1
|
Jain A, Verma A, Verma AK, Bajaj V. Tunable Q-factor wavelet transform based identification of diabetic patients using ECG signals. Comput Methods Biomech Biomed Engin 2024:1-10. [PMID: 38635476 DOI: 10.1080/10255842.2024.2342512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 04/08/2024] [Indexed: 04/20/2024]
Abstract
Diabetes is a chronic health condition that is characterized by increased levels of glucose (sugar) in the blood. It can have harmful effects on different parts of the body, such as the retina of the eyes, skin, nervous system, kidneys, and heart. Diabetes affects the structure of electrocardiogram (ECG) impulses by causing cardiovascular autonomic dysfunction. Multi-resolution analysis of the input ECG signal is utilized in this paper to develop a machine learning-based system for the automated detection of diabetic patients. In the first step, the input ECG signal is decomposed into sub-bands utilizing the tunable Q-factor wavelet transform (TQWT) technique. In the second step, four entropy-based characteristics are evaluated from each SB and elected using the K-W test method. To develop an automatic diabetes detection system, selected features are given as input with 10-fold validation to a SVM classifier using various kernel functions. The 3 rd sub-band of TQWT with the Coarse Gaussian kernel function kernel of the SVM classifier yields a classification accuracy of 91.5%. In the same dataset, the comparative analysis demonstrates that the proposed method outperforms other existing methods.
Collapse
Affiliation(s)
- Anuja Jain
- Teerthanker Mahaveer University, Moradabad, UP, India
| | - Anurag Verma
- Teerthanker Mahaveer University, Moradabad, UP, India
| | - Amit Kumar Verma
- Mahatama Jyotiba Phule Rohilkhand University, Bareilly, UP, India
| | - Varun Bajaj
- PDPM Indian Institute of Information Technology, Design & Manufacturing (IIITDM), Jabalpur, India
| |
Collapse
|
2
|
Li G, Wu S, Zhao H, Guan W, Zhou Y, Shi B. Non-invasive prognostic biomarker of lung cancer patients with brain metastases: Recurrence quantification analysis of heart rate variability. Front Physiol 2022; 13:987835. [PMID: 36148296 PMCID: PMC9486009 DOI: 10.3389/fphys.2022.987835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/15/2022] [Indexed: 11/28/2022] Open
Abstract
Background: It has previously been shown that the time-domain characteristic of heart rate variability (HRV) is an independent prognostic factor for lung cancer patients with brain metastasis (LCBM). However, it is unclear whether the nonlinear dynamic features contained in HRV are associated with prognosis in patients with LCBM. Recurrence quantification analysis (RQA) is a common nonlinear method used to characterize the complexity of heartbeat interval time series. This study was aimed to explore the association between HRV RQA parameters and prognosis in LCBM patients. Methods: Fifty-six LCBM patients from the Department of Radiation Oncology, the First Affiliated Hospital of Bengbu Medical College, were enrolled in this study. Five-minute ECG data were collected by a mini-ECG recorder before the first brain radiotherapy, and then heartbeat interval time series were extracted for RQA. The main parameters included the mean diagonal line length (Lmean), maximal diagonal line length (Lmax), percent of recurrence (REC), determinism (DET) and Shannon entropy (ShanEn). Patients were followed up (the average follow-up time was 19.2 months, a total of 37 patients died), and the relationships between the RQA parameters and survival of LCBM patients were evaluated by survival analysis. Results: The univariate analysis showed that an Lmax of >376 beats portended worse survival in LCBM patients. Multivariate Cox regression analysis revealed that the Lmax was still an independent prognostic factor for patients with LCBM after adjusting for confounders such as the Karnofsky performance status (KPS) (HR = 0.318, 95% CI: 0.151–0.669, p = 0.003). Conclusion: Reduced heartbeat complexity indicates a shorter survival time in patients with LCBM. As a non-invasive biomarker, RQA has the potential for application in evaluating the prognosis of LCBM patients.
Collapse
Affiliation(s)
- Guangqiao Li
- School of Medical Imaging, Bengbu Medical College, Bengbu, China
- Anhui Key Laboratory of Computational Medicine and Intelligent Health, Bengbu Medical College, Bengbu, China
| | - Shuang Wu
- Department of Radiation Oncology, First Affiliated Hospital, Bengbu Medical College, Bengbu, China
| | - Huan Zhao
- School of Medical Imaging, Bengbu Medical College, Bengbu, China
- Anhui Key Laboratory of Computational Medicine and Intelligent Health, Bengbu Medical College, Bengbu, China
| | - Weizheng Guan
- School of Medical Imaging, Bengbu Medical College, Bengbu, China
- Anhui Key Laboratory of Computational Medicine and Intelligent Health, Bengbu Medical College, Bengbu, China
| | - Yufu Zhou
- Department of Radiation Oncology, First Affiliated Hospital, Bengbu Medical College, Bengbu, China
| | - Bo Shi
- School of Medical Imaging, Bengbu Medical College, Bengbu, China
- Anhui Key Laboratory of Computational Medicine and Intelligent Health, Bengbu Medical College, Bengbu, China
- *Correspondence: Bo Shi,
| |
Collapse
|
3
|
A Human Defecation Prediction Method Based on Multi-Domain Features and Improved Support Vector Machine. Symmetry (Basel) 2022. [DOI: 10.3390/sym14091763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The difficulty of defecation seriously affects the quality of life of the bedridden elderly. To solve the problem that it is difficult to know the defecation time of the bedridden elderly, this paper proposed a human pre-defecation prediction method based on multi-domain features and improved support vector machine (SVM) using bowel sound as the original signal. The method includes three stages: multi-domain features extraction, feature optimization, and defecation prediction. In the stage of multi-domain features extraction, statistical analysis, fast Fourier transform (FFT), and wavelet packet transform are used to extract feature information in the time domain, frequency domain, and time-frequency domain. The symmetry of the bowel sound signal in the time domain, frequency domain, and time-frequency domain will change when the human has the urge to defecate. In the feature optimization stage, the Fisher Score (FS) algorithm is introduced to select meaningful and sensitive features according to the importance of each feature, aiming to remove redundant information and improve computational efficiency. In the stage of defecation prediction, SVM is optimized by the gray wolf optimization (GWO) algorithm to realize human defecation prediction. Finally, experimental analysis of the bowel sound data collected during the study is carried out. The experimental result shows that the proposed method could achieve an accuracy of 92.86% in defecation prediction, which proves the effectiveness of the proposed method.
