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Zhang J, Liu A, Gao M, Chen X, Zhang X, Chen X. ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network. Artif Intell Med 2020; 106:101856. [DOI: 10.1016/j.artmed.2020.101856] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/05/2020] [Accepted: 04/02/2020] [Indexed: 01/16/2023]
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102
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Lai D, Bu Y, Su Y, Zhang X, Ma CS. Non-Standardized Patch-Based ECG Lead Together With Deep Learning Based Algorithm for Automatic Screening of Atrial Fibrillation. IEEE J Biomed Health Inform 2020; 24:1569-1578. [DOI: 10.1109/jbhi.2020.2980454] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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103
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Lin GM, Lu HHS. A 12-Lead ECG-Based System With Physiological Parameters and Machine Learning to Identify Right Ventricular Hypertrophy in Young Adults. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2020; 8:1900510. [PMID: 32509473 PMCID: PMC7269457 DOI: 10.1109/jtehm.2020.2996370] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/07/2020] [Accepted: 05/14/2020] [Indexed: 12/21/2022]
Abstract
OBJECTIVE The presence of right ventricular hypertrophy (RVH) accounts for approximately 5-10% in young adults. The sensitivity estimated by commonly used 12-lead electrocardiographic (ECG) criteria for identifying the presence of RVH is under 20% in the general population. The aim of this study is to develop a 12-lead ECG system with the related information of age, body height and body weight via machine learning to increase the sensitivity and the precision for detecting RVH. METHOD In a sample of 1,701 males, aged 17-45 years, support vector machine is used for the training of 31 parameters including age, body height and body weight in addition to 28 ECG data such as axes, intervals and wave voltages as the inputs to link the output RVH. The RVH is defined on the echocardiographic finding for young males as right ventricular anterior wall thickness > 5.5 mm. RESULTS On the system goal for increasing sensitivity, the specificity is controlled around 70-75% and all data tested in the proposed method show competent sensitivity up to 70.3%. The values of area under curve of receiver operating characteristic curve and precision-recall curve using the proposed method are 0.780 and 0.285, respectively, which are better than 0.518 and 0.112 using the Sokolow-Lyon voltage criterion, respectively, for detecting unspecific RVH. CONCLUSION We present a method using simple physiological parameters with ECG data to effectively identify more than 70% of the RVH among young adults. Clinical Impact: This system provides a fast, precise and feasible diagnosis tool to screen RVH.
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Affiliation(s)
- Gen-Min Lin
- Department of Preventive MedicineFeinberg School of MedicineNorthwestern UniversityChicagoIL60611USA.,Department of MedicineHualien Armed Forces General HospitalHualien97144Taiwan.,Department of MedicineTri-Service General Hospital, National Defense Medical CenterTaipei11490Taiwan
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104
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Ping Y, Chen C, Wu L, Wang Y, Shu M. Automatic Detection of Atrial Fibrillation Based on CNN-LSTM and Shortcut Connection. Healthcare (Basel) 2020; 8:healthcare8020139. [PMID: 32443926 PMCID: PMC7348856 DOI: 10.3390/healthcare8020139] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/09/2020] [Accepted: 05/19/2020] [Indexed: 11/16/2022] Open
Abstract
Atrial fibrillation (AF) is one of the most common persistent arrhythmias, which has a close connection to a large number of cardiovascular diseases. However, if spotted early, the diagnosis of AF can improve the effectiveness of clinical treatment and effectively prevent serious complications. In this paper, a combination of an 8-layer convolutional neural network (CNN) with a shortcut connection and 1-layer long short-term memory (LSTM), named 8CSL, was proposed for the Electrocardiogram (ECG) classification task. Compared with recurrent neural networks (RNN) and multi-scale convolution neural networks (MCNN), not only can 8CSL extract features skillfully, but also deal with long-term dependency between data. In particular, 8CSL includes eight shortcut connections that can improve the speed of the data transmission and processing as a result of the shortcut connections. The model was evaluated on the base of the test set of the Computing in Cardiology Challenge 2017 dataset with the F1 score. The ECG recordings were cropped or padded to the same length. After 10-fold cross-validation, the average test F1 score was 84.89%, 89.55%, and 85.64% when the segment length was 5, 10, 20 s, respectively. The experiment results demonstrate excellent performance with potential practical applications.
