scholarly journals Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography

2021 ◽  
Vol 12 ◽  
Author(s):  
Mingyu Fu ◽  
Yitian Wang ◽  
Zixin Chen ◽  
Jin Li ◽  
Fengguo Xu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Single Channel ◽  
Parietal Lobe ◽  
Kappa Coefficient ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Cohen’S Kappa Coefficient

This study centers on automatic sleep staging with a single channel electroencephalography (EEG), with some significant findings for sleep staging. In this study, we proposed a deep learning-based network by integrating attention mechanism and bidirectional long short-term memory neural network (AT-BiLSTM) to classify wakefulness, rapid eye movement (REM) sleep and non-REM (NREM) sleep stages N1, N2 and N3. The AT-BiLSTM network outperformed five other networks and achieved an accuracy of 83.78%, a Cohen’s kappa coefficient of 0.766 and a macro F1-score of 82.14% on the PhysioNet Sleep-EDF Expanded dataset, and an accuracy of 81.72%, a Cohen’s kappa coefficient of 0.751 and a macro F1-score of 80.74% on the DREAMS Subjects dataset. The proposed AT-BiLSTM network even achieved a higher accuracy than the existing methods based on traditional feature extraction. Moreover, better performance was obtained by the AT-BiLSTM network with the frontal EEG derivations than with EEG channels located at the central, occipital or parietal lobe. As EEG signal can be easily acquired using dry electrodes on the forehead, our findings might provide a promising solution for automatic sleep scoring without feature extraction and may prove very useful for the screening of sleep disorders.

scholarly journals Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning

SLEEP ◽  
2020 ◽  
Vol 43 (11) ◽  
Author(s):  
Maurice Abou Jaoude ◽  
Haoqi Sun ◽  
Kyle R Pellerin ◽  
Milena Pavlova ◽  
Rani A Sarkis ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Cohen’S Kappa ◽  
Scalp Eeg ◽  
Cohen's Kappa ◽  
Eeg Recordings

Abstract Study Objectives Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings. Methods Using a clinical dataset of polysomnograms from 6,431 patients (MGH–PSG dataset), we trained a deep neural network to classify sleep stages based on scalp EEG data. The algorithm consists of a convolutional neural network for feature extraction, followed by a recurrent neural network that extracts temporal dependencies of sleep stages. The algorithm’s inputs are four scalp EEG bipolar channels (F3-C3, C3-O1, F4-C4, and C4-O2), which can be derived from any standard PSG or scalp EEG recording. We initially trained the algorithm on the MGH–PSG dataset and used transfer learning to fine-tune it on a dataset of long-term (24–72 h) scalp EEG recordings from 112 patients (scalpEEG dataset). Results The algorithm achieved a Cohen’s kappa of 0.74 on the MGH–PSG holdout testing set and cross-validated Cohen’s kappa of 0.78 after optimization on the scalpEEG dataset. The algorithm also performed well on two publicly available PSG datasets, demonstrating high generalizability. Performance on all datasets was comparable to the inter-rater agreement of human sleep staging experts (Cohen’s kappa ~ 0.75 ± 0.11). The algorithm’s performance on long-term scalp EEGs was robust over a wide age range and across common EEG background abnormalities. Conclusion We developed a deep learning algorithm that achieves human expert level sleep staging performance on long-term scalp EEG recordings. This algorithm, which we have made publicly available, greatly facilitates the use of large long-term EEG clinical datasets for sleep-related research.

scholarly journals Bayesian network-based methodology for selecting a cost-effective sewer asset management model

2020 ◽  
Author(s):  
Julián Guzmán-Fierro ◽  
Sharel Charry ◽  
Ivan González ◽  
Felipe Peña-Heredia ◽  
Nathalie Hernández ◽  
...  
Keyword(s):  
Asset Management ◽  
Kappa Coefficient ◽  
Wilcoxon Test ◽  
Cohen’S Kappa ◽  
Validation Data ◽  
Statistical Measures ◽  
Proactive Management ◽  
Cohen's Kappa ◽  
Cohen’S Kappa Coefficient

Abstract This paper presents a methodology based on Bayesian Networks (BN) to prioritise and select the minimal number of variables that allows predicting the structural condition of sewer assets to support the strategies in proactive management. The integration of BN models, statistical measures of agreement (Cohen's Kappa coefficient) and a statistical test (Wilcoxon test) were useful for a robust and straightforward selection of a minimum number of variables (qualitative and quantitative) that ensure a suitable prediction level of the structural conditions of sewer pipes. According to the application of the methodology to a specific case study (Bogotás sewer network, Colombia), it found that with only two variables (age and diameter) the model could achieve the same capacity of prediction (Cohen's Kappa coefficient = 0.43) as a model considering several variables. Furthermore, the methodology allows finding the calibration and validation percentage subsets that best fit (80% for calibration and 20% for validation data in the case study) in the model to increase the capacity of prediction with low variations. Furthermore, it found that a model, considering only pipes in critical and excellent conditions, increases the capacity of successful predictions (Cohen's Kappa coefficient from 0.2 to 0.43) for the proposed case study.

