Machine Learning vs Traditional Methods: Efficacy of Time Frequency Anal ysis in Diagnoses Applications and Imperative for Further Investment, Study, and Initiative
Abstract:
Neurocognitive diseases and viruses are the leading causes of fatalities in the US, and their effective treatment more often than not is converted into a high-risk scenario. Prominent traditional methods in the healthcare industry are arguably failing in terms of real-time efficiency, accuracy, and reliability. Time-frequency analysis (TFA) is an emerging signal processing and examination technique combining T-F representations with deep learning pattern prediction to accelerate results. Scientists are now exploring new ways to combine TFA with medieval techniques, such as Wavelet Transform (WT) and Higher Order Statistics (HOS), to create hybrid models that implement advanced Electroencephalographic (EEG) detection techniques through a combina tion of the best characteristics from both original standalone techniques. By automating early detection of previously thought intractable diseases, doctors are allowed more time to accurately measure the extent of the threat and provide strategic countermeasures, an element that is very much of the essence in life-threat ening situations. This work provides a comprehensive view of the science, computations, and mathematical applications of simultaneous Time-Frequency domain related signal processing techniques and the advancing integrations of Artificial Intelligence to present an argument for further development, research, and investment while coordinating an in-depth analysis of the pros and cons of static, non-DL based pattern prediction.
