1
|
Reduce Surface Electromyography Channels for Gesture Recognition by Multitask Sparse Representation and Minimum Redundancy Maximum Relevance. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9929684. [PMID: 34136113 PMCID: PMC8177973 DOI: 10.1155/2021/9929684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/09/2021] [Accepted: 05/21/2021] [Indexed: 11/21/2022]
Abstract
Surface electromyography- (sEMG-) based gesture recognition is widely used in rehabilitation training, artificial prosthesis, and human-computer interaction. The purpose of this study is to simplify the sEMG devices by reducing channels while achieving comparably high gesture recognition accuracy. We propose a compound channel selection scheme by combining the variable selection algorithms based on multitask sparse representation (MTSR) and minimum Redundancy Maximum Relevance (mRMR). Specifically, channelwise features are first extracted to compose channel-feature paired variables, for which variable selection procedures by MTSR and mRMR are carried out, respectively. Then, we rank all the channels according to their occurrences in each variable selection procedure and figure out a certain number of informative channels by fusing these rankings of channels. Finally, the gesture classification performance using the selected channels is evaluated by the support vector machine (SVM) classifier. Experiment results validate the effectiveness of this proposed method.
Collapse
|
2
|
Ashraf H, Waris A, Gilani SO, Kashif AS, Jamil M, Jochumsen M, Niazi IK. Evaluation of windowing techniques for intramuscular EMG-based diagnostic, rehabilitative and assistive devices. J Neural Eng 2021; 18. [PMID: 33217750 DOI: 10.1088/1741-2552/abcc7f] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Accepted: 11/20/2020] [Indexed: 11/12/2022]
Abstract
Objective.Intramuscular electromyography (iEMG) signals, invasively recorded, directly from the muscles are used to diagnose various neuromuscular disorders/diseases and to control rehabilitative and assistive robotic devices. iEMG signals are potentially being used in neurology, kinesiology, rehabilitation and ergonomics, to detect/diagnose various diseases/disorders. Electromyography-based classification and analysis systems are being designed and tested for the classification of various neuromuscular disorders and to control rehabilitative and assistive robotic devices. Many studies have explored parameters such as the pre-processing, feature extraction and selection of classifiers that can affect the performance and efficacy of iEMG-based classification systems. The pre-processing stage includes the removal of any unwanted noise from the original signal and windowing of the signal.Approach.This study investigated and presented the optimum windowing configurations for robust control and better performance results of an iEMG-based analysis system based on the stationarity rate (SR) and classification accuracy. Both disjoint and overlap, windowing techniques with varying window and overlap sizes have been investigated using a machine learning-based classification algorithm called linear discriminant analysis.Main results.The optimum window size ranges are from 200-300 ms for the disjoint and 225-300 ms for the overlap windowing technique, respectively. The inferred results show that for the overlap windowing technique the optimum range of overlap size is from 10%-30% of the length of window size. The mean classification accuracy (MCA) and mean stationarity rate (MSR) were found to be lower in the disjoint windowing technique compared to overlap windowing at all investigated overlap sizes. Statistical analysis (two-way analysis of variance test) showed that the MSR and MCA of the overlap windowing technique was significantly different at overlap sizes of 10%-30% (p-values < 0.05).Significance.The presented results can be used to achieve the best possible classification results and SR for any iEMG-based real-time diagnosis, detection and control system, which can enhance the performance of the system significantly.
