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Journal articles on the topic 'Classification of biomedical time series'

Author: Grafiati

Published: 21 December 2024

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1

Ramanujam, E., and S. Padmavathi. "Genetic time series motif discovery for time series classification." International Journal of Biomedical Engineering and Technology 31, no. 1 (2019): 47. http://dx.doi.org/10.1504/ijbet.2019.101051.

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Jin, Lin-peng, and Jun Dong. "Ensemble Deep Learning for Biomedical Time Series Classification." Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/6212684.

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Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimenta

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Ivaturi, Praharsh, Matteo Gadaleta, Amitabh C. Pandey, Michael Pazzani, Steven R. Steinhubl, and Giorgio Quer. "A Comprehensive Explanation Framework for Biomedical Time Series Classification." IEEE Journal of Biomedical and Health Informatics 25, no. 7 (2021): 2398–408. http://dx.doi.org/10.1109/jbhi.2021.3060997.

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Wang, Jin, Ping Liu, Mary F. H. She, Saeid Nahavandi, and Abbas Kouzani. "Bag-of-words representation for biomedical time series classification." Biomedical Signal Processing and Control 8, no. 6 (2013): 634–44. http://dx.doi.org/10.1016/j.bspc.2013.06.004.

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Ku-Maldonado, Carlos Alejandro, and Erik Molino-Minero-Re. "Performance Evaluation of Biomedical Time Series Transformation Methods for Classification Tasks." Revista Mexicana de Ingeniería Biomédica 44, no. 4 (2023): 105–16. http://dx.doi.org/10.17488/rmib.44.4.7.

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The extraction of time series features is essential across various fields, yet it remains a challenging endeavor. Therefore, it's crucial to identify appropriate methods capable of extracting pertinent information that can significantly enhance classification performance. Among these methods are those that translate time series into different domains. This study investigates three distinct time series transformation approaches for addressing time series classification challenges within biomedical data. The first method involves a response vector transformation, while the other two employ image

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Gupta, R., A. Mittal, K. Singh, V. Narang, and S. Roy. "Time-series approach to protein classification problem." IEEE Engineering in Medicine and Biology Magazine 28, no. 4 (2009): 32–37. http://dx.doi.org/10.1109/memb.2009.932903.

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7

Wang, Will Ke, Ina Chen, Leeor Hershkovich, et al. "A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications." Sensors 22, no. 20 (2022): 8016. http://dx.doi.org/10.3390/s22208016.

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Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for t

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Lemus, Mariano, João P. Beirão, Nikola Paunković, Alexandra M. Carvalho, and Paulo Mateus. "Information-Theoretical Criteria for Characterizing the Earliness of Time-Series Data." Entropy 22, no. 1 (2019): 49. http://dx.doi.org/10.3390/e22010049.

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Biomedical signals constitute time-series that sustain machine learning techniques to achieve classification. These signals are complex with measurements of several features over, eventually, an extended period. Characterizing whether the data can anticipate prediction is an essential task in time-series mining. The ability to obtain information in advance by having early knowledge about a specific event may be of great utility in many areas. Early classification arises as an extension of the time-series classification problem, given the need to obtain a reliable prediction as soon as possible

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Athavale, Yashodhan, Sridhar Krishnan, and Aziz Guergachi. "Pattern Classification of Signals Using Fisher Kernels." Mathematical Problems in Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/467175.

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The intention of this study is to gauge the performance of Fisher kernels for dimension simplification and classification of time-series signals. Our research work has indicated that Fisher kernels have shown substantial improvement in signal classification by enabling clearer pattern visualization in three-dimensional space. In this paper, we will exhibit the performance of Fisher kernels for two domains: financial and biomedical. The financial domain study involves identifying the possibility of collapse or survival of a company trading in the stock market. For assessing the fate of each com

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Carreiro, André V., Orlando Anunciação, João A. Carriço, and Sara C. Madeira. "Prognostic Prediction through Biclustering-Based Classification of Clinical Gene Expression Time Series." Journal of Integrative Bioinformatics 8, no. 3 (2011): 73–89. http://dx.doi.org/10.1515/jib-2011-175.

