Title:Evaluation of Current Trends in Biomedical Applications Using Soft
Computing
Volume: 18
Issue: 9
Author(s): Sachin Kumar and Karan Veer*
Affiliation:
- Department of Instrumentation and Control Engineering, DR. BR Ambedkar National Institute of Technology,
Jalandhar, India
Keywords:
Machine learning, deep learning, ECG, EEG, EMG, wrist pulse, signal processing, signal analysis.
Abstract: 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.