Myoelectric control systems—A survey

International Joint Conference on Neural Networks

https://www.repositorio.ufop.br/bitstream/123456789/7011/1/ARTIGO_ECGHeartBeat.pdf

ECG-based heartbeat classification for arrhythmia detection

A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification

User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of feedback, and difficult training are cited as primary reasons. Recently, researchers have focused on exploiting the new possibilities offered by advancements in prosthetic technology. Alternatively, researchers could focus on prosthesis acceptance by developing functional requirements based on activities users are likely to perform. In this article, we describe the process of determining such requirements and then the application of these requirements to evaluating the state of the art in myoelectric forearm prosthesis research. As part of a needs assessment, a workshop was organized involving clinicians (representing end users), academics, and engineers. The resulting needs included an increased number of functions, lower reaction and execution times, and intuitiveness of both control and feedback systems. Reviewing the state of the art of research in the main prosthetic subsystems (electromyographic [EMG] sensing, control, and feedback) showed that modern research prototypes only partly fulfill the requirements. We found that focus should be on validating EMG-sensing results with patients, improving simultaneous control of wrist movements and grasps, deriving optimal parameters for force and position feedback, and taking into account the psychophysical aspects of feedback, such as intensity perception and spatial acuity.

/pdf/myoelectric-forearm-prostheses-state-of-the-art-from-a-user-3zwrxoh1nz.pdf

Myoelectric Forearm Prostheses: State of the Art from a User-Centered Perspective

A fuzzy clustering neural network architecture for classification of ECG arrhythmias

Accurate and computationally efficient means of classifying surface myoelectric signals has been the subject of considerable research effort in recent years. The aim of this paper is to classify myoelectric signals using new fuzzy clustering neural network (NN) architectures to control multifunction prostheses. This paper presents a comparative study of the classification accuracy of myoelectric signals using multilayered perceptron NN using back-propagation, conic section function NN, and new fuzzy clustering NNs (FCNNs). The myoelectric signals considered are used in classifying six upper-limb movements: elbow flexion, elbow extension, wrist pronation and wrist supination, grasp, and resting. The results suggest that FCNN can generalize better than other NN algorithms and help the user learn better and faster. This method has the potential of being very efficient in real-time applications.

A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis

This paper presents an improved classifier for automated diagnostic systems of electrocardiogram (ECG) arrhythmias This diagnostic system consists of a combined Fuzzy Clustering Neural Network Algorithm for Classification of ECG Arrhythmias using type-2 fuzzy c-means clustering (T2FCM) algorithm and neural network Type-2 fuzzy c-means clustering is used to improve performance of neural network The aim of improving classifier's performance is to constitute the best classification system with high accuracy rate for ECG beats Ten types of ECG arrhythmias (normal beat, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter) obtained from MIT-BIH database were analyzed However, the presented structure was tested by experimental ECG records of 92 patients (40 male and 52 female, average age is 3975+/-1906) The classification accuracy of an improved classifier in training and testing, namely Type-2 Fuzzy Clustering Neural Network (T2FCNN), was compared with neural network (NN) and fuzzy clustering neural network (FCNN) In T2FCNN architecture, decision making has two stages: forming of the new training set obtained by selection of the best arrhythmia for each arrhythmia class using T2FCM and classification using neural network trained on the new training set The results are demonstrated that the proposed diagnostic systems achieved high (99%) accuracy rate

A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network

This paper presents a new automated diagnostic system to classification of electrocardiogram (ECG) arrhythmias. The diagnostic system is executed using type-2 fuzzy c-means clustering (T2FCM) algorithm, wavelet transform (WT) and neural network. Method of combining T2FCM and WT is used to improve performance of neural network. We aimed high accuracy rate to classification of ECG beats and constituted the automated diagnostic system to improve of classifier's performance. Ten types of ECG beats selected from MIT-BIH database were used to train the system. Then, this system was tested by the ECG signals of patients. The classification accuracy of the proposed classifier, type-2 fuzzy clustering wavelet neural network (T2FCWNN), is compared with the structures formed by type-1 FCM and WT. Process of T2FCWNN architecture is realized on three stages. First stage is formed the new training set obtained by selection of the best segments for each arrhythmia class using T2FCM. Second stage is feature extraction by WT on the new training set. Third stage is classification of the extracted features using neural network. The research showed that accuracy rate was found as 99% using this system.

Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier

Pervasive computing is often mentioned in the context of improving healthcare. This paper presents a novel approach for diagnosing diabetes using neural networks and pervasive healthcare computing technologies. The recent developments in small mobile devices and wireless communications provide a strong motivation to develop new software techniques and mobile services for pervasive healthcare computing. A distributed end-to-end pervasive healthcare system utilizing neural network computations for diagnosing illnesses was developed. This work presents the initial results for a simple client (patient's PDA) and server (powerful desktop PC) two-tier pervasive healthcare architecture. The computations of neural network operations on both client and server sides and wireless network communications between them are optimized for real time use of pervasive healthcare services.

Bekir Karlik

Papers

A fuzzy clustering neural network architecture for classification of ECG arrhythmias

A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis

A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network

Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier

Diagnosing diabetes using neural networks on small mobile devices