Preoperative amylmetacresol and dichlorobenzyl alcohol with lignocaine lozenge reduces postoperative sore throat following general anaesthesia using supraglottic airway devices: A double-blinded, randomised, placebo-controlled trial.

Background and Aims: Postoperative sore throat (POST) is an undesirable event reported in up to 62% of patients receiving general anaesthesia (GA). The incidence of POST following GA using a supraglottic airway device (SAD) is approximately 50%, with symptoms persisting up to 48 h. We examined the role of preoperative lozenges containing amylmetacresol and dichlorobenzyl alcohol (AMC/DCBA) with lignocaine (Strepsils® Max Plus) in reducing the incidence and intensity of POST following GA using SAD. Methods: We conducted a prospective, double-blinded, randomised, placebo-controlled trial involving 88 adults receiving GA for elective surgery using SAD not exceeding 2 h. Patients received either Strepsils Max Plus (Strepsils-LA group) or a placebo before induction of GA. The incidence and intensity of sore throat, dysphagia and dysphonia was measured using the Verbal Rating Scale at 30 min (early) and at 24 h (late) after removal of SAD. Results: Overall POST incidence was lower in the Strepsils-LA group (27.7% versus 56.8%, P = 0.007). Patients in the Strepsils-LA group reported a significantly lower incidence of early POST (14.9% versus 37.8%, P = 0.016) with a lower mean ± standard deviation intensity score (0.17 ± 0.43 versus 0.49 ± 0.69, P = 0.016). Although the overall incidence of dysphagia was lower (23.4% versus 48.6%, P = 0.016), more patients experienced dysphonia in the Strepsils-LA group. AMC/DCBA with lignocaine lozenges showed a relative risk reduction of 50% and a number needed to treat of 4 in reducing POST. Conclusion: AMC/DCBA with lignocaine lozenges administered before GA using SAD is a simple and safe method to reduce the incidence and severity of POST.

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 obstruction.

A systematic review of muscle activity assessment of the biceps brachii muscle using mechanomyography.

This systematic review aims to categorically analyses the literature on the assessment of biceps brachii (BB) muscle activity through mechanomyography (MMG). The application of our search criteria to five different databases identified 319 studies. A critical review of the 48 finally selected records, revealed the diversity of protocols and parameters that are employed in MMG-based assessments of BB muscle activity. The observations were categorized into the following: muscle torque, fatigue, strength and physiology. The available information on the muscle contraction protocol, sensor(s), MMG signal parameters and obtained results were then tabulated based on these categories for further analysis. The review affirms that - 1) MMG is suitable for skeletal muscle activity assessment and can be employed potentially for further investigation of the BB muscle activity and condition (e.g., force, torque, fatigue, and contractile properties), 2) a majority of the records focused on static contractions of the BB, and the analysis of dynamic muscle contractions using MMG is thus a research gap, and 3) very few studies have focused on the analysis of BB muscle activity under externally stimulated contractions. Taken together, the findings of this review on BB activity assessment using MMG affirm the potential of MMG as an alternative tool.

Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine.

BACKGROUND: Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases. From this perspective, we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds. METHODS: Energy and entropy features were extracted from the breath sound using the wavelet packet transform. The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA). The extracted features were inputted into the ELM classifier. RESULTS: The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%, respectively, whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%, respectively. In addition, maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features, respectively. CONCLUSION: The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features.

Injection speed of spinal anaesthesia for Caesarean delivery in Asian women and the incidence of hypotension: A randomised controlled trial.

STUDY OBJECTIVE: The purpose of this investigation was to determine if a slower speed of spinal anaesthesia injection would reduce the incidence of hypotension. STUDY DESIGN: Randomised controlled trial. SETTING: Tertiary level hospital in Malaysia. PATIENTS: 77 patients undergoing elective Caesarean delivery. INTERVENTION: Differing speeds of spinal injection. MEASUREMENTS: Systolic blood pressure was assessed every minute for the first 10min and incidence of hypotension (reduction in blood pressure of >30% of baseline) was recorded. The use of vasopressor and occurrence of nausea/vomiting were also recorded. MAIN RESULTS: 36 patients in SLOW group and 41 patients in FAST group were recruited into the study. There was no significant difference in blood pressure drop of >30% (p=0.497) between the two groups. There was no difference in the amount of vasopressor used and incidence of nausea/vomiting in both groups. CONCLUSION: In our study population, there was no difference in incidence of hypotension and nausea/vomiting when spinal injection time is prolonged beyond 15s to 60s. TRIAL REGISTRATION: ClinicalTrials.govNCT02275897. Registered on 15 October 2014.

Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation.

BACKGROUND: The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial. 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.

A novel approach to detect respiratory phases from pulmonary acoustic signals using normalised power spectral density and fuzzy inference system.

BACKGROUND: Monitoring respiration is important in several medical applications. One such application is respiratory rate monitoring in patients with sleep apnoea. The respiratory rate in patients with sleep apnoea disorder is irregular compared with the controls. Respiratory phase detection is required for a proper monitoring of respiration in patients with sleep apnoea. 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.

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.

Analysis of crosstalk in the mechanomyographic signals generated by forearm muscles during different wrist postures.

INTRODUCTION: In this study, we analyzed the crosstalk in mechanomyographic (MMG) signals generated by the extensor digitorum (ED), extensor carpi ulnaris (ECU), and flexor carpi ulnaris (FCU) muscles of the forearm during wrist flexion (WF) and extension (WE) and radial (RD) and ulnar (UD) deviations. METHODS: Twenty right-handed men (mean ± SD age=26.7 ± 3.83 years) performed the wrist postures. During each wrist posture, MMG signals were detected using 3 accelerometers. Peak cross-correlations were used to quantify crosstalk. RESULTS: The level of crosstalk ranged from 1.69 to 64.05%. The wrist postures except the RD did not influence the crosstalk significantly between muscle pairs. However, muscles of the forearm compartments influenced the level of crosstalk for each wrist posture significantly. CONCLUSIONS: The results may be used to improve our understanding of the mechanics of the forearm muscles during wrist postures.

Longitudinal, lateral and transverse axes of forearm muscles influence the crosstalk in the mechanomyographic signals during isometric wrist postures.

PROBLEM STATEMENT: In mechanomyography (MMG), crosstalk refers to the contamination of the signal from the muscle of interest by the signal from another muscle or muscle group that is in close proximity. PURPOSE: The aim of the present study was two-fold: i) to quantify the level of crosstalk in the mechanomyographic (MMG) signals from the longitudinal (Lo), lateral (La) and transverse (Tr) axes of the extensor digitorum (ED), extensor carpi ulnaris (ECU) and flexor carpi ulnaris (FCU) muscles during isometric wrist flexion (WF) and extension (WE), radial (RD) and ulnar (UD) deviations; and ii) to analyze whether the three-directional MMG signals influence the level of crosstalk between the muscle groups during these wrist postures. METHODS: Twenty, healthy right-handed men (mean ± SD: age = 26.7±3.83 y; height = 174.47±6.3 cm; mass = 72.79±14.36 kg) participated in this study. During each wrist posture, the MMG signals propagated through the axes of the muscles were detected using three separate tri-axial accelerometers. The x-axis, y-axis, and z-axis of the sensor were placed in the Lo, La, and Tr directions with respect to muscle fibers. The peak cross-correlations were used to quantify the proportion of crosstalk between the different muscle groups. RESULTS: The average level of crosstalk in the MMG signals generated by the muscle groups ranged from: 34.28-69.69% for the Lo axis, 27.32-52.55% for the La axis and 11.38-25.55% for the Tr axis for all participants and their wrist postures. The Tr axes between the muscle groups showed significantly smaller crosstalk values for all wrist postures [F (2, 38) = 14-63, p<0.05, η2 = 0.416-0.769]. SIGNIFICANCE: The results may be applied in the field of human movement research, especially for the examination of muscle mechanics during various types of the wrist postures.

Evaluation of repetitive isometric contractions on the heads of triceps brachii muscle during grip force exercise.

