# Special Issue on Signal Processing and Machine Learning for Biomedical Data
###### Abstract  
This Special Issue is focused on advanced techniques in signal processing, analysis, modelling, and classification, applied to a variety of medical diagnostic problems. Biomedical data play a fundamental role in many fields of research and clinical practice. Very often the complexity of these data and their large volume makes it necessary to develop advanced analysis techniques and systems. Furthermore, the introduction of new techniques and methodologies for diagnostic purposes, especially in the field of medical imaging, requires new signal processing and machine learning methods. The recent progress in machine learning techniques, and in particular deep learning, revolutionized various fields of  
[PERSON] et al. [3] proposed a fast reconstruction algorithm called self-learning subspace matrix factorization (SLSMF), which was applied to signals non-uniform sampling multidimensional nuclear magnetic resonance (NMR) spectroscopy. Results on the synthetic and realistic NMR data show that compared to the SLS, the fast SLS approach remarkably reduced computation time without sacrificing spectrum quality, and enabled faster reconstruction with parallel computing.  
In [4], the authors developed a system to classify Anomic and Wernicke's aphasia, based on the acoustic frequencies of speech signals. The system consisted of three diagnosis components--confrontation naming with 30 pictures to identify, single-word repetition of 15 single words, and comprehension components. The latter consisted of single-word comprehension and simple command comprehension. The evaluation was conducted on a total of 60 participants; 18 patients with Anomic Aphasia, 12 with Wernicke's Aphasia, and 30 non-aphasic.  
[PERSON] et al. [5] described a novel method for discriminating between lung cancer and non-cancer DNA sequences. The method focuses on network and graph theory, fractal geometry, and statistical pattern recognition, to define the prognostic value of HIF-1 expression in surgically treated lung cancer patients. DNA nucleotides are represented as a path in a graph, then fractal geometry is applied to measure the complexity of the graph. Comparison of the statistical and topological features extracted from the non-cancerous DNA sequences and cancerous DNA sequences showed that the fractal dimension decreased in the lung cancer network, while the topological properties of the network increased in the lung cancer network.  
In [6], [PERSON] et al. developed smartphone applications to monitor a patient with a cardiovascular disease. Through the use of the camera of the mobile device and without direct contact with the patient, the pulses per minute (PPM) was obtained. In particular, the authors showed that the analysis of the variations in skin color that are imperceptible to the eye allow estimation of the heart rate.  
The authors of [7] addressed the problem of automatic detection of myocardial infarction (MI) and for this purpose proposed a long short-term memory (Bi-LSTM) network. A heartbeat-attention mechanism was proposed to automatically weigh the difference between heartbeats based on 12-lead ECG records. The database used was the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database, with 549 ECG records from 290 patients. The results showed an accuracy of the order of 95%.  
[PERSON] et al. [8] proposed an algorithm to simultaneously estimate optimal rigid registration for the serial section images of biological tissues. The algorithm was non-iterative and it could simultaneously compute the transformations in a short time. The method was tested on 336 microscopic images of the serial section of a zebrafish acquired by scanning electron microscopy.