scholarly journals Detection of dementia on raw voice recordings using deep learning: A Framingham Heart Study

2021 ◽  
Author(s):  
Chonghua Xue ◽  
Cody Karjadi ◽  
Ioannis Ch. Paschalidis ◽  
Rhoda Au ◽  
Vijaya B. Kolachalama
Keyword(s):  
Deep Learning ◽  
Framingham Heart Study ◽  
Short Term Memory ◽  
Neuropsychological Testing ◽  
Heart Study ◽  
Audio Recordings ◽  
Lstm Network ◽  
Sensitivity Specificity ◽  
Normal Cognition ◽  
Mean Sensitivity

AbstractBackgroundIdentification of reliable, affordable and easy-to-use strategies for detection of dementia are sorely needed. Digital technologies, such as individual voice recordings, offer an attractive modality to assess cognition but methods that could automatically analyze such data without any pre-processing are not readily available.MethodsWe used a subset of 1264 digital voice recordings of neuropsychological examinations administered to participants from the Framingham Heart Study (FHS), a community-based longitudinal observational study. The recordings were 73 minutes in duration, on average, and contained at least two speakers (participant and clinician). Of the total voice recordings, 483 were of participants with normal cognition (NC), 451 recordings were of participants with mild cognitive impairment (MCI), and 330 were of participants with dementia. We developed two deep learning models (a two-level long short-term memory (LSTM) network and a convolutional neural network (CNN)), which used the raw audio recordings to classify if the recording included a participant with only NC or only dementia, and also to differentiate between recordings corresponding to non-demented (NC+MCI) and demented participants.FindingsBased on 5-fold cross-validation, the LSTM model achieved a mean (±std) area under the sensitivity-specificity curve (AUC) of 0.744±0.038, mean accuracy of 0.680±0.032, mean sensitivity of 0.719±0.112, and mean specificity of 0.652±0.089 in predicting cases with dementia from those with normal cognition. The CNN model achieved a mean AUC of 0.805±0.027, mean accuracy of 0.740±0.033, mean sensitivity of 0.735±0.094, and mean specificity of 0.750±0.083 in predicting cases with only dementia from those with only NC. For the task related to classification of demented participants from non-demented ones, the LSTM model achieved a mean AUC of 0.659±0.043, mean accuracy of 0.701±0.057, mean sensitivity of 0.245±0.161 and mean specificity of 0.856±0.105. The CNN model achieved a mean AUC of 0.730±0.039, mean accuracy of 0.735±0.046, mean sensitivity of 0.443±0.113, and mean specificity of 0.840±0.076 in predicting cases with dementia from those who were not demented.InterpretationThis proof-of-concept study demonstrates the potential that raw audio recordings of neuropsychological testing performed on individuals recruited within a community cohort setting can provide a level of screening for dementia.

scholarly journals Detection of dementia on voice recordings using deep learning: a Framingham Heart Study

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chonghua Xue ◽  
Cody Karjadi ◽  
Ioannis Ch. Paschalidis ◽  
Rhoda Au ◽  
Vijaya B. Kolachalama
Keyword(s):  
Deep Learning ◽  
Framingham Heart Study ◽  
Short Term Memory ◽  
Characteristic Curve ◽  
Neuropsychological Testing ◽  
Dementia Screening ◽  
Heart Study ◽  
Audio Recordings ◽  
Lstm Network ◽  
Individual Voice

