Muscle Fatigue in the Three Heads of the Triceps Brachii During a Controlled Forceful Hand Grip Task with Full Elbow Extension Using Surface Electromyography

The objective of the present study was to investigate the time to fatigue and compare the fatiguing condition among the three heads of the triceps brachii muscle using surface electromyography during an isometric contraction of a controlled forceful hand grip task with full elbow extension. Eighteen healthy subjects concurrently performed a single 90 s isometric contraction of a controlled forceful hand grip task and full elbow extension. Surface electromyographic signals from the lateral, long and medial heads of the triceps brachii muscle were recorded during the task for each subject. The changes in muscle activity among the three heads of triceps brachii were measured by the root mean square values for every 5 s period throughout the total contraction period. The root mean square values were then analysed to determine the fatiguing condition for the heads of triceps brachii muscle. Muscle fatigue in the long, lateral, and medial heads of the triceps brachii started at 40 s, 50 s, and 65 s during the prolonged contraction, respectively. The highest fatiguing rate was observed in the long head (slope = -2.863), followed by the medial head (slope = -2.412) and the lateral head (slope = -1.877) of the triceps brachii muscle. The results of the present study concurs with previous findings that the three heads of the triceps brachii muscle do not work as a single unit, and the fiber type/composition is different among the three heads

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Wheeze Sound Analysis Using Computer-Based Techniques: A Systematic Review

Wheezes are high pitched continuous respiratory acoustic sounds which are produced as a result of airway obstruction. Computer-based analyses of wheeze signals have been extensively used for parametric analysis, spectral analysis, identification of airway obstruction, feature extraction and diseases or pathology classification. While this area is currently an active field of research, the available literature has not yet been reviewed. This systematic review identified articles describing wheeze analyses using computer-based techniques on the SCOPUS, IEEE Xplore, ACM, PubMed and Springer and Elsevier electronic databases. After a set of selection criteria was applied, 41 articles were selected for detailed analysis. The findings reveal that 1) computerized wheeze analysis can be used for the identification of disease severity level or pathology, 2) further research is required to achieve acceptable rates of identification on the degree of airway obstruction with normal breathing, 3) analysis using combinations of features and on subgroups of the respiratory cycle has provided a pathway to classify various diseases or pathology that stem from airway obstructio

On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing

Objective: While Parkinson’s disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing. Method: EEG recordings from 14 scalp sites were collected from 20 PD patients and 30 age-matched normal controls. Multimodal (audio-visual) stimuli were presented to evoke specific targeted emotional states such as happiness, sadness, fear, anger, surprise and disgust. Absolute and relative power, frequency and asymmetry measures derived from spectrally analyzed EEGs were subjected to repeated ANOVA measures for group comparisons as well as to discriminate function analysis to examine their utility as classification indices. In addition, subjective ratings were obtained for the used emotional stimuli. Results: Behaviorally, PD patients showed no impairments in emotion recognition as measured by subjective ratings. Compared with normal controls, PD patients evidenced smaller overall relative delta, theta, alpha and beta power, and at bilateral anterior regions smaller absolute theta, alpha, and beta power and higher mean total spectrum frequency across different emotional states. Inter-hemispheric theta, alpha, and beta power asymmetry index differences were noted, with controls exhibiting greater right than left hemisphere activation. Whereas intra-hemispheric alpha power asymmetry reduction was exhibited in patients bilaterally at all regions. Discriminant analysis correctly classified 95.0% of the patients and controls during emotional stimuli. Conclusion: These distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients

Choice Of Mechanomyography Sensors For Diverse Types Of Muscle Activities

Skeletal muscles contribute to the movement produced in the human body.They are therefore of vital importance for the study of muscles in various applications of movement including exercise,sports,prosthesis,rehabilitation,etc. The movement produced by skeletal muscles can be analyzed through various techniques like mechanomyography (MMG) and electromyography (EMG).MMG is a novel technique to assess skeletal muscle function through the oscillations produced during muscle contractions.MMG advocates well for its reliability,performance,and ease in application to other presently used techniques.MMG employs several types of sensors to observe vibrations in skeletal muscles.These sensors vary widely from application to type of movement and muscle.This review provides a comprehensive chunk of information on MMG sensor selection according to its placement,muscle function and condition,and limb movement and application. Recommendations for the choice of MMG sensor are given through extensive literature search over here