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Affiliation(s)
- Yongjie Ping
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (Y.P.); (L.W.)
| | - Chao Chen
- Shandong Artificial Intelligence Institute, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China; (C.C.); (Y.W.)
| | - Lu Wu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (Y.P.); (L.W.)
- Shandong Artificial Intelligence Institute, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China; (C.C.); (Y.W.)
| | - Yinglong Wang
- Shandong Artificial Intelligence Institute, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China; (C.C.); (Y.W.)
| | - Minglei Shu
- Shandong Artificial Intelligence Institute, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China; (C.C.); (Y.W.)
- Correspondence:
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105
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Krasteva V, Ménétré S, Didon JP, Jekova I. Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2875. [PMID: 32438582 PMCID: PMC7285174 DOI: 10.3390/s20102875] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 05/13/2020] [Accepted: 05/15/2020] [Indexed: 11/16/2022]
Abstract
Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to self-extract significant features of the electrocardiogram (ECG) and can generally provide high-output diagnostic accuracy if subjected to robust training and optimization on large datasets at high computational cost. So far, limited research and optimization of DNNs in shock advisory systems is found on large ECG arrhythmia databases from out-of-hospital cardiac arrests (OHCA). The objective of this study is to optimize the hyperparameters (HPs) of deep convolutional neural networks (CNN) for detection of shockable (Sh) and nonshockable (NSh) rhythms, and to validate the best HP settings for short and long analysis durations (2-10 s). Large numbers of (Sh + NSh) ECG samples were used for training (720 + 3170) and validation (739 + 5921) from Holters and defibrillators in OHCA. An end-to-end deep CNN architecture was implemented with one-lead raw ECG input layer (5 s, 125 Hz, 2.5 uV/LSB), configurable number of 5 to 23 hidden layers and output layer with diagnostic probability p ∈ [0: Sh,1: NSh]. The hidden layers contain N convolutional blocks × 3 layers (Conv1D (filters = Fi, kernel size = Ki), max-pooling (pool size = 2), dropout (rate = 0.3)), one global max-pooling and one dense layer. Random search optimization of HPs = {N, Fi, Ki}, i = 1, … N in a large grid of N = [1, 2, … 7], Fi = [5;50], Ki = [5;100] was performed. During training, the model with maximal balanced accuracy BAC = (Sensitivity + Specificity)/2 over 400 epochs was stored. The optimization principle is based on finding the common HPs space of a few top-ranked models and prediction of a robust HP setting by their median value. The optimal models for 1-7 CNN layers were trained with different learning rates LR = [10-5; 10-2] and the best model was finally validated on 2-10 s analysis durations. A number of 4216 random search models were trained. The optimal models with more than three convolutional layers did not exhibit substantial differences in performance BAC = (99.31-99.5%). Among them, the best model was found with {N = 5, Fi = {20, 15, 15, 10, 5}, Ki = {10, 10, 10, 10, 10}, 7521 trainable parameters} with maximal validation performance for 5-s analysis (BAC = 99.5%, Se = 99.6%, Sp = 99.4%) and tolerable drop in performance (<2% points) for very short 2-s analysis (BAC = 98.2%, Se = 97.6%, Sp = 98.7%). DNN application in future-generation shock advisory systems can improve the detection performance of Sh and NSh rhythms and can considerably shorten the analysis duration complying with resuscitation guidelines for minimal hands-off pauses.