Collapse
|
4
|
Zanelli S, Ammi M, Hallab M, El Yacoubi MA. Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:4890. [PMID: 35808386 PMCID: PMC9269150 DOI: 10.3390/s22134890] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/17/2022] [Accepted: 06/21/2022] [Indexed: 06/15/2023]
Abstract
(1) Background: Diabetes mellitus (DM) is a chronic, metabolic disease characterized by elevated levels of blood glucose. Recently, some studies approached the diabetes care domain through the analysis of the modifications of cardiovascular system parameters. In fact, cardiovascular diseases are the first leading cause of death in diabetic subjects. Thanks to their cost effectiveness and their ease of use, electrocardiographic (ECG) and photoplethysmographic (PPG) signals have recently been used in diabetes detection, blood glucose estimation and diabetes-related complication detection. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (2) Method: We performed a systematic review based on articles that focus on the use of ECG and PPG signals in diabetes care. The search was focused on keywords related to the topic, such as "Diabetes", "ECG", "PPG", "Machine Learning", etc. This was performed using databases, such as PubMed, Google Scholar, Semantic Scholar and IEEE Xplore. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (3) Results: A total of 78 studies were included. The majority of the selected studies focused on blood glucose estimation (41) and diabetes detection (31). Only 7 studies focused on diabetes complications detection. We present these studies by approach: traditional, machine learning and deep learning approaches. (4) Conclusions: ECG and PPG analysis in diabetes care showed to be very promising. Clinical validation and data processing standardization need to be improved in order to employ these techniques in a clinical environment.
Collapse
Affiliation(s)
- Serena Zanelli
- University of Paris 8, LAGA, CNRS, Institut Galilée, 93200 Saint Denis, France;
- SAMOVAR Telecom SudParis, CNRS, Institut Polytechnique de Paris, 91764 Paris, France;
| | - Mehdi Ammi
- University of Paris 8, LAGA, CNRS, Institut Galilée, 93200 Saint Denis, France;
| | | | - Mounim A. El Yacoubi
- SAMOVAR Telecom SudParis, CNRS, Institut Polytechnique de Paris, 91764 Paris, France;
| |
Collapse
|
5
|
Sun LY, Ouyang Q, Cen WJ, Wang F, Tang WT, Shao JY. A Model Based on Artificial Intelligence Algorithm for Monitoring Recurrence of HCC after Hepatectomy. Am Surg 2021:31348211063549. [PMID: 34894786 DOI: 10.1177/00031348211063549] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND There is no satisfactory indicator for monitoring recurrence after resection of hepatocellular carcinoma (HCC). This retrospective study aimed to design and validate an HCC monitor recurrence (HMR) model for patients without metastasis after hepatectomy. METHODS A training cohort was recruited from 1179 patients with HCC without metastasis after hepatectomy between February 2012 and December 2015. An HMR model was developed using an AdaBoost classifier algorithm. The factors included patient age, TNM staging, tumor size, and pre/postoperative dynamic variations of alpha-fetoprotein (AFP). The diagnostic efficacy of the model was evaluated based on the area under the receiver operating characteristic curves (AUCs). The model was validated using a cohort of 695 patients. RESULTS In preoperative patients with positive or negative AFP, the AUC of the validation cohort in the HMR model was .8877, which indicated better diagnostic efficacy than that of serum AFP (AUC, .7348). The HMR model predicted recurrence earlier than computed tomography/magnetic resonance imaging did by 191.58 ± 165 days. In addition, the HMR model can predict the prognosis of patients with HCC after resection. CONCLUSIONS The HMR model established in this study is more accurate than serum AFP for monitoring recurrence after hepatectomy for HCC and can be used for real-time monitoring of the postoperative status in patients with HCC without metastasis.
Collapse
Affiliation(s)
- Li-Yue Sun
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Molecular Diagnostics, 71067Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Qing Ouyang
- Department of Hepatobiliary, 26470General Hospital of Southern Theatre Command of PLA, Guangzhou, China
| | - Wen-Jian Cen
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Molecular Diagnostics, 71067Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Fang Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Molecular Diagnostics, 71067Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wen-Ting Tang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Molecular Diagnostics, 71067Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jian-Yong Shao
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Molecular Diagnostics, 71067Sun Yat-sen University Cancer Center, Guangzhou, China
| |
Collapse
|
6
|
An absolute magnitude deviation of HRV for the prediction of prediabetes with combined artificial neural network and regression tree methods. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10040-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
7
|
Shashikant R, Chaskar U, Phadke L, Patil C. Gaussian process-based kernel as a diagnostic model for prediction of type 2 diabetes mellitus risk using non-linear heart rate variability features. Biomed Eng Lett 2021; 11:273-286. [PMID: 34350053 DOI: 10.1007/s13534-021-00196-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/03/2021] [Accepted: 06/20/2021] [Indexed: 01/07/2023] Open
Abstract
The main objective of the study was to develop a low-cost, non-invasive diagnostic model for the early prediction of T2DM risk and validation of this model on patients. The model was designed based on the machine learning classification technique using non-linear Heart rate variability (HRV) features. The electrocardiogram of the healthy subjects (n = 35) and T2DM subjects (n = 100) were recorded in the supine position for 15 min, and HRV features were extracted. The significant non-linear HRV features were identified through statistical analysis. It was found that Poincare plot features (SD1 and SD2) can differentiate the T2DM subject data from healthy subject data. Several machine learning classifiers, such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis, Naïve Bayes, and Gaussian Process Classifier (GPC), have classified the data based on the cross-validation approach. A GP classifier was implemented using three kernels, namely radial basis, linear, and polynomial kernel, considering the ability to handle the non-linear data. The classifier performance was evaluated and compared using performance metrics such as accuracy(AC), sensitivity(SN), specificity(SP), precision(PR), F1 score, and area under the receiver operating characteristic curve(AUC). Initially, all non-linear HRV features were selected for classification, but the specificity of the model was the limitation. Thus, only two Poincare plot features were used to design the diagnostic model. Our diagnostic model shows the performance using GPC based linear kernel as AC of 92.59%, SN of 96.07%, SP of 81.81%, PR of 94.23%, F1 score of 0.95, and AUC of 0.89, which are more extensive compared to other classification models. Further, the diagnostic model was deployed on the hardware module. Its performance on unknown/test data was validated on 65 subjects (healthy n = 15 and T2DM n = 50). Considering the desirable performance of the diagnostic model, it can be used as an initial screening test tool for a healthcare practitioner to predict T2DM risk.
Collapse
Affiliation(s)
- R Shashikant
- Department of Instrumentation and Control, College of Engineering, Pune, India
| | - Uttam Chaskar
- Department of Instrumentation and Control, College of Engineering, Pune, India
| | - Leena Phadke
- Department of Physiology, Smt. Kashibai Navale Medical College and General Hospital, Pune, India
| | - Chetankumar Patil
- Department of Instrumentation and Control, College of Engineering, Pune, India
| |
Collapse
|
8
|
Oh SL, Jahmunah V, Arunkumar N, Abdulhay EW, Gururajan R, Adib N, Ciaccio EJ, Cheong KH, Acharya UR. A novel automated autism spectrum disorder detection system. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00408-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
AbstractAutism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.