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Summary The constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyzes. It is now broadly accepted that considering a temporal perspective represents a great advantage to better understand disease progression and treatment results at a molecular level. In this context, biclustering algorithms emerged as an important tool to discover local expression patterns in biomedical applications, and CCC-Biclustering arose as an efficient algorithm relying on the temporal nature of data to identify all maximal temporal patterns in gene expressio

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Piepjohn, Patricia, Christin Bald, Gregor Kuhlenbäumer, Jos Steffen Becktepe, Günther Deuschl, and Gerhard Schmidt. "Real-time classification of movement patterns of tremor patients." Biomedical Engineering / Biomedizinische Technik 67, no. 2 (2022): 119–30. http://dx.doi.org/10.1515/bmt-2021-0140.

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Abstract The process of diagnosing tremor patients often leads to misdiagnoses. Therefore, existing technical methods for analysing tremor are needed to more effectively distinguish between different diseases. For this purpose, a system has been developed that classifies measured tremor signals in real time. To achieve this, the hand tremor of 561 subjects has been measured in different hand positions. Acceleration and surface electromyography are recorded during the examination. For this study, data from subjects with Parkinson’s Disease, Essential Tremor, and physiological tremor are conside

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Fulcher, Ben D., Max A. Little, and Nick S. Jones. "Highly comparative time-series analysis: the empirical structure of time series and their methods." Journal of The Royal Society Interface 10, no. 83 (2013): 20130048. http://dx.doi.org/10.1098/rsif.2013.0048.

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The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties

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Gamidullaeva, Leyla Ayvarovna, and Vsevolod Chernyshenko. "Using Decision-Making Block of Computer-Based Intelligent Biomedical Avatar for Applied Research in Bioinformatics." International Journal of Applied Research in Bioinformatics 9, no. 2 (2019): 24–34. http://dx.doi.org/10.4018/ijarb.2019070102.

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A biomedical task in which the definitions and properties of applied research indicators under study in bioinformatics is formalized. A wide range of traditional approaches used for predicting medical time series were reviewed. Advanced algorithms for predicting moments of reversals of biomedical trends based on machine learning tools were investigated as well. The effectiveness of different kinds of approaches was discussed, and related examples are given. An original securities price dynamics trend classification algorithm, based on the use of the sliding window methodology and biomedical av

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Alarcón, Ángel Serrano, Natividad Martínez Madrid, Ralf Seepold, and Juan Antonio Ortega Ramirez. "Main requirements of end-to-end deep learning models for biomedical time series classification in healthcare environments." Procedia Computer Science 207 (2022): 3038–46. http://dx.doi.org/10.1016/j.procs.2022.09.532.

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Carreiro, André V., Artur J. Ferreira, Mário A. T. Figueiredo, and Sara C. Madeira. "Towards a Classification Approach using Meta-Biclustering: Impact of Discretization in the Analysis of Expression Time Series." Journal of Integrative Bioinformatics 9, no. 3 (2012): 105–20. http://dx.doi.org/10.1515/jib-2012-207.

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Summary Biclustering has been recognized as a remarkably effective method for discovering local temporal expression patterns and unraveling potential regulatory mechanisms, essential to understanding complex biomedical processes, such as disease progression and drug response. In this work, we propose a classification approach based on meta-biclusters (a set of similar biclusters) applied to prognostic prediction. We use real clinical expression time series to predict the response of patients with multiple sclerosis to treatment with Interferon-β. As compared to previous approaches, the main ad

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Zhang, Yinghui, Fengyuan Zhang, Yantong Cui, and Ruoci Ning. "CLASSIFICATION OF BIOMEDICAL IMAGES USING CONTENT BASED IMAGE RETRIEVAL SYSTEMS." International Journal of Engineering Technologies and Management Research 5, no. 2 (2020): 181–89. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.161.

