Using Wearable Sensors and Deep Learning to Categorize and Detect Different Patterns of Breathing in Healthy Subjects

AbstractThe purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

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Diabetes classification application with efficient missing and outliers data handling algorithms

Complex & Intelligent Systems ◽

10.1007/s40747-021-00349-2 ◽

2021 ◽

Author(s):

Hanaa Torkey ◽

Elhossiny Ibrahim ◽

EZZ El-Din Hemdan ◽

Ayman El-Sayed ◽

Marwa A. Shouman

Keyword(s):

Deep Learning ◽

Healthcare System ◽

Wearable Sensors ◽

Model Performance ◽

Analysis Model ◽

Human Errors ◽

Web Based ◽

Machine Learning Model ◽

Complete Dataset ◽

Smart Wearable

AbstractCommunication between sensors spread everywhere in healthcare systems may cause some missing in the transferred features. Repairing the data problems of sensing devices by artificial intelligence technologies have facilitated the Medical Internet of Things (MIoT) and its emerging applications in Healthcare. MIoT has great potential to affect the patient's life. Data collected from smart wearable devices size dramatically increases with data collected from millions of patients who are suffering from diseases such as diabetes. However, sensors or human errors lead to missing some values of the data. The major challenge of this problem is how to predict this value to maintain the data analysis model performance within a good range. In this paper, a complete healthcare system for diabetics has been used, as well as two new algorithms are developed to handle the crucial problem of missed data from MIoT wearable sensors. The proposed work is based on the integration of Random Forest, mean, class' mean, interquartile range (IQR), and Deep Learning to produce a clean and complete dataset. Which can enhance any machine learning model performance. Moreover, the outliers repair technique is proposed based on dataset class detection, then repair it by Deep Learning (DL). The final model accuracy with the two steps of imputation and outliers repair is 97.41% and 99.71% Area Under Curve (AUC). The used healthcare system is a web-based diabetes classification application using flask to be used in hospitals and healthcare centers for the patient diagnosed with an effective fashion.

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Using Deep Learning for Energy Expenditure Estimation with wearable sensors

2015 17th International Conference on E-health Networking, Application & Services (HealthCom) ◽

10.1109/healthcom.2015.7454554 ◽

2015 ◽

Cited By ~ 12

Author(s):

Jindan Zhu ◽

Amit Pande ◽

Prasant Mohapatra ◽

Jay J. Han

Keyword(s):

Deep Learning ◽

Energy Expenditure ◽

Wearable Sensors

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Deep Learning for Medication Assessment of Individuals with Parkinson’s Disease Using Wearable Sensors

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2018.8513344 ◽

2018 ◽

Cited By ~ 1

Author(s):

Murtadha D. Hssayeni ◽

Jamie L. Adams ◽

Behnaz Ghoraani

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Wearable Sensors

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Enhancing perception for the visually impaired with deep learning techniques and low-cost wearable sensors

Pattern Recognition Letters ◽

10.1016/j.patrec.2019.03.008 ◽

2020 ◽

Vol 137 ◽

pp. 27-36 ◽

Cited By ~ 5

Author(s):

Zuria Bauer ◽

Alejandro Dominguez ◽

Edmanuel Cruz ◽

Francisco Gomez-Donoso ◽

Sergio Orts-Escolano ◽

...

Keyword(s):

Deep Learning ◽

Visually Impaired ◽

Low Cost ◽

Wearable Sensors ◽

Learning Techniques

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Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning

Sensors ◽

10.3390/s20226481 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6481

Author(s):

Kristin McClure ◽

Brett Erdreich ◽

Jason H. T. Bates ◽

Ryan S. McGinnis ◽

Axel Masquelin ◽

...

Keyword(s):

Obstructive Sleep Apnea ◽

Deep Learning ◽

Sleep Apnea ◽

Wearable Sensors ◽

Rapid Assessment ◽

Central Sleep Apnea ◽

Breathing Patterns ◽

Movement Data ◽

Normal Breathing ◽

Obstructive Sleep

Rapid assessment of breathing patterns is important for several emergency medical situations. In this research, we developed a non-invasive breathing analysis system that automatically detects different types of breathing patterns of clinical significance. Accelerometer and gyroscopic data were collected from light-weight wireless sensors placed on the chest and abdomen of 100 normal volunteers who simulated various breathing events (central sleep apnea, coughing, obstructive sleep apnea, sighing, and yawning). We then constructed synthetic datasets by injecting annotated examples of the various patterns into segments of normal breathing. A one-dimensional convolutional neural network was implemented to detect the location of each event in each synthetic dataset and to classify it as belonging to one of the above event types. We achieved a mean F1 score of 92% for normal breathing, 87% for central sleep apnea, 72% for coughing, 51% for obstructive sleep apnea, 57% for sighing, and 63% for yawning. These results demonstrate that using deep learning to analyze chest and abdomen movement data from wearable sensors provides an unobtrusive means of monitoring the breathing pattern. This could have application in a number of critical medical situations such as detecting apneas during sleep at home and monitoring breathing events in mechanically ventilated patients in the intensive care unit.

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