Collapse
|
9
|
Faust O, Lei N, Chew E, Ciaccio EJ, Acharya UR. A Smart Service Platform for Cost Efficient Cardiac Health Monitoring. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E6313. [PMID: 32872667 PMCID: PMC7504315 DOI: 10.3390/ijerph17176313] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 08/20/2020] [Accepted: 08/24/2020] [Indexed: 11/22/2022]
Abstract
AIM In this study we have investigated the problem of cost effective wireless heart health monitoring from a service design perspective. SUBJECT AND METHODS There is a great medical and economic need to support the diagnosis of a wide range of debilitating and indeed fatal non-communicable diseases, like Cardiovascular Disease (CVD), Atrial Fibrillation (AF), diabetes, and sleep disorders. To address this need, we put forward the idea that the combination of Heart Rate (HR) measurements, Internet of Things (IoT), and advanced Artificial Intelligence (AI), forms a Heart Health Monitoring Service Platform (HHMSP). This service platform can be used for multi-disease monitoring, where a distinct service meets the needs of patients having a specific disease. The service functionality is realized by combining common and distinct modules. This forms the technological basis which facilitates a hybrid diagnosis process where machines and practitioners work cooperatively to improve outcomes for patients. RESULTS Human checks and balances on independent machine decisions maintain safety and reliability of the diagnosis. Cost efficiency comes from efficient signal processing and replacing manual analysis with AI based machine classification. To show the practicality of the proposed service platform, we have implemented an AF monitoring service. CONCLUSION Having common modules allows us to harvest the economies of scale. That is an advantage, because the fixed cost for the infrastructure is shared among a large group of customers. Distinct modules define which AI models are used and how the communication with practitioners, caregivers and patients is handled. That makes the proposed HHMSP agile enough to address safety, reliability and functionality needs from healthcare providers.
Collapse
Affiliation(s)
- Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Ningrong Lei
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Eng Chew
- Faculty of Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia;
| | - Edward J. Ciaccio
- Department of Medicine—Cardiology, Columbia University, New York, NY 10027, USA;
| | - U Rajendra Acharya
- Biomedical Engineering Department, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- School of Management and Enterprise, University of Southern Queensland, Springfield, QLD 4350, Australia
| |
Collapse
|
10
|
Faust O, Ciaccio EJ, Acharya UR. A Review of Atrial Fibrillation Detection Methods as a Service. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E3093. [PMID: 32365521 PMCID: PMC7246533 DOI: 10.3390/ijerph17093093] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 04/19/2020] [Accepted: 04/24/2020] [Indexed: 12/28/2022]
Abstract
Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals.
Collapse
Affiliation(s)
- Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
| | - Edward J. Ciaccio
- Department of Medicine—Cardiology, Columbia University, New York, NY 10027, USA;
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Electronic & Computer Engineering, Singapore 599489, Singapore;
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| |
Collapse
|
11
|
Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals. Comput Biol Med 2019; 113:103387. [PMID: 31421276 DOI: 10.1016/j.compbiomed.2019.103387] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 08/08/2019] [Accepted: 08/08/2019] [Indexed: 11/24/2022]
Abstract
In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obtained from electrocardiogram (ECG) data. Recent progress in deep learning has contributed significantly to improvement in the quality of healthcare. In order for deep learning models to perform well, large datasets are required for training. However, a difficulty in the biomedical field is the lack of clinical data with expert annotation. A recent, commonly implemented technique to train deep learning models using small datasets is to transfer the weighting, developed from a large dataset, to the current model. This deep learning transfer strategy is generally employed for two-dimensional signals. Herein, the weighting of models pre-trained using two-dimensional large image data was applied to one-dimensional HR signals. The one-dimensional HR signals were then converted into frequency spectrum images, which were utilized for application to well-known pre-trained models, specifically: AlexNet, VggNet, ResNet, and DenseNet. The DenseNet pre-trained model yielded the highest classification average accuracy of 97.62%, and sensitivity of 100%, to detect DM subjects via HR signal recordings. In the future, we intend to further test this developed model by utilizing additional data along with cloud-based storage to diagnose DM via heart signal analysis.
Collapse
|
12
|
Faust O, Razaghi H, Barika R, Ciaccio EJ, Acharya UR. A review of automated sleep stage scoring based on physiological signals for the new millennia. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 176:81-91. [PMID: 31200914 DOI: 10.1016/j.cmpb.2019.04.032] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 04/03/2019] [Accepted: 04/29/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal. METHODS This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals. RESULTS Our review shows that all of these signals contain information for sleep stage scoring. CONCLUSIONS The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.
Collapse
Affiliation(s)
- Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom.
| | - Hajar Razaghi
- Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom
| | - Ragab Barika
- Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom
| | - Edward J Ciaccio
- Department of Medicine - Cardiology, Columbia University, New York, New York, USA
| | - U Rajendra Acharya
- Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| |
Collapse
|
13
|
Liu Y, Zhang Q, Zhao G, Qu Z, Liu G, Liu Z, An Y. Detecting Diseases by Human-Physiological-Parameter-Based Deep Learning. IEEE ACCESS 2019; 7:22002-22010. [DOI: 10.1109/access.2019.2893877] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
|
14
|
Albarado-Ibañez A, Arroyo-Carmona RE, Sánchez-Hernández R, Ramos-Ortiz G, Frank A, García-Gudiño D, Torres-Jácome J. The Role of the Autonomic Nervous System on Cardiac Rhythm during the Evolution of Diabetes Mellitus Using Heart Rate Variability as a Biomarker. J Diabetes Res 2019; 2019:5157024. [PMID: 31211146 PMCID: PMC6532312 DOI: 10.1155/2019/5157024] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 12/29/2018] [Accepted: 02/11/2019] [Indexed: 11/17/2022] Open
Abstract
Heart rate variability (HRV) is highly influenced by the Autonomic Nervous System (ANS). Several illnesses have been associated with changes in the ANS, thus altering the pattern of HRV. However, the variability of the heart rhythm is originated within the Sinus Atrial Node (SAN) which has its own variability. Still, although both oscillators produce HRV, the influence of the SAN on HRV has not yet been exhaustively studied. On the other hand, the complications of diabetes mellitus (DM), for instance, nephropathy, retinopathy, and neuropathy, increase cardiovascular morbidity and mortality. Traditionally, these complications are diagnosed only when the patient is already suffering from the negative symptoms these complications implicate. Consequently, it is of paramount importance to develop new techniques for early diagnosis prior to any deterioration on healthy patients. HRV has been proved to be a valuable, noninvasive clinical evidence for evaluating diseases and even for describing aging and behavior. In this study, several ECGs were recorded and their RR and PP intervals were analyzed to detect the interpotential interval (ii) of the SAN. Additionally, HRV reduction was quantified to identify alterations in the nervous system within the nodal tissue via measuring the SD1/SD2 ratio in a Poincaré plot. With 15 years of DM development, the data showed an age-dependent increase in HRV due to the axon retraction of ANS neurons from its effectors. In addition, these alterations modify the heart rhythm-producing fatal arrhythmias. Therefore, it is possible to avoid the consequences of DM identifying alterations in SAN previous to its symptomatic appearance. This could be used as an early diagnosis indicator.