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Affiliation(s)
- Vessela Krasteva
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl 105, 1113 Sofia, Bulgaria;
| | - Sarah Ménétré
- Schiller Médical, 4 Rue Louis Pasteur, 67160 Wissembourg, France; (S.M.); (J.-P.D.)
| | - Jean-Philippe Didon
- Schiller Médical, 4 Rue Louis Pasteur, 67160 Wissembourg, France; (S.M.); (J.-P.D.)
| | - Irena Jekova
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl 105, 1113 Sofia, Bulgaria;
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106
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Ghosh SK, Tripathy RK, Paternina MRA, Arrieta JJ, Zamora-Mendez A, Naik GR. Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network. J Med Syst 2020; 44:114. [PMID: 32388733 DOI: 10.1007/s10916-020-01565-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 03/31/2020] [Indexed: 12/14/2022]
Abstract
Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in turn, the Fractional norm (FN) feature is evaluated from the extracted coefficients at each subband. Then, the AF detection is carried out using a deep learning approach known as the Hierarchical Extreme Learning Machine (H-ELM) from the FN features. The proposed method is evaluated by considering normal and AF pathological ECG signals from public databases. The experimental results reveal that the proposed multi-rate cosine filter bank based on FN features is effective for the detection of AF pathology with an accuracy, sensitivity and specificity values of 99.40%, 98.77%, and 100%, respectively. The performance of the proposed diagnostic features of the ECG signal is compared with other existing features for the detection of AF. The low-frequency subband FN features found to be more significant with a difference of the mean values as 0.69 between normal and AF classes.
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Affiliation(s)
- S K Ghosh
- MLR Institute of Technology, Hyderabad, India
| | - R K Tripathy
- Birla Institute of Technology and Science Pilani, Hyderabad, India.
| | - Mario R A Paternina
- National Autonomous University of Mexico (UNAM), Mexico City, Mex. 04510, Mexico
| | | | | | - Ganesh R Naik
- Biomedical Engineering and Neuromorphic Systems (BENS) Research Group, MARCS Institute, Western Sydney University, Penrith, New South Wales, Australia
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107
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Niu J, Tang Y, Sun Z, Zhang W. Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks. IEEE J Biomed Health Inform 2020; 24:1321-1332. [DOI: 10.1109/jbhi.2019.2942938] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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108
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Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review. Comput Biol Med 2020; 120:103726. [DOI: 10.1016/j.compbiomed.2020.103726] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 03/16/2020] [Accepted: 03/21/2020] [Indexed: 01/03/2023]
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109
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Lin GM, Liu K. An Electrocardiographic System With Anthropometrics via Machine Learning to Screen Left Ventricular Hypertrophy among Young Adults. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2020; 8:1800111. [PMID: 32419990 PMCID: PMC7224269 DOI: 10.1109/jtehm.2020.2990073] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 02/25/2020] [Accepted: 04/06/2020] [Indexed: 12/12/2022]
Abstract
The prevalence of physiological and pathological left ventricular hypertrophy (LVH) among young adults is about 5%. A use of electrocardiographic (ECG) voltage criteria and machine learning for the ECG parameters to identify the presence of LVH is estimated only 20-30% in the general population. The aim of this study is to develop an ECG system with anthropometric data using machine learning to increase the accuracy and sensitivity for a screen of LVH. In a large sample of 2,196 males, aged 17–45 years, the support vector machine (SVM) classifier is used as the machine learning method for 31 characteristics including age, body height and body weight in addition to 28 ECG parameters such as axes, intervals and voltages to link the output of LVH. The diagnosis of LVH is based on the echocardiographic criteria for young males to be 116 gram/meter2 (left ventricular mass (LVM)/body surface area) or 49 gram/meter2.7 (LVM/body height2.7). On the purpose of increasing sensitivity, the specificity is adjusted around 70-75% and all data tested in proposed model reveal high sensitivity to 86.7%. The area under curve (AUC) of the Precision-Recall (PR) curve is 0.308 in the proposed model which is better than 0.109 and 0.077 using Cornell and Sokolow-Lyon voltage criteria for LVH, respectively. Our system provides a novel screening tool using age, body height, body weight and ECG data to identify most of the LVH among young adults. It provides a fast, accurate and practical diagnosis tool to identify LVH.
