eLearning

Latissimus Dorsi muscle as a graft applied to the heart: Evaluation of electrophysiological and mechanical characteristics

Idagene A. Cestari; Euclydes F. Marques; Helena T. T. Oyama; Adolfo A. Leirner 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1992

Experiments were performed in canine Latissimus Dorsi muscle, during Isometric contraction elicited by electrical stimulation. Measurements were carried out to evaluate the relationship between Force, Intramuscular Pressure, Electromyografic activity and Regional Thickness and to assess the muscle functional characteristics. Stimulation protocol consisted of stimulation with Increasing frequency (10-50 Hz) followed by a fatigue test (30 Hz). Force response correlated linearly ...

Fetal QRS complex detection using semi-blind source separation framework

Fatemeh Razavipour; Masoumeh Haghpanahi; Reza Sameni Computing in Cardiology 2013, 2013

Fetal heart rate variability (FHRV) is one of the valuable features of fetal electrocardiography that could be useful to obtain reliable information about the fetal heart activity. In noninvasive systems, the major obstacle for the accurate detection of the fetal QRS (fQRS) complex is the presence of abdominal noise and the maternal ECG (mECG). In this study, we proposed a ...

Source Selection for Real-Time User Intent Recognition Toward Volitional Control of Artificial Legs

Fan Zhang; He Huang IEEE Journal of Biomedical and Health Informatics, 2013

Various types of data sources have been used to recognize user intent for volitional control of powered artificial legs. However, there is still a debate on what exact data sources are necessary for accurately and responsively recognizing the user's intended tasks. Motivated by this widely interested question, in this study we aimed to 1) investigate the usefulness of different data ...

A PCA-assisted EMG-driven model to predict upper extremities' joint torque in dynamic movements

Shakiba Rafiee; Hossein Ehsani; Mostafa Rostami 2013 20th Iranian Conference on Biomedical Engineering (ICBME), 2013

To relate electromyographic signals (EMG) to net joint torque, different approaches have been taken into account. In this regard, some researchers chose to use Principal Component Analysis (PCA). A Study in 2001 reported a linear relationship between the PCA-processed EMG data and the joint torque while investigating isometric movements. In this project we questioned the possibility to use this method ...

Electromyogram decomposition using the single-lineage clustering algorithm and wavelets

P. Wellig; G. S. Moschytz Electronics, Circuits and Systems, 1999. Proceedings of ICECS '99. The 6th IEEE International Conference on, 1999

The decomposition of intramuscular myoelectric signals (EMG signals) can be considered as a classification problem, where both supervised and unsupervised classification techniques have to be used. Due to the low SNR and due to the lack of a priori knowledge, the unsupervised classification needs a lot of interactive tasks. In this paper, we describe the case when wavelet coefficients from ...

IEEE-USA E-Books

• Muscle Coordination, Motor Synergies, and Primitives from Surface EMG

  To investigate neural control strategies, muscle activity must be measured during motor behavior. Recent advances in the investigation of the neural control of movement have led to a re-examination of the mechanisms of sensorimotor integration in the central nervous system (CNS) and in the spinal circuitry in particular. This chapter considers different approaches used to uncover the modular organization of the motor output in human behaviors such as responding to postural perturbations, reaching with the arm, and locomotion, as well as its plasticity and flexibility in movement disorders. It also investigates the strategies that the CNS employs to coordinate the activation of many muscles start from electromyographic (EMG) signals recorded simultaneously from many muscles. Different muscle synergies models are used to decompose the EMG envelopes using appropriate factorization algorithms. The chapter further considers the spatiotemporal organization of the activity patterns of leg and trunk muscles during locomotion.

• Biophysics of the Generation of EMG Signals

  This chapter describes the basic concepts of generation and detection of EMG signals. Specific emphasis is devoted to the generation of muscle fiber action potentials at the fiber end plates, their propagation along the sarcolemma, and their extinction at the tendons. The chapter addresses the topics of crosstalk between nearby muscles and selectivity of the recording systems. It discusses the relationships between muscle force and the surface EMG. The EMG signal is generated by the electrical activity of the muscle fibers active during a contraction. The signal sources are the depolarizing and repolarizing zones of the muscle fibers. EMG signal features depend on a number of anatomical, physical, and detection system parameters. Considering all the factors related to the volume conductor and the signal sources that influence the characteristics of the EMG signal, a reliable relation between EMG amplitude and force needs a subject specific and condition specific calibration.