OBJECTIVES: Normally, surface electromyography electrodes are used to evaluate the activity of superficial muscles during various kinds of voluntary contractions of muscle fiber. The objective of the present study was to investigate the effect of repetitive isometric contractions on the three heads of the triceps brachii muscle during handgrip force exercise. METHODS: Myoelectric signals were recorded from the lateral, long and medial heads of the triceps brachii muscle in 13 healthy males during maximal isometric contractions for 10 s of concurrent handgrip force and elbow extension. The subjects were asked to perform their contraction task five times with 3 minutes interval between two successive contractions. RESULTS: Decreasing electromyographic activities were found for the lateral and long heads, and increasing for the medial head throughout the 5 different contractions. Electromyographic activities were found for the lateral head with mean=199.84, SD=7.65, CV=3.83%, the long head with mean=456.76, SD=18.10, CV=3.96%, and the medial head with mean=505.16, SD=8.47, CV=1.68%. Electromyographic activities among the three heads of triceps brachii were significantly different (F=3.82) at the alpha level of (p<0.05). CONCLUSIONS: These findings support that repetitive isometric contractions decrease the contractile activity in the lateral and long heads, and increases in the medial head of the triceps brachii muscle during handgrip force exercise with full elbow extension, and the electromyographic activity changes are observed to be more significant at the long head as compared to the lateral and medial heads.

A comparative study of the SVM and K-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals.

BACKGROUND: Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. RESULTS: The pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p < 0.001). The classification accuracies of the SVM and K-nn classifiers were found to be 92.19% and 98.26%, respectively. CONCLUSION: Although the data used to train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database.

Cross-talk in mechanomyographic signals from the forearm muscles during sub-maximal to maximal isometric grip force.

PURPOSE: This study aimed: i) to examine the relationship between the magnitude of cross-talk in mechanomyographic (MMG) signals generated by the extensor digitorum (ED), extensor carpi ulnaris (ECU), and flexor carpi ulnaris (FCU) muscles with the sub-maximal to maximal isometric grip force, and with the anthropometric parameters of the forearm, and ii) to quantify the distribution of the cross-talk in the MMG signal to determine if it appears due to the signal component of intramuscular pressure waves produced by the muscle fibers geometrical changes or due to the limb tremor. METHODS: Twenty, right-handed healthy men (mean ± SD: age  = 26.7±3.83 y; height  = 174.47±6.3 cm; mass  = 72.79±14.36 kg) performed isometric muscle actions in 20% increment from 20% to 100% of the maximum voluntary isometric contraction (MVIC). During each muscle action, MMG signals generated by each muscle were detected using three separate accelerometers. The peak cross-correlations were used to quantify the cross-talk between two muscles. RESULTS: The magnitude of cross-talk in the MMG signals among the muscle groups ranged from, R2(x, y) = 2.45-62.28%. Linear regression analysis showed that the magnitude of cross-talk increased linearly (r2 = 0.857-0.90) with the levels of grip force for all the muscle groups. The amount of cross-talk showed weak positive and negative correlations (r2 = 0.016-0.216) with the circumference and length of the forearm respectively, between the muscles at 100% MVIC. The cross-talk values significantly differed among the MMG signals due to: limb tremor (MMGTF), slow firing motor unit fibers (MMGSF) and fast firing motor unit fibers (MMGFF) between the muscles at 100% MVIC (p<0.05, η2 = 0.47-0.80). SIGNIFICANCE: The results of this study may be used to improve our understanding of the mechanics of the forearm muscles during different levels of the grip force.

Artificial intelligence techniques used in respiratory sound analysis--a systematic review.

Artificial intelligence (AI) has recently been established as an alternative method to many conventional methods. The implementation of AI techniques for respiratory sound analysis can assist medical professionals in the diagnosis of lung pathologies. This article highlights the importance of AI techniques in the implementation of computer-based respiratory sound analysis. Articles on computer-based respiratory sound analysis using AI techniques were identified by searches conducted on various electronic resources, such as the IEEE, Springer, Elsevier, PubMed, and ACM digital library databases. Brief descriptions of the types of respiratory sounds and their respective characteristics are provided. We then analyzed each of the previous studies to determine the specific respiratory sounds/pathology analyzed, the number of subjects, the signal processing method used, the AI techniques used, and the performance of the AI technique used in the analysis of respiratory sounds. A detailed description of each of these studies is provided. In conclusion, this article provides recommendations for further advancements in respiratory sound analysis.