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Affiliation(s)
- Gen-Min Lin
- 1Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIL60611USA.,2Department of MedicineHualien Armed Forces General HospitalHualien97144Taiwan.,3Tri-Service General HospitalNational Defense Medical CenterTaipei11490Taiwan
| | - Kiang Liu
- 4Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIL60611USA
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110
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Wen X, Huang Y, Wu X, Zhang B. A Feasible Feature Extraction Method for Atrial Fibrillation Detection From BCG. IEEE J Biomed Health Inform 2020; 24:1093-1103. [DOI: 10.1109/jbhi.2019.2927165] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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111
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Jangra M, Dhull SK, Singh KK. ECG arrhythmia classification using modified visual geometry group network (mVGGNet). JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191135] [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]
Affiliation(s)
- Manisha Jangra
- Department of ECE, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, India
| | - Sanjeev Kr. Dhull
- Department of ECE, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, India
| | - Krishna Kant Singh
- Department of Electronics and Communication Engineering, KIET Group of Institutions, Ghaziabad, India
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112
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Saadatnejad S, Oveisi M, Hashemi M. LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices. IEEE J Biomed Health Inform 2020; 24:515-523. [DOI: 10.1109/jbhi.2019.2911367] [Citation(s) in RCA: 150] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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113
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Zhang K, Aleexenko V, Jeevaratnam K. Computational approaches for detection of cardiac rhythm abnormalities: Are we there yet? J Electrocardiol 2020; 59:28-34. [PMID: 31954954 DOI: 10.1016/j.jelectrocard.2019.12.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 12/10/2019] [Accepted: 12/16/2019] [Indexed: 12/16/2022]
Abstract
The analysis of an electrocardiogram (ECG) is able to provide vital information on the electrical activity of the heart and is crucial for the accurate diagnosis of cardiac arrhythmias. Due to the nature of some arrhythmias, this might be a time-consuming and difficult to accomplish process. The advent of novel machine learning technologies in this field has a potential to revolutionise the use of the ECG. In this review, we outline key advances in ECG analysis for atrial, ventricular and complex multiform arrhythmias, as well as discuss the current limitations of the technology and the barriers that must be overcome before clinical integration is feasible.
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Affiliation(s)
- Kevin Zhang
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7AL, United Kingdom; School of Medicine, Imperial College London, United Kingdom
| | - Vadim Aleexenko
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7AL, United Kingdom
| | - Kamalan Jeevaratnam
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7AL, United Kingdom.
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114
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Shi H, Qin C, Xiao D, Zhao L, Liu C. Automated heartbeat classification based on deep neural network with multiple input layers. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105036] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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115
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Fan X, Zhao Y, Wang H, Tsui KL. Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals. BMC Med Inform Decis Mak 2019; 19:285. [PMID: 31888608 PMCID: PMC6937661 DOI: 10.1186/s12911-019-1012-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 12/19/2019] [Indexed: 11/18/2022] Open
Abstract
Background The accelerated growth of elderly population is creating a heavy burden to the healthcare system in many developed countries and regions. Electrocardiogram (ECG) analysis has been recognized as effective approach to cardiovascular disease diagnosis and widely utilized for monitoring personalized health conditions. Method In this study, we present a novel approach to forecasting one-day-forward wellness conditions for community-dwelling elderly by analyzing single lead short ECG signals acquired from a station-based monitoring device. More specifically, exponentially weighted moving-average (EWMA) method is employed to eliminate the high-frequency noise from original signals at first. Then, Fisher-Yates normalization approach is used to adjust the self-evaluated wellness score distribution since the scores among different individuals are skewed. Finally, both deep learning-based and traditional machine learning-based methods are utilized for building wellness forecasting models. Results The experiment results show that the deep learning-based methods achieve the best fitted forecasting performance, where the forecasting accuracy and F value are 93.21% and 91.98% respectively. The deep learning-based methods, with the merit of non-hand-crafted engineering, have superior wellness forecasting performance towards the competitive traditional machine learning-based methods. Conclusion The developed approach in this paper is effective in wellness forecasting for community-dwelling elderly, which can provide insights in terms of implementing a cost-effective approach to informing healthcare provider about health conditions of elderly in advance and taking timely interventions to reduce the risk of malignant events.