• Surface EMG Decomposition

  This chapter provides an overview of surface EMG decomposition techniques, along with their basic assumptions, properties, and limitations. Surface electrodes measure the electrical activity of several nearby muscle fibers that are active during a muscle contraction. The electrical activity of each fiber can be described by a single fiber action potential (SFAP) that propagates from the neuromuscular junction towards the tendons. There is large diversity of decomposition techniques that can roughly be categorized either as template matching or latent component analysis (blind source separation) approaches. Decomposition of surface EMG is a powerful tool enabling noninvasive insight not only into muscle control strategies, but also into peripheral muscle properties. It provides unambiguous information on physiological parameters of individual motor units that can easily be interpreted. The identification of motor units (MUs) discharge patterns from surface EMG signals, acquired during dynamic muscle contractions, needs to be addressed.

• Biofeedback Applications

  This chapter contains sections titled: Introduction Biofeedback Application to Impairment Syndromes SEMG Biofeedback Techniques Summary References

• Frontmatter

  The prelims comprise: Half-Title IEEE Press Editorial Board Title Copyright Contents Introduction Contributors

• Detection and Conditioning of Surface EMG Signals

  This chapter presents the detection and conditioning of surface electromyographic (EMG) signals. More advanced techniques are now widely used in research laboratories and are being adopted in clinical settings. Such techniques are based on multichannel detection by means of one dimensional (1-D) or two dimensional (2-D) electrode arrays. The chapter describes the electrode-skin interface and the front-end amplifier stage. The impedance between two electrodes is the sum of two electrode-skin impedances plus the interposed tissue impedance. Conventional electrodes, either wet or dry, behave like transducers converting ionic current (in tissue and gel) into flow of electrons in the metal. These electrical sensors require a careful skin preparation to reduce the impedance and noise associated to this interface. The biomedical sector offers small-sized, high-cost application-specific integrated circuits (ASIC) devices for biopotential measurements. Applications are expected to range from physiopathological investigations, to rehabilitation games, biofeedback applications, and sport training.

• Surface EMG in Ergonomics and Occupational Medicine

  Prevention of musculoskeletal disorders is one of the main goals in ergonomics. The development of methods to quantify muscle force, fatigue, and muscle involvement in a work task is important for this purpose surface electromyography (sEMG) and kinematic measurements are the main tools for the quantitative evaluation of risks related to work activity. The literature regarding the potential of sEMG techniques in ergonomic studies is extensive and concerns many applications, some of which are listed: work at display terminals and office workstations, production line workstations, work with surgical workstations and workspace optimization. Surface EMG has a remarkable potential role in the prevention, monitoring, documentation, and possibly treatment of work related disorders. Insufficient research funding and standardization efforts are still limiting its applications. Much greater research efforts are required to transform its potential into a practical technique with clinical and legal relevance and economic impact on work health.

• EMG Modeling and Simulation

  This chapter outlines the main components that should be considered in the development of physiologically based models of surface electromyographic (sEMG). One of the challenges in increasing the accuracy of current sEMG models is the correct identification of the model parameters. Parameter identification is particularly difficult for systems such as this in which it is not possible to accurately measure all of the physiological, anatomical, and physical properties of the system. The chapter explores the simulation of intramuscular EMG, in particular for clinical applications. One of the primary purposes of a modeling approach is to identify the mechanisms responsible for experimental observations. A successful EMG model can thereby help the researcher to relate the recorded electrical signal to the underlying processes associated with muscle contraction. Model validation remains one of the most challenging areas in EMG modeling.

• Control of Powered Upper Limb Prostheses

  This chapter contains sections titled: Introduction Myoelectric Signal as a Control Input Conventional Myoelectric Control Emerging MEC Strategies Summary References

• Applications in Musculoskeletal Physical Therapy

  Surface electromyography (sEMG) is typically applied in musculoskeletal physical therapy for the assessment of disturbed motor control and for monitoring change with rehabilitation. This chapter explores fundamental methods of EMG assessment as a means of evaluating neuromuscular impairment in patients with musculoskeletal disorders with a focus on two of the most common musculoskeletal complaints, namely, low back pain and neck pain. Distinct modifications of the sEMG signal can be identified during sustained voluntary or electrically elicited muscle contractions, and the analysis of myoelectric manifestations of muscle fatigue can provide important information about physiological changes evolving in the muscle. The amplitude of the surface EMG can be estimated by a scheme of demodulation, smoothing, and relinearization. An approach to monitoring the amplitude of activity of muscles with sEMG is to measure tuning curves. The chapter further reviews some of the most frequently applied analyses in musculoskeletal physical therapy research.