Normalised Mutual Information of High-Density Surface Electromyography during Muscle Fatigue

Bingham, Adrian; Arjunan, Sridhar P.; Jelfs, Beth; Kumar, Deepak

doi:10.3390/e19120697

Cited by 18 publications

(14 citation statements)

References 55 publications

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“…In this study, we have followed the methodology proposed in [65] to determine the NMI. It was calculated based on the probability distributions of the phonetic sound recordings considered in pairs.…”

Section: Normalised Mutual Informationmentioning

confidence: 99%

Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson’s Disease

et al. 2019

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In this paper, we have investigated the differences in the voices of Parkinson’s disease (PD) and age-matched control (CO) subjects when uttering three phonemes using two complexity measures: fractal dimension (FD) and normalised mutual information (NMI). Three sustained phonetic voice recordings, /a/, /u/ and /m/, from 22 CO (mean age = 66.91) and 24 PD (mean age = 71.83) participants were analysed. FD was first computed for PD and CO voice recordings, followed by the computation of NMI between the test groups: PD–CO, PD–PD and CO–CO. Four features reported in the literature—normalised pitch period entropy (Norm. PPE), glottal-to-noise excitation ratio (GNE), detrended fluctuation analysis (DFA) and glottal closing quotient (ClQ)—were also computed for comparison with the proposed complexity measures. The statistical significance of the features was tested using a one-way ANOVA test. Support vector machine (SVM) with a linear kernel was used to classify the test groups, using a leave-one-out validation method. The results showed that PD voice recordings had lower FD compared to CO (p < 0.008). It was also observed that the average NMI between CO voice recordings was significantly lower compared with the CO–PD and PD–PD groups (p < 0.036) for the three phonetic sounds. The average NMI and FD demonstrated higher accuracy (>80%) in differentiating the test groups compared with other speech feature-based classifications. This study has demonstrated that the voices of PD patients has reduced FD, and NMI between voice recordings of PD–CO and PD–PD is higher compared with CO–CO. This suggests that the use of NMI obtained from the sample voice, when paired with known groups of CO and PD, can be used to identify PD voices. These findings could have applications for population screening.

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Section: Normalised Mutual Informationmentioning

confidence: 99%

Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson’s Disease

et al. 2019

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“…It has been suggested that depletion of muscle glycogen and hypoglycaemia reduce muscle function during long-distance running (Bailey et al, 1993). Fatigue mechanisms may also include muscle metabolite accumulation (Bingham et al, 2017;Skurvydas et al, 2011), production of reactive oxygen species (ROS) (Nielson et al, 2008), and altered motor unit recruitment patterns (Bingham et al, 2017). Additionally, it is well documented that leakage of intracellular enzymes into plasma that are associated with decreased metabolic stability, such as creatine kinase (CK) and myoglobin could affect performance (Koo et al, 2014;Skurvydas et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Effects of Oral Branched‐Chain Amino Acids (BCAAs) Intake on Muscular and Central Fatigue During an Incremental Exercise

AbuMoh’d

Matalqah

Al-Abdulla

2020

Journal of Human Kinetics

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The aim of this study was to investigate the effects of oral branched-chain amino acids (BCAAs) intake on muscular (creatine kinase and myoglobin) and central (serotonin) fatigue during an incremental exercise protocol and to determine the time to exhaustion. Sixteen male long-distance runners (25.7 ± 2.0 yrs) performed two trials, 14 days apart. Using a double-blind, placebo-controlled, randomised crossover design, participants ingested either 20 g of BCAAs (BCAA trial) or a placebo 1 hour prior to performing an incremental exercise session on a treadmill. The starting speed was 8 km/h and this was increased by 1 km/h every 5 minutes until volitional exhaustion. Blood analysis indicated that plasma levels of serotonin were lower in the BCAA trial (259.3 ± 13.5 ng/ml) than the placebo trial (289.1 ± 14.5 ng/ml) (p < 0.05). There was a similar pattern of results for free fatty acid (p < 0.05). The creatine kinase level was higher in the BCAA trial (346.1 ± 33.7 U/L) than the placebo trial (307.3 ± 30.2 U/L). No significant difference between trials was observed regarding the level of myoglobin (p = 0.139). Time to exhaustion was longer in the BCAA trial (50.4 ± 2.3 min) than the placebo trial (46.6 ± 3.2 min). In conclusion, oral intake of 20 g of BCAAs 1 hour prior to an incremental treadmill exercise session increased time to exhaustion, probably due to the reduction in serotonin concentration. As myoglobin levels were within the normal range in both trials, we conclude that the participants did not reach muscular fatigue.

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“…The study analyzed HDEMG data from nine subjects performing isometric plantar flexion exercise at 40% maximum voluntary contraction until they are fatigued. HD EMG signals were recorded from the Tibialis anterior muscle using a 16x4 grid of electrodes at a sampling rate of 2441Hz [1]. After filtering with a bandpass filter (10-400Hz) and notch filter (50Hz), twenty (90% overlapping) windows of 2000 samples length (820 ms) from the start of the experiment and prior to the end of the experiment is considered as NF (n=180) and F (n=180) segments respectively [1].…”

Section: Methodsmentioning

confidence: 99%

Spatial Intensity Map of HDEMG Based Classification of Muscle Fatigue

Makaram

Arjunan

Kumar

et al. 2021

Studies in Health Technology and Informatics

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In this, study, we have investigated to identify the muscle fatigue using spatial maps of High-Density Electromyography (HDEMG). The experiment involves subjects performing plantar flexion at 40% maximum voluntary contraction until fatigue. During the experiment, HDEMG signal was recorded from the tibialis anterior muscle. The monopolar and bipolar spatial intensity maps were extracted from the HDEMG signal. The random forest classifier with different tree configurations was tested to distinguish nonfatigue and fatigue condition. The results indicate that selected electrodes from the differential intensity map results in an accuracy of 83.3% with the number of trees set at 17. This method of spatial analysis of HDEMG signals may be extended to assess fatigue in real life scenarios.

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Normalised Mutual Information of High-Density Surface Electromyography during Muscle Fatigue

Cited by 18 publications

References 55 publications

Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson’s Disease

Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson’s Disease

Effects of Oral Branched‐Chain Amino Acids (BCAAs) Intake on Muscular and Central Fatigue During an Incremental Exercise

Spatial Intensity Map of HDEMG Based Classification of Muscle Fatigue

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