Collapse
Affiliation(s)
- Hassan Ashraf
- Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Science and Technology (NUST), 44000 Islamabad, Pakistan
| | - Asim Waris
- Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Science and Technology (NUST), 44000 Islamabad, Pakistan
| | - Syed Omer Gilani
- Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Science and Technology (NUST), 44000 Islamabad, Pakistan
| | - Amer Sohail Kashif
- Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Science and Technology (NUST), 44000 Islamabad, Pakistan
| | - Mohsin Jamil
- Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Science and Technology (NUST), 44000 Islamabad, Pakistan.,Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, 240 Prince Phillip Drive, St John's NL A1B 3X5, Canada
| | - Mads Jochumsen
- Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark
| | - Imran Khan Niazi
- Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark.,Center of Chiropractic Research, New Zealand College of Chiropractic, 1149 Auckland, New Zealand.,Faculty of Health and Environmental Sciences, Health and Rehabilitation Research Institute, AUT University, Auckland 0627, New Zealand
| |
Collapse
|
3
|
Jose S, George ST, Subathra MSP, Handiru VS, Jeevanandam PK, Amato U, Suviseshamuthu ES. Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2020; 1:235-242. [PMID: 35402953 PMCID: PMC8975248 DOI: 10.1109/ojemb.2020.3017130] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 06/23/2020] [Accepted: 08/07/2020] [Indexed: 11/09/2022] Open
Affiliation(s)
- Shobha Jose
- School of Engineering and TechnologyKarunya Institute of Technology and Sciences Coimbatore 641-114 India
| | - S Thomas George
- School of Engineering and TechnologyKarunya Institute of Technology and Sciences Coimbatore 641-114 India
| | - M S P Subathra
- School of Engineering and TechnologyKarunya Institute of Technology and Sciences Coimbatore 641-114 India
| | - Vikram Shenoy Handiru
- Center for Mobility and Rehabilitation Engineering ResearchKessler Foundation West Orange NJ 07052 USA
| | | | - Umberto Amato
- Istituto di Scienze Applicate e Sistemi Intelligenti 'Eduardo Caianiello,' Consiglio Nazionale delle Ricerche 80131 Napoli Italy
| | | |
Collapse
|
4
|
Farzandipour M, Nabovati E, Saeedi S, Fakharian E. Fuzzy decision support systems to diagnose musculoskeletal disorders: A systematic literature review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 163:101-109. [PMID: 30119845 DOI: 10.1016/j.cmpb.2018.06.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 05/04/2018] [Accepted: 06/05/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Musculoskeletal disorders (MSDs) are one of the most important causes of disability with a high prevalence. The accurate and timely diagnosis of these disorders is often difficult. Clinical decision support systems (CDSSs) can help physicians to diagnose diseases quickly and accurately. Given the ambiguous nature of MSDs, fuzzy logic can be helpful in designing the CDSSs knowledge bases. The present study aimed to review the studies on fuzzy CDSSs to diagnose MSDs. METHODS A comprehensive search was conducted in Medline, Scopus, Cochrane Library, and ISI Web of Science databases to identify relevant studies published until March 15, 2016. Studies were included in which CDSSs were developed using fuzzy logic to diagnose MSDs, and tested their accuracy using real data from patients. RESULTS Of the 3188 papers examined, 23 papers included according to the inclusion criteria. The results showed that among all the designed CDSSs only one (CADIAG-2) was implemented in the clinical environment. In about half of the included studies (52%), CDSSs were designed to diagnose inflammatory/infectious disorder of the bone and joint. In most of the included studies (70%), the knowledge was extracted using a combination of three methods (acquiring from experts, analyzing the data, and reviewing the literature). The median accuracy of fuzzy rule-based CDSSs was 91% and it was 90% for other fuzzy models. The most frequently used membership functions were triangular and trapezoidal functions, and the most used method for inference was the Mamdani. CONCLUSIONS In general, fuzzy CDSSs have a high accuracy to diagnose MSDs. Despite the high accuracy, these systems have been used to a limited extent in the clinical environments. To design of knowledge base for CDSSs to diagnose MSDs, rule-based methods are used more than other fuzzy methods.
Collapse
Affiliation(s)
- Mehrdad Farzandipour
- Health Information Management Research Center, Kashan University of Medical Sciences, Kashan, Iran; Department of Health Information Management & Technology, School of Allied Health Professions, Kashan University of Medical Sciences, Kashan, Iran
| | - Ehsan Nabovati
- Health Information Management Research Center, Kashan University of Medical Sciences, Kashan, Iran; Department of Health Information Management & Technology, School of Allied Health Professions, Kashan University of Medical Sciences, Kashan, Iran.
