METHODS: Twenty male participants performed repetitive submaximal (60% MVIC) grip muscle contractions to induce muscle fatigue and the results were analyzed during the pre- and post-fatigue MVIC. MMG signals were recorded on the extensor digitorum (ED), extensor carpi radialis longus (ECRL), flexor digitorum superficialis (FDS) and flexor carpi radialis (FCR) muscles. The cross-correlation coefficient was used to quantify the cross-talk values in forearm muscle pairs (MP1, MP2, MP3, MP4, MP5 and MP6). In addition, the MMG RMS and MMG MPF were calculated to determine force production and muscle fatigue level, respectively.
RESULTS: The fatigue effect significantly increased the cross-talk values in forearm muscle pairs except for MP2 and MP6. While the MMG RMS and MMG MPF significantly decreased (p<0.05) based on the examination of the mean differences from pre- and post-fatigue MVIC.
CONCLUSION: The presented results can be used as a reference for further investigation of cross-talk on the fatigue assessment of extensor and flexor muscles' mechanic.
METHODS: Segmented and validated wheeze sounds was collected from 55 asthmatic patients from the trachea and lower lung base (LLB) during tidal breathing maneuvers. Segmented wheeze sounds have been grouped in to nine datasets based on auscultation location, breath phases and a combination of phase and location. Frequency based features F25, F50, F75, F90, F99 and mean frequency (MF) were calculated from normalized power spectrum. Subsequently, multivariate analysis was performed.
RESULTS: Generally frequency features observe statistical significance (p < 0.05) for the majority of datasets to differentiate severity level Ʌ = 0.432-0.939, F(12, 196-1534) = 2.731-11.196, p < 0.05, ɳ2 = 0.061-0.568. It was observed that selected features performed better (higher effect size) for trachea related samples Ʌ = 0.432-0.620, F(12, 196-498) = 6.575-11.196, p < 0.05, ɳ2 = 0.386-0.568.
CONCLUSIONS: The results demonstrated dthat severity levels of asthmatic patients with tidal breathing can be identified through computerized wheeze sound analysis. In general, auscultation location and breath phases produce wheeze sounds with different characteristics.
METHODS: MMG signals in longitudinal, lateral and transverse directions of muscle fibres were recorded from the elbow flexors of twenty-five male subjects using triaxial accelerometers. Cross-correlation coefficients were used to quantify the degree of crosstalk in all nine possible pairs of fibre axes, all muscle pairs and all exercises.
RESULTS: MMG root mean square (RMS) was statistically significant among the fibre axes (p<0.05, η2=0.17- 0.34) except for biceps brachii and brachioradialis in supination and brachialis in flexion. Overall mean crosstalk values in the three muscle pairs (biceps brachii & brachialis, brachialis & brachioradialis and brachioradialis & biceps brachii) were found to be 6.09-52.17%, 4.01-61.42% and 2.16-51.85%, respectively. Crosstalk values showed statistical significance among all nine axes pairs (p<0.05, η2=0.16-0.51) except for biceps brachii & brachialis during pronation. The transverse axes pair generated the lowest mean crosstalk values (2.16-9.14%).
CONCLUSION: MMG signals recorded using accelerometers from the transverse axes of muscle fibres in the elbow flexors are unique and yield the least amount of crosstalk.
METHODS: Twenty-five young and healthy university students performed a triceps push-down exercise at 45% one repetition maximum (1RM) with and without CS until task failure, and the rate of fatigue (ROF), endurance time (ET) and number of repetitions (NR) for both exercises were analyzed. In addition, the first and last six repetitions of each exercise were considered non-fatiguing (NF) and fatiguing (Fa), respectively, and the root mean square (RMS), mean power frequency (MPF) and median frequency (MDF) for each exercise repetition were evaluated.
