A Wearable Multi-Sensor Array Enables the Recording of Heart Sounds in Homecare

The home monitoring of patients affected by chronic heart failure (CHF) is of key importance in preventing acute episodes. Nevertheless, no wearable technological solution exists to date. A possibility could be offered by Cardiac Time Intervals extracted from simultaneous recordings of electrocardiographic (ECG) and phonocardiographic (PCG) signals. Nevertheless, the recording of a good-quality PCG signal requires accurate positioning of the stethoscope over the chest, which is unfeasible for a naïve user as the patient. In this work, we propose a solution based on multi-source PCG. We designed a flexible multi-sensor array to enable the recording of heart sounds by inexperienced users. The multi-sensor array is based on a flexible Printed Circuit Board mounting 48 microphones with a high spatial resolution, three electrodes to record an ECG and a Magneto-Inertial Measurement Unit. We validated the usability over a sample population of 42 inexperienced volunteers and found that all subjects could record signals of good to excellent quality. Moreover, we found that the multi-sensor array is suitable for use on a wide population of at-risk patients regardless of their body characteristics. Based on the promising findings of this study, we believe that the described device could enable the home monitoring of CHF patients soon

Key Aspects to Teach Medical Device Software Certification

Certification of Medical Device Software (MDS) according to the EU Medical Device Regulation 2017/745 requires demonstrating safety and effectiveness. Thus, the syllabus of a course on MDS development must provide tools for addressing these issues. To assure safety, risk analysis has to be performed using a four-step procedure. Effectiveness could be demonstrated by literature systematic review combined with meta-analysis, to compare the MDS performances with those of similar tools

Comparison of different similarity measures in hierarchical clustering

The management of datasets containing heterogeneous types of data is a crucial point in the context of precision medicine, where genetic, environmental, and life-style information of each individual has to be analyzed simultaneously. Clustering represents a powerful method, used in data mining, for extracting new useful knowledge from unlabeled datasets. Clustering methods are essentially distance-based, since they measure the similarity (or the distance) between two elements or one element and the cluster centroid. However, the selection of the distance metric is not a trivial task: it could influence the clustering results and, thus, the extracted information. In this study we analyze the impact of four similarity measures (Manhattan or L1 distance, Euclidean or L2 distance, Chebyshev or L∞ distance and Gower distance) on the clustering results obtained for datasets containing different types of variables. We applied hierarchical clustering combined with an automatic cut point selection method to six datasets publicly available on the UCI Repository. Four different clusterizations were obtained for every dataset (one for each distance) and were analyzed in terms of number of clusters, number of elements in each cluster, and cluster centroids. Our results showed that changing the distance metric produces substantial modifications in the obtained clusters. This behavior is particularly evident for datasets containing heterogeneous variables. Thus, the choice of the distance measure should not be done a-priori but evaluated according to the set of data to be analyzed and the task to be accomplished

Automatic Identification of the Best Auscultation Area for the Estimation of the Time of Closure of Heart Valves through Multi-Source Phonocardiography

In the latest years, multi-source phonocardiography (PCG) is gaining interest in relation to the home monitoring of cardiovascular diseases. An application of interest regards the monitoring of the time of closure of the four cardiac valves, which would enable the follow-up of at-risk patients for heart failure. In this work, we propose a hybrid system based on hierarchical clustering and Multi-Criteria Decision Analysis (MCDA) for automatically selecting the best auscultation area for the mentioned application through multi-source PCG. We simultaneously recorded 48 PCG signals from the subject's chest and divided them into morphologically homogenous groups using agglomerative hierarchical clustering, based on their correlation. Then, we explored three different approaches to select the best auscultation area, based respectively on the minimum latency, on the maximum signal-to-noise ratio, and on multiple criteria using ELECTRE III. The results obtained on the follow-up of a healthy subject over consecutive days show that a) the selection of the auscultation area using MCDA overcomes the limits of single-criteria approaches, b) the estimate of the time of closure of the heart valves using the proposed system is more robust than what obtained through the state-of-the-art single-source methodology

Design of a Service for the Management of Heart Failure Patients Using Telemedicine

The tremendous prevalence and mortality of heart failure (HF), along with the social and economic impact of its consequences, make an appropriate disease management utmost important. In this context, telemedicine offers promising possibilities. Current clinical guidelines and technological solutions do not address the problem of monitoring at-risk patients and patients affected by mild HF for prevention purposes. The goal of this work is to design a service based on a telemedicine framework for the management of heart failure patients. The proposed service grounds the monitoring of the patient on a custom multi-sensor array that we designed and developed for the purpose. The description of the processes involved in the service was carried out by means of Process Modelling tools, and in particular through Swim Lane Activity Diagrams. The results look promising for the implementation of the service in a real-life scenario. The main strength of the service resides in a) the use of noninvasive monitoring technologies to include patients with a mild HF or at-risk patients; and b) the integration of hospital and territory services to grant continuity and coherence in the treatment

Simulation of the Impact on the Workload of the Enlargement of the Clinical Staff of a Specialistic Reference Center.

Quality of care and patient satisfaction are important aspects of high standard care. If clinical staff is subject to an elevated workload there is a possible decrease of both. This justifies the development of tools to quantify the workload and to find organizational changes that will normalize it. We have previously developed a simulation system to quantify the workload of the staff working in a regional reference center for the treatment of bleeding and hemorrhagic disorders. The goal of this new work is to simulate, through an agent-based model, the impact of adding a physician to the staff. Ten sets of initial parameters were defined to simulate ten typical weeks. Results show that the introduction of the new physician together with a second ambulatory room can reduce the workload of all the staff to the expected 8-hour. In this situation, in which the staff workload does not exceed the daily capacity, we may suppose that an increase in the quality of care and patient satisfaction will be possible

Combining robotics with enhanced serotonin-driven cortical plasticity improves post-stroke motor recovery.

