Development of Health Parameter Model for Risk Prediction of CVD Using SVM.

Current methods of cardiovascular risk assessment are performed using health factors which are often based on the Framingham study. However, these methods have significant limitations due to their poor sensitivity and specificity. We have compared the parameters from the Framingham equation with linear regression analysis to establish the effect of training of the model for the local database. Support vector machine was used to determine the effectiveness of machine learning approach with the Framingham health parameters for risk assessment of cardiovascular disease (CVD). The result shows that while linear model trained using local database was an improvement on Framingham model, SVM based risk assessment model had high sensitivity and specificity of prediction of CVD. This indicates that using the health parameters identified using Framingham study, machine learning approach overcomes the low sensitivity and specificity of Framingham model.

Paraoxonase 1 status in keratoconus: a preliminary study of activity and polymorphism / Estatus de la paraoxonasa 1 en el queratocono: estudio preliminar de actividad y polimorfismo

OBJECTIVE: To determine the activity of paraoxonase 1 (PON1) in keratoconus in a Malaysian population in comparison with non-keratoconic subjects. METHODS: Clinical eye examinations were performed on patients with keratoconus and non-keratoconic subjects after questionnaires were completed. Blood samples were collected and subjected to spectrophotometry analysis of paraoxonase and diazoxonase activities for the determination of the status of PON1 of every individual. RESULTS: Of the 11 keratoconic patients and 55 non-keratoconic control samples collected, eight patients of Indian ethnicity were keratoconic (73%), whereas 33 non-Indians were non-keratoconic (60%; p = 0.047). Paraoxonase activity was lower in Indians compared to the non-Indians ie Malays and Chinese (p = 0.008). Keratoconic subjects had a lower paraoxonase activity compared to non-keratoconics (p = 0.038). CONCLUSIONS: The reduced paraoxonase activity in keratoconic patients suggests that the keratoconic corneas were more susceptible to oxidative stress. Reduced paraoxonase activity and keratoconus status appears to be associated with ethnicity.

OBJETIVO: Determinar la actividad de paraoxonasa 1 (Pon 1) en el queratocono en una población malaya, en comparación con sujetos no queratocónicos. MÉTODOS: Se realizaron exámenes clínicos oculares a pacientes con queratocono y a sujetos no queratocónicos luego que los mismos respondieran a los cuestionarios. Se recogieron muestras de sangre, que fueron entonces sometidas a análisis espectrofotométrico en relación con las actividades de la paraoxonasa y la diazoxonasa para la determinación del estatus de la paraoxonasa 1 de cada individuo. RESULTADOS: De los 11 pacientes queratocónicos y las 55 muestras de control no queratocónicas recogidas, 8 pacientes de etnicidad india fueron queratocónicos (73%), mientras que 33 no indios fueron no queratocónicos (60%; p = 0.047). La actividad de la paraoxonasa fue más baja en los indios en comparación con los no indios, es decir, los malayos y los chinos (p = 0.008). Los sujetos queratocónicos tenían una actividad de la paraoxonasa más baja, comparada con los no queratocónicos (p = 0.038). CONCLUSIONES: La actividad de la paraoxonasa reducida en los pacientes queratocónicos sugiere que las córneas queratocónicas son más susceptibles al estrés oxidativo. La actividad de la paraoxonasa reducida y el estatus del queratocono parecen estar asociados con la etnicidad.

Paraoxonase 1 status in keratoconus: a preliminary study of activity and polymorphism.

OBJECTIVE: To determine the activity of paraoxonase 1 (PON1) in keratoconus in a Malaysian population in comparison with non-keratoconic subjects. METHODS: Clinical eye examinations were performed on patients with keratoconus and non-keratoconic subjects after questionnaires were completed. Blood samples were collected and subjected to spectrophotometric analysis of paraoxonase and diazoxonase activities for the determination of the status of PON1 of every individual. RESULTS: Of the 11 keratoconic patients and 55 non-keratoconic control samples collected, eight patients of Indian ethnicity were keratoconic (73%), whereas 33 non-Indians were non-keratoconic (60%; p = 0.047). Paraoxonase activity was lower in Indians compared to the non-Indians ie Malays and Chinese (p = 0.008). Keratoconic subjects had a lower paraoxonase activity compared to non-keratoconics (p = 0.038). CONCLUSIONS: The reduced paraoxonase activity in keratoconic patients suggests that the keratoconic corneas were more susceptible to oxidative stress. Reduced paraoxonase activity and keratoconus status appears to be associated with ethnicity.

A machine learning based method for classification of fractal features of forearm sEMG using Twin Support vector machines.

Classification of surface electromyogram (sEMG) signal is important for various applications such as prosthetic control and human computer interface. Surface EMG provides a better insight into the strength of muscle contraction which can be used as control signal for different applications. Due to the various interference between different muscle activities, it is difficult to identify movements using sEMG during low-level flexions. A new set of fractal features - fractal dimension and Maximum fractal length of sEMG has been previously reported by the authors. These features measure the complexity and strength of the muscle contraction during the low-level finger flexions. In order to classify and identify the low-level finger flexions using these features based on the fractal properties, a recently developed machine learning based classifier, Twin Support vector machines (TSVM) has been proposed. TSVM works on basic learning methodology and solves the classification tasks as two SVMs for each classes. This paper reports the novel method on the machine learning based classification of fractal features of sEMG using the Twin Support vector machines. The training and testing was performed using two different kernel functions - Linear and Radial Basis Function (RBF).

Limitations and applications of ICA for surface electromyogram.

Surface electromyogram (SEMG) has numerous applications, but the presence of artefacts and noise, especially at low level of muscle activity make the recordings unreliable. Spectral and temporal overlap can make the removal of artefacts and noise, or separation of relevant signals from other bioelectric signals extremely difficult. Individual muscles may be considered as independent at the local level and this makes an argument for separating the signals using independent component analysis (ICA). In the recent past, due to the easy availability of ICA tools, numbers of researchers have attempted to use ICA for this application. This paper reports research conducted to evaluate the use of ICA for the separation of muscle activity and removal of the artefacts from SEMG. It discusses some of the conditions that could affect the reliability of the separation and evaluates issues related to the properties of the signals and number of sources. The paper also identifies the lack of suitable measure of quality of separation for bioelectric signals and it recommends and tests a more robust measure of separation. The paper also reports tests using Zibulevsky's technique of temporal plotting to identify number of independent sources in SEMG recordings. The theoretical analysis and experimental results demonstrate that ICA is suitable for SEMG signals. The results identify the unsuitability of ICA when the number of sources is greater than the number of recording channels. The results also demonstrate the limitations of such applications due to the inability of the system to identify the correct order and magnitude of the signals. The paper determines the suitability of the use of error measure using simulated mixing matrix and the estimated unmixing matrix as a means identifying the quality of separation of the output. The work demonstrates that even at extremely low level of muscle contraction, and with filtering using wavelets and band pass filters, it is not possible to get the data sparse enough to identify number of independent sources using Zibulevs.ky's technique.