ECG Signal Analysis: Enhancement and R-Peak Detection

The project has been inspired by the need to find an efficient method for ECG Signal Analysis which is simple and has good accuracy and less computation time. The initial task for efficient analysis is the removal of noise. It actually involves the extraction of the required cardiac components by rejecting the background noise. Enhancement of signal is achieved by the use of Empirical Mode Decomposition method. The use of EMD was inspired by its adaptive nature. The second task is that of R peak detection which is achieved by the use of Continuous Wavelet Transform. Efficiency of the method is measured in terms of detection error rate. Various other methods of R peak detection like Hilbert Transform and Difference Operation Method are implemented and the results when compared with the Continuous Wavelet Transform prove that CWT is a better method. The simulation is done in MATLAB environment. The experiments are carried out on MIT-BIH database. The results show that our proposed method is very effective and an efficient method for fast computation of R peak detection

Epidemiology of obesity and its related morbidities among rural population attending a primary health centre of Odisha, India

Context: Overweight and obesity has become a major contributor to global burden of chronic diseases and disability. Obesity among rural India is emerging as a major health problem because of change in lifestyle and food habits, thereby increases the risk of multiple morbid conditions among rural population. Aims: This study aims to find out the association of overweight/obesity with different socio-demographic factors and explore the co-morbidities among overweight and obese in a rural setting. Settings and Design: A cross-sectional study was done in a randomly selected primary health centre of Khurda district, Odisha for 4 months. Materials and Methods: This study was done among 183 patients aged >20 years with BMI >25 kg/m2 after taking their consent. Anthropometric measurements were done and data were collected using a semi-structured questionnaire. Statistical Analysis Used: Descriptive statistics such as proportion, mean and standard deviation were calculated and inferential statistics such as Chi-square test, univariate and multivariate regression was done using the SPSS version 20.0. Results: Mean age of participants was 45.7 (±13.8) years. About 93.4% were Grade II obese while 51.9% were at risk according to their waist–hip ratio. Around 53.6% of participants had multi-morbidity. Age, occupation and number of children were significantly associated with obesity. Morbidity was significantly associated with age, occupation, marital status and number of children. Increased grade in obesity the more is the probability of having morbidity. Conclusions: There is an urgent need to screen for obesity at rural health facility and early management for prevention from co-morbidities