Abstract 14963: Accelerometer-measured Activity in Non-obstructive Hypertrophic Cardiomyopathy: Patient-generated Activity Measures Correlate With, and Are Convolutional Neural Network Predictors of, Clinical Parameters in the MAVERICK-HCM Study

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Priyanka Agarwal ◽  
Anna Shcherbina ◽  
Sharlene Day ◽  
Sara Saberi ◽  
Matthew E Mealiffe ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Hypertrophic Cardiomyopathy ◽  
Convolutional Neural Network ◽  
Pearson Correlation ◽  
Step Count ◽  
Clinical Parameters ◽  
Clinical Markers ◽  
Double Blind ◽  
Activity Measures

Introduction: Overall activity characteristics for patients with hypertrophic cardiomyopathy (HCM) have not been quantified previously. The relationship between physical activity quantified by accelerometry and biomarkers, exercise capacity, and quality of life in patients with HCM is also unknown. Methods: MAVERICK-HCM was a double-blind, placebo-controlled, 16-week study of mavacamten in 59 patients with symptomatic non-obstructive HCM. Patients were asked to wear ActiGraph GT9X Link wrist-worn monitors for ≥11 days between screening and day 1, and between weeks 12 and 16. Features derived from raw accelerometry data included average daily accelerometer units (ADAU) and step count. Univariate Pearson correlation coefficients were calculated between accelerometry data and clinical parameters among all patients. A multi-task convolutional neural network (CNN) was trained on raw accelerometry datapoints to jointly predict clinical markers of HCM severity. Test and training sets were derived by randomly segmenting each patient’s triaxial accelerometry data into non-overlapping minute intervals. Results: Fifty patients wore the accelerometer for ≥1 compliant day. Mean wear time was 12 days during screening and 10 days during treatment. Activity measures are summarized and average step count was 3,076 steps at baseline ( Table ). Activity features correlated with peak oxygen uptake (pVO 2 ), log NT-proBNP, and KCCQ score ( Table ). CNN predictions of clinical measures from activity data found Spearman R correlations of 0.82 for pVO 2 , 0.92 for log NT-proBNP, 0.82 for KCCQ, and 0.79 for E/e’. Conclusions: HCM patients in the MAVERICK study averaged only 3,000 steps/day. Markers of physical activity drawn from accelerometry are associated with standard clinical markers of HCM severity. Deep learning models can be constructed to predict markers of HCM severity from patients’ raw accelerometry data.

scholarly journals Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study

10.2196/18142 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18142
Author(s):  
Ramin Mohammadi ◽  
Mursal Atif ◽  
Amanda Jayne Centi ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Machine Learning ◽  
Learning Model ◽  
Step Count ◽  
Daily Step ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Machine Learning Model ◽  
Daily Step Count

Background It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. Objective The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user’s activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. Methods We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. Results Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. Conclusions Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual’s level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

Convolutional Neural Network-Based Automatic Measurement of Joint Space Width to Predict Radiographic Severity and Progression of Knee Osteoarthritis

2021 ◽  
Author(s):  
James Chung Wai Cheung ◽  
Yiu Chow TAM ◽  
Lok Chun CHAN ◽  
Ping Keung CHAN ◽  
Chunyi WEN
Keyword(s):  
Neural Network ◽  
Knee Osteoarthritis ◽  
Convolutional Neural Network ◽  
Pearson Correlation ◽  
Joint Space Width ◽  
Joint Space ◽  
Receiver Operating Curve ◽  
Radiographic Severity ◽  
X Ray ◽  
Space Width

