Evaluation of a task-based community oriented teaching model in family medicine for undergraduate medical students in Iraq

BACKGROUND: The inclusion of family medicine in medical school curricula is essential for producing competent general practitioners. The aim of this study is to evaluate a task-based, community oriented teaching model of family medicine for undergraduate students in Iraqi medical schools. METHODS: An innovative training model in family medicine was developed based upon tasks regularly performed by family physicians providing health care services at the Primary Health Care Centre (PHCC) in Mosul, Iraq. Participants were medical students enrolled in their final clinical year. Students were assigned to one of two groups. The implementation group (28 students) was exposed to the experimental model and the control group (56 students) received the standard teaching curriculum. The study took place at the Mosul College of Medicine and at the Al-Hadba PHCC in Mosul, Iraq, during the academic year 1999–2000. Pre- and post-exposure evaluations comparing the intervention group with the control group were conducted using a variety of assessment tools. RESULTS: The primary endpoints were improvement in knowledge of family medicine and development of essential performance skills. Results showed that the implementation group experienced a significant increase in knowledge and performance skills after exposure to the model and in comparison with the control group. Assessment of the model by participating students revealed a high degree of satisfaction with the planning, organization, and implementation of the intervention activities. Students also highly rated the relevancy of the intervention for future work. CONCLUSION: A model on PHCC training in family medicine is essential for all Iraqi medical schools. The model is to be implemented by various relevant departments until Departments of Family medicine are established

Risk factors for pre-term birth in Iraq: a case-control study

BACKGROUND: Preterm birth (PTB)is a major clinical problem associated with perinatal mortality and morbidity. The aim of the present study is to identify risk factors associated with PTB in Mosul, Iraq. METHODS: A case-control study was conducted in Mosul, Iraq, from 1(st )September, 2003 to 28(th )February, 2004. RESULTS: A total of 200 cases of PTB and 200 controls of full-term births were screened and enrolled in the study. Forward logistic regression analysis was used in the analysis. Several significant risk associations between PTB and the following risk factors were identified: poor diet (OR = 4.33), heavy manual work (OR = 1.70), caring for domestic animals (OR = 5.06), urinary tract infection (OR = 2.85), anxiety (OR = 2.16), cervical incompetence (OR = 4.74), multiple pregnancies (OR = 7.51), direct trauma to abdomen (OR = 3.76) and abortion (OR = 6.36). CONCLUSION: The main determinants of PTB in Iraq were low socio-economic status and factors associated with it, such as heavy manual work and caring for domestic animals, in addition to urinary tract infections and poor obstetric history

Optimal feature set for finger movement classification based on sEMG

© 2018 IEEE. One of the most important electrophysiological signal is the Electromyography (EMG) signal, which is widely used in medical and engineering studies. This signal contains a wealth of information about muscle functions. Therefore, the EMG signal is becoming increasingly important and has started to be used in many applications like finger movement rehabilitation. However, an advanced EMG signal analysis method is required for efficient usage of such applications. This signal analysis can include signal detection, decomposition, processing, and classification. There are many approaches in studying the EMG signals, however, one of the important factor of analyzing is to get the most efficient and effective features that can be extracted from the raw signal. This paper presents the best feature extraction set compared to previous studies. Where eighteen well-known features algorithm has been tested using the sequential forward searching (SFS) method to get excellent classification accuracy in a minimum processing time. Among these novel features only four combinations have been selected with perfect results, which are; Hjorth Time Domain parameters (HTD), Mean Absolute Value (MAV), Root Mean Square (RMS) and Wavelet Packet Transform (WPT). The superiority of this feature set has been proven experimentally, and the results show that the classification accuracy could reach up to 99% to recognize the individual and combined for ten classes of finger movements using only two EMG channels

Augmentation of transient stability margin based on rapid assessment of rate of change of kinetic energy

