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https://www.readbyqxmd.com/read/29663090/human-activity-recognition-from-body-sensor-data-using-deep-learning
#1
Mohammad Mehedi Hassan, Shamsul Huda, Md Zia Uddin, Ahmad Almogren, Majed Alrubaian
In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities...
April 16, 2018: Journal of Medical Systems
https://www.readbyqxmd.com/read/29651694/classification-of-ecg-beats-using-deep-belief-network-and-active-learning
#2
Sayantan G, Kien P T, Kadambari K V
A new semi-supervised approach based on deep learning and active learning for classification of electrocardiogram signals (ECG) is proposed. The objective of the proposed work is to model a scientific method for classification of cardiac irregularities using electrocardiogram beats. The model follows the Association for the Advancement of medical instrumentation (AAMI) standards and consists of three phases. In phase I, feature representation of ECG is learnt using Gaussian-Bernoulli deep belief network followed by a linear support vector machine (SVM) training in the consecutive phase...
April 12, 2018: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/29570642/competitive-deep-belief-networks-for-underwater-acoustic-target-recognition
#3
Honghui Yang, Sheng Shen, Xiaohui Yao, Meiping Sheng, Chen Wang
Underwater acoustic target recognition based on ship-radiated noise belongs to the small-sample-size recognition problems. A competitive deep-belief network is proposed to learn features with more discriminative information from labeled and unlabeled samples. The proposed model consists of four stages: (1) A standard restricted Boltzmann machine is pretrained using a large number of unlabeled data to initialize its parameters; (2) the hidden units are grouped according to categories, which provides an initial clustering model for competitive learning; (3) competitive training and back-propagation algorithms are used to update the parameters to accomplish the task of clustering; (4) by applying layer-wise training and supervised fine-tuning, a deep neural network is built to obtain features...
March 23, 2018: Sensors
https://www.readbyqxmd.com/read/29558485/a-hybrid-technique-for-speech-segregation-and-classification-using-a-sophisticated-deep-neural-network
#4
Khurram Ashfaq Qazi, Tabassam Nawaz, Zahid Mehmood, Muhammad Rashid, Hafiz Adnan Habib
Recent research on speech segregation and music fingerprinting has led to improvements in speech segregation and music identification algorithms. Speech and music segregation generally involves the identification of music followed by speech segregation. However, music segregation becomes a challenging task in the presence of noise. This paper proposes a novel method of speech segregation for unlabelled stationary noisy audio signals using the deep belief network (DBN) model. The proposed method successfully segregates a music signal from noisy audio streams...
2018: PloS One
https://www.readbyqxmd.com/read/29495410/an-unsupervised-deep-hyperspectral-anomaly-detector
#5
Ning Ma, Yu Peng, Shaojun Wang, Philip H W Leong
Hyperspectral image (HSI) based detection has attracted considerable attention recently in agriculture, environmental protection and military applications as different wavelengths of light can be advantageously used to discriminate different types of objects. Unfortunately, estimating the background distribution and the detection of interesting local objects is not straightforward, and anomaly detectors may give false alarms. In this paper, a Deep Belief Network (DBN) based anomaly detector is proposed. The high-level features and reconstruction errors are learned through the network in a manner which is not affected by previous background distribution assumption...
February 26, 2018: Sensors
https://www.readbyqxmd.com/read/29492098/development-of-models-for-classification-of-action-between-heat-clearing-herbs-and-blood-activating-stasis-resolving-herbs-based-on-theory-of-traditional-chinese-medicine
#6
Zhao Chen, Yanfeng Cao, Shuaibing He, Yanjiang Qiao
Background: Action (" gongxiao " in Chinese) of traditional Chinese medicine (TCM) is the high recapitulation for therapeutic and health-preserving effects under the guidance of TCM theory. TCM-defined herbal properties (" yaoxing " in Chinese) had been used in this research. TCM herbal property (TCM-HP) is the high generalization and summary for actions, both of which come from long-term effective clinical practice in two thousands of years in China. However, the specific relationship between TCM-HP and action of TCM is complex and unclear from a scientific perspective...
2018: Chinese Medicine
https://www.readbyqxmd.com/read/29487619/deep-learning-for-computer-vision-a-brief-review
#7
REVIEW
Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis
Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation...