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Because of the numerous application of Content-based image retrieval (CBIR) system in various areas, it has always remained a topic of keen interest by the researchers. Fetching of the most similar image from the complete repository by comparing it to the input image in the minimum span of time is the main task of the CBIR. The purpose of the CBIR can vary from different types of requirements like a diagnosis of the illness by the physician, crime investigation, product recommendation by the e-commerce companies, etc. In the present work, CBIR is used for finding the similar patients having Br

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Lipponen, Jukka A., and Mika P. Tarvainen. "A robust algorithm for heart rate variability time series artefact correction using novel beat classification." Journal of Medical Engineering & Technology 43, no. 3 (2019): 173–81. http://dx.doi.org/10.1080/03091902.2019.1640306.

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18

Jackson, Rhydon, Debra Knisley, Cecilia McIntosh, and Phillip Pfeiffer. "Predicting Flavonoid UGT Regioselectivity." Advances in Bioinformatics 2011 (June 30, 2011): 1–15. http://dx.doi.org/10.1155/2011/506583.

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Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Novel indices characterizing graphical models of residues were proposed and found to be widely distributed among existing amino acid indices and to cluster residues appropriately. UGT subsequences biochemically linked to regioselectivity were modeled as sets of index sequences. Several learning techniques incorporating these UGT models were compared with classifications based on standard sequence alignment scor

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JO, YONG-UN, and DO-CHANG OH. "REAL-TIME HAND GESTURE CLASSIFICATION USING CRNN WITH SCALE AVERAGE WAVELET TRANSFORM." Journal of Mechanics in Medicine and Biology 20, no. 10 (2020): 2040028. http://dx.doi.org/10.1142/s021951942040028x.

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It is very useful in the human computer interface to quickly and accurately recognize human hand movements in real time. In this paper, we aimed to robustly recognize hand gestures in real time using Convolutional Recurrent Neural Network (CRNN) with pre-processing and overlapping window. The CRNN is a deep learning model that combines Long Short-Term Memory (LSTM) for time-series information classification and Convolutional Neural Network (CNN) for feature extraction. The sensor for hand gesture detection uses Myo-armband, and six hand gestures are recognized and classified, including two gri

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20

Gao, Yongxiang, Zhi Zhao, Yimin Chen, et al. "Automatic epileptic seizure classification in multichannel EEG time series with linear discriminant analysis." Technology and Health Care 28, no. 1 (2020): 23–33. http://dx.doi.org/10.3233/thc-181548.

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21

Chambon, Stanislas, Mathieu N. Galtier, Pierrick J. Arnal, Gilles Wainrib, and Alexandre Gramfort. "A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series." IEEE Transactions on Neural Systems and Rehabilitation Engineering 26, no. 4 (2018): 758–69. http://dx.doi.org/10.1109/tnsre.2018.2813138.

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22

Arami, Arash, Antonios Poulakakis-Daktylidis, Yen F. Tai, and Etienne Burdet. "Prediction of Gait Freezing in Parkinsonian Patients: A Binary Classification Augmented With Time Series Prediction." IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, no. 9 (2019): 1909–19. http://dx.doi.org/10.1109/tnsre.2019.2933626.

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23

Dursun, Gizem, Dunja Bijelić, Neşe Ayşit, et al. "Combined segmentation and classification-based approach to automated analysis of biomedical signals obtained from calcium imaging." PLOS ONE 18, no. 2 (2023): e0281236. http://dx.doi.org/10.1371/journal.pone.0281236.

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Automated screening systems in conjunction with machine learning-based methods are becoming an essential part of the healthcare systems for assisting in disease diagnosis. Moreover, manually annotating data and hand-crafting features for training purposes are impractical and time-consuming. We propose a segmentation and classification-based approach for assembling an automated screening system for the analysis of calcium imaging. The method was developed and verified using the effects of disease IgGs (from Amyotrophic Lateral Sclerosis patients) on calcium (Ca2+) homeostasis. From 33 imaging v

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24

Liu, Chenxi, Israel Cohen, Rotem Vishinkin, and Hossam Haick. "Nanomaterial-Based Sensor Array Signal Processing and Tuberculosis Classification Using Machine Learning." Journal of Low Power Electronics and Applications 13, no. 2 (2023): 39. http://dx.doi.org/10.3390/jlpea13020039.