WAVELET FEATURES BASED SLEEP STAGES DETECTION USING SINGLE CHANNEL EEG

2017 ◽  
Vol 5 (4) ◽  
pp. 99-102
Author(s):  
Vandana Roy ◽  
Anand Prakash ◽  
Shailja Shukla
Keyword(s):  
Feature Extraction ◽  
Wavelet Decomposition ◽  
Single Channel ◽  
Kappa Coefficient ◽  
Sleep Stages ◽  
Chi Square ◽  
Wavelet Features ◽  
Stage Classification ◽  
Selection Of

The sleep stages determination is important for the identification and diagnosis of different diseases. An efficient algorithm of wavelet decomposition is used for feature extraction of single channel EEG. The Chi-Square method is applied for the selection of the best attributes from the extracted features. The classification of different staged techniques is applied with the help AdaBoost.M1 algorithm. The accuracy of 89.82% achieved in the six stage classification. The weighted sensitivity of all stages is 89.8% and kappa coefficient of 77.93% is obtained in the six stage classification.

scholarly journals Automatic Sleep Stage Classification Using 1D Convolutional Neural Network

2020 ◽  
Author(s):  
Asma Salamatian ◽  
Ali Khadem
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Eeg Signal ◽  
Stage Classification ◽  
Sleep Stage Classification

Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affects different aspects of human life. Sleep monitoring and sleep stage classification play an important role in the diagnosis of sleeprelated diseases and neurological disorders. Empirically, classification of sleep stages is a time-consuming, tedious, and complex task, which heavily depends on the experience of the experts. As a result, there is a crucial need for an automatic efficient sleep staging system. Materials and Methods: This study develops a 13-layer 1D Convolutional Neural Network (CNN) using singlechannel Electroencephalogram (EEG) signal for extracting features automatically and classifying the sleep stages. To overcome the negative effect of an imbalance dataset, we have used the Synthetic Minority Oversampling Technique (SMOTE). In our study, the single-channel EEG signal is given to a 1D CNN, without any feature extraction/selection processes. This deep network can self-learn the discriminative features from the EEG signal. Results: Applying the proposed method to sleep-EDF dataset resulted in overall accuracy, sensitivity, specificity, and Precision of 94.09%, 74.73%, 96.43%, and 71.02%, respectively, for classifying five sleep stages. Using single-channel EEG and providing a network with fewer trainable parameters than most of the available deep learning-based methods are the main advantages of the proposed method. Conclusion: In this study, a 13-layer 1D CNN model was proposed for sleep stage classification. This model has an end-to-end complete architecture and does not require any separate feature extraction/selection and classification stages. Having a low number of network parameters and layers while still having high classification accuracy, is the main advantage of the proposed method over most of the previous deep learning-based approaches.

scholarly journals Automatic Sleep Stage Classification Based on Two-channel EOG and One-channel EMG

2021 ◽  
Author(s):  
Yanjun LI ◽  
Xianglin Yang ◽  
Zhi Xu ◽  
Yu Zhang ◽  
Zhongping Cao
Keyword(s):  
Sleep Stage ◽  
Kappa Coefficient ◽  
Sleep Monitoring ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Feature Normalization ◽  
Leave One Out ◽  
Cohen’S Kappa Coefficient

Abstract The sleep monitoring with PSG severely degrades the sleep quality. In order to simplify the hygienic processing and reduce the load of sleep monitoring, an approach to automatic sleep stage classification without electroencephalogram (EEG) was explored. Totally 108 features from two-channel electrooculogram (EOG) and 6 features from one-channel electromyogram (EMG) were extracted. After feature normalization, the random forest (RF) was used to classify five stages, including wakefulness, REM sleep, N1 sleep, N2 sleep and N3 sleep. Using 114 normalized features from the combination of EOG (108 features) and EMG (6 features), the Cohen’s kappa coefficient was 0.749 and the accuracy was 80.8% by leave-one -out cross-validation (LOOCV) for 124 records from ISRUC-Sleep. As a reference for AASM standard, the Cohen’s kappa coefficient was 0.801 and the accuracy was 84.7% for the same dataset based on 438 normalized features from the combination of EEG (324 features), EOG (108 features) and EMG (6 features). In conclusion, the approach by EOG+EMG with the normalization can reduce the load of sleep monitoring, and achieves comparable performances with the "gold standard" EEG+EOG+EMG on sleep classification.