A fast-load injection through a resistive dynamic brake with appropriate power dissipation capacity can absorb the excess transient energy caused by a large and sudden disturbance and thus improve the transient stability margin of a power system. However, fast assessment of the transient stability and the effective insertion/removal instants of the brake are longstanding challenges. This paper proposes a new criterion based on the rate of change of kinetic energy to rapidly evaluate system transient stability and identify conditions of effective insertion/removal instants of a dynamic brake. Unlike reported studies where the superiority of this criterion was only demonstrated through off-line simulation, both the theoretical modeling and practical implementation of this criterion is presented here using the one machine infinite bus system. A microprocessor controller based on a single-variable measurement, i.e. generator deviation speed, is proposed and implemented to control the dynamic brake during the disturbance periods. The observed behavior of the power system under sudden disturbances and the effect of timely insertion/removal of the dynamic brake on the transient stability of the power system under study are presented and evaluated. The proposed method has been successfully validated, demonstrating its suitability for practical and rapid assessment of transient stability

Spatially Filtered Low-Density EMG and Time-Domain Descriptors Improves Hand Movement Recognition.

Surface Electromyogram (EMG) pattern recognition has long been utilized for controlling multifunctional myoelectric prostheses. In such an application, a number of EMG channels are usually utilized to acquire more information about the underlying activity of the remaining muscles in the amputee stump. However, despite the multichannel nature of this application, the extracted features are usually acquired from each channel individually, without consideration for the interaction between the different muscles recruited to achieve a specific movement. In this paper, we proposed an approach of spatial filtering, denoted as Range Spatial Filtering (RSF), to increase the number of EMG channels available for feature extraction, by considering the range of all possible logical combinations of each n channels. The proposed RSF method is then combined with conventional time-domain (TD) feature extraction, as an extension of the conventional single channel TD features that are heavily considered in this field. We then show how the addition of a new feature, specifically the minimum absolute value of the range of each two windowed EMG signals, can significantly reduce the different patterns misclassification rate achieved by conventional TD features (with and without our RSF method). The performance of the proposed method is verified on EMG data collected from nine transradial amputees (seven traumatic and two congenital), with six grip and finger movements, for three different levels of forces (low, medium, and high). The classification results showed significant reduction in classification error rates compared to other methods (nearly 10% for some individual TD features and 5% for combined TD features, with Bonferroni corrected p-values <; 0.01)

Recurrent Fusion of Time-Domain Descriptors Improves EMG-based Hand Movement Recognition.

Controlling powered prostheses with myoelectric pattern recognition (PR) provides a natural human-robot interfacing scheme for amputees who lost their limbs. Research in this direction reveals that the challenges prohibiting reliable clinical translation of myoelectric interfaces are mainly driven by the quality of the extracted features. Hence, developing accurate and reliable feature extraction techniques is of vital importance for facilitating clinical implementation of Electromyogram (EMG) PR systems. To overcome this challenge, we proposed a combination of Range Spatial Filtering (RSF) and Recurrent Fusion of Time Domain Descriptors (RFTDD) in order to improve the classifier performance and make the prosthetic hand control more appropriate for clinical applications. RSF is used to increase the number of EMG signals available for feature extraction by focusing on the spatial information between all possible logical combinations of the physical EMG channels. RFTDD is then used to capture the temporal information by applying a recurrent data fusion process on the resulting orientation-based time-domain (TD) features, with a sigmoidal function to limit the features range and overcome the vanishing amplitudes problem. The main advantages of the proposed method include 1) its potential in capturing the temporal-spatial dependencies of the EMG signals, leading to reduced classification errors, and 2) the simplicity with which the features are extracted, as any kind of simple TD features can be adopted with this method. The performance of the proposed RFTDD is then benchmarked across many well-known TD features individually and as sets to prove the power of the RFTDD method on two EMG datasets with a total of 31 subjects. Testing results revealed an approximate reduction of 12% in classification errors across all subjects when using the proposed method against traditional feature extraction methods.Clinical Relevance-Establishing significance and importance of RFTDD, with simple time-domain features, for robust and low-cost clinical applications