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Tuberculosis (TB) has long been recognized as a significant health concern worldwide. Recent advancements in noninvasive wearable devices and machine learning (ML) techniques have enabled rapid and cost-effective testing for the real-time detection of TB. However, small datasets are often encountered in biomedical and chemical engineering domains, which can hinder the success of ML models and result in overfitting issues. To address this challenge, we propose various data preprocessing methods and ML approaches, including long short-term memory (LSTM), convolutional neural network (CNN), Grami

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Arunachalam, S. P., S. Kapa, S. K. Mulpuru, P. A. Friedman, and E. G. Tolkacheva. "Improved Multiscale Entropy Technique with Nearest-Neighbor Moving-Average Kernel for Nonlinear and Nonstationary Short-Time Biomedical Signal Analysis." Journal of Healthcare Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/8632436.

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Analysis of biomedical signals can yield invaluable information for prognosis, diagnosis, therapy evaluation, risk assessment, and disease prevention which is often recorded as short time series data that challenges existing complexity classification algorithms such as Shannon entropy (SE) and other techniques. The purpose of this study was to improve previously developed multiscale entropy (MSE) technique by incorporating nearest-neighbor moving-average kernel, which can be used for analysis of nonlinear and non-stationary short time series physiological data. The approach was tested for robu

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Tripathy, R. K., and U. Rajendra Acharya. "Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework." Biocybernetics and Biomedical Engineering 38, no. 4 (2018): 890–902. http://dx.doi.org/10.1016/j.bbe.2018.05.005.

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Cuesta-Frau, David, Juan Pablo Murillo-Escobar, Diana Alexandra Orrego, and Edilson Delgado-Trejos. "Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications." Entropy 21, no. 4 (2019): 385. http://dx.doi.org/10.3390/e21040385.

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Permutation Entropy (PE) is a time series complexity measure commonly used in a variety of contexts, with medicine being the prime example. In its general form, it requires three input parameters for its calculation: time series length N, embedded dimension m, and embedded delay τ . Inappropriate choices of these parameters may potentially lead to incorrect interpretations. However, there are no specific guidelines for an optimal selection of N, m, or τ , only general recommendations such as N > > m ! , τ = 1 , or m = 3 , … , 7 . This paper deals specifically with the study of the practi

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Wang, Jialing, Shiwei Cheng, Jieming Tian, and Yuefan Gao. "A 2D CNN-LSTM hybrid algorithm using time series segments of EEG data for motor imagery classification." Biomedical Signal Processing and Control 83 (May 2023): 104627. http://dx.doi.org/10.1016/j.bspc.2023.104627.

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BAI, G. MERCY, and P. VENKADESH. "TAYLOR–MONARCH BUTTERFLY OPTIMIZATION-BASED SUPPORT VECTOR MACHINE FOR ACUTE LYMPHOBLASTIC LEUKEMIA CLASSIFICATION WITH BLOOD SMEAR MICROSCOPIC IMAGES." Journal of Mechanics in Medicine and Biology 21, no. 06 (2021): 2150041. http://dx.doi.org/10.1142/s021951942150041x.