247 Automated sleep staging using wrist-worn device and deep neural networks

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A100-A100
Author(s):  
Niranjan Sridhar ◽  
Atiyeh Ghoreyshi ◽  
Lance Myers ◽  
Zachary Owens
Keyword(s):  
Heart Rate ◽  
Low Cost ◽  
Population Level ◽  
Health Study ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Free Living ◽  
Cohen’S Kappa ◽  
Class Prediction ◽  
Cohen's Kappa

Abstract Introduction Heart rate is well-known to be modulated by sleep stages. If clinically useful sleep scoring can be performed using only cardiac rhythms, then existing medical and consumer-grade devices that can measure heart rate can enable low-cost sleep evaluations. Methods We trained a neural network which uses dilated convolutional blocks to learn both local and long range features of heart rate extracted from ECG R-wave timing to predict for every non-overlapping 30s epoch of the input the probabilities of the epoch being in one of four classes—wake, light sleep, deep sleep or REM. The largest probability is chosen as the network’s class prediction and used to form the hypnogram. We used the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis Study (MESA) and Physionet Computing in Cardiology (CinC) dataset (over 10000 nights) for training and evaluation. Then we deployed the algorithm on PPG based heart rate measured by a wrist-worn device worn by subjects in a free-living setting. Results On the held out test SHHS dataset (800 nights, 561 subjects), the overall 4-class staging accuracy was 77% and Cohen’s kappa was 0.66. On the CinC dataset (993 nights, 993 subjects), the overall 4 class accuracy was 72% and Cohen’s kappa was 0.55. The study on free-living subjects is underway and these novel results will be collated and presented upon completion. Conclusion We hope these results build more trust in automated heart rate based sleep staging and encourage further research into its clinical application in screening and diagnosis of sleep disorders. Low cost, high efficacy devices which can be used in longitudinal studies can lead to breakthroughs in clinical applications of sleep staging for early diagnosis of chronic conditions and novel treatment endpoints. Support (if any) We recently published the training/testing of the algorithm as well a population level analysis showing differences in predicted sleep stages between disease cohorts. The article was published in NPJ Digital Medicine in Aug 2020. The study on free living subjects is currently underway and these new results will be presented at the sleep conference. Preliminary results indicate high concordance with our published results.

scholarly journals Patient-Specific Explanations for Predictions of Clinical Outcomes

ACI Open ◽  
2019 ◽  
Vol 03 (02) ◽  
pp. e88-e97
Author(s):  
Mohammadamin Tajgardoon ◽  
Malarkodi J. Samayamuthu ◽  
Luca Calzoni ◽  
Shyam Visweswaran
Keyword(s):  
Machine Learning ◽  
Clinical Outcomes ◽  
Characteristic Curve ◽  
Community Acquired Pneumonia ◽  
Kappa Coefficient ◽  
Patient Specific ◽  
P Value ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Cohen’S Kappa Coefficient

Abstract Background Machine learning models that are used for predicting clinical outcomes can be made more useful by augmenting predictions with simple and reliable patient-specific explanations for each prediction. Objectives This article evaluates the quality of explanations of predictions using physician reviewers. The predictions are obtained from a machine learning model that is developed to predict dire outcomes (severe complications including death) in patients with community acquired pneumonia (CAP). Methods Using a dataset of patients diagnosed with CAP, we developed a predictive model to predict dire outcomes. On a set of 40 patients, who were predicted to be either at very high risk or at very low risk of developing a dire outcome, we applied an explanation method to generate patient-specific explanations. Three physician reviewers independently evaluated each explanatory feature in the context of the patient's data and were instructed to disagree with a feature if they did not agree with the magnitude of support, the direction of support (supportive versus contradictory), or both. Results The model used for generating predictions achieved a F1 score of 0.43 and area under the receiver operating characteristic curve (AUROC) of 0.84 (95% confidence interval [CI]: 0.81–0.87). Interreviewer agreement between two reviewers was strong (Cohen's kappa coefficient = 0.87) and fair to moderate between the third reviewer and others (Cohen's kappa coefficient = 0.49 and 0.33). Agreement rates between reviewers and generated explanations—defined as the proportion of explanatory features with which majority of reviewers agreed—were 0.78 for actual explanations and 0.52 for fabricated explanations, and the difference between the two agreement rates was statistically significant (Chi-square = 19.76, p-value < 0.01). Conclusion There was good agreement among physician reviewers on patient-specific explanations that were generated to augment predictions of clinical outcomes. Such explanations can be useful in interpreting predictions of clinical outcomes.

scholarly journals Seroprevalence of SARS-CoV-2 Assessed by Four Chemiluminescence Immunoassays and One Immunocromatography Test for SARS-Cov-2