2018: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29471111/mortality-prediction-in-intensive-care-units-icus-using-a-deep-rule-based-fuzzy-classifier
#8
Raheleh Davoodi, Mohammad Hassan Moradi
Electronic health records (EHRs) contain critical information useful for clinical studies. Early assessment of patients' mortality in intensive care units is of great importance. In this paper, a Deep Rule-Based Fuzzy System (DRBFS) was proposed to develop an accurate in-hospital mortality prediction in the intensive care unit (ICU) patients employing a large number of input variables. Our main contribution is proposing a system, which is capable of dealing with big data with heterogeneous mixed categorical and numeric attributes...
February 19, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29356346/predicting-the-hearing-outcome-in-sudden-sensorineural-hearing-loss-via-machine-learning-models
#9
D Bing, J Ying, J Miao, L Lan, D Wang, L Zhao, Z Yin, L Yu, J Guan, Q Wang
OBJECTIVE: Sudden sensorineural hearing loss (SSHL) is a multifactorial disorder with high heterogeneity, thus the outcomes vary widely. This study aimed to develop predictive models based on four machine learning methods for SSHL, identifying the best performer for clinical application. DESIGN: Single-centre retrospective study. SETTING: Chinese People's liberation army (PLA) hospital, Beijing, China. PARTICIPANTS: A total of 1220 in-patient SSHL patients were enrolled between June 2008 and December 2015...
January 21, 2018: Clinical Otolaryngology
https://www.readbyqxmd.com/read/29181238/development-of-a-stress-classification-model-using-deep-belief-networks-for-stress-monitoring
#10
Se-Hui Song, Dong Keun Kim
Objectives: Stress management is related to public healthcare and quality of life; an accurate stress classification method is necessary for the design of stress monitoring systems. Therefore, the goal of this study was to design a novel stress classification model using a deep learning method. Methods: In this paper, we present a stress classification model using the dataset from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze stress-related health data...
October 2017: Healthcare Informatics Research
https://www.readbyqxmd.com/read/29095063/a-hybrid-classifier-based-on-nonlinear-pca-and-deep-belief-networks-with-applications-in-dysphagia-diagnosis
#11
Chong Su, Yue Gao, Yuxiao Xie, Yong Xue, Lijun Ge, Hongguang Li
Traditional dysphagia prescreening diagnostic methods require doctors specialists to give patients a total score based on a water swallow test scale. This method is limited by the high dimensionality of the diagnostic elements in the water swallow test scale with heavy workload (Towards each patient, the scale requires the doctors give score for 18 diagnostic elements respectively) as well as the difficulties of extracting and using the diagnostic scale data's non-linear features and hidden expertise information (Even with the scale scores, specific diagnostic conclusions are still given by expert doctors under the expertise)...
November 2, 2017: Computer Assisted Surgery (Abingdon, England)
https://www.readbyqxmd.com/read/29072144/a-new-method-for-enhancer-prediction-based-on-deep-belief-network
#12
Hongda Bu, Yanglan Gan, Yang Wang, Shuigeng Zhou, Jihong Guan
BACKGROUND: Studies have shown that enhancers are significant regulatory elements to play crucial roles in gene expression regulation. Since enhancers are unrelated to the orientation and distance to their target genes, it is a challenging mission for scholars and researchers to accurately predicting distal enhancers. In the past years, with the high-throughout ChiP-seq technologies development, several computational techniques emerge to predict enhancers using epigenetic or genomic features...
October 16, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29060616/a-separated-feature-learning-based-dbn-structure-for-classification-of-ssmvep-signals
#13
Yaguang Jia, Jun Xie, Guanghua Xu, Min Li, Sicong Zhang, Ailing Luo, Xingliang Han
Signal processing is one of the key points in brain computer interface (BCI) application. The common methods in BCI signal classification include canonical correlation analysis (CCA), support vector machine (SVM) and so on. However, because BCI signals are very complex and valid signals often come with confounded background noise, many current classification methods would lose meaningful information embedded in human EEGs. Otherwise, due to the huge inter-subject variability with respect to characteristics and patterns of BCI signals, there often exists large difference of classification accuracy among different subjects...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29060497/recognition-physical-activities-with-optimal-number-of-wearable-sensors-using-data-mining-algorithms-and-deep-belief-network
#14
Ali H Al-Fatlawi, Hayder K Fatlawi, Sai Ho Ling
Daily physical activities monitoring is benefiting the health care field in several ways, in particular with the development of the wearable sensors. This paper adopts effective ways to calculate the optimal number of the necessary sensors and to build a reliable and a high accuracy monitoring system. Three data mining algorithms, namely Decision Tree, Random Forest and PART Algorithm, have been applied for the sensors selection process. Furthermore, the deep belief network (DBN) has been investigated to recognise 33 physical activities effectively...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29018640/a-multi-classifier-system-for-automatic-mitosis-detection-in-breast-histopathology-images-using-deep-belief-networks
#15
K Sabeena Beevi, Madhu S Nair, G R Bindu
Mitotic count is an important diagnostic factor in breast cancer grading and prognosis. Detection of mitosis in breast histopathology images is very challenging mainly due to diffused intensities along object boundary and shape variation in different stages of mitosis. This paper demonstrates an accurate technique for detecting the mitotic cells in Hematoxyline and Eosin stained images by step by step refinement of segmentation and classification stages. Krill Herd Algorithm-based localized active contour model precisely segments cell nuclei from background stroma...
