dataesr/scientific-paragraphs-categorization huggingface dataset:
Sensors and Datasets A home monitoring system using a ballistocardiography (BCG) hydraulic sensor has been developed to monitor sleep at home (see Fig. 1 ). The hydraulic bed sensor has been developed at the Center for Eldercare and Rehabilitation Technology (CERT) at the University of Missouri. A BCG device provides a noninvasive, low-cost, and robust solution for capturing physiological parameters such as heart rate and respiration rate, during sleep  [6, 10, 11]. The BCG sensor has four transducers. Each transducer is composed of a water tube with a pressure sensor placed at one end. The water tube is 50 cm long and 6 cm wide and it is filled with about 0. 4 liters of water. The BCG sensor is placed under the mattress, parallel to the body direction, to provide sleeping comfort and not to disturb a person's normal sleep pattern.
The pressure outputs are coupled to a Maxim MAX7401 which is a filtering circuit that consists of a 741 operational amplifier and an 8th-order integrated Bessel filter  [3, 9]. The four-channel signal is sampled and quantized to 12-bit precision. The BCG signal acquired from the sensor is superimposed over the respiration signal. The four matching transducers are independent; consequently, the data quality collected by those transducers might vary depending on the subject's sleeping position, type of bed (e. g. , material, thickness, etc. ) and the physical characteristics of the subject such as age and body mass index (BMI). In this study, two kinds of datasets have been collected utilizing our bed sensor: sleep posture and sleep stages.
Posture Dataset A total of 56 young healthy subjects (About 75. 8% of males and 24. 2% of females) were asked to lie still in our lab for one minute on each of the four main postures, supine, prone, left lateral and right lateral. The data collection procedure was approved by the University of Missouri Institutional Review Board (MUIRB). The bed sensor produced data sampled at 100 Hz. The subjects age ranged between 18 and 49 years (mean 29. 27 years); the weight ranged between 48 and 184 kg (mean 77. 40 kg); the height ranged between 100 and 190 cm (mean 173. 04 cm); the average body mass index was 27. 34 kg/m 2 , ranged between 18. 1 and 184 kg/m 2.
Sleep Stage Dataset Sleep stage data was collected from consenting patients by a sleep-credentialed physician during the regularly scheduled PSG studies conducted in the sleep lab of the Boone Hospital Center (BHC) in Columbia, MO, USA. In addition to the regular PSG equipment, we placed our hydraulic bed sensor under the study bed mattress. The scoring system for staging the sleep was based on the American Academy of Sleep Medicine Manual (AASM)  [1] , which is the standard for scoring sleep stages and it provides guidelines for associated events during sleep. Sleep stage scoring in 30-second epochs is required for the AASM protocol. A program produced by Natus SleepWorks (Natus Medical Inc. , San Carlos, CA, USA) was used in the BHC sleep lab to assist the staff in monitoring a patient throughout the night.
Next, all the epochs classified as sleep are fed into the next layer to classify the REM and NREM epochs further. The last block of the architecture, shown in Fig. 3  is used to fine-tune the sleep stage data. In the first phase, the optimal hyperparameters for the model are carefully chosen for a given dataset. The model is at that time fine-tuned on the given dataset with these hyperparameter settings. Training overnight raw sleep stage data using DNN is time-consuming. Moreover, the ground truth of the posture data is more reliable than the ground truth of sleep stage data. Deep learning models with transfer learning can, however, successfully converge on short-time data (sleep posture data) split across multiple subjects. By training a model over multiple posture data subjects, we obtained an initialization that is then used for fine-tuning our model on the sleep stage data.
Conclusion In this paper, we have developed a deep learning-based hierarchical classification method for automatic sleep stage classification based on BCG data. The deep learning model is a state-of-the-art algorithm that consists of a stacked CNN-LSTM model. The proposed model was trained using a transfer learning approach that achieved a significantly improved performance in comparison to similar studies. The results of the leave-one-out cross-validation strategy showed potential in automatically classifying sleep stage epochs. Our sleep monitoring system based on the BCG can indeed provide a more natural way of diagnosing sleep problems, and make long-term sleep monitoring possible. However, to validate our algorithms we used a selected, balanced dataset with a reduced level of noise. We are currently working to increase the robustness of our methodology and validate it for patients with reduced REM and high AHI.
In fact, both networks achieved 30% sensitivity, but with 6 false alarms per day (FA/24h) for LSTM and 21 FA/24h for GRU. Choi et al. [3]  proposed a multi-scale 3D-CNN with a bidirectional GRU model for cross-patient seizure detection. The authors used STFT to get spectral and temporal features from EEG signals. They also considered spatial features extracted from electrode positions. The proposed model  [3]  was evaluated on two data sets: the Boston Children's Hospital MIT scalp EEG data set (CHB-MIT) and the Seoul National University Hospital (SNUH) Scalp EEG data set. The approach achieved a sensitivity of 89% with a FPR of 0. 5/h on the first database and 89% sensitivity with FPR of 0. 6/h on SNUH data.