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Acute lymphoblastic leukemia (ALL) is a serious hematological neoplasis that is characterized by the development of immature and abnormal growth of lymphoblasts. However, microscopic examination of bone marrow is the only way to achieve leukemia detection. Various methods are developed for automatic leukemia detection, but these methods are costly and time-consuming. Hence, an effective leukemia detection approach is designed using the proposed Taylor–monarch butterfly optimization-based support vector machine (Taylor–MBO-based SVM). However, the proposed Taylor–MBO is designed by integrating

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Chang, Yuan-Hsiang, Kuniya Abe, Hideo Yokota, Kazuhiro Sudo, Yukio Nakamura, and Ming-Dar Tsai. "HUMAN INDUCED PLURIPOTENT STEM CELL REGION DETECTION IN BRIGHT-FIELD MICROSCOPY IMAGES USING CONVOLUTIONAL NEURAL NETWORKS." Biomedical Engineering: Applications, Basis and Communications 31, no. 02 (2019): 1950009. http://dx.doi.org/10.4015/s1016237219500091.

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Human induced pluripotent stem (iPS) cells represent an ideal source for patient specific cell-based regenerative medicine. For practical uses of iPS cells, large-scale, cost- and time-effective production of fully reprogrammed iPS cells from a number of patients should be achieved. To achieve this goal, culture protocols for inducing iPS cells as well as methods for selecting fully reprogrammed iPS cells in a mixture of cells which are still in reprogramming and non-iPS differentiated cells, should be improved. This paper proposes a convolutional neural network (CNN) structure to classify a b

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Resta, Michele, Anna Monreale, and Davide Bacciu. "Occlusion-Based Explanations in Deep Recurrent Models for Biomedical Signals." Entropy 23, no. 8 (2021): 1064. http://dx.doi.org/10.3390/e23081064.

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The biomedical field is characterized by an ever-increasing production of sequential data, which often come in the form of biosignals capturing the time-evolution of physiological processes, such as blood pressure and brain activity. This has motivated a large body of research dealing with the development of machine learning techniques for the predictive analysis of such biosignals. Unfortunately, in high-stakes decision making, such as clinical diagnosis, the opacity of machine learning models becomes a crucial aspect to be addressed in order to increase the trust and adoption of AI technolog

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Dissanayake, W. M. N. D., and Maheshi B. Dissanayake. "A Novel LSTM-based Data Synthesis Approach for Performance Improvement in Detecting Epileptic Seizures." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 20 (October 10, 2023): 132–39. http://dx.doi.org/10.37394/23208.2023.20.13.

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Bio-electrical time signals play a significant role in assisting non-invasive observational procedures in healthcare. These bioelectrical signals are weak signals with inherently low voltage and low frequency, hidden mostly under relatively large high-voltage noise signals. Hence it is extra challenging to analyze them. In modern clinical data analysis, these signals could be further analyzed using conventional machine learning (ML) methods. Also, in the recent past, two-dimensional spectrum-based classification, predominantly with Convolutional Neural Networks (CNN), has been tried with time-

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Zhu, Mengyun, Ximin Fan, Weijing Liu, et al. "Artificial Intelligence-Based Echocardiographic Left Atrial Volume Measurement with Pulmonary Vein Comparison." Journal of Healthcare Engineering 2021 (December 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/1336762.

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This paper combines echocardiographic signal processing and artificial intelligence technology to propose a deep neural network model adapted to echocardiographic signals to achieve left atrial volume measurement and automatic assessment of pulmonary veins efficiently and quickly. Based on the echocardiographic signal generation mechanism and detection method, an experimental scheme for the echocardiographic signal acquisition was designed. The echocardiographic signal data of healthy subjects were measured in four different experimental states, and a database of left atrial volume measurement

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Szigeti, Balázs, Ajinkya Deogade, and Barbara Webb. "Searching for motifs in the behaviour of larval Drosophila melanogaster and Caenorhabditis elegans reveals continuity between behavioural states." Journal of The Royal Society Interface 12, no. 113 (2015): 20150899. http://dx.doi.org/10.1098/rsif.2015.0899.

