scholarly journals Automatic Segmentation Scheme for Effective Synchronization of EMG-EEG Quantification

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Motor Cortex ◽  
Muscle Contraction ◽  
Muscle Relaxation ◽  
Time Windows ◽  
Automatic Segmentation ◽  
Discrete Wavelet ◽  
Long Duration ◽  
Binary Segmentation ◽  
Flexor Digitorum ◽  
Muscle Connectivity

Effective segmentation of electromyography (EMG) burst that synchronizes with electroencephalography (EEG) for long-duration recording is important steps to better understand the quantification of brain-muscle connectivity in periodic motoric activities. The work proposes an alternative automatic EMG segmentation scheme consists of four main steps, i.e. denoising of EMG burst signal using discrete wavelet transform, enveloping signal using time-windows averaging of RMS amplitude, an adaptive threshold to detect start/end burst envelope with accommodation of muscle contraction characteristic and the final step is conversion enveloping signal to binary segmentation signal.The proposed scheme is evaluated to detect contraction period/duration of EMG for the subject under repetitive holding and releasing grasp using a physiotherapy device. During exercise, the bio-amplifier board is customized to acquire simultaneous EEG and EMG from the region of flexor digitorum superficialis (FDS) of muscle and cortical motor of the brain, with total 284 EMG burst that counting by manual segmentation. The automatic segmentation can detect the total EMG burst by 6.25% error of false burst detection.The usefulness of proposed scheme is also tested to association analysis according to the power of EMG burst and the power of mu-wave of EEG recorded on the motor cortex. The changing trend of the power of mu-wave associated with muscle relaxation, muscle contraction strength and the synchronization level on the motor cortex during exercise are analyzed with integrated information that is relevant with biofeedback concept. The results demonstrate that proposed scheme has potential to be an effective method for the evaluation of biofeedback rehabilitation exercise.

scholarly journals AUTOMATICALLY DETECTING "SIGNIFICANT EVENTS" ON SenseCam

2013 ◽  
Vol 11 (06) ◽  
pp. 1350050 ◽  
Author(s):  
NA LI ◽  
MARTIN CRANE ◽  
HEATHER J. RUSKIN
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Time Windows ◽  
Fluctuation Analysis ◽  
Discrete Wavelet ◽  
Dominant Eigenvalue ◽  
Multiple Time ◽  
Correlation Relationship ◽  
Significant Events ◽  
Detrended Fluctuation

SenseCam is an effective memory-aid device that can automatically record images and other data from the wearer's whole day. The main issue is that, while SenseCam produces a sizeable collection of images over the time period, the vast quantity of captured data contains a large percentage of routine events, which are of little interest to review. In this article, the aim is to detect "Significant Events" for the wearers. We use several time series analysis methods such as Detrended Fluctuation Analysis (DFA), Eigenvalue dynamics and Wavelet Correlations to analyse the multiple time series generated by the SenseCam. We show that Detrended Fluctuation Analysis exposes a strong long-range correlation relationship in SenseCam collections. Maximum Overlap Discrete Wavelet Transform (MODWT) was used to calculate equal-time Correlation Matrices over different time scales and then explore the granularity of the largest eigenvalue and changes of the ratio of the sub-dominant eigenvalue spectrum dynamics over sliding time windows. By examination of the eigenspectrum, we show that these approaches enable detection of major events in the time SenseCam recording, with MODWT also providing useful insight on details of major events. We suggest that some wavelet scales (e.g., 8 minutes–16 minutes) have the potential to identify distinct events or activities.

Proximal Coiling of the Profundus Tendon After Laceration of the Finger

1989 ◽  
Vol 14 (4) ◽  
pp. 416-418
Author(s):  
B. J. GAINOR
Keyword(s):  
Muscle Contraction ◽  
Primary Repair ◽  
Flexor Digitorum Profundus ◽  
Flexor Digitorum ◽  
Unusual Finding

Three patients who had delayed primary repair of a severed flexor digitorum profundus in the finger were found to have proximal coiling of the tendon in the palm. These patients’ hands had been inadequately immobilised during the interval between injury and surgery. The most likely pathomechanics of this unusual finding is secondary retraction and coiling of the severed tendon from unrestrained muscle contraction after division of the tendon. Precautions should be taken when retrieving the tendon stump for tenorrhaphy.

scholarly journals Arm movements induced by noninvasive optogenetic stimulation of the motor cortex in the common marmoset

2019 ◽  
Vol 116 (45) ◽  
pp. 22844-22850 ◽  
Author(s):  
Teppei Ebina ◽  
Keitaro Obara ◽  
Akiya Watakabe ◽  
Yoshito Masamizu ◽  
Shin-Ichiro Terada ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Nonhuman Primates ◽  
Arm Movements ◽  
Common Marmoset ◽  
Blue Laser ◽  
Optogenetic Stimulation ◽  
Long Duration ◽  
The Common ◽  
Forelimb Movements ◽  
Stimulation Of

