Biomedical Signal Processing for Community Health

Résumé fourni par la source

Biomedical signal processing entails the acquisition and preprocessing of physiological signals, followed by information retrieval to analyze signals for patterns and trends. Sources of biomedical signals are neural signals, cardiac rhythm, muscle movements, and normal activity signals. Research presents a novel framework, the White Shark Optimized Refined Random Forest (WSO-RRF), which leverages biomedical signal processing techniques to enhance community health monitoring and disease detection. White Shark Optimization (WSO) improves disease detection and community health monitoring by enhancing biomedical signal processing. The electrocardiogram signal data was gathered from the open-source Kaggle website. During the pre-processing phase, min-max scaling assists in converting biomedical signal data into a specified range, helping in noise minimization and improving the quality of subsequent analysis. Medical signals hold both temporal and frequency information that is crucial for analysis. The wavelet transform is used to extract multi-resolution features from medical signals. The research proposes a WSO-RRF model and shows the model's performance through various evaluation metrics. The WSO-RRF achieved the highest performance metrics with an accuracy of 94.6%, RMSE of 0.19, and MAE of 0.24. Biomedical signal processing offers an innovative strategy that can be harnessed to enhance community health. It directly impacts the healthcare sector by promoting early diagnosis, providing feedback, and making treatment per individual available worldwide.

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