| | - Soheila Saeedi
- Department of Health Information Management & Technology, School of Allied Health Professions, Kashan University of Medical Sciences, Kashan, Iran; Student research committee, Kashan University of Medical sciences, Kashan, Iran
| | - Esmaeil Fakharian
- Trauma Research Center, Kashan University of Medical Sciences, Kashan, Iran
| |
Collapse
|
5
|
Alcan V, Canal MR, Zinnuroğlu M. Using fuzzy logic for diagnosis and classification of spasticity. Turk J Med Sci 2017; 47:148-160. [PMID: 28263483 DOI: 10.3906/sag-1512-65] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 05/17/2016] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND/AIM Spasticity is generally defined as a sensory-motor control disorder. However, there is no pathophysiological mechanism or appropriate measurement and evaluation standards that can explain all aspects of a possible spasticity occurrence. The objective of this study is to develop a fuzzy logic classifier (FLC) diagnosis system, in which a quantitative evaluation is performed by surface electromyography (EMG), and investigate underlying pathophysiological mechanisms of spasticity. MATERIALS AND METHODS Surface EMG signals recorded from the tibialis anterior and medial gastrocnemius muscles of hemiplegic patients with spasticity and a healthy control group were analyzed in standing, resting, dorsal flexion, and plantar flexion positions. The signals were processed with different methods: by using their amplitudes in the time domain, by applying short-time Fourier transform, and by applying wavelet transform. A Mamdani-type multiple-input, single-output FLC with 64 rules was developed to analyze EMG signals. RESULTS The wavelet transform provided better positive findings among all three methods used in this study. The FLC test results showed that the test was 100% sensitive to identify spasticity with 95.8% accuracy and 93.8% specificity. CONCLUSION A FLC was successfully designed to detect and identify spasticity in spite of existing measurement difficulties in its nature.
Collapse
Affiliation(s)
- Veysel Alcan
- Department of Electricity-Electronics Technology, Ministry of National Education, Ankara, Turkey
| | - Mehmet Rahmi Canal
- Department of Computer Engineering, Faculty of Technology, Gazi University, Ankara, Turkey
| | - Murat Zinnuroğlu
- Department of Physical Medicine and Rehabilitation, Faculty of Medicine, Gazi University, Ankara, Turkey
| |
Collapse
|
6
|
Artameeyanant P, Sultornsanee S, Chamnongthai K. An EMG-based feature extraction method using a normalized weight vertical visibility algorithm for myopathy and neuropathy detection. SPRINGERPLUS 2017; 5:2101. [PMID: 28053831 PMCID: PMC5174015 DOI: 10.1186/s40064-016-3772-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2016] [Accepted: 11/30/2016] [Indexed: 04/03/2024]
Abstract
Background Electromyography (EMG) signals recorded from healthy, myopathic, and amyotrophic lateral sclerosis (ALS) subjects are nonlinear, non-stationary, and similar in the time domain and the frequency domain. Therefore, it is difficult to classify these various statuses. Methods This study proposes an EMG-based feature extraction method based on a normalized weight vertical visibility algorithm (NWVVA) for myopathy and ALS detection. In this method, sampling points or nodes based on sampling theory are extracted, and features are derived based on relations among the vertical visibility nodes with their amplitude differences as weights. The features are calculated via selective statistical mechanics and measurements, and the obtained features are assembled into a feature matrix as classifier input. Finally, powerful classifiers, such as k-nearest neighbor, multilayer perceptron neural network, and support vector machine classifiers, are utilized to differentiate signals of healthy, myopathy, and ALS cases. Results Performance evaluation experiments are carried out, and the results revealed 98.36% accuracy, which corresponds to approximately a 2% improvement compared with conventional methods. Conclusions An EMG-based feature extraction method using a NWVVA is proposed and implemented to detect healthy, ALS, and myopathy statuses.
