# AI-Based Biomedical Signal Processing
###### Abstract
The results show that the investigation of an automated process to detect and monitor Alzheimer's disease may improve speech-based remote healthcare. Moving to the next main topic, deep learning methods are continuously gaining interest from the biomedical research community [16; 17; 18], considering their ability to capture patterns exploitable for data augmentation and information transfer, and provide temporal and frequency information from different data types. In this Special Issue, very different approaches in a variety of biomedical domains have been proposed. A very particular example is the one by [PERSON], [PERSON], and [PERSON] [19], who propose a novel Voice Conversion (VC) approach to enhance Mandarin electro-laryngeal speech. This technique is usually prone to producing unnatural and difficult-to-understand speech in the case of tonal languages. Therefore, the Authors propose a CycleGAN-based VC, exploiting a 2D-Conformer-1D-Transformer-2D-Conformer generation to capture both temporal and frequency information characteristic of speech signals. The CycleGAN receives as an input Mel-spectrograms from Electro-laryngeal fixed and variable tone speech signals. Objective and subjective evaluations were used to confirm the efficacy of the approach. Thus, in the objective evaluation, Mel-Cepstral distortion and root mean square error of CodeAP are used to evaluate spectrum features, while speech enhancement was detected with the correlation coefficient of fundamental frequency and error of the logarithmic fundamental frequency. Instead, for the subjective evaluation, 10 Chinese listeners were enrolled to provide a transcription with tones of the audio they heard, and give a mean opinion score of the audio's understandability and naturalness. The results show an enhancement of Mandarin electro-laryngeal speech by improving the tone accuracy and reducing the word error perception rate while considering an increase in intelligibility and naturalness compared to other literature works. Notice that the speech files are available. Other works have instead focused on (i) the use of bidirectional long-short-term-memory-based models for lower-limb rehabilitation improvement through the combination of extended reality, robotic ankle prosthetic leg, and EMG signals; (ii) the classification of heart sounds to detect cardiovascular diseases through an ensemble of deep learning models using features extracted from phonocardiogram signals; (iii) a novel lightweight encoder-decoder network designed for semantic and instance-level segmentation of human chromosomes in microscopic images; (iv) working on photoacoustic imaging developing YoLov8-MedSAM, specifically designed for precise brain tumor identification and segmentation; and (v) proposing a R-CNN-based system to provide indications on cutting specific regions during thoracoscopic surgery. The last topic is the one related to model explainability, interpretability, and personalization [13; 20]. Debated in the literature, explainability refers to the ability to describe how a model arrives at its predictions, while interpretability focuses on how easily a human can understand the model's decisions and reasoning process. Among the most challenging issues, interpretability and explainability are essential to guarantee reliability and transparency [21]. The so-called \"black-box\" nature of many high-performing models still hinders their acceptance by medical practitioners, who often need clear, evidence-based justifications for algorithmic decisions, especially when they impact patient care. In this context, the work of [PERSON], [PERSON], and [PERSON] [22] is very innovative. The work aimed to correctly detect bipolar disorder and schizophrenia, which are usually studied separately, confused, and diagnosed at advanced stages. Particularly, they propose a ResNet50-based deep learning model intended to work on structural magnetic resonance images.