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Cimr D, Busovsky D, Fujita H, Studnicka F, Cimler R, Hayashi T. Classification of health deterioration by geometric invariants. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 239:107623. [PMID: 37276760 DOI: 10.1016/j.cmpb.2023.107623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/18/2023] [Accepted: 05/24/2023] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVES Prediction of patient deterioration is essential in medical care, and its automation may reduce the risk of patient death. The precise monitoring of a patient's medical state requires devices placed on the body, which may cause discomfort. Our approach is based on the processing of long-term ballistocardiography data, which were measured using a sensory pad placed under the patient's mattress. METHODS The investigated dataset was obtained via long-term measurements in retirement homes and intensive care units (ICU). Data were measured unobtrusively using a measuring pad equipped with piezoceramic sensors. The proposed approach focused on the processing methods of the measured ballistocardiographic signals, Cartan curvature (CC), and Euclidean arc length (EAL). RESULTS For analysis, 218,979 normal and 216,259 aberrant 2-second samples were collected and classified using a convolutional neural network. Experiments using cross-validation with expert threshold and data length revealed the accuracy, sensitivity, and specificity of the proposed method to be 86.51 CONCLUSIONS: The proposed method provides a unique approach for an early detection of health concerns in an unobtrusive manner. In addition, the suitability of EAL over the CC was determined.
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Affiliation(s)
- Dalibor Cimr
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Damian Busovsky
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Hamido Fujita
- Faculty of Information Technology, HUTECH University, Ho Chi Minh City 700000, Vietnam; Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia; DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada, Spain; Regional Research Center, Iwate Prefectural University, Iwate 0200611, Japan.
| | - Filip Studnicka
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Richard Cimler
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Toshitaka Hayashi
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
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Cimr D, Fujita H, Tomaskova H, Cimler R, Selamat A. Automatic seizure detection by convolutional neural networks with computational complexity analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107277. [PMID: 36463672 DOI: 10.1016/j.cmpb.2022.107277] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/20/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVES Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems. METHODS The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network. RESULTS Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset. CONCLUSIONS Through the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support.
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Affiliation(s)
- Dalibor Cimr
- Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic
| | - Hamido Fujita
- Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam; DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada, Spain; Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia; Regional Research Center, Iwate Prefectural University, Iwate Japan.
| | - Hana Tomaskova
- Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic
| | - Richard Cimler
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Ali Selamat
- Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
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Balali P, Rabineau J, Hossein A, Tordeur C, Debeir O, van de Borne P. Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22239565. [PMID: 36502267 PMCID: PMC9737480 DOI: 10.3390/s22239565] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/11/2022] [Accepted: 11/28/2022] [Indexed: 05/29/2023]
Abstract
Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body's center of mass and on the chest, respectively. Since their inception, their potential for evaluating cardiovascular health has been studied. However, both BCG and SCG are impacted by respiration, leading to a periodic modulation of these signals. As a result, data processing algorithms have been developed to exclude the respiratory signals, or recording protocols have been designed to limit the respiratory bias. Reviewing the present status of the literature reveals an increasing interest in applying these techniques to extract respiratory information, as well as cardiac information. The possibility of simultaneous monitoring of respiratory and cardiovascular signals via BCG or SCG enables the monitoring of vital signs during activities that require considerable mental concentration, in extreme environments, or during sleep, where data acquisition must occur without introducing recording bias due to irritating monitoring equipment. This work aims to provide a theoretical and practical overview of cardiopulmonary interaction based on BCG and SCG signals. It covers the recent improvements in extracting respiratory signals, computing markers of the cardiorespiratory interaction with practical applications, and investigating sleep breathing disorders, as well as a comparison of different sensors used for these applications. According to the results of this review, recent studies have mainly concentrated on a few domains, especially sleep studies and heart rate variability computation. Even in those instances, the study population is not always large or diversified. Furthermore, BCG and SCG are prone to movement artifacts and are relatively subject dependent. However, the growing tendency toward artificial intelligence may help achieve a more accurate and efficient diagnosis. These encouraging results bring hope that, in the near future, such compact, lightweight BCG and SCG devices will offer a good proxy for the gold standard methods for assessing cardiorespiratory function, with the added benefit of being able to perform measurements in real-world situations, outside of the clinic, and thus decrease costs and time.