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We present a novel method for the unsupervised discovery of behavioural motifs in larval Drosophila melanogaster and Caenorhabditis elegans . A motif is defined as a particular sequence of postures that recurs frequently. The animal's changing posture is represented by an eigenshape time series, and we look for motifs in this time series. To find motifs, the eigenshape time series is segmented, and the segments clustered using spline regression. Unlike previous approaches, our method can classify sequences of unequal duration as the same motif. The behavioural motifs are used as the basis of a

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Chatterjee, Shre Kumar, Saptarshi Das, Koushik Maharatna, et al. "Exploring strategies for classification of external stimuli using statistical features of the plant electrical response." Journal of The Royal Society Interface 12, no. 104 (2015): 20141225. http://dx.doi.org/10.1098/rsif.2014.1225.

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Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In th

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Uyulan, Caglar, Türker Tekin Ergüzel, and Nevzat Tarhan. "Entropy-based feature extraction technique in conjunction with wavelet packet transform for multi-mental task classification." Biomedical Engineering / Biomedizinische Technik 64, no. 5 (2019): 529–42. http://dx.doi.org/10.1515/bmt-2018-0105.

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Abstract Event-related mental task information collected from electroencephalography (EEG) signals, which are functionally related to different brain areas, possesses complex and non-stationary signal features. It is essential to be able to classify mental task information through the use in brain-computer interface (BCI) applications. This paper proposes a wavelet packet transform (WPT) technique merged with a specific entropy biomarker as a feature extraction tool to classify six mental tasks. First, the data were collected from a healthy control group and the multi-signal information compri

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Makhir, Abdelmalek, My Hachem El Yousfi Alaoui, and Larbi Belarbi. "Comprehensive Cardiac Ischemia Classification Using Hybrid CNN-Based Models." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 03 (2024): 154–65. http://dx.doi.org/10.3991/ijoe.v20i03.45769.

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This study addresses the critical issue of classifying cardiac ischemia, a disease with significant global health implications that contributes to the global mortality rate. In our study, we tackle the classification of ischemia using six diverse electrocardiogram (ECG) datasets and a convolutional neural network (CNN) as the primary methodology. We combined six separate datasets to gain a more comprehensive understanding of cardiac electrical activity, utilizing 12 leads to obtain a broader perspective. A discrete wavelet transform (DWT) preprocessing was used to eliminate irrelevant informat

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Cuesta-Frau, David, Jakub Schneider, Eduard Bakštein, Pavel Vostatek, Filip Spaniel, and Daniel Novák. "Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study." Entropy 22, no. 11 (2020): 1243. http://dx.doi.org/10.3390/e22111243.

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Bipolar Disorder (BD) is an illness with high prevalence and a huge social and economic impact. It is recurrent, with a long-term evolution in most cases. Early treatment and continuous monitoring have proven to be very effective in mitigating the causes and consequences of BD. However, no tools are currently available for a massive and semi-automatic BD patient monitoring and control. Taking advantage of recent technological developments in the field of wearables, this paper studies the feasibility of a BD episodes classification analysis while using entropy measures, an approach successfully

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Khorasani, Abed, Mohammad Reza Daliri, and Mohammad Pooyan. "Recognition of amyotrophic lateral sclerosis disease using factorial hidden Markov model." Biomedical Engineering / Biomedizinische Technik 61, no. 1 (2016): 119–26. http://dx.doi.org/10.1515/bmt-2014-0089.

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Abstract Amyotrophic lateral sclerosis (ALS) is a common disease among neurological disorders that can change the pattern of gait in human. One of the effective methods for recognition and analysis of gait patterns in ALS patients is utilizing stride interval time series. With proper preprocessing for removing unwanted artifacts from the raw stride interval times and then extracting meaningful features from these data, the factorial hidden Markov model (FHMM) was used to distinguish ALS patients from healthy subjects. The results of classification accuracy evaluated using the leave-one-out (LO

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BALOGLU, ULAS BARAN, and ÖZAL YILDIRIM. "CONVOLUTIONAL LONG-SHORT TERM MEMORY NETWORKS MODEL FOR LONG DURATION EEG SIGNAL CLASSIFICATION." Journal of Mechanics in Medicine and Biology 19, no. 01 (2019): 1940005. http://dx.doi.org/10.1142/s0219519419400050.