Epileptic Seizure Prediction Truong et al. [21]  applied a CNN to three databases: (1) the Freiburg Hospital intra-cranial EEG (iEEG) data set, (2) the CHB-MIT data set, and (3) the American Epilepsy Society (AES) Seizure Prediction Challenge data set. They considered only data from 13 patients over 24 cases when considering the CHB-MIT database. These patients have less than 10 seizures per day and at least 3 h of inter-ictal signals. STFT has been applied on raw data to build spectrograms. The proposed model is patient-oriented. It achieved a sensitivity of 81. 4%, 81. 2%, and 75% and a FPR of 0. 06/h, 0. 16/h, and 0. 21/h, for the 3 data sets mentioned above, respectively. In the context of the AES Seizure Prediction Challenge organised by Kaggle, Korshunova  [11]  implemented two models for seizure prediction.
Database: The CHB-MIT Scalp EEG database is used in this paper. It was collected by an MIT team in partnership with the pediatric hospital in Boston and can be downloaded from the Physionet platform  [7]. The data set is composed of approximately 1136 h of brain activity recordings and 198 seizures. It includes many types of seizures and was collected from both genders with different ages. One fifth of the data was recorded from men aged from 3 to 22 years. The remaining data concerns females aged from 1. 5 to 19 years  [18]. In a few records, other non-EEG signals were also recorded, such as electrocardiogram (ECG) or vagal nerve stimulus (VNS). Furthermore, we used electrode positions and nomenclature different from the International 10-20 system in some records. Consequently, one step to do was data refitting to get the same channels for all files.
Experimental Evaluation The proposed CGRNN was implemented in Python 3. 5 using Tensorflow 1. 9. 0 backend and Keras 2. 2. Models for different patients were configured to run on an NVIDIA GeForce GTX 1080 GPU. We used 25% of the data for validation and 75% for training. Categorical cross-entropy was adopted as our loss function and the ADAM optimization algorithm was used for model optimization. It is an extension of stochastic gradient descent. To test our model, we consider that one signal clip is preceding a seizure only if at least 8 of 10 successive samples of this sequence are detected as pre-ictal. The calculated metrics are accuracy, sensitivity, and FPR. We also implemented a CNN model with the same architecture as proposed by Truong et al. [21]. This model considers the same defined classes in  [21] : the inter-ictal signal which is about 4 h before and after a seizure and the pre-ictal signal as the period from 35 min to 5 min before the ictal phase.
Environmental Conditions Unlike the patient activity dataset, challenges in assembling environmental data were easier, where we found many open sources that describe environmental conditions of indoor and outdoor spaces at long intervals. One of the most interesting outdoor datasets for environmental information is published by the ministry of environment and climate change strategy in British Columbia in Canada  [21]. These data sets contain continuous readings of meteorological and pollutants index from air quality monitoring stations across the province from 1980 till the end of 2017. Simulation of the internal environment is also important where the patient spends most of his time at home. The indoor data is tracked via GAMS indoor air quality monitor  [22].
Implementation The experimental dataset consists of the activities that have been generated using the MATLAB, the environmental conditions which are captured by real sensors and the biomarkers that obtained from medical sources. The simulated scenarios revolve around creating sequential records over 12 months period for COPD patients with different levels of disease severity and autonomy. In this section, we briefly describe the implementation steps of this project using protégé (Fig. 4 ). The initial implementation of the project will be done in the following manner: (1) the simulated data stored in Excel spreadsheet files will be uploaded directly to Ontology-based knowledge using Cellfie plugin. (2)  The SPARQL query engine accesses to the Knowledge Base to retrieve information of patient's profile, current location, and activity, etc.
The proposed approach is based on analyzing 3D facial expression and spoken language. It has been evaluated on the DAIC-WOZ dataset  [4]. The proposed approach requires an active intervention from the patient, which makes it unusable in some cases. Actually, it is hard to ask a patient to daily record videos, due to privacy issues. In contrast, our approach is passive, analyzes spontaneous phone calls, and preserves data privacy. Su et al. [14]  present a diagnosis assistance system that is based on deep learning and fusion techniques. The proposed system makes use of two deep learning models. The first one is used for speech classification, while the second one processes facial expression. This work is aimed at assisting doctors to avoid misdiagnosis in the case of BD. Actually, BD is usually confused with MDD.