An Overview Of Breath Phase Detection – Techniques & Applications

The main aim of this study is to provide an overview on the state of the art techniques (acoustic and non-acoustic approaches) involved in breath phase detection and to highlight applications where breath phase detection is vital. Both acoustic and non-acoustic approaches are summarized in detail. The non-acoustic approach involves placement of sensors or flow measurement devices to estimate the breath phases, whereas the acoustic approach involves the use of sophisticated signal processing methods on respiratory sounds to detect breath phases. This article also briefly discusses the advantages and disadvantages of the acoustic and non-acoustic approaches of breath phase detection. The literature reveals that recent advancements in computing technology open avenues for researchers to apply sophisticated signal processing techniques and artificial intelligence algorithms to detect the breath phases in a non-invasive way. Future works that can be implemented after detecting the breath phases are also highlighted in this article

Identification Of Asthma Severity Levels Through Wheeze Sound Characterization And Classification Using Integrated Power Features

This study aimed to investigate and classify wheeze sound characteristics according to asthma severity levels (mild, moderate and severe) using integrated power (IP) features. Method: Validated and segmented wheeze sounds were obtained from the lower lung base (LLB) and trachea recordings of 55 asthmatic patients with different severity levels during tidal breathing manoeuvres. From the segments, nine datasets were obtained based on the auscultation location, breath phases and their combination. In this study, IP features were extracted for assessing asthma severity. Subsequently, univariate and multivariate (MANOVA) statistical analyses were separately implemented to analyse behaviour of wheeze sounds according to severity levels. Furthermore, the ensemble (ENS), knearest- neighbour (KNN) and support vector machine (SVM) classifiers were applied to classify the asthma severity levels. Results and conclusion: The univariate results of this study indicated that the majority of features significantly discriminated (p < 0.05) the severity levels in all the datasets. The MANOVA results yielded significantly (p < 0.05) large effect size in all datasets (including LLB-related) and almost all post hoc results were significant(p < 0.05). A comparison ofthe performance of classifiers revealed that eight ofthe nine datasets showed improved performance with the ENS classifier. The Trachea inspiratory (T-Inspir) dataset produced the highest performance. The overall best positive predictive rate (PPR) for the mild, moderate and severe severity levels were 100% (KNN), 92% (SVM) and 94% (ENS) respectively. Analysis related to auscultation locations revealed that tracheal wheeze sounds are more specific and sensitive predictors of asthma severity. Additionally, phase related investigations indicated that expiratory and inspiratory wheeze sounds are equally informative for the classification of asthma severit

Correlation of Objective Assessment of Facial Paralysis with House-Brackmann Score

This article illustrated a brief review of some objective methods in assessing facial nerve function for facial nerve paralysis which were correlated with House-Brackmann Grading System (HBGS). A rigorous search of online databases such as Springer, Elsevier and IEEE was conducted from June, 2015 to November, 2016 to discover and analyze the previous works in facial nerve assessment methods for facial paralysis. Several domains such as facial grading system and methods used to evaluate the facial nerve function were extracted for further analysis. Different keywords were used to acquire the studies based on the desire criteria. A total of 8 articles were identified and were analyzed for inclusion in this search. In conclusion, this review has presented an initial overview for further improvements in objective facial nerve assessment which has to be correlated with subjective assessment to make it more reliable and useful in clinical practice.

Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features

This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features. 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 < 0.05) among the severity levels with few exceptions. In the classification experiments, the ensemble classifier exhibited better performance in terms of sensitivity, specificity and positive predictive value (PPV). The trachea inspiratory group showed the highest classification performance compared with all the other groups. Overall, the best PPV for the mild, moderate and severe samples were 95% (ENS), 88% (ENS) and 90% (SVM), respectively. With respect to location, the tracheal related wheeze sounds were most sensitive and specific predictors of asthma severity levels. In addition, the classification performances of the inspiratory and expiratory related groups were comparable, suggesting that the samples from these locations are equally informativ

Fuzzy Logic Controller Design for Intelligent Air-Conditioning System

Inefficient air cooling systems may cause of wasting energy in a great amount specially in the urban area. Being the most popular cooling system, air-conditioners have been used in domestic usage as well as in industrial applications. However, the unintelligent nature of such cooling system gives rise to excess energy consumption which causes a huge problem in the current energy hungry world. In this paper, we present design of a fuzzy logic controller for the intelligent air-conditioning system. The performance of the controller is also simulated. The proposed controller has the adaptive nature to control fan and compressor speed which leads to reducing power consumption. Also, the system controls the operation mode to retain the healthy oxygen level and humid condition of the indoor environment