Collapse
Affiliation(s)
- Alondra Albarado-Ibañez
- Universidad Nacional Autónoma de México, Centro de las Ciencias de la Complejidad, Circuito Mario de la Cueva 20, Insurgentes Sur, Delegación Coyoacán, C.P. 04510 Cd. de México, Mexico
- Benemérita Universidad Autónoma de Puebla, Instituto de Fisiología, 14 Sur 6301, Colonia Jardines de San Manuel, C.P. 72570 Puebla, Pue., Mexico
| | - Rosa Elena Arroyo-Carmona
- Benemérita Universidad Autónoma de Puebla, Facultad de Ciencias Químicas, 18 sur y avenida San Claudio colonia Jardines de San Manuel, C.P. 72570 Puebla, Pue., Mexico
| | - Rommel Sánchez-Hernández
- Benemérita Universidad Autónoma de Puebla, Instituto de Fisiología, 14 Sur 6301, Colonia Jardines de San Manuel, C.P. 72570 Puebla, Pue., Mexico
| | - Geovanni Ramos-Ortiz
- Universidad de Puebla, Escuela de Ciencias Químicas, Colonia Guadalupe Hidalgo, Puebla, Pue., Mexico
| | - Alejandro Frank
- Universidad Nacional Autónoma de México, Centro de las Ciencias de la Complejidad, Circuito Mario de la Cueva 20, Insurgentes Sur, Delegación Coyoacán, C.P. 04510 Cd. de México, Mexico
| | - David García-Gudiño
- Universidad Nacional Autónoma de México, Centro de las Ciencias de la Complejidad, Circuito Mario de la Cueva 20, Insurgentes Sur, Delegación Coyoacán, C.P. 04510 Cd. de México, Mexico
| | - Julián Torres-Jácome
- Benemérita Universidad Autónoma de Puebla, Instituto de Fisiología, 14 Sur 6301, Colonia Jardines de San Manuel, C.P. 72570 Puebla, Pue., Mexico
| |
Collapse
|
15
|
Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR. Deep learning for healthcare applications based on physiological signals: A review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 161:1-13. [PMID: 29852952 DOI: 10.1016/j.cmpb.2018.04.005] [Citation(s) in RCA: 363] [Impact Index Per Article: 60.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 03/23/2018] [Accepted: 04/02/2018] [Indexed: 05/06/2023]
Abstract
BACKGROUND AND OBJECTIVE We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. METHODS An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. RESULTS During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. CONCLUSIONS This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.
Collapse
Affiliation(s)
- Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom.
| | - Yuki Hagiwara
- Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Tan Jen Hong
- Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Oh Shu Lih
- Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - U Rajendra Acharya
- Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| |
Collapse
|
16
|
G S, Kp S, R V. Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.procs.2018.05.041] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
17
|
HAGIWARA YUKI, FAUST OLIVER. NONLINEAR ANALYSIS OF CORONARY ARTERY DISEASE, MYOCARDIAL INFARCTION, AND NORMAL ECG SIGNALS. J MECH MED BIOL 2017. [DOI: 10.1142/s0219519417400061] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
In this study, we analyze nonlinear feature extraction methods in terms of their ability to support the diagnosis of coronary artery disease (CAD) and myocardial infarction (MI). The nonlinear features were extracted from electrocardiogram (ECG) signals that were measured from CAD patients, MI patients as well as normal controls. We tested 34 recurrence quantification analysis (RQA) features, 14 bispectrum, and 136 cumulant features. The features were extracted from 10,546 normal, 41,545 CAD, and 40,182 MI heart beats. The feature quality was assessed with Student’s [Formula: see text]-test and the [Formula: see text]-value was used for feature ranking. We found that nonlinear features can effectively represent the physiological realities of the human heart.
Collapse
Affiliation(s)
- YUKI HAGIWARA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - OLIVER FAUST
- Department of Engineering and Mathematics, Sheffield Hallam University, UK
| |
Collapse
|
18
|
TRIPATHY RK, PATERNINA MARIORARRIETA, ARRIETA JUANG, PATTANAIK P. AUTOMATED DETECTION OF ATRIAL FIBRILLATION ECG SIGNALS USING TWO STAGE VMD AND ATRIAL FIBRILLATION DIAGNOSIS INDEX. J MECH MED BIOL 2017. [DOI: 10.1142/s0219519417400449] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Atrial fibrillation (AF) is a common atrial arrhythmia occurring in clinical practice and can be diagnosed using electrocardiogram (ECG) signal. The conventional diagnostic features of ECG signal are not enough to quantify the pathological variations during AF. Therefore, an automated detection of AF pathology using the new diagnostic features of ECG signal is required. This paper proposes a novel method for the detection of AF using ECG signals. In this work, we are using a novel nonlinear method namely, the two-stage variational mode decomposition (VMD) to analyze ECG and deep belief network (DBN) for automated AF detection. First, the ECG signals of both normal sinus rhythm (NSR) and AF classes are decomposed into different modes using VMD. The first mode of VMD is decomposed in the second stage as this mode captures the atrial activity (AA) information during AF. The remaining modes of ECG captures the ventricular activity information. The sample entropy (SE) and the VMD estimated center frequency features are extracted from the sub-modes of AA mode and ventricular activity modes. These extracted features coupled with DBN classifier is able to classify normal and AF ECG signals with an accuracy, sensitivity and specificity values of 98.27%, 97.77% and 98.67%, respectively. We have developed an atrial fibrillation diagnosis index (AFDI) using selected SE and center frequency features to detect AF with a single number. The system is ready to be tested on huge database and can be used in main hospitals to detect AF ECG classes.
