Voice signals database of ALS patients with different dysarthria severity and healthy controls.

This paper describes a new publicly-available database of VOiCe signals acquired in Amyotrophic Lateral Sclerosis (ALS) patients (VOC-ALS) and healthy controls performing different speech tasks. This dataset consists of 1224 voice signals recorded from 153 participants: 51 healthy controls (32 males and 19 females) and 102 ALS patients (65 males and 37 females) with different severity of dysarthria. Each subject's voice was recorded using a smartphone application (Vox4Health) while performing several vocal tasks, including a sustained phonation of the vowels /a/, /e/, /i/, /o/, /u/ and /pa/, /ta/, /ka/ syllable repetition. Basic derived speech metrics such as harmonics-to-noise ratio, mean and standard deviation of fundamental frequency (F0), jitter and shimmer were calculated. The F0 standard deviation of vowels and syllables showed an excellent ability to identify people with ALS and to discriminate the different severity of dysarthria. These data represent the most comprehensive database of voice signals in ALS and form a solid basis for research on the recognition of voice impairment in ALS patients for use in clinical applications.

Special Issue: "Intelligent Systems for Clinical Care and Remote Patient Monitoring".

The year 2020 was definitely like no other [...].

A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction.

In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our framework show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about 3.03% for precision, 1.56% for recall, 3.17% for F1, and 1.56% for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place.

Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment.

Diabetes is a heterogeneous group of diseases that share a common trait of elevated blood glucose levels. Insulin lowers this level by promoting glucose utilization, thus avoiding short- and long-term organ damage due to the elevated blood glucose level. A patient with diabetes uses an insulin pump to dose insulin. The pump uses a controller to compute and dose the correct amount of insulin to keep blood glucose levels in a safe range. Insulin-pump controller development is an ongoing process aiming at fully closed-loop control. Controllers entering the market must be evaluated for safety. We propose an evaluation method that exploits an FDA-approved diabetic patient simulator. The method evaluates a Cartesian product of individual insulin-pump parameters with a fine degree of granularity. As this is a computationally intensive task, the simulator executes on a distributed cluster. We identify safe and risky combinations of insulin-pump parameter settings by applying the binomial model and decision tree to this product. As a result, we obtain a tool for insulin-pump settings and controller safety assessment. In this paper, we demonstrate the tool with the Low-Glucose Suspend and OpenAPS controllers. For average ± standard deviation, LGS and OpenAPS exhibited 1.7 ± 0.6% and 3.2 ± 1.8% of local extrema (i.e., good insulin-pump settings) out of all the entire Cartesian products, respectively. A continuous region around the best-discovered settings (i.e., the global extremum) of the insulin-pump settings spread across 4.0 ± 1.1% and 4.1 ± 1.3% of the Cartesian products, respectively.

A Two-Step Approach for Classification in Alzheimer's Disease.

The classification of images is of high importance in medicine. In this sense, Deep learning methodologies show excellent performance with regard to accuracy. The drawback of these methodologies is the fact that they are black boxes, so no explanation is given to users on the reasons underlying their choices. In the medical domain, this lack of transparency and information, typical of black box models, brings practitioners to raise concerns, and the result is a resistance to the use of deep learning tools. In order to overcome this problem, a different Machine Learning approach to image classification is used here that is based on interpretability concepts thanks to the use of an evolutionary algorithm. It relies on the application of two steps in succession. The first receives a set of images in the inut and performs image filtering on them so that a numerical data set is generated. The second is a classifier, the kernel of which is an evolutionary algorithm. This latter, at the same time, classifies and automatically extracts explicit knowledge as a set of IF-THEN rules. This method is investigated with respect to a data set of MRI brain imagery referring to Alzheimer's disease. Namely, a two-class data set (non-demented and moderate demented) and a three-class data set (non-demented, mild demented, and moderate demented) are extracted. The methodology shows good results in terms of accuracy (100% for the best run over the two-class problem and 91.49% for the best run over the three-class one), F_score (1.0000 and 0.9149, respectively), and Matthews Correlation Coefficient (1.0000 and 0.8763, respectively). To ascertain the quality of these results, they are contrasted against those from a wide set of well-known classifiers. The outcome of this comparison is that, in both problems, the methodology achieves the best results in terms of accuracy and F_score, whereas, for the Matthews Correlation Coefficient, it has the best result over the two-class problem and the second over the three-class one.

