A Program Evaluation of Competency Training Within a College Student Health Center

Integrated primary care (IPC) is a method for providing medical and psychotherapy services within a single primary care setting. Doctoral psychology students pursuing a career in psychotherapy may receive training in IPC as doctoral students. However, the field of IPC has a limited understanding of the current quality of IPC training for doctoral psychology students. The current study utilized professional competency guidelines for practicing psychology in IPC settings to evaluate doctoral training provided at the Utah State University Student Health Center. Doctoral psychology training at the Student Health Center was evaluated for how well it provides training in IPC competencies and how well it develops competencies among students who have competed the training. Competency training provided and developed in doctoral students was measured using existing student evaluations, the training course syllabus, and surveys of 14 doctoral students, 4 medical providers, and the training supervisor. Moderate examples of training and competence skills were found in the Science and Systems competency clusters, consistently high ratings of training and competence were found in the Professionalism, Relationships, and Application clusters, and minimal evidence of training was found in the Education cluster. Future development of training is suggested in the team approach to patient care, communication of expectations for students and medical providers, and teaching about IPC. This study contributes to the understanding of the current state of IPC training and the degree to which doctoral psychology students are prepared for careers in IPC

Shallow Neural Network for Biometrics from the ECG-WATCH

Applications such as surveillance, banking and healthcare deal with sensitive data whose confidentiality and integrity depends on accurate human recognition. In this sense, the crucial mechanism for performing an effective access control is authentication, which unequivocally yields user identity. In 2018, just in North America, around 445K identity thefts have been denounced. The most adopted strategy for automatic identity recognition uses a secret for encrypting and decrypting the authentication information. This approach works very well until the secret is kept safe. Electrocardiograms (ECGs) can be exploited for biometric purposes because both the physiological and geometrical differences in each human heart correspond to uniqueness in the ECG morphology. Compared with classical biometric techniques, e.g. fingerprints, ECG-based methods can definitely be considered a more reliable and safer way for user authentication due to ECG inherent robustness to circumvention, obfuscation and replay attacks. In this paper, the ECG WATCH, a non-expensive wristwatch for recording ECGs anytime, anywhere, in just 10 s, is proposed for user authentication. The ECG WATCH acquisitions have been used to train a shallow neural network, which has reached a 99% classification accuracy and 100% intruder recognition rate

Development and Validation of an Algorithm for the Digitization of ECG Paper Images

The electrocardiogram (ECG) signal describes the heart’s electrical activity, allowing it to detect several health conditions, including cardiac system abnormalities and dysfunctions. Nowadays, most patient medical records are still paper-based, especially those made in past decades. The importance of collecting digitized ECGs is twofold: firstly, all medical applications can be easily implemented with an engineering approach if the ECGs are treated as signals; secondly, paper ECGs can deteriorate over time, therefore a correct evaluation of the patient’s clinical evolution is not always guaranteed. The goal of this paper is the realization of an automatic conversion algorithm from paper-based ECGs (images) to digital ECG signals. The algorithm involves a digitization process tested on an image set of 16 subjects, also with pathologies. The quantitative analysis of the digitization method is carried out by evaluating the repeatability and reproducibility of the algorithm. The digitization accuracy is evaluated both on the entire signal and on six ECG time parameters (R-R peak distance, QRS complex duration, QT interval, PQ interval, P-wave duration, and heart rate). Results demonstrate the algorithm efficiency has an average Pearson correlation coefficient of 0.94 and measurement errors of the ECG time parameters are always less than 1 mm. Due to the promising experimental results, the algorithm could be embedded into a graphical interface, becoming a measurement and collection tool for cardiologists

Double Channel Neural Non Invasive Blood Pressure Prediction

Cardiovascular Diseases represent the leading cause of deaths in the world. Arterial Blood Pressure (ABP) is an important physiological parameter that should be properly monitored for the purposes of prevention. This work applies the neural network output-error (NNOE) model to ABP forecasting. Three input configurations are proposed based on ECG and PPG for estimating both systolic and diastolic blood pressures. The double channel configuration is the best performing one by means of the mean absolute error w.r.t the corresponding invasive blood pressure signal (IBP); indeed, it is also proven to be compliant with the ANSI/AAMI/ISO 81060-2:2013 regulation for non invasive ABP techniques. Both ECG and PPG correlations to IBP signal are further analyzed using Spearman’s correlation coefficient. Despite it suggests PPG is more closely related to ABP, its regression performance is worse than ECG input configuration one. However, this behavior can be explained looking to human biology and ABP computation, which is based on peaks (systoles) and valleys (diastoles) extraction

