APPROACH: Several master templates are initially generated by applying principal component analysis to data obtained from the PhysioNet MIMIC II database. The master template is then updated with each incoming clean PPG pulse. The correlation coefficient is used to classify the PPG pulse into either good or bad quality categories. The performance of our algorithm was evaluated using data obtained from two different sources: (i) our own data collected from 19 healthy subjects using the wearable Sotera Visi Mobile system (Sotera Wireless Inc.) as they performed various movement types; and (ii) ICU data provided by the PhysioNet MIMIC II database. The developed algorithm was evaluated against a manually annotated 'gold standard' (GS).
MAIN RESULTS: Our algorithm achieved an overall accuracy of 91.5% ± 2.9%, with a sensitivity of 94.1% ± 2.7% and a specificity of 89.7% ± 5.1%, when tested on our own data. When applying the algorithm to data from the PhysioNet MIMIC II database, it achieved an accuracy of 98.0%, with a sensitivity and specificity of 99.0% and 96.1%, respectively.
SIGNIFICANCE: The proposed method is simple and robust against individual variations in the PPG characteristics, thus making it suitable for a diverse range of datasets. Integration of the proposed artefact detection technique into remote monitoring devices could enhance reliability of the PPG-derived physiological parameters.
RESULT: The localization error is validated on the two datasets with superior performance over the state-of-the-art methods and variation in the expression is visualized using Principal Components (PCs). The deformations show various expression regions in the faces. The results indicate that Sad expression has the lowest recognition accuracy on both datasets. The classifier achieved a recognition accuracy of 99.58 and 99.32% on Stirling/ESRC and Bosphorus, respectively.
CONCLUSION: The results demonstrate that the method is robust and in agreement with the state-of-the-art results.
METHOD: A convenience sample of 102 patients was recruited from four Cure and Care Service Centres in Malaysia.
RESULTS: Principal component analysis with varimax rotation supported two-factor solutions for each subscale: problem recognition, desire for help and treatment readiness, which accounted for 63.5%, 62.7% and 49.1% of the variances, respectively. The Cronbach's alpha coefficients were acceptable for the overall measures (24 items: ∝ = 0.89), the problem recognition scale (10 items; ∝ = 0.89), desire for help (6 items; ∝ = 0.64) and treatment readiness scale (8 items; ∝ = 0.60). The results also indicated significant motivational differences for different modalities, with inpatients having significantly higher motivational scores in each scale compared to outpatients.
CONCLUSION: The present study pointed towards the favourable psychometric properties of a motivation for treatment scale, which can be a useful instrument for clinical applications of drug use changes and treatment.
DESIGNS AND PARTICIPANTS: The results from an evidence synthesis and the outcomes from an expert panel discussion were used to shape CHBMS scale content into an assessment of beliefs about CRC screening (CHBMS-CRC). This questionnaire assessment was translated into the official language of Malaysia. An initial study tested the face validity of the new scale or questionnaire with 30 men and women from various ethnic groups. Factorial or structural validity was investigated in a community sample of 954 multiethnic Malaysians.
SETTING: Selangor state, Malaysia.
RESULTS: The new scale was culturally acceptable to the three main ethnic groups in Malaysia and achieved good face validity. Cronbach's alpha coefficients ranged from 0.66 to 0.93, indicating moderate to good internal consistency. Items relating to perceived susceptibility to CRC 'loaded' on Factor 1 (with loadings scoring above 0.90); perceived benefits of CRC screening items loaded on factor 2 and were correlated strongly (loadings ranged between 0.63 and 0.83) and perceived barriers (PBA) to CRC screening (PBA) items loaded on factor 3 (range 0.30-0.72).
CONCLUSION: The newly developed CHBMS-CRC-M fills an important gap by providing a robust scale with which to investigate and assess CRC screening beliefs and contribute to efforts to enhance CRC screening uptake and early detection of CRC in Malaysia and in other Malay-speaking communities in the region.