scholarly journals Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2594 ◽  
Author(s):  
Delaram Jarchi ◽  
Javier Andreu-Perez ◽  
Mehrin Kiani ◽  
Oldrich Vysata ◽  
Jiri Kuchynka  ◽  
...  
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Sleep Disorders ◽  
Detection Algorithm ◽  
Learning Framework ◽  
Peak Detection Algorithm ◽  
Obstructive Sleep ◽  
Patient Groups ◽  
Synchrosqueezed Wavelet Transform ◽  
Pulse Peak

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem.

scholarly journals Deep Learning based Arrhythmia Classification with an ECG Acquisition System

2019 ◽  
Vol 9 (2) ◽  
pp. 3849-3852
Keyword(s):  
Deep Learning ◽  
Premature Ventricular Contraction ◽  
Detection Algorithm ◽  
Raspberry Pi ◽  
Data Set ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Peak Detection Algorithm ◽  
Different Types ◽  
Testing Accuracy

One of the issues that the human body faces is arrhythmia, a condition where the human heartbeat is either irregular, too slow or too fast. One of the ways to diagnose arrhythmia is by using ECG signals, the best diagnostic tool for detection of arrhythmia. This paper describes a deep learning approach to check whether signs of arrhythmia, in a given input signal, are present or not. A batch normalized CNN is used to classify the ECG signals based on the different types of arrhythmia. The model has achieved 96.39% training accuracy and 97% testing accuracy. The ECG signals are classified into five classes namely: Normal beats, Premature Ventricular Contraction (PVC) beats, Right Bundle Branch Block (RBBB) beats, Left Bundle Branch Block (LBBB) beats and Paced beats. A peak detection algorithm with six simple steps is designed to detect R-peaks from the ECG signals. A hardware device is built using Raspberry Pi to acquire ECG signals, which are then sent to the trained CNN for classification. The data-set for training is obtained from the MIT-BIH repository. Keras and Tensorflow libraries are used to design and develop the CNN and an application is designed using ’MEAN’ stack and ’Flask’ based servers.

scholarly journals DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning

2019 ◽  
Author(s):  
Jacob M. Graving ◽  
Daniel Chae ◽  
Hemal Naik ◽  
Liang Li ◽  
Benjamin Koger ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Detection Algorithm ◽  
Estimation Methods ◽  
Body Parts ◽  
Software Toolkit ◽  
Multi Scale ◽  
Scientific Disciplines ◽  
Peak Detection Algorithm ◽  
Deep Learning Model

AbstractQuantitative behavioral measurements are important for answering questions across scientific disciplines—from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal’s body parts directly from images or videos. However, currently-available animal pose estimation methods have limitations in speed and robustness. Here we introduce a new easy-to-use software toolkit,DeepPoseKit, that addresses these problems using an eZcient multi-scale deep-learning model, calledStacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2× with no loss in accuracy compared to currently-available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings—including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.

scholarly journals DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Jacob M Graving ◽  
Daniel Chae ◽  
Hemal Naik ◽  
Liang Li ◽  
Benjamin Koger ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Detection Algorithm ◽  
Estimation Methods ◽  
Body Parts ◽  
Software Toolkit ◽  
Multi Scale ◽  
Scientific Disciplines ◽  
Peak Detection Algorithm ◽  
Deep Learning Model

Quantitative behavioral measurements are important for answering questions across scientific disciplines—from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal’s body parts directly from images or videos. However, currently available animal pose estimation methods have limitations in speed and robustness. Here, we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings—including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.

A Deep Learning Framework for Prediction of Retinal Disorders

2020 ◽  
Author(s):  
Raniyaharini R ◽  
Madhumitha K ◽  
Mishaa S ◽  
Virajaravi R
Keyword(s):  
Deep Learning ◽  
Learning Framework ◽  
Retinal Disorders
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