2017: IEEE Journal of Translational Engineering in Health and Medicine
https://www.readbyqxmd.com/read/28991060/hemodynamic-instability-and-cardiovascular-events-after-traumatic-brain-injury-predict-outcome-after-artifact-removal-with-deep-belief-network-analysis
#16
Hakseung Kim, Seung-Bo Lee, Yunsik Son, Marek Czosnyka, Dong-Joo Kim
BACKGROUND: Hemodynamic instability and cardiovascular events heavily affect the prognosis of traumatic brain injury. Physiological signals are monitored to detect these events. However, the signals are often riddled with faulty readings, which jeopardize the reliability of the clinical parameters obtained from the signals. A machine-learning model for the elimination of artifactual events shows promising results for improving signal quality. However, the actual impact of the improvements on the performance of the clinical parameters after the elimination of the artifacts is not well studied...
October 5, 2017: Journal of Neurosurgical Anesthesiology
https://www.readbyqxmd.com/read/28946991/deep-learning-ensemble-with-asymptotic-techniques-for-oscillometric-blood-pressure-estimation
#17
Soojeong Lee, Joon-Hyuk Chang
BACKGROUND AND OBJECTIVE: This paper proposes a deep learning based ensemble regression estimator with asymptotic techniques, and offers a method that can decrease uncertainty for oscillometric blood pressure (BP) measurements using the bootstrap and Monte-Carlo approach. While the former is used to estimate SBP and DBP, the latter attempts to determine confidence intervals (CIs) for SBP and DBP based on oscillometric BP measurements. METHOD: This work originally employs deep belief networks (DBN)-deep neural networks (DNN) to effectively estimate BPs based on oscillometric measurements...
November 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28919042/new-tools-for-the-visualization-of-biological-pathways
#18
Tomojit Ghosh, Xiaofeng Ma, Michael Kirby
This paper presents several geometrically motivated techniques for the visualization of high-dimensional biological data sets. The Grassmann manifold provides a robust framework for measuring data similarity in a subspace context. Sparse radial basis function classification as a visualization technique leverages recent advances in radial basis function learning via convex optimization. In the spirit of deep belief networks, supervised centroid-encoding is proposed as a way to exploit class label information...
January 1, 2018: Methods: a Companion to Methods in Enzymology
https://www.readbyqxmd.com/read/28875051/statistics-and-deep-belief-network-based-cardiovascular-risk-prediction
#19
Jaekwon Kim, Ungu Kang, Youngho Lee
OBJECTIVES: Cardiovascular predictions are related to patients' quality of life and health. Therefore, a risk prediction model for cardiovascular conditions is needed. METHODS: In this paper, we propose a cardiovascular disease prediction model using the sixth Korea National Health and Nutrition Examination Survey (KNHANES-VI) 2013 dataset to analyze cardiovascular-related health data. First, statistical analysis was performed to find variables related to cardiovascular disease using health data related to cardiovascular disease...
July 2017: Healthcare Informatics Research
https://www.readbyqxmd.com/read/28737705/emotion-recognition-from-chinese-speech-for-smart-affective-services-using-a-combination-of-svm-and-dbn
#20
Lianzhang Zhu, Leiming Chen, Dehai Zhao, Jiehan Zhou, Weishan Zhang
Accurate emotion recognition from speech is important for applications like smart health care, smart entertainment, and other smart services. High accuracy emotion recognition from Chinese speech is challenging due to the complexities of the Chinese language. In this paper, we explore how to improve the accuracy of speech emotion recognition, including speech signal feature extraction and emotion classification methods. Five types of features are extracted from a speech sample: mel frequency cepstrum coefficient (MFCC), pitch, formant, short-term zero-crossing rate and short-term energy...
July 24, 2017: Sensors
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