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Background and objective: Deep learning structures have recently achieved remarkable success in the field of machine learning. Convolutional neural networks (CNN) in image processing and long-short term memory (LSTM) in the time-series analysis are commonly used deep learning algorithms. Healthcare applications of deep learning algorithms provide important contributions for computer-aided diagnosis research. In this study, convolutional long-short term memory (CLSTM) network was used for automatic classification of EEG signals and automatic seizure detection. Methods: A new nine-layer deep net

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Bogdanov, M. R., G. R. Shakhmametova, and N. N. Oskin. "Possibility of Using the Attention Mechanism in Multimodal Recognition of Cardiovascular Diseases." Programmnaya Ingeneria 15, no. 11 (2024): 578–88. http://dx.doi.org/10.17587/prin.15.578-588.

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The paper is about studying the possibility of using the attention mechanism in diagnosing various cardiovascular diseases. Biomedical data were presented in different modalities (text, images, and time series). A comparison of the efficiency of 5 transformers based on the attention mechanism (Dosovitsky transformer, compact convolutional transformer, transformer with external attention, transformer based on tokenization with patch shift and local self-attention, transformer based on multiple deep attention) was carried out with the Exception convolutional neural network, three fully connecte

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Amarantidis, Lampros Chrysovalantis, and Daniel Abásolo. "Interpretation of Entropy Algorithms in the Context of Biomedical Signal Analysis and Their Application to EEG Analysis in Epilepsy." Entropy 21, no. 9 (2019): 840. http://dx.doi.org/10.3390/e21090840.

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Biomedical signals are measurable time series that describe a physiological state of a biological system. Entropy algorithms have been previously used to quantify the complexity of biomedical signals, but there is a need to understand the relationship of entropy to signal processing concepts. In this study, ten synthetic signals that represent widely encountered signal structures in the field of signal processing were created to interpret permutation, modified permutation, sample, quadratic sample and fuzzy entropies. Subsequently, the entropy algorithms were applied to two different databases

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Zhu, Lingxia, Zhiping Xu, and Ting Fang. "Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning." Journal of Healthcare Engineering 2021 (October 27, 2021): 1–8. http://dx.doi.org/10.1155/2021/6050433.

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Cardiovascular disease remains a substantial cause of morbidity and mortality in the developed world and is becoming an increasingly important cause of death in developing countries too. While current cardiovascular treatments can assist to reduce the risk of this disease, a large number of patients still retain a high risk of experiencing a life-threatening cardiovascular event. Thus, the advent of new treatments methods capable of reducing this residual risk remains an important healthcare objective. This paper proposes a deep learning-based method for section recognition of cardiac ultrasou

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Jing, Junyuan, Jing Zhang, Aiping Liu, Min Gao, Ruobing Qian, and Xun Chen. "ECG-Based Multiclass Arrhythmia Classification Using Beat-Level Fusion Network." Journal of Healthcare Engineering 2023 (November 29, 2023): 1–10. http://dx.doi.org/10.1155/2023/1755121.

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Cardiovascular disease (CVD) is one of the most severe diseases threatening human life. Electrocardiogram (ECG) is an effective way to detect CVD. In recent years, many methods have been proposed to detect arrhythmia using 12-lead ECG. In particular, deep learning methods have been proven to be effective and have been widely used. The attention mechanism has attracted extensive attention in many fields in a series of deep learning methods. Off-the-shelf solutions based on deep learning and attention mechanism for ECG classification mostly give weights to time points. None of the existing metho

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Heo, Suncheol, Jae Yong Yu, Eun Ae Kang, et al. "Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach." Healthcare Informatics Research 29, no. 3 (2023): 246–55. http://dx.doi.org/10.4258/hir.2023.29.3.246.

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Objectives: The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea.Methods: A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI

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Nigat, Tsedenya Debebe, Tilahun Melak Sitote, and Berihun Molla Gedefaw. "Fungal Skin Disease Classification Using the Convolutional Neural Network." Journal of Healthcare Engineering 2023 (May 30, 2023): 1–9. http://dx.doi.org/10.1155/2023/6370416.