However, our work is based on the analysis of spontaneous phone calls (i. e. , recorded on a daily basis), thanks to our deep learning model that is locally running on a patient's smartphone. Huang et al. [8]  define two speech features based on speech landmark bigrams (i. e. , bigram count and LDA bigram) for depression detection. Both features could be extracted from naturalistic phone calls including 6 elicitation tasks such as measuring the diadochokinetic rate. Landmarks are extracted using the SpeechMark R toolbox 5  , which is a Matlab tool that runs under Windows 7, Windows XP, Apple OSX (Lion or Mountain Lion), as mentioned on their web page. This means that the proposed work needs to send recorded phone calls to an external server for feature extraction. However, our work allows us to locally extract features on the smartphone.
Architecture Figure  1  shows the architecture of DL4DED. Our approach (1) records spontaneous phone calls, (2) stores them temporarily in a buffer belonging to the local memory of a patient's phone, (3) processes them via our "mobile" deep learning model, (4) identifies the state of the recorded voice (i. e. , depressed or not depressed), (  5 ) discards the recorded voice (i. e. , phone call), and finally (6) sends and stores the obtained decision to a database that is installed on a cloud server. The communication links between the smartphone and the cloud server are enabled via the HTTP protocol. A web dashboard is available for both patients and psychiatrists. It is used to display the patient's mood on the basis of the voice status. To identify the status of the voice, we use a novel deep learning model that is described in Sect.
Implementation Issues Figure  4  shows the technical architecture of DL4DED. The smartphone is in charge of recording and analyzing phone calls, while running our deep learning model that has been implemented using the Keras 6  library. Generated decisions are then sent to a Flask RESTful API 1. 0. 2, a web service platform that stores received decisions in a RethinkDB 2. 3. 6 0xenial (GCC 5. 3. 1) database. A nodeJS v4. 2. 6 server is installed to build a real-time web application. It is composed of a set of dashboards, displaying analysis results and an estimation of the patient's mood/status. To predict depressed voice, recorded phone calls are pre-processed first. Afterwards, the deep learning model is loaded into a mobile application to trigger predictions. Implementation details are presented below.
Performance The main objective of this group of experiments is to compare our "optimized deep learning model" (i. e. , with model compression, running on a smartphone) and the "original deep learning model" (i. e. , the originally created deep learning model without compression and optimization), in terms of accuracy that has been calculated using Tensorflow libraries. This experiment does not depend on the used data since it assesses the accuracy loss related to the use of DL4DED. Therefore, we evaluate both models on the same database (i. e. , the DAIC-WOZ dataset). The obtained results demonstrate that the accuracy of DL4DED (0. 5) is slightly lower than the accuracy of the original deep learning model (0. 52). This means that the applied compression techniques do not significantly alter the analysis results.
Conclusion We presented a novel mobile deep learning approach for depressive episode detection, called DL4DED. It is a combination of CNN and LSTM networks. The proposed deep learning model was optimized using compression techniques to be loaded onto smartphones. DL4DED records spontaneous phone calls on a daily basis, stores them temporarily locally on a smartphone, and loads our optimized deep learning model to allow real-time detection of depressed voice. DL4DED preserves data privacy since recorded phone calls are not sent to external servers. DL4DED was evaluated on the DAIC-WOZ database. The results demonstrated the efficiency of DL4DED in terms of accuracy and power consumption. There are several directions for future research. First, we aim to consider the number of recorded phone calls in our study to improve analysis results.
Actually, the absence of phone calls could be seen as a severe sign of depression. Second, we plan to extend our deep learning approach to detect manic episodes in BD. Third, we aim to build a realistic and balanced database to improve the performance of DL4DED. Finally, further optimization methods should be investigated to improve our implementation on mobile devices. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
The System This project has sought to incorporate real-time integration of data in order to provide HCPs with notifications and monitoring capabilities. The system links multiple smart technology devices and funnels the data into one database. This system has been comprised of two software innovations; the Lawson Integrated Database (LIDB) and the Collaborative Health Record (CHR). The LIDB is an information management platform that collates and manages client health information behind the St. Joseph's Health Care hospital firewall. The LIDB keeps health data segregated in its own database schema but is capable of matching patient data across HCPs. With funds from the Canada Health Infoway, the research team was able to ensure the security of the LIDB through a third-party Privacy Impact Analysis and Threat Risk Assessment.
Selfassessments (known in the software as "Qnaires © ") are also completed within the CHR by the participant which allows the HCP and care team to monitor changes and potential crises. These self-assessments can include standardised tools used such as Patient Health Questionnaire (PHQ-9) and fully customisable tools created by the HCP within the CHR. The data from these assessments are then backed up to the participant's profile in the LIDB. In the event of a crisis (e. g. a participant indicates suicidal ideation on a Qnaire), an alert is sent to the care team so that they can act accordingly.