Collapse
Affiliation(s)
- R. K. TRIPATHY
- Faculty of Engineering and Technology (ITER), S‘O’A University, Bhubaneswar 751030, India
| | - MARIO R. ARRIETA PATERNINA
- Department of Electrical Engineering, National Autonomous University of Mexico, Mexico City 04510, Mexico
| | | | - P. PATTANAIK
- Faculty of Engineering and Technology (ITER), S‘O’A University, Bhubaneswar 751030, India
| |
Collapse
|
19
|
Abstract
Diabetes mellitus (DM) is a critical and long-term disorder due to the insufficient production of insulin by the pancreas or ineffective use of insulin by the body. Importantly, cardiovascular disease (CVD) has long been thought to be linked with diabetes. Despite more diabetic individuals surviving from better medications and treatments, there has been significant rise in the morbidity and mortality from CVD. Indeed, the classification of DM based on the electrocardiogram signals of the heart will be an advantageous system. Further, computer-aided classification of DM with integrated algorithms may enhance the execution of the system. In this paper, we have reviewed various studies using heart rate variability signals for automated classification of diabetes. Furthermore, the different techniques used to extract the features and the efficiency of the classification systems are discussed.
Collapse
Affiliation(s)
- MUHAMMAD ADAM
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - JEN HONG TAN
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - EDDIE Y. K. NG
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| |
Collapse
|
20
|
TRIPATHY R, PATERNINA MARIORARRIETA, PATTANAIK P. A NEW METHOD FOR AUTOMATED DETECTION OF DIABETES FROM HEART RATE SIGNAL. J MECH MED BIOL 2017. [DOI: 10.1142/s0219519417400012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Diabetes Mellitus (DM) is a chronic disease and it is characterized based on the increase in the sugar level in the blood. The other diseases such as the cardiomyopathy, neuropathy and retinopathy may occur due to the DM pathology. The RR-time series or heart rate (HR) signal quantifies the beat-to-beat variations in the electrocardiogram (ECG) and it has been widely used for the detection of various cardiac diseases. Detection of DM based on the features of HR signal is a challenging problem. This paper copes with a new method for the detection of Diabetes Mellitus (DM) based on the features extracted from the HR signal. The Singular Spectrum Analysis (SSA) of HR signal and the Kernel Sparse Representation Classifier (KSRC) are the mathematical foundations used to achieve the detection. SSA is used to decompose the HR signal into sub-signals, and diagnostic features such as the maximum value of each sub-signal and eigenvalues are evaluated. Then, the KSRC uses the proposed diagnostic features as inputs for detecting diabetes. The experimental results reveal that the proposal attains the accuracy, sensitivity, and specificity values of 92.18%, 93.75% and 90.62%, respectively, employing the KSRC and the hold-out cross-validation approach. The method is compared with existing approaches for detecting diabetes from HR signal.
Collapse
Affiliation(s)
- R. K. TRIPATHY
- Faculty of Engineering (ITER), S‘O’A University, Bhubaneswar 751030, India
| | - MARIO R. ARRIETA PATERNINA
- Department of Electrical Engineering, National Autonomous University of Mexico, Mexico City 04510, Mexico
| | - P. PATTANAIK
- Faculty of Engineering (ITER), S‘O’A University, Bhubaneswar 751030, India
| |
Collapse
|
21
|
KANG YIDA, ZHUO DEMING, FOO RUIENANNE, LIM CHOOMIN, FAUST OLIVER, HAGIWARA YUKI. ALGORITHM FOR THE DETECTION OF CONGESTIVE HEART FAILURE INDEX. J MECH MED BIOL 2017. [DOI: 10.1142/s0219519417400437] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
This study documents our efforts to provide computer support for the diagnosis of congestive heart failure (CHF). That computer support takes the form of an index value. A high index value indicates a low probability of CHF, and an index value below a threshold of 25.6 suggests a high probability of CHF. To create that index, we have designed a sophisticated algorithm chain which takes electrocardiogram signals as input. The signals are pre-processed before they are sent to a range of nonlinear feature extraction algorithms. The top 10 feature extraction methods were used to create the CHF index. By using objective feature extraction algorithms, we avoid the problem of inter- and intra-observer variability. We observed that the nonlinear feature extraction methods reflect the nature of the human heart very well. That observation is based on the fact that the nonlinear features achieved low [Formula: see text]-values and high feature ranking criterion scores.
Collapse
Affiliation(s)
- YI DA KANG
- Department of Electronic and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - DEMING ZHUO
- Department of Electronic and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - RUI EN ANNE FOO
- Department of Electronic and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - CHOO MIN LIM
- Department of Electronic and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - OLIVER FAUST
- Department of Engineering and Mathematics, Sheffield Hallam University, UK
| | - YUKI HAGIWARA
- Department of Electronic and Computer Engineering, Ngee Ann Polytechnic, Singapore
| |
Collapse
|
22
|
Carricarte Naranjo C, Sanchez-Rodriguez LM, Brown Martínez M, Estévez Báez M, Machado García A. Permutation entropy analysis of heart rate variability for the assessment of cardiovascular autonomic neuropathy in type 1 diabetes mellitus. Comput Biol Med 2017; 86:90-97. [DOI: 10.1016/j.compbiomed.2017.05.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 05/05/2017] [Accepted: 05/06/2017] [Indexed: 01/29/2023]
|
23
|
Kim TH, Ku B, Bae JH, Shin JY, Jun MH, Kang JW, Kim J, Lee JH, Kim JU. Hemodynamic changes caused by acupuncture in healthy volunteers: a prospective, single-arm exploratory clinical study. Altern Ther Health Med 2017; 17:274. [PMID: 28532415 PMCID: PMC5440909 DOI: 10.1186/s12906-017-1787-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Accepted: 05/11/2017] [Indexed: 11/10/2022]
Abstract
BACKGROUND Radial pressure pulse wave (RPPW) examination has been a key diagnostic component of traditional Chinese medicine. The objective of this study was to investigate the changes in RPPW along with various hemodynamic variables after acupuncture stimulation and to examine the validity of pulse diagnosis as a modern diagnostic tool. METHODS We conducted acupuncture stimulation at both ST36 acupuncture points in 25 healthy volunteers. We simultaneously assessed the RPPW by pulse tonometry; heart rate variability (HRV) by electrocardiogram; photoplethysmogram (PPG) signals, respiration rate, peripheral blood flow velocity and arterial depth by ultrasonography; and cardiac output by impedance cardiography, before, during and after a session of acupuncture stimulation. RESULTS We observed consistent patterns of increased spectral energy at low frequency (<10 Hz) and pulse power using RPPW examination and in the amplitude and systolic area of the PPG signal during the entire acupuncture session. The low- and high-frequency domains of HRV increased and decreased, respectively, during the acupuncture session. The peripheral blood velocity rose shortly after needle insertion, reached a maximum in the middle of the session and decreased afterwards. The augmentation index (AIX) and pulse transit time (PTT) obtained from RPPW did not change significantly. CONCLUSION Acupuncture stimulation at ST36 in healthy subjects increased the peripheral pulse amplitudes (pressure pulse wave (PPW) and PPG), blood flow velocity (ultrasonography) and sympathetic nerve activity (HRV). The lack of changes in the AIX and PTT suggests that the increased pulse amplitudes and blood flow velocity may result from increased cardiac output. TRIAL REGISTRATION Clinical Research Information Service ( KCT0001663 ).