RESULTS: The lateral and long head showed significant differences (P<0.05) in the ROF between the two exercises, and all the heads showed significant (P<0.05) differences in the RMS between the two exercises under NF conditions. Only the long head showed a significant difference (P<0.05) in the MPF and MDF between the two exercises. CS increases the ET (24.74%) and NR (27%) of the exercise. The three heads showed significant differences (P<0.05) in the RMS, MPF and MDF under all exercise conditions.
CONCLUSION: A lower ROF was obtained with CS. In addition, the RMS was found to be better approximator of CS, whereas MPF and MDF were more resistant to the effect of CS. The results showed that the three heads worked independently under all conditions, and the non-synergist and synergist head pairs showed similar behavior under Fa conditions. The findings from this study provide additional insights regarding the functioning of each TB head.
METHODS: Twenty-five subjects performed isometric elbow extension until failure, and the rate of fatigue (ROF), time to fatigue (TTF) and normalized TTF (NTTF) were statistically analysed. Subsequently, the behaviour of root-mean-square (RMS), mean-power frequency (MPF) and median-power frequency (MDF) under pre-, onset- and post-fatigue conditions were compared.
RESULTS: The findings indicated that, among the heads, ROF was statistically significant at 30% and 45% MVC (P<0.05) but TTF and NTTF at all intensities was statistically insignificant (P>0.05). For every head, only TTF was statistically significant (P<0.05) at different intensities. MPF and MDF under pre-, onset- and post-fatigue conditions were statistically significant (P<0.05) among the heads at all intensities, whereas RMS showed no such behaviour.
CONCLUSION: The investigated parameters reveal that the three heads of TB act independently before fatigue onset and appear to work in union after fatigue. Synergist head pairs exhibit similar spectral and temporal behaviour in contrast to the non-synergist TB head pair. We find spectral parameters to be more specific predictors of fatigue.
METHODS: An initial search of the SCOPUS database using an appropriate set of keywords yielded 290 studies, and 59 potential studies were selected after all the records were screened using the eligibility criteria. This review on crosstalk revealed that signal contamination due to crosstalk remains a major challenge in the application of surface myography techniques. Various methods have been employed in previous studies to identify, quantify and reduce crosstalk in surface myographic signals.
RESULTS: Although correlation-based methods for crosstalk quantification are easy to use, there is a possibility that co-contraction could be interpreted as crosstalk. High-definition EMG has emerged as a new technique that has been successfully applied to reduce crosstalk.
CONCLUSIONS: The phenomenon of crosstalk needs to be investigated carefully because it depends on many factors related to muscle task and physiology. This review article not only provides a good summary of the literature on crosstalk in myographic signals but also discusses new directions related to techniques for crosstalk identification, quantification and reduction. The review also provides insights into muscle-related issues that impact crosstalk in myographic signals.
METHOD: Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods.
RESULTS AND CONCLUSION: All statistical comparisons exhibited a significant difference (p
OBJECTIVES: This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system.
METHODS: The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset.
RESULTS: The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069.
CONCLUSION: The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS.
AIMS: To develop a model to detect the respiratory phases present in the pulmonary acoustic signals and to evaluate the performance of the model in detecting the respiratory phases.
METHODS: Normalised averaged power spectral density for each frame and change in normalised averaged power spectral density between the adjacent frames were fuzzified and fuzzy rules were formulated. The fuzzy inference system (FIS) was developed with both Mamdani and Sugeno methods. To evaluate the performance of both Mamdani and Sugeno methods, correlation coefficient and root mean square error (RMSE) were calculated.
RESULTS: In the correlation coefficient analysis in evaluating the fuzzy model using Mamdani and Sugeno method, the strength of the correlation was found to be r = 0.9892 and r = 0.9964, respectively. The RMSE for Mamdani and Sugeno methods are RMSE = 0.0853 and RMSE = 0.0817, respectively.
CONCLUSION: The correlation coefficient and the RMSE of the proposed fuzzy models in detecting the respiratory phases reveals that Sugeno method performs better compared with the Mamdani method.