Abstract Objectives To develop a deep convolutional neural network (CNN) for the segmentation of femur and tibia on plain x-ray radiographs, hence enabling an automated measurement of joint space width (JSW) to predict the severity and progression of knee osteoarthritis (KOA). Methods A CNN with ResU-Net architecture was developed for knee X-ray imaging segmentation. The efficiency was evaluated by the Intersection over Union (IoU) score by comparing the outputs with the annotated contour of the distal femur and proximal tibia. By leveraging imaging segmentation, the minimal and multiple JSWs in the tibiofemoral joint were estimated and then validated by radiologists’ measurements in the Osteoarthritis Initiative (OAI) dataset using Pearson correlation and Bland–Altman plot. The estimated JSWs were deployed to predict the radiographic severity and progression of KOA defined by Kellgren-Lawrence (KL) grades using the XGBoost model. The classification performance was assessed using F1 and area under receiver operating curve (AUC). Results The network has attained a segmentation efficiency of 98.9% IoU. Meanwhile, the agreement between the CNN-based estimation and radiologist’s measurement of minimal JSW reached 0.7801 (p < 0.0001). Moreover, the 32-point multiple JSW obtained the highest AUC score of 0.656 to classify KL-grade of KOA. Whereas the 64-point multiple JSWs achieved the best performance in predicting KOA progression defined by KL grade change within 48 months, with AUC of 0.621. The multiple JSWs outperform the commonly used minimum JSW with 0.587 AUC in KL-grade classification and 0.554 AUC in disease progression prediction. Conclusion Fine-grained characterization of joint space width of KOA yields comparable performance to the radiologist in assessing disease severity and progression. We provide a fully automated and efficient radiographic assessment tool for KOA.

scholarly journals Spatial-Temporal 3D Residual Correlation Network for Urban Traffic Status Prediction

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 33
Author(s):  
Yin-Xin Bao ◽  
Quan Shi ◽  
Qin-Qin Shen ◽  
Yang Cao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Pearson Correlation ◽  
Urban Traffic ◽  
Correlation Network ◽  
Convolutional Network ◽  
Residual Correlation ◽  
Temporal Characteristics ◽  
Temporal Features

Accurate traffic status prediction is of great importance to improve the security and reliability of the intelligent transportation system. However, urban traffic status prediction is a very challenging task due to the tight symmetry among the Human–Vehicle–Environment (HVE). The recently proposed spatial–temporal 3D convolutional neural network (ST-3DNet) effectively extracts both spatial and temporal characteristics in HVE, but ignores the essential long-term temporal characteristics and the symmetry of historical data. Therefore, a novel spatial–temporal 3D residual correlation network (ST-3DRCN) is proposed for urban traffic status prediction in this paper. The ST-3DRCN firstly introduces the Pearson correlation coefficient method to extract a high correlation between traffic data. Then, a dynamic spatial feature extraction component is constructed by using 3D convolution combined with residual units to capture dynamic spatial features. After that, based on the idea of long short-term memory (LSTM), a novel architectural unit is proposed to extract dynamic temporal features. Finally, the spatial and temporal features are fused to obtain the final prediction results. Experiments have been performed using two datasets from Chengdu, China (TaxiCD) and California, USA (PEMS-BAY). Taking the root mean square error (RMSE) as the evaluation index, the prediction accuracy of ST-3DRCN on TaxiCD dataset is 21.4%, 21.3%, 11.7%, 10.8%, 4.7%, 3.6% and 2.3% higher than LSTM, convolutional neural network (CNN), 3D-CNN, spatial–temporal residual network (ST-ResNet), spatial–temporal graph convolutional network (ST-GCN), dynamic global-local spatial–temporal network (DGLSTNet), and ST-3DNet, respectively.

No Reference Video Quality Assessment with authentic distortions using 3-D Deep Convolutional Neural Network

2020 ◽  
Vol 2020 (9) ◽  
pp. 168-1-168-7
Author(s):  
Roger Gomez Nieto ◽  
Hernan Dario Benitez Restrepo ◽  
Roger Figueroa Quintero ◽  
Alan Bovik
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Pearson Correlation ◽  
Video Quality ◽  
Perceptual Quality ◽  
Support Vector ◽  
Quality Prediction ◽  
Video Quality Assessment ◽  
Different Color

Video Quality Assessment (VQA) is an essential topic in several industries ranging from video streaming to camera manufacturing. In this paper, we present a novel method for No-Reference VQA. This framework is fast and does not require the extraction of hand-crafted features. We extracted convolutional features of 3-D C3D Convolutional Neural Network and feed one trained Support Vector Regressor to obtain a VQA score. We did certain transformations to different color spaces to generate better discriminant deep features. We extracted features from several layers, with and without overlap, finding the best configuration to improve the VQA score. We tested the proposed approach in LIVE-Qualcomm dataset. We extensively evaluated the perceptual quality prediction model, obtaining one final Pearson correlation of 0:7749±0:0884 with Mean Opinion Scores, and showed that it can achieve good video quality prediction, outperforming other state-of-the-art VQA leading models.