Optogenetics is now a fundamental tool for investigating the relationship between neuronal activity and behavior. However, its application to the investigation of motor control systems in nonhuman primates is rather limited, because optogenetic stimulation of cortical neurons in nonhuman primates has failed to induce or modulate any hand/arm movements. Here, we used a tetracycline-inducible gene expression system carrying CaMKII promoter and the gene encoding a Channelrhodopsin-2 variant with fast kinetics in the common marmoset, a small New World monkey. In an awake state, forelimb movements could be induced when Channelrhodopsin-2−expressing neurons in the motor cortex were illuminated by blue laser light with a spot diameter of 1 mm or 2 mm through a cranial window without cortical invasion. Forelimb muscles responded 10 ms to 50 ms after photostimulation onset. Long-duration (500 ms) photostimulation induced discrete forelimb movements that could be markerlessly tracked with charge-coupled device cameras and a deep learning algorithm. Long-duration photostimulation mapping revealed that the primary motor cortex is divided into multiple domains that can induce hand and elbow movements in different directions. During performance of a forelimb movement task, movement trajectories were modulated by weak photostimulation, which did not induce visible forelimb movements at rest, around the onset of task-relevant movement. The modulation was biased toward the movement direction induced by the strong photostimulation. Combined with calcium imaging, all-optical interrogation of motor circuits should be possible in behaving marmosets.

scholarly journals Unidirectional brain to muscle connectivity reveals motor cortex control of leg muscles during stereotyped walking

NeuroImage ◽  
2017 ◽  
Vol 159 ◽  
pp. 403-416 ◽  
Author(s):  
Fiorenzo Artoni ◽  
Chiara Fanciullacci ◽  
Federica Bertolucci ◽  
Alessandro Panarese ◽  
Scott Makeig ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Leg Muscles ◽  
Muscle Connectivity

scholarly journals Effects of Aqueous Extract of Curcuma Longa Rhizome On Motility of Isolated Rabbit Jejunum

2019 ◽  
Vol 2 (1) ◽  
pp. 13-22
Author(s):  
B Umaru
Keyword(s):  
Smooth Muscle ◽  
Muscle Contraction ◽  
Aqueous Extract ◽  
Curcuma Longa ◽  
Muscle Relaxation ◽  
Soxhlet Extraction ◽  
Smooth Muscle Contraction ◽  
Smooth Muscle Relaxation ◽  
Rabbit Jejunum

Turmeric (curcuma longa) is a rhizomatous herbaceous perennial plant of the ginger family and the order Zingerberales. It is widely cultivated and used in the treatment of various ailments. In this study, the effect of aqueous extract of C. longa on isolated rabbit jejunum was investigated in vitro using Physiograph (Meditech, India). The rhizome of Curcumin was extracted using Soxhlet extraction method and distilled water was used as a solvent. The elemental analysis was determined using AAS and the result revealed the presence of Potassium, Magnesium, Iron and Nitrogen. The percentage concentrations of trace elements in the aqueous Curcumin rhizome were within the WHO standard limit. The aqueous extract at concentration tested (100 mg/ml) significantly decreased (p<0.05) jejunum smooth muscle contraction. Addition of Atropine (1mM) or Propranolol (1mM) further decreased the amplitude of jejunum smooth muscle contraction. Curcumin rhizome (100 mg/ml) blocked contraction induced by Ach (0.001μg/ml). The result of this work has shown that rhizome of C. longa produced jejunum smooth muscle relaxation, plant extract with antispasmodic activity may reduce gastrointestinal motility thereby delay gastric emptying and may be important in treatment of disease ailments like diarrhoea and colic.

scholarly journals An Efficient Method for Automated Breast Mass Segmentation and Classification in Digital Mammograms

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
Behrouz Niroomand Fam ◽  
Alireza Nikravanshalmani ◽  
Madjid Khalilian
Keyword(s):  
Breast Imaging ◽  
Wavelet Coefficient ◽  
Automatic Segmentation ◽  
Area Under The Curve ◽  
Histogram Equalization ◽  
Screening Mammography ◽  
Discrete Wavelet ◽  
Mass Detection ◽  
Comparable Performance

Background: Automatic detection and classification of breast masses in mammograms are still challenging tasks. Today, computer-aided diagnosis (CAD) systems are being developed to assist radiologists in interpreting mammograms. Objectives: This study aimed to provide a novel method for automatic segmentation and classification of masses in mammograms to help radiologists make an accurate diagnosis. Materials and Methods: For an efficient mass diagnosis in mammograms, we proposed an automatic scheme to perform both mass detection and classification. First, a combination of several image enhancement algorithms, including contrast-limited adaptive histogram equalization (CLAHE), guided imaging, and median filtering, was investigated to enhance the visual features of breast area and increase the accuracy of segmentation outcomes. Second, the density of discrete wavelet coefficient density (DDWCs), based on the quincunx lifting scheme (QLS), was proposed to find suspicious mass regions or regions of interest (ROIs). Finally, mass lesions that appeared in the mammogram were classified into four categories of benign, probably benign, malignant, and probably malignant, based on the morphological shape. The proposed method was evaluated among 1593 images from the Curated Breast Imaging Subset-Digital Database for Screening Mammography (CBIS-DDSM) dataset. Results: The experimental results revealed that the suspected region localization had 100% sensitivity, with a mean of 6.4 ± 4.5 false positive (FP) detections per image. Moreover, the results showed an overall accuracy of 85.9% and an area under the curve (AUC) of 0.901 for the mass classification algorithm. Conclusion: The present results showed the comparable performance of our proposed method to that of the state-of-the-art methods.

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