Collapse
|
24
|
Acharya UR, Mookiah MRK, Koh JE, Tan JH, Bhandary SV, Rao AK, Hagiwara Y, Chua CK, Laude A. Automated diabetic macular edema (DME) grading system using DWT, DCT Features and maculopathy index. Comput Biol Med 2017; 84:59-68. [DOI: 10.1016/j.compbiomed.2017.03.016] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 03/16/2017] [Accepted: 03/17/2017] [Indexed: 12/11/2022]
|
25
|
França da Silva AK, Penachini da Costa de Rezende Barbosa M, Marques Vanderlei F, Destro Christofaro DG, Marques Vanderlei LC. Application of Heart Rate Variability in Diagnosis and Prognosis of Individuals with Diabetes Mellitus: Systematic Review. Ann Noninvasive Electrocardiol 2017; 21:223-35. [PMID: 27226209 DOI: 10.1111/anec.12372] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The use of heart rate variability as a tool capable of discriminating individuals with diabetes mellitus is still little explored, as its use has been limited to comparing those with and without the disease. Thus, the purpose of this study was to verify the use of heart rate variability as a tool for diagnostic and prognostic evaluation in person with diabetes and to identify whether there are cutoff points generated from the use of this tool in these individuals. METHODS A search was conducted in the electronic databases MEDLINE, Cochrane Library, Web of Science, EMBASE, and LILACS starting from the oldest records until January 2015, by means of descriptors related to the target condition, evaluated tool, and evaluation method. All the studies were evaluated for methodological quality using the QUADAS-2 instrument. RESULTS Eight studies were selected. In general, the studies showed that the heart rate variability is useful to discriminate cardiac autonomic neuropathy in person with diabetes, and the sample entropy, SD1/SD2 indices, SDANN, HF, and slope of TFC have better discriminatory power to detect autonomic dysfunction, with sensitivity and specificity values ranging from 72% to 100% and 71% to 97%, respectively. CONCLUSION Although there are methodological differences in indices used, in general, this tool demonstrated good sensitivity and specificity and can be used as an additional and/or complementary tool to the conventional autonomic tests, in order to obtain safer and more effective diagnostic, collaborating for better risk stratification conditions of these patients.
Collapse
Affiliation(s)
| | | | - Franciele Marques Vanderlei
- Department of Physical Therapy, Faculty of Science and Technology, Paulista State University, Presidente Prudente, São Paulo, Brazil
| | - Diego Giuliano Destro Christofaro
- Department of Physical Education, Faculty of Science and Technology, Paulista State University, Presidente Prudente, São Paulo, Brazil
| | - Luiz Carlos Marques Vanderlei
- Department of Physical Therapy, Faculty of Science and Technology, Paulista State University, Presidente Prudente, São Paulo, Brazil
| |
Collapse
|
26
|
Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Bhandary SV, Rao AK, Fujita H, Hagiwara Y, Chua CK, Laude A. Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index. Comput Biol Med 2016; 75:54-62. [PMID: 27253617 DOI: 10.1016/j.compbiomed.2016.04.015] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Revised: 04/07/2016] [Accepted: 04/22/2016] [Indexed: 11/27/2022]
Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore 599491, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Malaysia
| | | | - Joel E W Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Jen Hong Tan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Sulatha V Bhandary
- Department of Ophthalmology, Kasturba Medical College, Manipal, 576104, India
| | - A Krishna Rao
- Department of Ophthalmology, Kasturba Medical College, Manipal, 576104, India
| | - Hamido Fujita
- Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate, 020-0693, Japan
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Chua Kuang Chua
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Augustinus Laude
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, 308433, Singapore
| |
Collapse
|
27
|
FAUST OLIVER, YU WENWEI. FORMAL AND MODEL DRIVEN DESIGN OF THE BRIGHT LIGHT THERAPY SYSTEM LUXAMET. J MECH MED BIOL 2016. [DOI: 10.1142/s0219519416500652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Seasonal depression seriously diminishes the quality of life for many patients. To improve their condition, we propose LUXAMET, a bright light therapy system. This system has the potential to relieve patients from some of the symptoms caused by seasonal depression. The system was designed with a formal and model driven design methodology. This methodology enabled us to minimize systemic hazards, like blinding patients with an unhealthy dose of light. This was achieved by controlling race conditions and memory leaks, during design time. We prove that the system specification is deadlock as well as livelock free and there are no invariant violations. These proofs, together with the similarity between specification model and implementation code, make us confident that the implemented system is a reliable tool which can help patients during seasonal depression.
Collapse
Affiliation(s)
- OLIVER FAUST
- School of Engineering, University of Aberdeen, Scotland, UK
| | - WENWEI YU
- School of Science and Engineering, Habib University, Karachi, Pakistan
| |
Collapse
|
28
|
Fujita H, Acharya UR, Sudarshan VK, Ghista DN, Sree SV, Eugene LWJ, Koh JE. Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.02.049] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
|
29
|
Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Noronha K, Bhandary SV, Rao AK, Hagiwara Y, Chua CK, Laude A. Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features. Comput Biol Med 2016; 73:131-40. [PMID: 27107676 DOI: 10.1016/j.compbiomed.2016.04.009] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Revised: 04/10/2016] [Accepted: 04/14/2016] [Indexed: 11/18/2022]
Abstract
Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening.
Collapse
Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, 599491, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Malaysia
| | | | - Joel E W Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Jen Hong Tan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Kevin Noronha
- Department of Electronics and Telecommunication, St. Francis Institute of Technology, Mumbai 400103, India
| | - Sulatha V Bhandary
- Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India
| | - A Krishna Rao
- Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Chua Kuang Chua
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Augustinus Laude
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, 308433, Singapore
| |
Collapse
|
30
|
FAUST OLIVER, ACHARYA URAJENDRA, NG EYK, FUJITA HAMIDO. A REVIEW OF ECG-BASED DIAGNOSIS SUPPORT SYSTEMS FOR OBSTRUCTIVE SLEEP APNEA. J MECH MED BIOL 2016. [DOI: 10.1142/s0219519416400042] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy.