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Skin is the outer cover of our body, which protects vital organs from harm. This important body part is often affected by a series of infections caused by fungus, bacteria, viruses, allergies, and dust. Millions of people suffer from skin diseases. It is one of the common causes of infection in sub-Saharan Africa. Skin disease can also be the cause of stigma and discrimination. Early and accurate diagnosis of skin disease can be vital for effective treatment. Laser and photonics-based technologies are used for the diagnosis of skin disease. These technologies are expensive and not affordable,

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Cuesta-Frau, David, Daniel Novák, Vacláv Burda, et al. "Influence of Duodenal–Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods." Complexity 2019 (February 14, 2019): 1–10. http://dx.doi.org/10.1155/2019/6070518.

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Diabetes is a disease of great and rising prevalence, with the obesity epidemic being a significant contributing risk factor. Duodenal–jejunal bypass liner (DJBL) is a reversible implant that mimics the effects of more aggressive surgical procedures, such as gastric bypass, to induce weight loss. We hypothesized that DJBL also influences the glucose dynamics in type II diabetes, based on the induced changes already demonstrated in other physiological characteristics and parameters. In order to assess the validity of this assumption, we conducted a quantitative analysis based on several nonline

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Ahammed, Kawser, and Mosabber Uddin Ahmed. "QUANTIFICATION OF MENTAL STRESS USING COMPLEXITY ANALYSIS OF EEG SIGNALS." Biomedical Engineering: Applications, Basis and Communications 32, no. 02 (2020): 2050011. http://dx.doi.org/10.4015/s1016237220500118.

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Detection of mental stress has been receiving great attention from the researchers for many years. Many studies have analyzed electroencephalogram signals in order to estimate mental stress using linear methods. In this paper, a novel nonlinear stress assessment method based on multivariate multiscale entropy has been introduced. Since the multivariate multiscale entropy method characterizes the complexity of nonlinear time series, this research determines the mental stress of human during cognitive workload using complexity of electroencephalogram (EEG) signals. To perform this work, 36 subje

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Alrowais, Fadwa, Faiz Abdullah Alotaibi, Abdulkhaleq Q. A. Hassan, Radwa Marzouk, Mrim M. Alnfiai, and Ahmed Sayed. "Enhanced Pelican Optimization Algorithm with Deep Learning-Driven Mitotic Nuclei Classification on Breast Histopathology Images." Biomimetics 8, no. 7 (2023): 538. http://dx.doi.org/10.3390/biomimetics8070538.

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Breast cancer (BC) is a prevalent disease worldwide, and accurate diagnoses are vital for successful treatment. Histopathological (HI) inspection, particularly the detection of mitotic nuclei, has played a pivotal function in the prognosis and diagnosis of BC. It includes the detection and classification of mitotic nuclei within breast tissue samples. Conventionally, the detection of mitotic nuclei has been a subjective task and is time-consuming for pathologists to perform manually. Automatic classification using computer algorithms, especially deep learning (DL) algorithms, has been develope

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Maheshwari, Saumil, Aman Agarwal, Anupam Shukla, and Ritu Tiwari. "A comprehensive evaluation for the prediction of mortality in intensive care units with LSTM networks: patients with cardiovascular disease." Biomedical Engineering / Biomedizinische Technik 65, no. 4 (2020): 435–46. http://dx.doi.org/10.1515/bmt-2018-0206.

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AbstractIntensive care units (ICUs) are responsible for generating a wealth of useful data in the form of electronic health records. We aimed to build a mortality prediction model on a Medical Information Mart for Intensive Care (MIMIC-III) database and to assess whether the use of deep learning techniques like long short-term memory (LSTM) can effectively utilize the temporal relations among clinical variables. The models were built on clinical variable dynamics of the first 48 h of ICU admission of 12,550 records from the MIMIC-III database. A total of 36 variables including 33 time series v

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