Contactless respiration rate measurement using optical method and empirical mode decomposition.

BACKGROUND: At the emergency triage center, assessment of the present of the danger signs and measurement of vital signs are measured according to the guidelines. The respiration rate is still posing a challenge to the doctor as it is impractical to use conventional devices. Attaching measurement devices to the patient will induce artificial measurements (self-awareness stress effects) besides being time-consuming. Currently, the medical officers visually count the number of times the chest movement in a minute, sometimes poses cultural challenges especially for female patients. OBJECTIVE: The main objective of this paper is to develop a robust algorithm to extract respiration rate using the contactless displacement sensor. METHODS: In this study, chest movements were used as an indicative of inspiration and expiration to measure respiratory rate using the contactless displacement sensor. The contactless optical signals were recorded from 32 healthy subjects in four different controlled breathing conditions: rest, coughing, talking and hand movement to obtain the motion artifacts that the patients may have in the emergency department. The Empirical mode decomposition (EMD) algorithm was used to derive continuous RR signal from the contactless optical signal. RESULTS: The analysis showed that there is a good correlation (0.9702) with RMSE of 0.33 breaths per minutes between the contact respiration rate and contactless respiration rate using empirical mode decomposition method. CONCLUSION: It can be concluded that the empirical mode decomposition method can extract the respiration rate of the contactless optical signal from chest movement.

Secondary triage classification using an ensemble random forest technique.

BACKGROUND: Triage of patients in the emergency department is a complex task based on several uncertainties and ambiguous information. Triage must be implemented within two to five minutes to avoid potential fatality and increased waiting time. OBJECTIVE: An intelligent triage system has been proposed for use in a triage environment to reduce human error. METHODS: This system was developed based on the objective primary triage scale (OPTS) that is currently used in the Universiti Kebangsaan Malaysia Medical Center. Both primary and secondary triage models are required to develop this system. The primary triage model has been reported previously; this work focused on secondary triage modelling using an ensemble random forest technique. The randomized resampling method was proposed to balance the data unbalance prior to model development. RESULTS: The results showed that the 300% resampling gave a low out-of-bag error of 0.02 compared to 0.37 without pre-processing. This model has a sensitivity and specificity of 0.98 and 0.89, respectively, for the unseen data. CONCLUSION: With this combination, the random forest reduces the variance, and the randomized resembling reduces the bias, leading to the reduced out-of-bag error.

Tumoral calcinosis: a case report and review of the literature.

Tumoral calcinosis is an uncommon and seemingly unrecognised disease of obscure aetiology. A case in a young Caucasian boy aged 14 is reported. The characteristic clinical features, radiological and pathological findings are described. The condition must be differentiated from other varieties of pathological calcification such as chronic vitamin D intoxication, the milk-alkali syndrome, chronic nephritis, primary hyperparathyroidism, calcinosis universalis, calcinosis circumscripta and dystrophic calcification.