Classification of Covid-19 chest X-ray images by means of an interpretable evolutionary rule-based approach.

In medical practice, all decisions, as for example the diagnosis based on the classification of images, must be made reliably and effectively. The possibility of having automatic tools helping doctors in performing these important decisions is highly welcome. Artificial Intelligence techniques, and in particular Deep Learning methods, have proven very effective on these tasks, with excellent performance in terms of classification accuracy. The problem with such methods is that they represent black boxes, so they do not provide users with an explanation of the reasons for their decisions. Confidence from medical experts in clinical decisions can increase if they receive from Artificial Intelligence tools interpretable output under the form of, e.g., explanations in natural language or visualized information. This way, the system outcome can be critically assessed by them, and they can evaluate the trustworthiness of the results. In this paper, we propose a new general-purpose method that relies on interpretability ideas. The approach is based on two successive steps, the former being a filtering scheme typically used in Content-Based Image Retrieval, whereas the latter is an evolutionary algorithm able to classify and, at the same time, automatically extract explicit knowledge under the form of a set of IF-THEN rules. This approach is tested on a set of chest X-ray images aiming at assessing the presence of COVID-19.

The Effects of Physical Exercise on Cognition: How Heart Rate Variability Can Predict Cognitive Performances.

Objectives: Investigate and identify the relationship between physical exercise and cognitive performance measured by using different cognitive tests taken from Cambridge Brain Science (CBS). Methods: Thirty subjects, divided into two groups (aerobic and effort), undergo twelve cognitive tests from CBS. A comparison between the pre- and post-exercise results in terms of cognitive performance differences is carried out. Regression analysis between Heart Rate Variability (HRV) features and CBS tests results is performed. Results: In most CBS tests, there is an improvement, or at least a confirmation, of the subject's cognitive ability, for both groups. Reasoning (80-100%), concentration (80-87%), and planning tests (93-100%) seem to undergo critical positive changes. The regression analysis, performed by using a set of different algorithms, has demonstrated that it is possible, by monitoring the HRV during the exercise, to predict to some extent the cognitive performance, i.e., the CBS tests results. The best performing regression algorithms are Simple Linear (Quade Test-aerobic group: 2.098, effort group: 3.350, both groups: 2.747) and REPTree (Quade Test-aerobic group: 2.955, effort group: 3.315, both groups: 3.121). The statistical analysis has proved that physical activity is statistically useful for the subjects in improving their cognitive performance. Conclusions: This study has numerically appraised the improvement, the conservation, or the worsening on different aspects of cognition. The found mathematical relationship between physical exercise and cognitive performance suggests that it is possible to predict the beneficial effect of various exercises on executive and attentive control.

An automatic rules extraction approach to support OSA events detection in an mHealth system.

Detection and real time monitoring of obstructive sleep apnea (OSA) episodes are very important tasks in healthcare. To suitably face them, this paper proposes an easy-to-use, cheap mobile-based approach relying on three steps. First, single-channel ECG data from a patient are collected by a wearable sensor and are recorded on a mobile device. Second, the automatic extraction of knowledge about that patient takes place offline, and a set of IF…THEN rules containing heart-rate variability (HRV) parameters is achieved. Third, these rules are used in our real-time mobile monitoring system: the same wearable sensor collects the single-channel ECG data and sends them to the same mobile device, which now processes those data online to compute HRV-related parameter values. If these values activate one of the rules found for that patient, an alarm is immediately produced. This approach has been tested on a literature database with 35 OSA patients. A comparison against five well-known classifiers has been carried out.

Monitoring Obstructive Sleep Apnea by means of a real-time mobile system based on the automatic extraction of sets of rules through Differential Evolution.