scholarly journals Automatic Extraction and Detection of Characteristic Movement Patterns in Children with ADHD Based on a Convolutional Neural Network (CNN) and Acceleration Images

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3924 ◽  
Author(s):  
Mario Muñoz-Organero ◽  
Lauren Powell ◽  
Ben Heller ◽  
Val Harpin ◽  
Jack Parker
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Convolutional Neural Network ◽  
Statistical Significance ◽  
Neurodevelopmental Disorder ◽  
Control Group ◽  
P Value ◽  
Moderate Physical Activity ◽  
Children With Adhd ◽  
Axial Acceleration

Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental disorder, which is characterized by inattention, hyperactivity and impulsive behaviors. In particular, children have difficulty keeping still exhibiting increased fine and gross motor activity. This paper focuses on analyzing the data obtained from two tri-axial accelerometers (one on the wrist of the dominant arm and the other on the ankle of the dominant leg) worn during school hours by a group of 22 children (11 children with ADHD and 11 paired controls). Five of the 11 ADHD diagnosed children were not on medication during the study. The children were not explicitly instructed to perform any particular activity but followed a normal session at school alternating classes of little or moderate physical activity with intermediate breaks of more prominent physical activity. The tri-axial acceleration signals were converted into 2D acceleration images and a Convolutional Neural Network (CNN) was trained to recognize the differences between non-medicated ADHD children and their paired controls. The results show that there were statistically significant differences in the way the two groups moved for the wrist accelerometer (t-test p-value <0.05). For the ankle accelerometer statistical significance was only achieved between data from the non-medicated children in the experimental group and the control group. Using a Convolutional Neural Network (CNN) to automatically extract embedded acceleration patterns and provide an objective measure to help in the diagnosis of ADHD, an accuracy of 0.875 for the wrist sensor and an accuracy of 0.9375 for the ankle sensor was achieved.

scholarly journals Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram

2020 ◽  
Vol 75 (7) ◽  
pp. 722-733 ◽  
Author(s):  
Wei-Yin Ko ◽  
Konstantinos C. Siontis ◽  
Zachi I. Attia ◽  
Rickey E. Carter ◽  
Suraj Kapa ◽  
...  
Keyword(s):  
Neural Network ◽  
Hypertrophic Cardiomyopathy ◽  
Convolutional Neural Network

scholarly journals Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network

2020 ◽  
Author(s):  
Anil Kumar Hanumanthappa ◽  
Jaswinder Singh ◽  
Kuldip Paliwal ◽  
Jaspreet Singh ◽  
Yaoqi Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Solvent Accessibility ◽  
Pearson Correlation ◽  
Functional Characterization ◽  
Homology Search ◽  
Supplementary Information ◽  
Functional Regions ◽  
Comparable Performance ◽  
Single Sequence

Abstract Motivation RNA solvent accessibility, similar to protein solvent accessibility, reflects the structural regions that are accessible to solvents or other functional biomolecules, and plays an important role for structural and functional characterization. Unlike protein solvent accessibility, only a few tools are available for predicting RNA solvent accessibility despite the fact that millions of RNA transcripts have unknown structures and functions. Also, these tools have limited accuracy. Here, we have developed RNAsnap2 that uses a dilated convolutional neural network with a new feature, based on predicted base-pairing probabilities from LinearPartition. Results Using the same training set from the recent predictor RNAsol, RNAsnap2 provides an 11% improvement in median Pearson Correlation Coefficient (PCC) and 9% improvement in mean absolute errors for the same test set of 45 RNA chains. A larger improvement (22% in median PCC) is observed for 31 newly deposited RNA chains that are non-redundant and independent from the training and the test sets. A single-sequence version of RNAsnap2 (i.e. without using sequence profiles generated from homology search by Infernal) has achieved comparable performance to the profile-based RNAsol. In addition, RNAsnap2 has achieved comparable performance for protein-bound and protein-free RNAs. Both RNAsnap2 and RNAsnap2 (SingleSeq) are expected to be useful for searching structural signatures and locating functional regions of non-coding RNAs. Availability and implementation Standalone-versions of RNAsnap2 and RNAsnap2 (SingleSeq) are available at https://github.com/jaswindersingh2/RNAsnap2. Direct prediction can also be made at https://sparks-lab.org/server/rnasnap2. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above. Supplementary information Supplementary data are available at Bioinformatics online.

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