research and clinical settings. The tediousness of this manual task has motivated efforts for its automation. Although some progress has been made, increasing the temporal accuracy of spindle scoring and improving the performance assessment methodology are two aspects needing more attention. In this paper, four open-access automated spindle detectors with fine temporal resolution are proposed and tested against expert scoring of two proprietary and two open-access databases. Results highlight several findings: (1) that expert scoring and polysomnographic databases are important confounders when comparing the performance of spindle detectors tested using different databases or scorings; (2) because spindles are sparse events, specificity estimates are potentially misleading for assessing automated detector performance; (3) reporting the performance of spindle detectors exclusively with sensitivity and specificity estimates, as is often seen in the literature, is insufficient; including sensitivity, precision and a more comprehensive statistic such as Matthew's correlation coefficient, F1-score, or Cohen's κ is necessary for adequate evaluation; (4) reporting statistics for some reasonable range of decision thresholds provides a much more complete and useful benchmarking; (5) performance differences between tested automated detectors were found to be similar to those between available expert scorings; (6) much more development is needed to effectively compare the performance of spindle detectors developed by different research teams. Finally, this work clarifies a long-standing but only seldomly posed question regarding whether expert scoring truly is a reliable gold standard for sleep spindle assessment.