Real-time Obstructive Sleep Apnea (OSA) episode detection and monitoring are important for society in terms of an improvement in the health of the general population and of a reduction in mortality and healthcare costs. Currently, to diagnose OSA patients undergo PolySomnoGraphy (PSG), a complicated and invasive test to be performed in a specialized center involving many sensors and wires. Accordingly, each patient is required to stay in the same position throughout the duration of one night, thus restricting their movements. This paper proposes an easy, cheap, and portable approach for the monitoring of patients with OSA, which collects single-channel ElectroCardioGram (ECG) data only. It is easy to perform from the patient's point of view because only one wearable sensor is required, so the patient is not restricted to keeping the same position all night long, and the detection and monitoring can be carried out in any place through the use of a mobile device. Our approach is based on the automatic extraction, from a database containing information about the monitored patient, of explicit knowledge in the form of a set of IF…THEN rules containing typical parameters derived from Heart Rate Variability (HRV) analysis. The extraction is carried out off-line by means of a Differential Evolution algorithm. This set of rules can then be exploited in the real-time mobile monitoring system developed at our Laboratory: the ECG data is gathered by a wearable sensor and sent to a mobile device, where it is processed in real time. Subsequently, HRV-related parameters are computed from this data, and, if their values activate some of the rules describing the occurrence of OSA, an alarm is automatically produced. This approach has been tested on a well-known literature database of OSA patients. The numerical results show its effectiveness in terms of accuracy, sensitivity, and specificity, and the achieved sets of rules evidence the user-friendliness of the approach. Furthermore, the method is compared against other well known classifiers, and its discrimination ability is shown to be higher.

A medical diagnosis support system based on automatic knowledge extraction from databases through differential evolution.

An intelligent system for supporting medical diagnosis is presented in this paper. The system automatically extracts knowledge from databases as sets of IF-THEN rules. The approach chosen to fulfil this task is based on the differential evolution (DE) algorithm and its implementation results in a tool called DEREx. This tool is aimed at supporting clinicians in their decision making in the diagnostic process, by providing them with clear explanations on the reasons why each item is assigned to a given class. Performance of the tool has been evaluated over seven medical databases and compared against that of fifteen well-known classification tools. Numerical results in terms of classification accuracy and their statistical analysis, have evidenced the effectiveness of the proposed approach, so DEREx is preferable because of its added value, i.e. the knowledge extracted automatically and provided to users in an easily comprehensible form.

An evolutionary-fuzzy DSS for assessing health status in multiple sclerosis disease.

Assisted Living provides a long-term care option that combines supportive systems and services for monitoring and assessing the health status with activities of daily living and health care. Daily monitoring of the health status in subjects characterized by chronic and/or degenerative conditions is not possible in all those cases where the disease progression has to be evaluated only by a direct interaction between the patients and the healthcare structures on a regular basis, over time and for life. In this respect, this work proposes an evolutionary-fuzzy decision support system (DSS) for assessing the health status of subjects affected by multiple sclerosis (MS) during the disease progression over time. Such a DSS has been defined and implemented exploiting a novel approach devised to facilitate the design of fuzzy DSSs for medical problems. The approach is aimed at: (i) introducing a set of design criteria to encode the medical knowledge elicited from clinical experts in terms of linguistic variables, linguistic values and fuzzy rules with the final aim of granting the interpretability; (ii) defining a fuzzy inference technique to best fit the structure of medical knowledge and the peculiarities of the medical inference; (iii) defining an evolutionary technique to tune the formalized knowledge by optimizing the shapes of the membership functions for each linguistic variable involved in the rules. An experimental session has been carried out for evaluating, first of all, the approach on five medical databases commonly diffused in literature and for comparing it with other systems. After that, the evolutionary-fuzzy DSS for assessing MS patient's health status has been quantitatively evaluated on 120 patients affected by MS and compared with other approaches. The achieved results have shown that our approach is very effective on the five databases, since it provides, on average, the second highest accuracy when compared to eight tools. Furthermore, as far as the classification of multiple sclerosis lesions is considered, the proposed system has turned out to outperform nine popular tools.