MyMedR

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

Palaniappan R, Sundaraj K, Sundaraj S

BMC Bioinformatics, 2014;15:223.

PMID: 24970564 DOI: 10.1186/1471-2105-15-223

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.

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

Palaniappan R, Sundaraj K, Sundaraj S

Comput Methods Programs Biomed, 2017 Jul;145:67-72.

PMID: 28552127 DOI: 10.1016/j.cmpb.2017.04.013

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

Palaniappan R, Sundaraj K, Sundaraj S, Huliraj N, Revadi SS

Clin Respir J, 2016 Jul;10(4):486-94.

PMID: 25515741 DOI: 10.1111/crj.12250

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.

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

Talib I, Sundaraj K, Lam CK, Sundaraj S

J Musculoskelet Neuronal Interact, 2018 12 01;18(4):446-462.

PMID: 30511949

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.

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

Islam A, Sundaraj K, Ahmad RB, Sundaraj S, Ahamed NU, Ali MA

Muscle Nerve, 2015 Jun;51(6):899-906.

PMID: 25204740 DOI: 10.1002/mus.24454

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.

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

Islam MA, Sundaraj K, Ahmad RB, Sundaraj S, Ahamed NU, Ali MA

PLoS One, 2014;9(8):e104280.

PMID: 25090008 DOI: 10.1371/journal.pone.0104280

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.

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

Ali MA, Sundaraj K, Ahmad RB, Ahamed NU, Islam MA, Sundaraj S

Technol Health Care, 2014;22(4):617-25.

PMID: 24990168 DOI: 10.3233/THC-140833

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.

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

Islam MA, Sundaraj K, Ahmad RB, Sundaraj S, Ahamed NU, Ali MA

PLoS One, 2014;9(5):e96628.

PMID: 24802858 DOI: 10.1371/journal.pone.0096628

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.

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

Chiang CF, Hasan MS, Tham SW, Sundaraj S, Faris A, Ganason N

J Clin Anesth, 2017 Jun;39:82-86.

PMID: 28494915 DOI: 10.1016/j.jclinane.2017.03.025

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.

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

Ali A, Sundaraj K, Badlishah Ahmad R, Ahamed NU, Islam A, Sundaraj S

J Hum Kinet, 2015 Jun 27;46:69-76.

PMID: 26240650 DOI: 10.1515/hukin-2015-0035

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.

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