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Affiliation(s)
- Paniz Balali
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Laboratory of Image Synthesis and Analysis, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Jeremy Rabineau
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Amin Hossein
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Cyril Tordeur
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Olivier Debeir
- Laboratory of Image Synthesis and Analysis, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Philippe van de Borne
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Department of Cardiology, Erasme Hospital, Université Libre de Bruxelles, 1050 Brussels, Belgium
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OCSTN: One-class time-series classification approach using a signal transformation network into a goal signal. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.09.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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FHRGAN: Generative adversarial networks for synthetic fetal heart rate signal generation in low-resource settings. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.01.070] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Fetanat M, Stevens M, Jain P, Hayward C, Meijering E, Lovell NH. Fully Elman Neural Network: A Novel Deep Recurrent Neural Network Optimized by an Improved Harris Hawks Algorithm for Classification of Pulmonary Arterial Wedge Pressure. IEEE Trans Biomed Eng 2021; 69:1733-1744. [PMID: 34813462 DOI: 10.1109/tbme.2021.3129459] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Heart failure (HF) is one of the most prevalent life-threatening cardiovascular diseases in which 6.5 million people are suffering in the USA and more than 23 million worldwide. Mechanical circulatory support of HF patients can be achieved by implanting a left ventricular assist device (LVAD) into HF patients as a bridge to transplant, recovery or destination therapy and can be controlled by measurement of normal and abnormal pulmonary arterial wedge pressure (PAWP). While there are no commercial long-term implantable pressure sensors to measure PAWP, real-time non-invasive estimation of abnormal and normal PAWP becomes vital. In this work, first an improved Harris Hawks optimizer algorithm called HHO+ is presented and tested on 24 unimodal and multimodal benchmark functions. Second, a novel fully Elman neural network (FENN) is proposed to improve the classification performance. Finally, four novel 18-layer deep learning methods of convolutional neural networks (CNNs) with multi-layer perceptron (CNN-MLP), CNN with Elman neural networks (CNN-ENN), CNN with fully Elman neural networks (CNN-FENN), and CNN with fully Elman neural networks optimized by HHO+ algorithm (CNN-FENN-HHO+) for classification of abnormal and normal PAWP using estimated HVAD pump flow were developed and compared. The estimated pump flow was derived by a non-invasive method embedded into the commercial HVAD controller. The proposed methods are evaluated on an imbalanced clinical dataset using 5-fold cross-validation. The proposed CNN-FENN-HHO+ method outperforms the proposed CNN-MLP, CNN-ENN and CNN-FENN methods and improved the classification performance metrics across 5-fold cross-validation with an average sensitivity of 79%, accuracy of 78% and specificity of 76%. The proposed methods can reduce the likelihood of hazardous events like pulmonary congestion and ventricular suction for HF patients and notify identified abnormal cases to the hospital, clinician and cardiologist for emergency action, which can diminish the mortality rate of patients with HF.
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Wang J. An intelligent computer-aided approach for atrial fibrillation and atrial flutter signals classification using modified bidirectional LSTM network. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.06.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Sadek I, Abdulrazak B. A comparison of three heart rate detection algorithms over ballistocardiogram signals. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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Pastor-Serrano O, Lathouwers D, Perkó Z. A semi-supervised autoencoder framework for joint generation and classification of breathing. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106312. [PMID: 34392000 DOI: 10.1016/j.cmpb.2021.106312] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 07/21/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE One of the main problems with biomedical signals is the limited amount of patient-specific data and the significant amount of time needed to record the sufficient number of samples needed for diagnostic and treatment purposes. In this study, we present a framework to simultaneously generate and classify biomedical time series based on a modified Adversarial Autoencoder (AAE) algorithm and one-dimensional convolutions. Our work is based on breathing time series, with specific motivation to capture breathing motion during radiotherapy lung cancer treatments. METHODS First, we explore the potential in using the Variational Autoencoder (VAE) and AAE algorithms to model breathing signals from individual patients. We then extend the AAE algorithm to allow joint semi-supervised classification and generation of different types of signals within a single framework. To simplify the modeling task, we introduce a pre-processing and post-processing compressing algorithm that transforms the multi-dimensional time series into vectors containing time and position values, which are transformed back into time series through an additional neural network. RESULTS The resulting models are able to generate realistic and varied samples of breathing. By incorporating 4% and 12% of the labeled samples during training, our model outperforms other purely discriminative networks in classifying breathing baseline shift irregularities from a dataset completely different from the training set, achieving an average macro F1-score of 94.91% and 96.54%, respectively. CONCLUSION To our knowledge, the presented framework is the first approach that unifies generation and classification within a single model for this type of biomedical data, enabling both computer aided diagnosis and augmentation of labeled samples within a single framework.
