Papers by aditya chintapalli
International journal of bio- …, Jan 1, 1995
Identification and classification of the dysphagic patient at risk of aspiration is important fro... more Identification and classification of the dysphagic patient at risk of aspiration is important from a clinical point of view. Recently, we have developed techniques to quantify various biomechanical parameters that characterize the dysphagic patient, and have developed an expert system to classify patients based on these measurements. The purpose of the present investigation was to develop a fuzzy logic diagnosis system for classification of the patient with pharyngeal dysphagia into four categories of risk for aspiration. Non-invasive acceleration and swallow pressure measurements were obtained and five parameters were extracted from these measurements. A set of membership functions were defined for each parameter. The measured parameter values were fuzzified and fed to a rule base which provided a set of output membership values corresponding to each of the categories. The set of output values were defuzzified to obtain a continuous measure of classification. The fuzzy system was evaluated using the data obtained from 22 subjects. There was a complete agreement between the fuzzy system classification and the clinician's classification in 18 of the 22 patients. The fuzzy system overestimated the risk by half a category in two patients and underestimated by half a category in two patients. The fuzzy logic diagnosis system, together with the biomechanical measures, provides a tool for continued patient assessment on a daily basis to identify the patient who needs further videofluorography examination.
Proc. of the 3rd international Brain-Computer Interface …, Jan 1, 2006
This paper introduces the use of a Fuzzy Inference System (FIS) for classification in EEG-based B... more This paper introduces the use of a Fuzzy Inference System (FIS) for classification in EEG-based Brain-Computer Interfaces (BCI) systems. We present our FIS algorithm and compare it, on motor imagery signals, with three other popular classifiers, widely used in the BCI community. Our results show that FIS outperformed a Linear Classifier and reached the same level of accuracy as Support Vector Machine and neural networks. Thus, FIS-based classification is suitable for BCI design. Furthermore, FIS algorithms have two additionnal advantages: they are readable and easily extensible.
Rule extraction by fuzzy modeling algorithm for fuzzy logic control of cisatracurium as a neuromuscular block
Engineering Applications of Artificial …, Jan 1, 2009
... or FLM is a set of linguistic control rules related by the dual concepts of fuzzy implication... more ... or FLM is a set of linguistic control rules related by the dual concepts of fuzzy implication and ... The approach of calculating rules possibility is based on the concept that the more important rules ... one of the 15 sets of clinical data results, including information on Time, EMG level (T1 ...
… IEEE Transactions on, Jan 1, 2000
This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for mu... more This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification. In order to obtain acceptable training speed and realistic fuzzy system structure, these features are clustered without supervision using the Basic Isodata algorithm at the beginning of the training phase, and the clustering results are used in initializing the fuzzy system parameters. Afterwards, fuzzy rules in the system are trained with the back-propagation algorithm. The fuzzy approach was compared with an artificial neural network (ANN) method on four subjects, and very similar classification results were obtained. It is superior to the latter in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. Some potential advantages of the fuzzy approach over the ANN approach are also discussed.
Neural Systems and Rehabilitation …, Jan 1, 2005
The user has requested enhancement of the downloaded file.
A survey on analysis and design of model-based fuzzy control systems
Fuzzy Systems, IEEE Transactions on, Jan 1, 2006
Fuzzy logic control was originally introduced and developed as a model free control design approa... more Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control
Fuzzy neural network in case-based diagnostic system
Fuzzy Systems, IEEE Transactions on, Jan 1, 1997
AbstractDiagnosing electronic systems for symptoms supplied by customers is often difficult as h... more AbstractDiagnosing electronic systems for symptoms supplied by customers is often difficult as human descriptions of symptoms are for the most part uncertain and ambiguous. As a result, traditional expert systems are not effective in providing reliable analysis, often ...
Medical engineering & physics, Jan 1, 1999
99)00055-7 · Source: PubMed CITATIONS 75 READS 97 4 authors:
Wavelet based neuro-fuzzy classification for emg control
… , Circuits and Systems and …, Jan 1, 2002
... Fuzzy EMG classification for Prosthesis Control. IEEE Transactions on Rehabilitation Engine... more ... Fuzzy EMG classification for Prosthesis Control. IEEE Transactions on Rehabilitation Engineering, Vol. 8(3), 305-3 1 I, 2000. [5] Yupu Yang, Xiaoming Xu, Wenyuan Zhang. Design neural networksbased fuzzy logic, Fuzzy Sefs and Systems, Vol. 114, pp.325-328, 1998.
