1,721,036 research outputs found
A wearable smart device to monitor multiple vital parameters—VITAL ECG
Full text linkSmart devices are more and more present in every aspect of everyday life. From smartphones, which are now like mini-computers, through systems for monitoring sleep or fatigue, to specific sensors for the recording of vital parameters. A particular class of the latter regards health monitoring. Indeed, through the use of such devices, several vital parameters can be acquired and automatically monitored, even remotely. This paper presents the second generation of VITAL-ECG, a smart device designed to monitor the most important vital parameters as a “one touch” device, anywhere, at low cost. It is a wearable device that coupled with a mobile app can track bio-parameters such as: electrocardiogram, SpO2, skin temperature, and physical activity of the patient. Even if it not yet a medical device, a comprehensive comparison with a golden standard electrocardiograph is presented to demonstrate the quality of the recorded signals and the validity of the proposed approach
Anytime ecg monitoring through the use of a low-cost, user-friendly, wearable device
Full text linkEvery year cardiovascular diseases kill the highest number of people worldwide. Among these, pathologies characterized by sporadic symptoms, such as atrial fibrillation, are difficult to be detected as state-of-the-art solutions, e.g., 12-leads electrocardiogram (ECG) or Holter devices, often fail to tackle these kinds of pathologies. Many portable devices have already been proposed, both in literature and in the market. Unfortunately, they all miss relevant features: they are either not wearable or wireless and their usage over a long-term period is often unsuitable. In addition, the quality of recordings is another key factor to perform reliable diagnosis. The ECG WATCH is a device designed for targeting all these issues. It is inexpensive, wearable (size of a watch), and can be used without the need for any medical expertise about positioning or usage. It is non-invasive, it records single-lead ECG in just 10 s, anytime, anywhere, without the need to physically travel to hospitals or cardiologists. It can acquire any of the three peripheral leads; results can be shared with physicians by simply tapping a smartphone app. The ECG WATCH quality has been tested on 30 people and has successfully compared with an electrocardiograph and an ECG simulator, both certified. The app embeds an algorithm for automatically detecting atrial fibrillation, which has been successfully tested with an official ECG simulator on different severity of atrial fibrillation. In this sense, the ECG WATCH is a promising device for anytime cardiac health monitoring
Shallow Neural Network for Biometrics from the ECG-WATCH
Full text linkApplications 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
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Governance di sistema e processo direzionale
No full textIn seguito alla V Conferenza di Trieste nella quale il Dipartimento Politiche Antidroga della Presidenza del Consiglio dei Ministri, aveva segnalato la necessit di poter orientare i Dipartimenti delle Dipendenze verso i consumatori di cocaina che fino a qualche anno fa non erano presenti allinterno dei servizi, sono state attivate numerose iniziative che necessariamente dovevano essere supportate da una serie di Linee di indirizzo tecnico-scientifiche che potessero meglio orientare gli operatori sia per gli aspetti diagnostici, sia per il trattamento e la riabilitazione delle persone dipendenti da cocaina. Per questo motivo stato attivato uno specifico progetto, di cui questo manuale ne uno dei frutti. Con la Regione Lombardia e, in particolare, con il Dipartimento delle Dipendenze dellASL di Varese al fine di dare concreta attuazione agli intenti sopra riportati.
Queste Linee operative affrontano non solo i problemi clinici ma anche, proprio per la concretezza che vogliono avere, i problemi gestionali ed organizzativi al fine di poter realizzare interventi sostenibili ed efficaci
Double Channel Neural Non Invasive Blood Pressure Prediction
Full text linkCardiovascular 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
Noninvasive Arterial Blood Pressure Estimation using ABPNet and VITAL-ECG
Full text linkArterial Blood Pressure (ABP) is an important physiological parameter that should be properly monitored for the purposes of prevention and detection of cardiovascular diseases, which represent one of the leading causes of death in the world. Currently, the most common adopted noninvasive blood pressure measurement system is sphygmomanometer, which works by inflating and deflating a cuff around the arm. This work presents ABPNet, a new prediction technique, based on a multilayer perceptron (MLP), which uses ECG and PPG to estimate both systolic and diastolic blood pressure. To train the neural network, signals are gathered from the Physionet MIMIC database. The proposed architecture performances are evaluated w.r.t. both the invasive blood pressure signal and the noninvasive sphygmomanometer measurements. The experimental results are quite promising; they are compliant with the ANSI/AAMI/ ISO 81060-2:2013 for sphygmomanometer certification because the network predicted values are within +/-5 mmHg w.r.t. real invasive measurements, as imposed by the legislation. Finally, it is shown how ABPNet can be used to improve the VITAL-ECG, a wearable device designed to acquire vital parameters, such as electrocardiographic (ECG) and photoplethysmographic (PLETH/PPG) signals; indeed, by embedding the ABPNet neural network, VITAL-ECG can be upgraded to estimate, also, ABP. As a consequence, the device could be used to fight cardiovascular diseases and prevent their dangerous effects
Development and Validation of an Algorithm for the Digitization of ECG Paper Images
Full text linkThe 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
A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction
Full text linkContinuous 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
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