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Affiliation(s)
- Xiaomao Fan
- School of Data Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China
| | - Yang Zhao
- Center for System Informatics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China.
| | - Hailiang Wang
- Center for System Informatics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China
| | - Kwok Leung Tsui
- School of Data Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China.,Center for System Informatics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China
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116
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Yamabe M, Horie K, Shiokawa H, Funato H, Yanagisawa M, Kitagawa H. MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks. Sci Rep 2019; 9:15793. [PMID: 31672998 PMCID: PMC6823352 DOI: 10.1038/s41598-019-51269-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 09/27/2019] [Indexed: 12/03/2022] Open
Abstract
Automated sleep stage scoring for mice is in high demand for sleep research, since manual scoring requires considerable human expertise and efforts. The existing automated scoring methods do not provide the scoring accuracy required for practical use. In addition, the performance of such methods has generally been evaluated using rather small-scale datasets, and their robustness against individual differences and noise has not been adequately verified. This research proposes a novel automated scoring method named “MC-SleepNet”, which combines two types of deep neural networks. Then, we evaluate its performance using a large-scale dataset that contains 4,200 biological signal records of mice. The experimental results show that MC-SleepNet can automatically score sleep stages with an accuracy of 96.6% and kappa statistic of 0.94. In addition, we confirm that the scoring accuracy does not significantly decrease even if the target biological signals are noisy. These results suggest that MC-SleepNet is very robust against individual differences and noise. To the best of our knowledge, evaluations using such a large-scale dataset (containing 4,200 records) and high scoring accuracy (96.6%) have not been reported in previous related studies.
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Affiliation(s)
- Masato Yamabe
- Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Japan
| | - Kazumasa Horie
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan.
| | - Hiroaki Shiokawa
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan
| | - Hiromasa Funato
- International Institute for Integrative Sleep Medicine, University of Tsukuba, Tsukuba, Japan
| | - Masashi Yanagisawa
- International Institute for Integrative Sleep Medicine, University of Tsukuba, Tsukuba, Japan
| | - Hiroyuki Kitagawa
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan
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117
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A novel hybrid network of fusing rhythmic and morphological features for atrial fibrillation detection on mobile ECG signals. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04318-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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118
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Abstract
Background Wearable sensors (wearables) have been commonly integrated into a wide variety of commercial products and are increasingly being used to collect and process raw physiological parameters into salient digital health information. The data collected by wearables are currently being investigated across a broad set of clinical domains and patient populations. There is significant research occurring in the domain of algorithm development, with the aim of translating raw sensor data into fitness- or health-related outcomes of interest for users, patients, and health care providers. Objectives The aim of this review is to highlight a selected group of fitness- and health-related indicators from wearables data and to describe several algorithmic approaches used to generate these higher order indicators. Methods A systematic search of the Pubmed database was performed with the following search terms (number of records in parentheses): Fitbit algorithm (18), Apple Watch algorithm (3), Garmin algorithm (5), Microsoft Band algorithm (8), Samsung Gear algorithm (2), Xiaomi MiBand algorithm (1), Huawei Band (Watch) algorithm (2), photoplethysmography algorithm (465), accelerometry algorithm (966), ECG algorithm (8287), continuous glucose monitor algorithm (343). The search terms chosen for this review are focused on algorithms for wearable devices that dominated the commercial wearables market between 2014-2017 and that were highly represented in the biomedical literature. A second set of search terms included categories of algorithms for fitness-related and health-related indicators that are commonly used in wearable devices (e.g. accelerometry, PPG, ECG). These papers covered the following domain areas: fitness; exercise; movement; physical activity; step count; walking; running; swimming; energy expenditure; atrial fibrillation; arrhythmia; cardiovascular; autonomic nervous system; neuropathy; heart rate variability; fall detection; trauma; behavior change; diet; eating; stress detection; serum glucose monitoring; continuous glucose monitoring; diabetes mellitus type 1; diabetes mellitus type 2. All studies uncovered through this search on commercially available device algorithms and pivotal studies on sensor algorithm development were summarized, and a summary table was constructed using references generated by the literature review as described (Table 1). Conclusions Wearable health technologies aim to collect and process raw physiological or environmental parameters into salient digital health information. Much of the current and future utility of wearables lies in the signal processing steps and algorithms used to analyze large volumes of data. Continued algorithmic development and advances in machine learning techniques will further increase analytic capabilities. In the context of these advances, our review aims to highlight a range of advances in fitness- and other health-related indicators provided by current wearable technologies.
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