Collapse
Affiliation(s)
- OLIVER FAUST
- Faculty of Arts, Computing, Engineering and Sciences, Sheffield Hallam University, UK
| | | | | | | |
Collapse
|
31
|
FAUST OLIVER, NG EYK. COMPUTER AIDED DIAGNOSIS FOR CARDIOVASCULAR DISEASES BASED ON ECG SIGNALS: A SURVEY. J MECH MED BIOL 2016. [DOI: 10.1142/s0219519416400017] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The interpretation of Electroencephalography (ECG) signals is difficult, because even subtle changes in the waveform can indicate a serious heart disease. Furthermore, these waveform changes might not be present all the time. As a consequence, it takes years of training for a medical practitioner to become an expert in ECG-based cardiovascular disease diagnosis. That training is a major investment in a specific skill. Even with expert ability, the signal interpretation takes time. In addition, human interpretation of ECG signals causes interoperator and intraoperator variability. ECG-based Computer-Aided Diagnosis (CAD) holds the promise of improving the diagnosis accuracy and reducing the cost. The same ECG signal will result in the same diagnosis support regardless of time and place. This paper introduces both the techniques used to realize the CAD functionality and the methods used to assess the established functionality. This survey aims to instill trust in CAD of cardiovascular diseases using ECG signals by introducing both a conceptional overview of the system and the necessary assessment methods.
Collapse
Affiliation(s)
- OLIVER FAUST
- Faculty of Arts, Computing, Engineering and Sciences, Sheffield Hallam University, Sheffield, UK
| | - E. Y. K. NG
- School of Mechanical & Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore
| |
Collapse
|
32
|
ACHARYA URAJENDRA, FUJITA HAMIDO, BHAT SHREYA, KOH JOELEW, ADAM MUHAMMAD, GHISTA DHANJOON, SUDARSHAN VIDYAK, CHUA KOKPOO, CHUA KUANGCHUA, MOLINARI FILIPPO, NG EYK, TAN RUSAN. AUTOMATED DIAGNOSIS OF DIABETES USING ENTROPIES AND DIABETIC INDEX. J MECH MED BIOL 2016. [DOI: 10.1142/s021951941640008x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Diabetes Mellitus (DM) is a chronic metabolic disorder that hampers the body’s energy absorption capacity from the food. It is either caused by pancreatic malfunctioning or the body cells being inactive to insulin production. Prolonged diabetes results in severe complications, such as retinopathy, neuropathy, cardiomyopathy and cardiovascular diseases. DM is an incurable disorder. Thus, diagnosis and monitoring of diabetes is essential to prevent the body organs from severe damage. Heart Rate Variability (HRV) signal processing can be used as one of the methods for the diagnosis of DM. Our paper introduces a noninvasive technique of automated diabetic diagnosis using HRV signals. The R-R interval signals are decomposed using Shearlet transforms integrated with Continuous Wavelet Transform (CWT), and their characteristic features are extracted by using Shannon’s, Renyi’s, Kapur entropies, energy and Higher Order Spectra (HOS). Then, Locality Sensitive Discriminant Analysis (LSDA) is employed to remove insignificant features and reduce the number of employed features. These redundant features are eliminated by using six feature selection algorithms: Student’s t-test, Receiver Operating Characteristic Curve (ROC), Wilcoxon signed-rank test, Bhattacharyya distance, Information entropy and Fuzzy Max-Relevance and Min-Redundancy (MRMR). This step is followed by classification of normal and diabetic signals using different classifiers, such as discriminant classifiers, Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Naïve Bayes (NB), Fuzzy Sugeno (FSC), Gaussian Mixture Model (GMM), AdaBoost and k-Nearest Neighbor (k-NN) classifier. In these classifiers, the selected features are employed to distinguish diabetic signals from normal signals. These classifiers are trained and then tested to validate their accuracy to make accurate diagnosis. The FSC classifier is shown to have the highest (100%) accuracy. Nevertheless, we go one step further in formulating another novel classifier in the form of the diabetic index, and have shown how distinctly it is able to separate diabetic signals from normal signals.
Collapse
Affiliation(s)
- U. RAJENDRA ACHARYA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
| | - HAMIDO FUJITA
- Iwate Prefectural University (IPU), Faculty of Software and Information Science, Iwate, Japan
| | - SHREYA BHAT
- Department of Psychiatry, St. John’s Research Institute, Bangalore 560034, India
| | - JOEL EW KOH
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - MUHAMMAD ADAM
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | | | - VIDYA K. SUDARSHAN
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - KOK POO CHUA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - KUANG CHUA CHUA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - FILIPPO MOLINARI
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - E. Y. K. NG
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | - RU SAN TAN
- Department of Cardiology, National Heart Centre, Singapore
| |
Collapse
|
33
|
PACHORI RAMBILAS, KUMAR MOHIT, AVINASH PAKALA, SHASHANK KORA, ACHARYA URAJENDRA. AN IMPROVED ONLINE PARADIGM FOR SCREENING OF DIABETIC PATIENTS USING RR-INTERVAL SIGNALS. J MECH MED BIOL 2016. [DOI: 10.1142/s0219519416400030] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Diabetes Mellitus (DM) which is a chronic disease and difficult to cure. If diabetes is not treated in a timely manner, it may cause serious complications. For timely treatment, an early detection of the disease is of great interest. Diabetes can be detected by analyzing the RR-interval signals. This work presents a methodology for classification of diabetic and normal RR-interval signals. Firstly, empirical mode decomposition (EMD) method is applied to decompose the RR-interval signals in to intrinsic mode functions (IMFs). Then five parameters namely, area of analytic signal representation (AASR), mean frequency computed using Fourier-Bessel series expansion (MFFB), area of ellipse evaluated from second-order difference plot (ASODP), bandwidth due to frequency modulation (BFM) and bandwidth due to amplitude modulation (BAM) are extracted from IMFs obtained from RR-interval signals. Statistically significant features are fed to least square-support vector machine (LS-SVM) classifier. The three kernels namely, Radial Basis Function (RBF), Morlet wavelet, and Mexican hat wavelet kernels have been studied to obtain the suitable kernel function for the classification of diabetic and normal RR-interval signals. In this work, we have obtained the highest classification accuracy of 95.63%, using Morlet wavelet kernel function with 10-fold cross-validation. The classification system proposed in this work can help the clinicians to diagnose diabetes using electrocardiogram (ECG) signals.