Abstract Background Identification of reliable, affordable, and easy-to-use strategies for detection of dementia is sorely needed. Digital technologies, such as individual voice recordings, offer an attractive modality to assess cognition but methods that could automatically analyze such data are not readily available. Methods and findings We used 1264 voice recordings of neuropsychological examinations administered to participants from the Framingham Heart Study (FHS), a community-based longitudinal observational study. The recordings were 73 min in duration, on average, and contained at least two speakers (participant and examiner). Of the total voice recordings, 483 were of participants with normal cognition (NC), 451 recordings were of participants with mild cognitive impairment (MCI), and 330 were of participants with dementia (DE). We developed two deep learning models (a two-level long short-term memory (LSTM) network and a convolutional neural network (CNN)), which used the audio recordings to classify if the recording included a participant with only NC or only DE and to differentiate between recordings corresponding to those that had DE from those who did not have DE (i.e., NDE (NC+MCI)). Based on 5-fold cross-validation, the LSTM model achieved a mean (±std) area under the receiver operating characteristic curve (AUC) of 0.740 ± 0.017, mean balanced accuracy of 0.647 ± 0.027, and mean weighted F1 score of 0.596 ± 0.047 in classifying cases with DE from those with NC. The CNN model achieved a mean AUC of 0.805 ± 0.027, mean balanced accuracy of 0.743 ± 0.015, and mean weighted F1 score of 0.742 ± 0.033 in classifying cases with DE from those with NC. For the task related to the classification of participants with DE from NDE, the LSTM model achieved a mean AUC of 0.734 ± 0.014, mean balanced accuracy of 0.675 ± 0.013, and mean weighted F1 score of 0.671 ± 0.015. The CNN model achieved a mean AUC of 0.746 ± 0.021, mean balanced accuracy of 0.652 ± 0.020, and mean weighted F1 score of 0.635 ± 0.031 in classifying cases with DE from those who were NDE. Conclusion This proof-of-concept study demonstrates that automated deep learning-driven processing of audio recordings of neuropsychological testing performed on individuals recruited within a community cohort setting can facilitate dementia screening.

scholarly journals Real-Time Detection of Dictionary DGA Network Traffic Using Deep Learning

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Kate Highnam ◽  
Domenic Puzio ◽  
Song Luo ◽  
Nicholas R. Jennings
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Traffic ◽  
Short Term Memory ◽  
Domain Names ◽  
Control Networks ◽  
Detection Techniques ◽  
Lstm Network ◽  
And Control

AbstractBotnets and malware continue to avoid detection by static rule engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the “bagging” model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, $$F_1$$ F 1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large enterprise. In 4 h of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.

scholarly journals Automatic Detection of Microsleep Episodes With Deep Learning

2021 ◽  
Vol 15 ◽  
Author(s):  
Alexander Malafeev ◽  
Anneke Hertig-Godeschalk ◽  
David R. Schreier ◽  
Jelena Skorucak ◽  
Johannes Mathis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Internal Representation ◽  
Automatic Detection ◽  
Maintenance Of Wakefulness Test ◽  
Lstm Network ◽  
Definition Of ◽  
Artificial Neuronal Network ◽  
Electroencephalogram Eeg ◽  
Laborious Work

Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. Scoring of the MWT in most sleep-wake centers is limited to classical definition of sleep (30 s epochs), and MSEs are mostly not considered in the absence of established scoring criteria defining MSEs but also because of the laborious work. We aimed for automatic detection of MSEs with machine learning, i.e., with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients. Experts visually scored wakefulness, and according to recently developed scoring criteria MSEs, microsleep episode candidates (MSEc), and episodes of drowsiness (ED). We implemented segmentation algorithms based on convolutional neural networks (CNNs) and a combination of a CNN with a long-short term memory (LSTM) network. A LSTM network is a type of a recurrent neural network which has a memory for past events and takes them into account. Data of 53 patients were used for training of the classifiers, 12 for validation and 11 for testing. Our algorithms showed a good performance close to human experts. The detection was very good for wakefulness and MSEs and poor for MSEc and ED, similar to the low inter-expert reliability for these borderline segments. We performed a visualization of the internal representation of the data by the artificial neuronal network performing best using t-distributed stochastic neighbor embedding (t-SNE). Visualization revealed that MSEs and wakefulness were mostly separable, though not entirely, and MSEc and ED largely intersected with the two main classes. We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts. The code of the algorithms (https://github.com/alexander-malafeev/microsleep-detection) and data (https://zenodo.org/record/3251716) are available.

scholarly journals Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 517 ◽  
Author(s):  
Ali M. Hasan ◽  
Mohammed M. AL-Jawad ◽  
Hamid A. Jalab ◽  
Hadil Shaiba ◽  
Rabha W. Ibrahim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Accurate Method ◽  
Short Term ◽  
Lung Scan ◽  
Deep Learning Features ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Ct Slices

Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists’ efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.