Collapse
Affiliation(s)
- Patcharin Artameeyanant
- Department of Electronic and Telecommunication Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha-uthit Rd., Bangmod, Thungkhru, Bangkok, 10140 Thailand
| | - Sivarit Sultornsanee
- School of Business, University of the Thai Chamber of Commerce, 126/1 Vibhavadi Rd., Dindang, Bangkok, 10400 Thailand
| | - Kosin Chamnongthai
- Department of Electronic and Telecommunication Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha-uthit Rd., Bangmod, Thungkhru, Bangkok, 10140 Thailand
| |
Collapse
|
7
|
Classifying neuromuscular diseases using artificial neural networks with applied Autoregressive and Cepstral analysis. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2383-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
8
|
Verikas A, Vaiciukynas E, Gelzinis A, Parker J, Olsson MC. Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness. SENSORS (BASEL, SWITZERLAND) 2016; 16:E592. [PMID: 27120604 PMCID: PMC4851105 DOI: 10.3390/s16040592] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Revised: 04/11/2016] [Accepted: 04/17/2016] [Indexed: 11/16/2022]
Abstract
This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features derived from the properties of two highest peaks as important predictors of personal shot effectiveness. Activation sequence profiles helped in analyzing muscle orchestration during golf shot, exposing a specific avalanche pattern, but data from more players are needed for stronger conclusions. Results demonstrate that information arising from an EMG signal stream is useful for predicting golf shot success, in terms of club head speed and ball carry distance, with acceptable accuracy. Surface EMG data, collected with a goal to automatically evaluate golf player's performance, enables wearable computing in the field of ambient intelligence and has potential to enhance exercising of a long carry distance drive.
Collapse
Affiliation(s)
- Antanas Verikas
- Intelligent Systems Laboratory, Centre for Applied Intelligent Systems Research, Halmstad University, Kristian IV:s väg 3, PO Box 823, Halmstad S-30118, Sweden.
- Department of Electrical Power Systems, Kaunas University of Technology, Studentu 50, Kaunas LT-51368, Lithuania.
| | - Evaldas Vaiciukynas
- Department of Electrical Power Systems, Kaunas University of Technology, Studentu 50, Kaunas LT-51368, Lithuania.
- Department of Information Systems, Kaunas University of Technology, Studentu 50, Kaunas LT-51368, Lithuania.
| | - Adas Gelzinis
- Department of Electrical Power Systems, Kaunas University of Technology, Studentu 50, Kaunas LT-51368, Lithuania.
| | - James Parker
- School of Business, Engineering and Science, Halmstad University, Kristian IV:s väg 3, PO Box 823, Halmstad S-30118, Sweden.
| | - M Charlotte Olsson
- School of Business, Engineering and Science, Halmstad University, Kristian IV:s väg 3, PO Box 823, Halmstad S-30118, Sweden.
| |
Collapse
|
9
|
Arosha Senanayake S, Malik OA, Iskandar PM, Zaheer D. A knowledge-based intelligent framework for anterior cruciate ligament rehabilitation monitoring. Appl Soft Comput 2014. [DOI: 10.1016/j.asoc.2013.11.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
10
|
Discrimination of axonal neuropathy using sensitivity and specificity statistical measures. Neural Comput Appl 2014. [DOI: 10.1007/s00521-014-1622-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
11
|
Gokgoz E, Subasi A. Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders. J Med Syst 2014; 38:31. [PMID: 24696395 DOI: 10.1007/s10916-014-0031-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2014] [Accepted: 03/10/2014] [Indexed: 12/14/2022]
Abstract
Different approaches have been applied for quantitative analysis of EMG signals. This study introduces the effect of Multiscale Principal Component Analysis (MSPCA) denoising method in ElectroMyoGram (EMG) signal classification. The effect of the MSPCA denoising method discussed on EMG signal classification. In addition, effect of Multiple Single Classification (MUSIC) feature extraction method presented and compared for the classification of EMG signals. The results were accomplished on the basis of EMG signal data to classify into normal, ALS or myopathic. Furthermore, total accuracy of classifiers such as k-Nearest Neighbor (k-NN), Artificial Neural Network (ANN) and Support Vector Machines (SVMs) were discussed. Significant results were found by using MSPCA denoising method. The comparisons between the developed classifiers were based on a number of scalar performances such as sensitivity, specificity, accuracy, F-measure and area under ROC curve (AUC). The results show that MSPCA de-noising has considerably increased the accuracy as compared to EMG data without MSPCA de-noising.
