Background: Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psychiatry, research based on functional magnetic resonance imaging has yielded remarkable results, but these tools tend to be too expensive for everyday clinical use. Objective: This review focuses on an affordable data-driven approach based on electroencephalographic recordings. Web-based applications via public or private cloud-based platforms would be a logical next step. We aim to compare several different approaches to the detection of depression from electroencephalographic recordings using various features and machine learning models. Methods: To detect depression, we reviewed published detection studies based on resting-state electroencephalogram with final machine learning, and to predict therapy outcomes, we reviewed a set of interventional studies using some form of stimulation in their methodology. Results: We reviewed 14 detection studies and 12 interventional studies published between 2008 and 2019. As direct comparison was not possible due to the large diversity of theoretical approaches and methods used, we compared them based on the steps in analysis and accuracies yielded. In addition, we compared possible drawbacks in terms of sample size, feature extraction, feature selection, classification, internal and external validation, and possible unwarranted optimism and reproducibility. In addition, we suggested desirable practices to avoid misinterpretation of results and optimism. Conclusions: This review shows the need for larger data sets and more systematic procedures to improve the use of the solution for clinical diagnostics. Therefore, regulation of the pipeline and standard requirements for methodology used should become mandatory to increase the reliability and accuracy of the complete methodology for it to be translated to modern psychiatry.

There is strong clinical evidence from the current literature that certain psychological and physiological indicators are closely related to mood changes. However, patients with mental illnesses who present similar behavior may be diagnosed differently, which is why a personalized study of each patient is necessary. Following previous promising results in the detection of depression, in this work, supervised machine learning (ML) algorithms were applied to classify the different states of patients diagnosed with bipolar depressive disorder (BDD). The purpose of this study was to provide relevant information to medical staff and patients’ relatives in order to help them make decisions that may lead to a better management of the disease. The information used was collected from BDD patients through wearable devices (smartwatches), daily self-reports, and medical observation at regular appointments. The variables were processed and then statistical techniques of data analysis, normalization, noise reduction, and feature selection were applied. An individual analysis of each patient was carried out. Random Forest, Decision Trees, Logistic Regression, and Support Vector Machine algorithms were applied with different configurations. The results allowed us to draw some conclusions. Random Forest achieved the most accurate classification, but none of the applied models were the best technique for all patients. Besides, the classification using only selected variables produced better results than using all available information, though the amount and source of the relevant variables differed for each patient. Finally, the smartwatch was the most relevant source of information.

In this paper, a synergy of signal processing techniques and intelligent strategies is applied in order to identify different types of human brain tumours, so that to help to confirm the histological diagnosis. The wavelet-SVM (support vector machine) classifier merges wavelet transform to reduce the size of the biomedical spectra and to extract the main features, with SVM to classify them. The influence of some of the configuration parameters of each of those techniques on the clustering is analysed. The classification results are promising specially taking into account that medical knowledge has not been considered.

Human physiology is among the most complex systems in nature, characterized by intricate structural and functional networks and rich temporal dynamics. Electrophysiological signals produced by different tissues/organs reflect physiological activity, and are inherently non-stationary, non-linear, and noisy. This work focuses on fractal analysis, a framework that captures the self-similar and scale-free properties of electrophysiological signals, which is considered to act as an output of complex physiological structures that generate complex processes. Central to this approach is the principle of ‘decomplexification’, whereby aging and disease are associated with a loss of physiological complexity. We discuss key algorithms, particularly Higuchi’s fractal dimension, which is often combined with other nonlinear measures and machine-learning models for real-time analysis of electrophysiological signals. Evidence shows that fractal metrics enable the early detection and monitoring of neurological and psychiatric disorders, outperforming traditional spectral measures. In movement disorders and mood disorders, fractal and nonlinear features show high diagnostic accuracy. Beyond diagnostics, we discuss therapeutic applications, including the prediction of responsiveness to non-invasive brain stimulation. Here, we envisage the evolution of one fractal or nonlinear measure use, to several measures applied, then use it as a feature for machine learning, and then realize that a whole cluster of biomarkers must be used to reflect the state of autonomic profile, which then can be used for ontology-based application profiles that can be machine-actionable. In addition, we discuss the fractal and fractional description of transport processes, which offer innovative improvement for a much more accurate description of physiological reality as a prerequisite for further modeling: for example, this is needed for digital twins to support the clinical translation of fractal analysis for personalized medicine. In essence, if one is trying to mathematically describe or quantify structures or processes in human physiology, fractal and fractional are the supreme and adequate approach to accurately model that reality.