Data Analysis Data is entered and stored on REDCap, a secure web-based database application by a research assistant. For quantitative analysis, the research team uses SPSS Statistics Software to generate descriptive statistics. It should be noted that data from the CHR's Qnaires have not, and will not, be analysed. The primary outcome of interest is the total score from the CIQ-R. Further quantitative analyses will investigate the housing history of the participant to assess housing stability, the health status of the participant, and experience with health, social and justice services to evaluate service usage. Individual interviews are held with all participants enrolled in the study. Focus groups, as described by Krueger  [16] , are held with HCPs in both phases as well as participants in the community phase.
A well-known example is Inception-v3, a convolutional neural network trained on more than a million images from the ImageNet database (http://www. image-net. org). The network has 48 layers and can classify images into one thousand object categories, including trees and many animals. Another interesting example is represented by the Painters  [5]  networks, developed to automatically classify Paintings of famous artists. In principle, any of those methods can be used following the transfer learning paradigm to represent images coming from urban landscape of New York City.
The Data Analysis Pipeline The pipeline implemented in our work is described in Fig. 2. The NAIP NYC image has been subdivided into image square blocks having size of 256 Â 256 pixels, corresponding to a 512 m edge. Therefore, it was possible to estimate the value of each of the 27 variables collected by "500 Cities" for each block. During this process, blocks out of the tracts or over the sea have been excluded, thus reducing the dataset. The images have been then processed by a pretrained deep model, thus extracting the final features for each image. Images are clustered by resorting to k-means clustering, and the clusters, confirmed with visual inspection, are associated to the healthcare indexes by statistical analysis.
Image Blocks The NAIP NYC image has been subdivided into 8336 images of 256 Â 256 pixels. Each image is a square with edge equal to 512 m. It must be underlined that the original image is georeferenced, meaning that each single pixel is precisely located in space. The small derived tiles are georeferenced as well and can be effectively overlapped to the health and well-being maps. The images have been processed resorting to the Matlab Image Processing and Mapping toolboxes, which are capable of properly managing georeferenced images. Figure  3  shows some examples of the resulting images. It is possible to note that some of the squares have white areas, corresponding to unmapped zones, due to the irregular borders of the image and to the presence of sea, rivers. Due to the availability of the vector map of the borders of NYC, we have been able to quantify, for each tile, the amount of its surface lying inside the borders of the city; we then filtered the original tile set and maintained only those having a minimal overlapping of 90%.
Estimation of the Healthcare Indexes for Each Image Block The healthcare indexes of the 500 Cities database are collected for census tracts and NYC has, as already reported, 2166 census tracts. In order to carry out our analysis, we had to determine the value of the considered variables for each image block. In fact, a given tile overlaps, in general, several tracts. Therefore, we had to implement a simple estimator of the healthcare index of the block, as: hci block ð Þ¼ P j w j hci j ð Þ P j w j where hci(j) is the value of the generic health care index for the j-th census tract and w j is the percentage of the image block covered by the mentioned tract. An example is shown in Figs. 3  and 4. In order to properly quantify hcis, the blocks with a white area greater than 10% of the image have been removed.
Correlation and Statistical Analysis The final step of the data analysis pipeline is represented by the search of statistical correlations between the clusters and their hcis. Univariate multinomial logistic regression was applied to estimate the probability to belong to a specific cluster given single variables' values. Multivariate multinomial logistic regression was performed after removal of samples characterized by missing values. A backward stepwise selection procedure based on AIC was applied to identify the most informative set of variables jointly modulating the probability to belong to the clusters. Multinomial logistic regression and the stepwise selection procedure were implemented in the R packages "nnet" and "stats", respectively. Analyses were performed by the R software tool version 3.
Services Delivery The proposed architecture is intended for an entire city. It must then be able to be deployed to multiple participants. This scalability according to the number of seniors must not affect the quality of services offered. The AAL system must also guarantee security as it covers private issues. The microservice architecture structures an application as a collection of services that are highly maintainable and testable, loosely coupled, independently deployable and organized around business capabilities. The microservice architecture enables the continuous delivery and deployment of large and complex applications. AAL system can be spread across multiple servers or even multiple databases. The architecture has better fault isolation; if one microservice fails, the others will continue to work.
It is useful for connections to remote sites where a small code footprint is required or a reduced network bandwidth. It implements a channel subscription mechanism. Whenever an event occurs, it notifies all entities registered in the channel. Thus, when a new event occurs all registered components are notified, avoiding an active standby. The microservice of persistence and data management deals with data backup in the appropriate databases. As part of this architecture, we deployed two database management systems. One deals with raw data and the other with processed data to increase efficiency in some monitoring operations. The protocol bridge is a microservice that bundles devices and other technologies into a single solution. It provides a uniform user interface and a common approach to automating actions and rules across the system.