Collapse
Affiliation(s)
- Ercan Gokgoz
- Faculty of Engineering and Information Technologies, International Burch University, Francuske Revolucije bb. Ilidza, Sarajevo, 71000, Bosnia and Herzegovina,
| | | |
Collapse
|
12
|
Xie HB, Guo T, Bai S, Dokos S. Hybrid soft computing systems for electromyographic signals analysis: a review. Biomed Eng Online 2014; 13:8. [PMID: 24490979 PMCID: PMC3922626 DOI: 10.1186/1475-925x-13-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2013] [Accepted: 01/30/2014] [Indexed: 11/12/2022] Open
Abstract
Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis.
Collapse
Affiliation(s)
- Hong-Bo Xie
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney 2052, Australia
| | - Tianruo Guo
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney 2052, Australia
| | - Siwei Bai
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney 2052, Australia
| | - Socrates Dokos
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney 2052, Australia
| |
Collapse
|
13
|
Arosha Senanayake SMN, Ahmed Malik O, Mohammad Iskandar P, Zaheer D. Assessing post-anterior cruciate ligament reconstruction ambulation using wireless wearable integrated sensors. J Med Eng Technol 2013; 37:498-510. [DOI: 10.3109/03091902.2013.837529] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
|
14
|
Godil SS, Shamim MS, Enam SA, Qidwai U. Fuzzy logic: A "simple" solution for complexities in neurosciences? Surg Neurol Int 2011; 2:24. [PMID: 21541006 PMCID: PMC3050069 DOI: 10.4103/2152-7806.77177] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2010] [Accepted: 01/03/2011] [Indexed: 11/24/2022] Open
Abstract
Background: Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum. Methods: This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology. Results: The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures. Conclusions: In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences.
Collapse
Affiliation(s)
- Saniya Siraj Godil
- Faculty of Health Sciences, Medical College, Aga Khan University, Karachi, Pakistan
| | | | | | | |
Collapse
|
15
|
|
16
|
Lopes AJ, Capone D, Mogami R, Lanzillotti RS, Melo PLD, Jansen JM. Severity classification for idiopathic pulmonary fibrosis by using fuzzy logic. Clinics (Sao Paulo) 2011; 66:1015-9. [PMID: 21808868 PMCID: PMC3129967 DOI: 10.1590/s1807-59322011000600016] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2011] [Revised: 02/19/2011] [Accepted: 03/10/2011] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVE To set out a severity classification for idiopathic pulmonary fibrosis (IPF) based on the interaction of pulmonary function parameters with high resolution computed tomography (CT) findings. INTRODUCTION Despite the contribution of functional and radiological methods in the study of IPF, there are few classification proposals for the disease based on these examinations. METHODS A cross-sectional study was carried out, in which 41 non-smoking patients with IPF were evaluated. The following high resolution CT findings were quantified using a semi-quantitative scoring system: reticular abnormality, honeycombing and ground-glass opacity. The functional variables were measured by spirometry, forced oscillation technique, helium dilution method, as well as the single-breath method of diffusing capacity of carbon monoxide. With the interaction between functional indexes and high resolution CT scores through fuzzy logic, a classification for IPF has been built. RESULTS Out of 41 patients studied, 26 were male and 15 female, with a mean age of 70.8 years. Volume measurements were the variables which showed the best interaction with the disease extension on high resolution CT, while the forced vital capacity showed the lowest estimative errors in comparison to total lung capacity. A classification for IPF was suggested based on the 95% confidence interval of the forced vital capacity %: mild group (>92.7); moderately mild (76.9-92.6); moderate (64.3-76.8%); moderately severe (47.1-64.2); severe (24.3-47.0); and very severe (<24.3). CONCLUSION Through fuzzy logic, an IPF classification was built based on forced vital capacity measurement with a simple practical application.
Collapse
Affiliation(s)
- Agnaldo José Lopes
- Pulmonary Function Laboratory, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | | | | | | | | | | |
Collapse
|