2021 ◽  
Vol 9 ◽  
Author(s):  
Pellegrino Cerino ◽  
Alfonso Gallo ◽  
Biancamaria Pierri ◽  
Carlo Buonerba ◽  
Denise Di Concilio ◽  
...  
Keyword(s):  
Screening Program ◽  
Kappa Coefficient ◽  
Cohen’S Kappa ◽  
Campania Region ◽  
Entire Cohort ◽  
Cohen's Kappa ◽  
Rapid Tests ◽  
Second Wave ◽  
Cohen’S Kappa Coefficient ◽  
Good Agreement

The onset of the new SARS-CoV-2 coronavirus encouraged the development of new serologic tests that could be additional and complementary to real-time RT-PCR-based assays. In such a context, the study of performances of available tests is urgently needed, as their use has just been initiated for seroprevalence assessment. The aim of this study was to compare four chemiluminescence immunoassays and one immunochromatography test for SARS-Cov-2 antibodies for the evaluation of the degree of diffusion of SARS-CoV-2 infection in Salerno Province (Campania Region, Italy). A total of 3,185 specimens from citizens were tested for anti-SARS-CoV-2 antibodies as part of a screening program. Four automated immunoassays (Abbott and Liaison SARS-CoV-2 CLIA IgG and Roche and Siemens SARS-CoV-2 CLIA IgM/IgG/IgA assays) and one lateral flow immunoassay (LFIA Technogenetics IgG–IgM COVID-19) were used. Seroprevalence in the entire cohort was 2.41, 2.10, 1.82, and 1.85% according to the Liaison IgG, Abbott IgG, Siemens, and Roche total Ig tests, respectively. When we explored the agreement among the rapid tests and the serologic assays, we reported good agreement for Abbott, Siemens, and Roche (Cohen's Kappa coefficient 0.69, 0.67, and 0.67, respectively), whereas we found moderate agreement for Liaison (Cohen's kappa coefficient 0.58). Our study showed that Abbott and Liaison SARS-CoV-2 CLIA IgG, Roche and Siemens SARS-CoV-2 CLIA IgM/IgG/IgA assays, and LFIA Technogenetics IgG-IgM COVID-19 have good agreement in seroprevalence assessment. In addition, our findings indicate that the prevalence of IgG and total Ig antibodies against SARS-CoV-2 at the time of the study was as low as around 3%, likely explaining the amplitude of the current second wave.

Prevalence and Risk Factors for Bilateral Meniscal Tears Identified during Treatment for Cranial Cruciate Ligament Disease Via Tibial Plateau Levelling Osteotomy in Dogs

2020 ◽  
Author(s):  
Rebecca L. Laube ◽  
Kyle K. Kerstetter
Keyword(s):  
Risk Factors ◽  
Tibial Plateau ◽  
Cruciate Ligament ◽  
Significant Risk ◽  
Kappa Coefficient ◽  
Meniscal Tears ◽  
Cohen’S Kappa ◽  
Cranial Cruciate Ligament ◽  
Cohen's Kappa ◽  
Cohen’S Kappa Coefficient

Abstract Objective The aim of this study was to report the prevalence and risk factors of bilateral meniscal tears during a tibial plateau levelling osteotomy (TPLO). Methods Data from 362 dogs that underwent staged or simultaneous TPLO between January 2006 and April 2019 were retrospectively collected. Variables such as breed, sex, weight change and intervals between surgeries were analysed with logistic regression. Preoperative tibial plateau angle, age, cranial cruciate ligament status and body weight were analysed with a generalized linear mixed model. All analyses were performed to assess the likelihood of bilateral meniscal tears versus unilateral tears and no tears. Correlation of meniscal tears between stifles was assessed with Cohen's kappa coefficient. Results Prevalence of bilateral meniscal tears was 48.0% (95% confidence interval [CI]: 43.0–53.0%). There was moderate agreement of the presence of meniscal tears between stifles (Cohen's kappa coefficient = 0.41, 95% CI: 0.31–0.51).The odds for bilateral meniscal tears were higher for Rottweilers (odds ratio [OR:] 4.5 [95% CI 1.1–30.3], p = 0.033), older dogs (OR: 1.2 [95% CI: 1.1–1.4 per year], p < 0.0001), smaller dogs (OR: 0.98 [95% CI: 0.97–0.99 per 0.45-kg], p = 0.001), stifles with complete cranial cruciate ligament tears (OR: 21.1 [95% CI: 7.1–62.4], p < 0.0001). Conclusion Contralateral meniscal tears, breed, older age, lower patient weight and complete cranial cruciate ligament tear were significant risk factors for bilateral meniscal tears. Surgeons can use these results to determine prognoses and propensities for meniscal tears in at-risk dogs.

Sign in / Sign up

Export Citation Format

Share Document