# AI-Based Biomedical Signal Processing
###### Abstract  
Motivated by the numerous studies working towards innovative processing of biomedical signals, especially exploiting Artificial Intelligence (AI)-based methodologies, this Special Issue was proposed in 2022. This initiative aims to provide a comprehensive overview of the most recent advancements in the field, highlighting both theoretical contributions and practical implementations. As well-depicted by [PERSON] et al. [2], the learning ability of AI techniques is being leveraged to aid diagnoses and improve prediction accuracy while considering proper raw data processing and/or feature extraction. These advancements are made possible through careful preprocessing of raw biomedical data, as well as through the extraction of informative and discriminative features that feed into AI models. Moreover, the literature confirms this research trend, particularly applied to heart-related data, such as electrocardiographic (ECG) [3, 4, 5, 6, 7, 8, 9], electromyographic (EMG) [3, 10], and electroencephalographic (EEG) signals [3, 2, 11]. Furthermore, AI techniques are also being successfully applied to the analysis and interpretation of biomedical imaging data [13], opening new frontiers in diagnostic imaging and computer-aided detection. This Special Issue thus serves as a timely platform for the dissemination of novel AI-driven solutions in biomedical data processing, fostering interdisciplinary collaboration among engineers, computer scientists, and healthcare professionals. Notice that a total of 14 contributions were collected, focusing mainly on (i) feature engineering, (ii) deep-learning applications, and (iii) model explainability, interpretability, and personalization, as per the previously cited papers. These aspects will be briefly presented, and a general overview of the Special Issue studies provided. Two key aspects of biomedical signal processing are feature extraction and selection [14, 15] and their use to evaluate the physiology of the systems or their clinical use. This process was recently named feature engineering and is fundamental to provide a correct data characterization as well as reduce signal dimensionality [15]. Many AI-based algorithms have aimed to use feature engineering as a biomedical signal-processing step. For this purpose, the Authors of this SI have proposed different strategies, spanning from the use of a combination of decomposition transforms for signal analysis and least-squares support vector machines to reliably detect atrial fibrillation to the extraction of RR-interval-related features in ECG as inputs to artificial neural networks and adaptive neuro-fuzzy interference systems. Other ECG feature types were investigated in the context of COVID-19 mortality risk detection or extracted through the use of convolutional neural networks (CNNs). Another case lies in seizure detection on EEG data. Spontaneous alpha and beta rhythms are selected by exploiting the discrete wavelet transform and features extracted from these rhythms by employing the Mallat algorithm. An interesting approach is the one by [PERSON] et al. [15], who propose an innovative detection and monitoring of [PERSON]'s disease by using automatic speech analysis, exploiting voice features related to the concept of emotional temperature. Particularly, they work with the 2019 Cross-Sectional Alzheimer Prognosis database containing spontaneous and induced voice samples and thus analyzing the differences between emotional temperature features derived from a human or an automatic interview process. The automatic speech analysis is conducted through (i) emotional temperature calculation with a voice activity detector providing features that (ii) undergo a statistical and (iii) univariate analysis. Afterwards, (iv) a multivariate analysis using a linear discriminant analysis, logistic regression, and k-nearest neighbor to detect if there is [PERSON]'s disease or not and its severity (mild, moderate, or healthy control). Notice that a feature selection step is also conducted through a neighborhood component analysis.