Despite recent progresses in robotic rehabilitation technologies, their efficacy for post-stroke motor recovery is still limited. Such limitations might stem from the insufficient enhancement of plasticity mechanisms, crucial for functional recovery. Here, we designed a clinically relevant strategy that combines robotic rehabilitation with chemogenetic stimulation of serotonin release to boost plasticity. These two approaches acted synergistically to enhance post-stroke motor performance. Indeed, mice treated with our combined therapy showed substantial functional gains that persisted beyond the treatment period and generalized to non-trained tasks. Motor recovery was associated with a reduction in electrophysiological and neuroanatomical markers of GABAergic neurotransmission, suggesting disinhibition in perilesional areas. To unveil the translational potentialities of our approach, we specifically targeted the serotonin 1A receptor by delivering Buspirone, a clinically approved drug, in stroke mice undergoing robotic rehabilitation. Administration of Buspirone restored motor impairments similarly to what observed with chemogenetic stimulation, showing the immediate translational potential of this combined approach to significantly improve motor recovery after stroke

Artificial intelligence for target prostate biopsy outcomes prediction the potential application of fuzzy logic

Background: In current precision prostate cancer (PCa) surgery era the identification of the best patients candidate for prostate biopsy still remains an open issue. The aim of this study was to evaluate if the prostate target biopsy (TB) outcomes could be predicted by using artificial intelligence approach based on a set of clinical pre-biopsy. Methods: Pre-biopsy characteristics in terms of PSA, PSA density, digital rectal examination (DRE), previous prostate biopsies, number of suspicious lesions at mp-MRI, lesion volume, lesion location, and Pi-Rads score were extracted from our prospectively maintained TB database from March 2014 to December 2019. Our approach is based on Fuzzy logic and associative rules mining, with the aim to predict TB outcomes. Results: A total of 1448 patients were included. Using the Frequent-Pattern growth algorithm we extracted 875 rules and used to build the fuzzy classifier. 963 subjects were classified whereas for the remaining 484 subjects were not classified since no rules matched with their input variables. Analyzing the classified subjects we obtained a specificity of 59.2% and sensitivity of 90.8% with a negative and the positive predictive values of 81.3% and 76.6%, respectively. In particular, focusing on ISUP ≥ 3 PCa, our model is able to correctly predict the biopsy outcomes in 98.1% of the cases. Conclusions: In this study we demonstrated that the possibility to look at several pre-biopsy variables simultaneously with artificial intelligence algorithms can improve the prediction of TB outcomes, outclassing the performance of PSA, its derivates and MRI alone

Machine-Learning-Based Tool to Predict Target Prostate Biopsy Outcomes: An Internal Validation Study

Abstract: The aim of this study is to present a personalized predictive model (PPM) with a machine learning (ML) system that is able to identify and classify patients with suspected prostate cancer (PCa) following mpMRI. We extracted all the patients who underwent fusion biopsy (FB) from March 2014 to December 2019, while patients from August 2020 to April 2021 were included as a validation set. The proposed system was based on the following four ML methods: a fuzzy inference system (FIS), the support vector machine (SVM), k-nearest neighbors (KNN), and self-organizing maps (SOMs). Then, a system based on fuzzy logic (FL) + SVM was compared with logistic regression (LR) and standard diagnostic tools. A total of 1448 patients were included in the training set, while 181 patients were included in the validation set. The area under the curve (AUC) of the proposed FIS + SVM model was comparable with the LR model but outperformed the other diagnostic tools. The FIS + SVM model demonstrated the best performance, in terms of negative predictive value (NPV), on the training set (78.5%); moreover, it outperformed the LR in terms of specificity (92.1% vs. 83%). Considering the validation set, our model outperformed the other methods in terms of NPV (60.7%), sensitivity (90.8%), and accuracy (69.1%). In conclusion, we successfully developed and validated a PPM tool using the FIS + SVM model to calculate the probability of PCa prior to a prostate FB, avoiding useless ones in 15% of the cases

Radiation hard polyimide-coated FBG optical sensors for relative humidity monitoring in the CMS experiment at CERN

This work investigates the performance and the radiation hardness capability of optical thermo-hygrometers based on Fibre Bragg Gratings (FBG) for humidity monitoring in the Compact Muon Solenoid (CMS), one of the four experiments running at CERN in Geneva. A thorough campaign of characterization was performed on 80 specially produced Polyimide-coated RH FBG sensors and 80 commercial temperature FBG sensors. Sensitivity, repeatability and accuracy were studied on the whole batch, putting in evidence the limits of the sensors, but also showing that they can be used in very dry conditions. In order to extract the humidity measurements from the sensor readings, commercial temperature FBG sensors were characterized in the range of interest. Irradiation campaigns with ionizing radiation (gamma-rays from a Co-60 source) at incremental absorbed doses (up to 210 kGy for the T sensors and up to 90 kGy for the RH sensors) were performed on sample of T and RH-Sensors. The results show that the sensitivity of the sensors is unchanged up to the level attained of the absorbed dose, while the natural wavelength peak of each sensor exhibits a radiation-induced shift (signal offset). The saturation properties of this shift are discussed