scholarly journals Using Deep Learning to Forecast Maritime Vessel Flows

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1761 ◽  
Author(s):  
Xiangyu Zhou ◽  
Zhengjiang Liu ◽  
Fengwu Wang ◽  
Yajuan Xie ◽  
Xuexi Zhang
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Short Term Memory ◽  
Absolute Error ◽  
Transportation Systems ◽  
Traffic Information ◽  
Automatic Identification ◽  
Identification System ◽  
System Data ◽  
Lstm Network

Forecasting vessel flows is important to the development of intelligent transportation systems in the maritime field, as real-time and accurate traffic information has favorable potential in helping a maritime authority to alleviate congestion, mitigate emission of GHG (greenhouse gases) and enhance public safety, as well as assisting individual vessel users to plan better routes and reduce additional costs due to delays. In this paper, we propose three deep learning-based solutions to forecast the inflow and outflow of vessels within a given region, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and the integration of a bidirectional LSTM network with a CNN (BDLSTM-CNN). To apply those solutions, we first divide the given maritime region into M × N grids, then we forecast the inflow and outflow for all the grids. Experimental results based on the real AIS (Automatic Identification System) data of marine vessels in Singapore demonstrate that the three deep learning-based solutions significantly outperform the conventional method in terms of mean absolute error and root mean square error, with the performance of the BDLSTM-CNN-based hybrid solution being the best.

scholarly journals A Physics-Guided Deep Learning Model for 10-h Dead Fuel Moisture Content Estimation

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 933
Author(s):  
Chunquan Fan ◽  
Binbin He
Keyword(s):  
Deep Learning ◽  
Moisture Content ◽  
Short Term Memory ◽  
Simulation Models ◽  
Fuel Moisture ◽  
Site Characteristics ◽  
Important Input ◽  
Lstm Network ◽  
Fuel Moisture Content ◽  
Process Based Models

Dead fuel moisture content (DFMC) is a key driver for fire occurrence and is often an important input to many fire simulation models. There are two main approaches to estimating DFMC: empirical and process-based models. The former mainly relies on empirical methods to build relationships between the input drivers (weather, fuel and site characteristics) and observed DFMC. The latter attempts to simulate the processes that occur in the fuel with energy and water balance conservation equations. However, empirical models lack explanations for physical processes, and process-based models may provide an incomplete representation of DFMC. To combine the benefits of empirical and process-based models, here we introduced the Long Short-Term Memory (LSTM) network and its combination with an effective physics process-based model fuel stick moisture model (FSMM) to estimate DFMC. The LSTM network showed its powerful ability in describing the temporal dynamic changes of DFMC with high R2 (0.91), low RMSE (3.24%) and MAE (1.97%). When combined with a FSMM model, the physics-guided model FSMM-LSTM showed betterperformance (R2 = 0.96, RMSE = 2.21% and MAE = 1.41%) compared with the other models. Therefore, the combination of the physics process and deep learning estimated 10-h DFMC more accurately, allowing the improvement of wildfire risk assessments and fire simulating.

scholarly journals Including Image-Based Perception in Disturbance Observer for Warehouse Drones

2020 ◽  
Author(s):  
Zhu Chen ◽  
Xiao Liang ◽  
Minghui Zheng
Keyword(s):  
Deep Learning ◽  
Disturbance Observer ◽  
Feedback Loop ◽  
Short Term Memory ◽  
External Disturbance ◽  
Short Term ◽  
Object A ◽  
Numerical Studies ◽  
Long Short Term Memory ◽  
Lstm Network

Abstract Grasping and releasing objects would cause oscillations to delivery drones in the warehouse. To reduce such undesired oscillations, this paper treats the to-be-delivered object as an unknown external disturbance and presents an image-based disturbance observer (DOB) to estimate and reject such disturbance. Different from the existing DOB technique that can only compensate for the disturbance after the oscillations happen, the proposed image-based one incorporates image-based disturbance prediction into the control loop to further improve the performance of the DOB. The proposed image-based DOB consists of two parts. The first one is deep-learning-based disturbance prediction. By taking an image of the to-be-delivered object, a sequential disturbance signal is predicted in advance using a connected pre-trained convolutional neural network (CNN) and a long short-term memory (LSTM) network. The second part is a conventional DOB in the feedback loop with a feedforward correction, which utilizes the deep learning prediction to generate a learning signal. Numerical studies are performed to validate the proposed image-based DOB regarding oscillation reduction for delivery drones during the grasping and releasing periods of the objects.