Collapse
Affiliation(s)
- RAM BILAS PACHORI
- Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 452017, India
| | - MOHIT KUMAR
- Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 452017, India
| | - PAKALA AVINASH
- Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 452017, India
| | - KORA SHASHANK
- Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 452017, India
| | - U. RAJENDRA ACHARYA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| |
Collapse
|
34
|
An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures. ENTROPY 2015. [DOI: 10.3390/e17085218] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
|
35
|
Acharya UR, Fujita H, Sudarshan VK, Sree VS, Eugene LWJ, Ghista DN, Tan RS. An integrated index for detection of Sudden Cardiac Death using Discrete Wavelet Transform and nonlinear features. Knowl Based Syst 2015. [DOI: 10.1016/j.knosys.2015.03.015] [Citation(s) in RCA: 86] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
36
|
Rajendra Acharya U, Vidya KS, Ghista DN, Lim WJE, Molinari F, Sankaranarayanan M. Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method. Knowl Based Syst 2015. [DOI: 10.1016/j.knosys.2015.02.005] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
37
|
Tarvainen MP, Laitinen TP, Lipponen JA, Cornforth DJ, Jelinek HF. Cardiac autonomic dysfunction in type 2 diabetes - effect of hyperglycemia and disease duration. Front Endocrinol (Lausanne) 2014; 5:130. [PMID: 25152747 PMCID: PMC4126058 DOI: 10.3389/fendo.2014.00130] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Accepted: 07/19/2014] [Indexed: 01/15/2023] Open
Abstract
Heart rate variability (HRV) is reduced in diabetes mellitus (DM) patients, suggesting dysfunction of cardiac autonomic regulation and an increased risk for cardiac events. The aim of this paper was to examine the associations of blood glucose level (BGL), glycated hemoglobin (HbA1c), and duration of diabetes with cardiac autonomic regulation assessed by HRV analysis. Resting electrocardiogram (ECG), recorded over 20 min in supine position, and clinical measurements of 189 healthy controls and 93 type 2 DM (T2DM) patients were analyzed. HRV was assessed using several time-domain, frequency-domain, and non-linear methods. HRV parameters showed a clear difference between healthy controls and T2DM patients. Hyperglycemia was associated with increase in mean heart rate and decrease in HRV, indicated by negative correlations of BGL and HbA1c with mean RR interval and most of the HRV parameters. Duration of diabetes was strongly associated with decrease in HRV, the most significant decrease in HRV was found within the first 5-10 years of the disease. In conclusion, elevated blood glucose levels have an unfavorable effect on cardiac autonomic function and this effect is pronounced in long-term T2DM patients. The most significant decrease in HRV related to diabetes and thus presence of autonomic neuropathy was observed within the first 5-10 years of disease progression.
Collapse
Affiliation(s)
- Mika P. Tarvainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
- *Correspondence: Mika P. Tarvainen, Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, Kuopio 70211, Finland e-mail:
| | - Tomi P. Laitinen
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Jukka A. Lipponen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - David J. Cornforth
- School of Design, Communication and IT, University of Newcastle, Newcastle, NSW, Australia
| | - Herbert F. Jelinek
- School of Community Health, Centre for Research in Complex Systems, Charles Sturt University, Albury, NSW, Australia
| |
Collapse
|
38
|
Mookiah MRK, Acharya UR, Chua CK, Lim CM, Ng EYK, Laude A. Computer-aided diagnosis of diabetic retinopathy: a review. Comput Biol Med 2013; 43:2136-55. [PMID: 24290931 DOI: 10.1016/j.compbiomed.2013.10.007] [Citation(s) in RCA: 168] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2013] [Revised: 09/27/2013] [Accepted: 10/04/2013] [Indexed: 11/29/2022]
Abstract
Diabetes mellitus may cause alterations in the retinal microvasculature leading to diabetic retinopathy. Unchecked, advanced diabetic retinopathy may lead to blindness. It can be tedious and time consuming to decipher subtle morphological changes in optic disk, microaneurysms, hemorrhage, blood vessels, macula, and exudates through manual inspection of fundus images. A computer aided diagnosis system can significantly reduce the burden on the ophthalmologists and may alleviate the inter and intra observer variability. This review discusses the available methods of various retinal feature extractions and automated analysis.
Collapse
|
39
|
Wallot S, Fusaroli R, Tylén K, Jegindø EM. Using complexity metrics with R-R intervals and BPM heart rate measures. Front Physiol 2013; 4:211. [PMID: 23964244 PMCID: PMC3741573 DOI: 10.3389/fphys.2013.00211] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Accepted: 07/24/2013] [Indexed: 11/16/2022] Open
Abstract
Lately, growing attention in the health sciences has been paid to the dynamics of heart rate as indicator of impending failures and for prognoses. Likewise, in social and cognitive sciences, heart rate is increasingly employed as a measure of arousal, emotional engagement and as a marker of interpersonal coordination. However, there is no consensus about which measurements and analytical tools are most appropriate in mapping the temporal dynamics of heart rate and quite different metrics are reported in the literature. As complexity metrics of heart rate variability depend critically on variability of the data, different choices regarding the kind of measures can have a substantial impact on the results. In this article we compare linear and non-linear statistics on two prominent types of heart beat data, beat-to-beat intervals (R-R interval) and beats-per-min (BPM). As a proof-of-concept, we employ a simple rest-exercise-rest task and show that non-linear statistics—fractal (DFA) and recurrence (RQA) analyses—reveal information about heart beat activity above and beyond the simple level of heart rate. Non-linear statistics unveil sustained post-exercise effects on heart rate dynamics, but their power to do so critically depends on the type data that is employed: While R-R intervals are very susceptible to non-linear analyses, the success of non-linear methods for BPM data critically depends on their construction. Generally, “oversampled” BPM time-series can be recommended as they retain most of the information about non-linear aspects of heart beat dynamics.
Collapse
Affiliation(s)
- Sebastian Wallot
- Interacting Minds Centre, Department of Culture and Society, Aarhus University Aarhus, Denmark
| | | | | | | |
Collapse
|
40
|
Song Z, Ji Z, Ma JG, Sputh B, Acharya UR, Faust O. A systematic approach to embedded biomedical decision making. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012; 108:656-664. [PMID: 22136936 DOI: 10.1016/j.cmpb.2011.11.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2011] [Revised: 10/03/2011] [Accepted: 11/07/2011] [Indexed: 05/31/2023]
Abstract
An embedded decision making is a key feature for many biomedical systems. In most cases human life directly depends on correct decisions made by these systems, therefore they have to work reliably. This paper describes how we applied systems engineering principles to design a high performance embedded classification system in a systematic and well structured way. We introduce the structured design approach by discussing requirements capturing, specifications refinement, implementation and testing. Thereby, we follow systems engineering principles and execute each of these processes as formal as possible. The requirements, which motivate the system design, describe an automated decision making system for diagnostic support. These requirements are refined into the implementation of a support vector machine (SVM) algorithm which enables us to integrate automated decision making in embedded systems. With a formal model we establish functionality, stability and reliability of the system. Furthermore, we investigated different parallel processing configurations of this computationally complex algorithm. We found that, by adding SVM processes, an almost linear speedup is possible. Once we established these system properties, we translated the formal model into an implementation. The resulting implementation was tested using XMOS processors with both normal and failure cases, to build up trust in the implementation. Finally, we demonstrated that our parallel implementation achieves the speedup, predicted by the formal model.
Collapse
Affiliation(s)
- Zhe Song
- School of Electronic Engineering, Tianjin University, China.
| | | | | | | | | | | |
Collapse
|