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Affiliation(s)
- Oscar Pastor-Serrano
- Delft University of Technology, Department of Radiation Science and Technology, Mekelweg 15, Delft 2629JB, Netherlands.
| | - Danny Lathouwers
- Delft University of Technology, Department of Radiation Science and Technology, Mekelweg 15, Delft 2629JB, Netherlands
| | - Zoltán Perkó
- Delft University of Technology, Department of Radiation Science and Technology, Mekelweg 15, Delft 2629JB, Netherlands
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Cimr D, Studnicka F, Fujita H, Cimler R, Slegr J. Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 207:106149. [PMID: 34015736 DOI: 10.1016/j.cmpb.2021.106149] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 04/29/2021] [Indexed: 06/12/2023]
Abstract
Background and Objectives Automatic detection of breathing disorders plays an important role in the early signalization of respiratory diseases. Measuring methods can be based on electrocardiogram (ECG), sound, oximetry, or respiratory analysis. However, these approaches require devices placed on the human body or they are prone to disturbance by environmental influences. To solve these problems, we proposed a heart contraction mechanical trigger for unobtrusive detection of respiratory disorders from the mechanical measurement of cardiac contractions. We designed a novel method to calculate this mechanical trigger purely from measured mechanical signals without the use of ECG. Methods The approach is a built-on calculation of the so-called euclidean arc length from the signals. In comparison to previous researches, this system does not require any equipment attached to a person. This is achieved by locating the tensometers on the bed. Data from sensors are fused by the Cartan curvatures method to beat-to-beat vector input for the Convolutional neural network (CNN) classifier. Results In sum, 2281 disordered and 5130 normal breathing samples was collected for analysis. The experiments with use of 10-fold cross validation show that accuracy, sensitivity, and specificity reach values of 96.37%, 92.46%, and 98.11% respectively. Conclusions By the approach for detection, the system offers a novel way for a completely unobtrusive diagnosis of breathing-related health problems. The proposed solution can effectively be deployed in all clinical or home environments.
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Affiliation(s)
- Dalibor Cimr
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Filip Studnicka
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Hamido Fujita
- Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam; DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada, Spain; Regional Research Center, Iwate Prefectural University, Iwate, Japan.
| | - Richard Cimler
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Jan Slegr
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
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Chen Z, Ono N, Chen W, Tamura T, Altaf-Ul-Amin MD, Kanaya S, Huang M. The feasibility of predicting impending malignant ventricular arrhythmias by using nonlinear features of short heartbeat intervals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 205:106102. [PMID: 33933712 DOI: 10.1016/j.cmpb.2021.106102] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 04/05/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Malignant ventricular arrhythmias (MAs) occur unpredictably and lead to emergencies. A new approach that uses a timely tracking device e.g., photoplethysmogram (PPG) solely to predict MAs would be irreplaceably valuable and it is natural to expect the approach can predict the occurrence as early as possible. METHOD We assumed that with an appropriate metric based on signal complexity, the heartbeat interval time series (HbIs) can be used to manifest the intrinsic characteristics of the period immediately precedes the MAs (preMAs). The approach first characterizes the patterns of preMAs by a new complexity metric (the refined composite multi-scale entropy). The MAs detector is then constructed by checking the discriminability of the MAs against the sinus rhythm and other prevalent arrhythmias (atrial fibrillation and premature ventricular contraction) of three machine-learning models (SVM, Random Forest, and XGboost). RESULTS Two specifications are of interest: the length of the HbIs needed to delineate the preMAs patterns sufficiently (lspec) and how long before the occurrence of MAs will the HbIs manifest specific patterns that are distinct enough to predict the impending MAs (tspec). Our experimental results confirmed the best performance came from a Random-Forest model with an average precision of 99.99% and recall of 88.98% using a HbIs of 800 heartbeats (the lspec), 108 seconds (the tspec) before the occurrence of MAs. CONCLUSION By experimental validation of the unique pattern of the preMAs in HbIs and using it in the machine learning model, we showed the high possibility of MAs prediction in a broader circumstance, which may cover daily healthcare using the alternative sensor in HbIs monitoring. Therefore, this research is theoretically and practically significant in cardiac arrest prevention.
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Affiliation(s)
- Zheng Chen
- Graduate School of Science and Technology, Nara Insitute of Science and Technology, Japan
| | - Naoaki Ono
- Graduate School of Science and Technology, Nara Insitute of Science and Technology, Japan; Data Science Center, Nara Insitute of Science and Technology, Japan
| | - Wei Chen
- Center for Intelligent Medical Electronics, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Toshiyo Tamura
- Institute for Healthcare Robotics, Waseda university, Japan
| | - M D Altaf-Ul-Amin
- Graduate School of Science and Technology, Nara Insitute of Science and Technology, Japan
| | - Shigehiko Kanaya
- Graduate School of Science and Technology, Nara Insitute of Science and Technology, Japan; Data Science Center, Nara Insitute of Science and Technology, Japan
| | - Ming Huang
- Graduate School of Science and Technology, Nara Insitute of Science and Technology, Japan.
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Automated Sleep apnea detection using optimal duration-frequency concentrated wavelet-based features of pulse oximetry signals. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02422-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Hernandez-Matamoros A, Fujita H, Perez-Meana H. A novel approach to create synthetic biomedical signals using BiRNN. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.06.019] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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