Hybrid fuzzy logic committee neural networks for recognition of swallow acceleration signals
Computer Methods and Programs in …, Jan 1, 2001
Biological signals are complex and often require intelligent systems for recognition of character... more Biological signals are complex and often require intelligent systems for recognition of characteristic signals. In order to improve the reliability of the recognition or automated diagnostic systems, hybrid fuzzy logic committee neural networks were developed and the system was used for recognition of swallow acceleration signals from artifacts. Two sets of fuzzy logic-committee networks (FCN) each consisting of seven member networks were developed, trained and evaluated. The FCN-I was used to recognize dysphagic swallow from artifacts, and the second committee FCN-II was used to recognize normal swallow from artifacts. Several networks were trained and the best seven were recruited into each committee. Acceleration signals from the throat were bandpass filtered, and several parameters were extracted and fed to the fuzzy logic block of either FCN-I or FCN-II. The fuzzified membership values were fed to the committee of neural networks which provided the signal classification. A majority opinion of the member networks was used to arrive at the final decision. Evaluation results revealed that FCN correctly identified 16 out of 16 artifacts and 31 out of 33 dysphagic swallows. In two cases, the decision was ambiguous due to the lack of a majority opinion. FCN-II correctly identified 24 out of 24 normal swallows, and 28 out of 29 artifacts. In one case, the decision was ambiguous due to the lack of a majority opinion. The present hybrid intelligent system consisting of fuzzy logic and committee networks provides a reliable tool for recognition and classification of acceleration signals due to swallowing.
… , IEEE Transactions on, Jan 1, 1997
This study examines the design of a rational stimulation pattern for electrical stimulation and a... more This study examines the design of a rational stimulation pattern for electrical stimulation and a robust closed-loop control scheme to improve cycling system efficacy for subjects with paraplegia. The stimulation patterns were designed by analyzing gravitation potential needed for the cycling movement of the lower limbs against a frictionless cycling ergometer and the response delay of electrically stimulated muscles. To simplify the cycling control system, the stimulation patterns were fixed and only the single gain of the stimulation patterns was adjusted via a feedback control algorithm. To circumvent the complexity involved with exactly modeling a stimulated muscle and cycling ergometer, a model-free fuzzy logic controller (FLC) was adopted herein for our control scheme. Comparison between FLC and conventional proportional-derivative (PD) controllers demonstrated that the FLC with asymmetrical membership function enabled the subject with paraplegia to maintain varied desired cycling speeds, particularly at lower cycling speed. By incorporating the rational stimulation patterns, the FLC can produce a smooth and prolonged cycling movement deemed necessary for designing various training protocols.
Clin. Neurophysiol, Jan 1, 1999
Objective: A multi-stage system for automated detection of epileptiform activity in the EEG has b... more Objective: A multi-stage system for automated detection of epileptiform activity in the EEG has been developed and tested on prerecorded data from 43 patients. Methods: The system is centred on the use of an arti®cial neural network, known as the self-organising feature map (SOFM), as a novel pattern classi®er. The role of the SOFM is to assign a probability value to incoming candidate epileptiform discharges (on a single channel basis). The multi-stage detection system consists of three major stages: mimetic, SOFM, and fuzzy logic. Fuzzy logic is introduced in order to incorporate spatial contextual information in the detection process. Through fuzzy logic it has been possible to develop an approximate model of the spatial reasoning performed by the electroencephalographer. Results: The system was trained on 35 epileptiform EEGs containing over 3000 epileptiform events and tested on a different set of eight EEGs containing 190 epileptiform events (including one normal EEG). Results show that the system has a sensitivity of 55.3% and a selectivity of 82% with a false detection rate of just over seven per hour.
Biomedical Engineering, IEEE …, Jan 1, 2003
Biological procedures online, Jan 1, 2006
Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardwa... more Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.
Journal of Intelligent …, Jan 1, 2003
One of the major difficulties faced by those who are fitted with prosthetic devices is the great ... more One of the major difficulties faced by those who are fitted with prosthetic devices is the great mental effort needed during the first stages of training. When working with myoelectric prosthesis, that effort increases dramatically. In this sense, the authors decided to devise a mechanism to help patients during the learning stages, without actually having to wear the prosthesis. The system is based on a real hardware and software for detecting and processing electromyografic (EMG) signals. The association of autoregressive (AR) models and a neural network is used for EMG pattern discrimination. The outputs of the neural network are then used to control the movements of a virtual prosthesis that mimics what the real limb should be doing. This strategy resulted in rates of success of 100% when discriminating EMG signals collected from the upper arm muscle groups. The results show a very easy-to-use system that can greatly reduce the duration of the training stages.
On automatic identification of upper-limb movements using small-sized training sets of EMG signals
Medical engineering & physics, Jan 1, 2000
We evaluate the performance of a variety of neural and fuzzy networks for discrimination among th... more We evaluate the performance of a variety of neural and fuzzy networks for discrimination among three planar arm-pointing movements by means of electromyographic (EMG) signals, when learning is based on small-sized training sets. The aim of this work is to underline the importance that the sparse data problem has in designing pattern classifiers with good generalisation properties. The results indicate that one of the proposed fuzzy networks is more robust than the other classifiers when working with small training sets.
Uploads
Papers by aditya chintapalli