scholarly journals Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning

Telecom ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 446-471
Author(s):  
Percy Kapadia ◽  
Boon-Chong Seet
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Access Network ◽  
Edge Computing ◽  
Fifth Generation ◽  
Radio Access ◽  
Ping Pong ◽  
Lstm Network ◽  
Multi Access

This paper proposes a potential enhancement of handover for the next-generation multi-tier cellular network, utilizing two fifth-generation (5G) enabling technologies: multi-access edge computing (MEC) and machine learning (ML). MEC and ML techniques are the primary enablers for enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC). The subset of ML chosen for this research is deep learning (DL), as it is adept at learning long-term dependencies. A variant of artificial neural networks called a long short-term memory (LSTM) network is used in conjunction with a look-up table (LUT) as part of the proposed solution. Subsequently, edge computing virtualization methods are utilized to reduce handover latency and increase the overall throughput of the network. A realistic simulation of the proposed solution in a multi-tier 5G radio access network (RAN) showed a 40–60% improvement in overall throughput. Although the proposed scheme may increase the number of handovers, it is effective in reducing the handover failure (HOF) and ping-pong rates by 30% and 86%, respectively, compared to the current 3GPP scheme.

scholarly journals SL-BiLSTM: A Signal-Based Bidirectional LSTM Network for Over-the-Horizon Target Localization

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wanting Yu ◽  
Hongyi Yu ◽  
Ding Wang ◽  
Jianping Du ◽  
Mengli Zhang
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Technology ◽  
Long Distance ◽  
Position Information ◽  
Performance Loss ◽  
Low Snr ◽  
Regularization Techniques ◽  
Positioning Method ◽  
Lstm Network

Deep learning technology provides novel solutions for localization in complex scenarios. Conventional methods generally suffer from performance loss in the long-distance over-the-horizon (OTH) scenario due to uncertain ionospheric conditions. To overcome the adverse effects of the unknown and complex ionosphere on positioning, we propose a deep learning positioning method based on multistation received signals and bidirectional long short-term memory (BiLSTM) network framework (SL-BiLSTM), which refines position information from signal data. Specifically, we first obtain the form of the network input by constructing the received signal model. Second, the proposed method is developed to predict target positions using an SL-BiLSTM network, consisting of three BiLSTM layers, a maxout layer, a fully connected layer, and a regression layer. Then, we discuss two regularization techniques of dropout and randomization which are mainly adopted to prevent network overfitting. Simulations of OTH localization are conducted to examine the performance. The parameters of the network have been trained properly according to the scenario. Finally, the experimental results show that the proposed method can significantly improve the accuracy of OTH positioning at low SNR. When the number of training locations increases to 200, the positioning result of SL-BiLSTM is closest to CRLB at high SNR.

Demand Forecasting in Supply Chain Management Using Different Deep Learning Methods

2021 ◽  
pp. 140-170
Author(s):  
Asma Husna ◽  
Saman Hassanzadeh Amin ◽  
Bharat Shah
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Deep Learning ◽  
Supply Chain Management ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Learning Approaches ◽  
Chain Management ◽  
Lstm Network ◽  
Artificial Neural Network Ann

Supply chain management (SCM) is a fast growing and largely studied field of research. Forecasting of the required materials and parts is an important task in companies and can have a significant impact on the total cost. To have a reliable forecast, some advanced methods such as deep learning techniques are helpful. The main goal of this chapter is to forecast the unit sales of thousands of items sold at different chain stores located in Ecuador with holistic techniques. Three deep learning approaches including artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM) are adopted here for predictions from the Corporación Favorita grocery sales forecasting dataset collected from Kaggle website. Finally, the performances of the applied models are evaluated and compared. The results show that LSTM network tends to outperform the other two approaches in terms of performance. All experiments are conducted using Python's deep learning library and Keras and Tensorflow packages.

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