Isometric fatigue patterns in time and time–frequency domains of triceps surae muscle in different knee positions

2011 ◽  
Vol 21 (4) ◽  
pp. 572-578 ◽  
Author(s):  
Glauber Ribeiro Pereira ◽  
Liliam Fernandes de Oliveira ◽  
Jurandir Nadal
Author(s):  
G.R. González Toledo ◽  
H. Pérez Pérez ◽  
L. Brage Martín ◽  
V. Castro López-Tarruella

2013 ◽  
Vol 48 (4) ◽  
pp. 477-482 ◽  
Author(s):  
David O. Draper ◽  
Amanda R. Hawkes ◽  
A. Wayne Johnson ◽  
Mike T. Diede ◽  
Justin H. Rigby

Context: A new continuous diathermy called ReBound recently has been introduced. Its effectiveness as a heating modality is unknown. Objective: To compare the effects of the ReBound diathermy with an established deep-heating diathermy, the Megapulse II pulsed shortwave diathermy, on tissue temperature in the human triceps surae muscle. Design:  Crossover study. Setting: University research laboratory. Patients or Other Participants: Participants included 12 healthy, college-aged volunteers (4 men, 8 women; age = 22.2 ± 2.25 years, calf subcutaneous fat thickness = 7.2 ± 1.9 mm). Intervention(s):  Each modality treatment was applied to the triceps surae muscle group of each participant for 30 minutes. After 30 minutes, we removed the modality and recorded temperature decay for 20 minutes. Main Outcome Measure(s): We horizontally inserted an implantable thermocouple into the medial triceps surae muscle to measure intramuscular tissue temperature at 3 cm deep. We measured temperature every 5 minutes during the 30-minute treatment and each minute during the 20-minute temperature decay. Results: Tissue temperature at a depth of 3 cm increased more with Megapulse II than with ReBound diathermy over the course of the treatment (F6,66 = 10.78, P < .001). ReBound diathermy did not produce as much intramuscular heating, leading to a slower heat dissipation rate than the Megapulse II (F20,220 = 28.84, P < .001). Conclusions:  During a 30-minute treatment, the Megapulse II was more effective than ReBound diathermy at increasing deep, intramuscular tissue temperature of the triceps surae muscle group.


2021 ◽  
Author(s):  
Iman Kalaji

Abnormalities in the rhythmic electromechanical contractions of the heart results in cardiac arrhythmias. When these abnormalities rise from the ventricles of the heart, they are classified as ventricular arrhythmias. The two major types of ventricular arrhythmias are ventricular tachycardia (VT) and ventricular fibrillation (VF). Ventricular fibrillation is the most dangerous among the two arrhythmias, that usually leads to sudden cardiac death if not treated immediately. Annually about 40,000 sudden cardiac deaths are reported in Canada. Due to high mortality rate and serious impact on quality of life, researchers have been focusing on characterizing ventricular arrhythmias that may lead to delivering optimized treatment options in improving the survival rates. In this thesis two major types of ventricular arrhythmias were analyzed and quantified by performing discriminative sparse coding analysis called label consistent K-SVD using time frequency dictionaries that are well localized in time and frequency domains. The analyzed signals were 670 ECG ventricular arrhythmia segments from 33 patients extracted from the Malignant Ventricular Ectopy and Creighton University Tachy-Arrhythmia databases. Using the LCKSVD dictionary learning approach, an overall maximum classification accuracy of 73.3% was achieved with a hybrid optimized wavelet dictionary. Based on the comparative analysis, the trained (learned) dictionaries yielded better performance than the untrained dictionaries. The results indicate that discriminative sparse coding approach has greater potential in extracting signal adaptive and morphologically discriminative time-frequency structures in studying ventricular arrhythmias.


2021 ◽  
Author(s):  
Iman Kalaji

Abnormalities in the rhythmic electromechanical contractions of the heart results in cardiac arrhythmias. When these abnormalities rise from the ventricles of the heart, they are classified as ventricular arrhythmias. The two major types of ventricular arrhythmias are ventricular tachycardia (VT) and ventricular fibrillation (VF). Ventricular fibrillation is the most dangerous among the two arrhythmias, that usually leads to sudden cardiac death if not treated immediately. Annually about 40,000 sudden cardiac deaths are reported in Canada. Due to high mortality rate and serious impact on quality of life, researchers have been focusing on characterizing ventricular arrhythmias that may lead to delivering optimized treatment options in improving the survival rates. In this thesis two major types of ventricular arrhythmias were analyzed and quantified by performing discriminative sparse coding analysis called label consistent K-SVD using time frequency dictionaries that are well localized in time and frequency domains. The analyzed signals were 670 ECG ventricular arrhythmia segments from 33 patients extracted from the Malignant Ventricular Ectopy and Creighton University Tachy-Arrhythmia databases. Using the LCKSVD dictionary learning approach, an overall maximum classification accuracy of 73.3% was achieved with a hybrid optimized wavelet dictionary. Based on the comparative analysis, the trained (learned) dictionaries yielded better performance than the untrained dictionaries. The results indicate that discriminative sparse coding approach has greater potential in extracting signal adaptive and morphologically discriminative time-frequency structures in studying ventricular arrhythmias.


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