Evaluation of Current Trends in Biomedical Applications Using Soft Computing

With the rapid advancement in analyzing high-volume and complex data, machine learning has become one of the most critical and essential tools for classification and prediction. This study reviews machine learning (ML) and deep learning (DL) methods for the classification and prediction of biological signals. The effective utilization of the latest technology in numerous applications, along with various challenges and possible solutions, is the main objective of this present study. A PICO-based systematic review is performed to analyze the applications of ML and DL in different biomedical signals, viz. electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and wrist pulse signal from 2015 to 2022. From this analysis, one can measure machine learning's effectiveness and key characteristics of deep learning. This literature survey finds a clear shift toward deep learning techniques compared to machine learning used in the classification of biomedical signals.

Automated summary

With the rapid development of high-volume and complex data analysis, machine learning has become a critical tool for classification and prediction. This study reviews the methods of machine learning and deep learning for the classification and prediction of biological signals and conducts a systematic review on their applications in different biomedical signals from 2015 to 2022. The findings show a clear shift towards deep learning techniques in the classification of biomedical signals compared to machine learning.