A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction

Continuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino

Towards Uncovering Feature Extraction from Temporal Signals in Deep CNN: The ECG Case Study

Despite all the progress made in biomedical field, the Electrocardiogram (ECG) is still one of the most commonly used signal in medical examinations. Over the years, the problem of ECG classification has been approached in many different ways, most of which rely on the extraction of features from the signal in the form of temporal or morphological characteristics. Although feature engineering can led to adequately good results, it mostly relies on human ability and experience in selecting the correct feature set. In the last decade, a growing class of techniques based on Convolutional Neural Network (CNN) has been proposed in opposition to feature engineering. The efficiency and accuracy of CNN-based approaches is indisputable, however their ability in extracting and using temporal features from raw signal is poorly understood. The main objective of this work was to uncover the differences and the relationships between CNN feature maps and human-curated temporal features, towards a deeper understanding of neural-based approaches for ECG. In fact, the proposed study succeeded in finding a similarity between the output stage of the first layers of a deep 1D-CNN with several temporal features, demonstrating that not only that the engineered features effectively works in ECG classification tasks, but also that CNN can improve those features by elaborating them towards an higher level of abstraction

Homogenization of magnitudes of the ISC Bulletin

We implemented an automatic procedure to download the hypocentral data of the online Bulletin of the International Seismological Centre (ISC) in order to produce in near real-time a homogeneous catalogue of the Global and EuroMediterranean instrumental seismicity to be used for forecasting experiments and other statistical analyses. For the interval covered by the reviewed ISC Bulletin, we adopt the ISC locations and convert the surface wave magnitude (Ms) and short-period body-wave magnitude (mb) as computed by the ISC to moment magnitude (Mw), using empirical relations. We merge the so obtained proxies with real Mw provided by global and EuroMediterranean moment tensor catalogues. For the most recent time interval (about 2 yr) for which the reviewed ISC Bulletin is not available, we do the same but using the preferred (prime) location provided by the ISC Bulletin and converting to Mw the Ms and mb provided by some authoritative agencies. For computing magnitude conversion equations, we use curvilinear relations defined in a previous work and the chi-square regression method that accounts for the uncertainties of both x and y variables

A Short-Range FMCW Radar-Based Approach for Multi-Target Human-Vehicle Detection

In this article, a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets crossing a monitored area is proposed. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by low-cost off-The-shelf components, i.e., a 24 GHz frequency-modulated continuous wave (FMCW) radar module and a Raspberry Pi mini-PC. The developed method is based on an ad hoc processing chain to accomplish the automatic target recognition (ATR) task, which consists of blocks performing clutter and leakage removal with an infinite impulse response (IIR) filter, clustering with a density-based spatial clustering of applications with noise (DBSCAN) approach, tracking using a Benedict-Bordner $alpha$-$eta$ filter, features extraction, and finally classification of targets by means of a $k$-nearest neighbor ( $k$-NN) algorithm. The approach is validated in real experimental scenarios, showing its capabilities in correctly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks)

Validation of the Italian version of the Patient Reported Experience Measures for intermediate care services

Background: Intermediate care (IC) services are a key component of integrated care for elderly people, providing a link between hospital and home through provision of rehabilitation and health and social care. The Patient Reported Experience Measures (PREMs) are designed to measure user experience of care in IC settings. Objective: To examine the feasibility and the scaling properties of the Italian version of PREMs questionnaires for use in IC services. Methods: A cross-sectional survey was conducted on consecutive users of 1 home-based and 4 bed-based IC services in Emilia-Romagna (Italy). The main outcome measure was the PREMs questionnaire results. PREMs for each home- and bed-based IC services were translated, back-translated, and adapted through consensus among the members of the advisory board and pilot testing of face validity in 15 patients. A total of 199 questionnaires were returned from users of bed-based services and 185 were returned by mail from users of home-based services. The return rates and responses were examined. Mokken analysis was used to examine the scaling properties of the PREMs. Results: Analysis performed on the bed-based PREMs (N=154) revealed that 13 items measured the same construct and formed a moderate-strength scale (Loevinger H=0.488) with good reliability (Cronbach’s alpha =0.843). Analysis of home-based PREMs (N=134 records) revealed that 15 items constituted a strong scale (Loevinger H=0.543) with good reliability (Cronbach’s alpha =0.875). Conclusion: The Italian versions of the bed- and home-based IC-PREMs questionnaires proved to be valid and reliable tools to assess patients’ experience of care. Future plans include monitoring user experience over time in the same facilities and in other Italian IC settings for between-service benchmarking