scholarly journals Automated Arrhythmia Detection Based on RR Intervals

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1446
Author(s):  
Oliver Faust ◽  
Murtadha Kareem ◽  
Ali Ali ◽  
Edward J. Ciaccio ◽  
U. Rajendra Acharya
Keyword(s):  
Learning Algorithm ◽  
Cost Effective ◽  
Arrhythmia Detection ◽  
Rr Interval ◽  
Deep Learning Algorithm ◽  
Number Of Patients ◽  
Life Threatening ◽  
Term Monitoring ◽  
Fold Cross Validation

Abnormal heart rhythms, also known as arrhythmias, can be life-threatening. AFIB and AFL are examples of arrhythmia that affect a growing number of patients. This paper describes a method that can support clinicians during arrhythmia diagnosis. We propose a deep learning algorithm to discriminate AFIB, AFL, and NSR RR interval signals. The algorithm was designed with data from 4051 subjects. With 10-fold cross-validation, the algorithm achieved the following results: ACC = 99.98%, SEN = 100.00%, and SPE = 99.94%. These results are significant because they show that it is possible to automate arrhythmia detection in RR interval signals. Such a detection method makes economic sense because RR interval signals are cost-effective to measure, communicate, and process. Having such a cost-effective solution might lead to widespread long-term monitoring, which can help detecting arrhythmia earlier. Detection can lead to treatment, which improves outcomes for patients.

Development and Assembly of a MEMS Based High-Sensitivity Relative Gravimeter for Multi-Pixel Imaging Applications 

2021 ◽  
Author(s):  
Karl Toland ◽  
Abhinav Prasad ◽  
Andreas Noack ◽  
Kristian Anastasiou ◽  
Richard Middlemiss ◽  
...  
Keyword(s):  
High Sensitivity ◽  
Cost Effective ◽  
Mount Etna ◽  
Earth Tides ◽  
Gravity Measurements ◽  
Stepper Motors ◽  
Long Term Monitoring ◽  
The Stability ◽  
Term Monitoring

<p>The manufacture and production of a high-sensitivity cost-effective gravimeter has the potential to change the methodology and efficiency of gravity measurements. Currently, the most common method to conduct a survey is by using a single gravimeter, usually costing tens of thousands of Dollars, with measurements taken at multiple locations to obtain the required data. The availability of a cost-effective gravimeter however would allow the user to install multiple gravimeters, at the same cost of a single gravimeter, to increase the efficiency of surveys and long-term monitoring.  </p><p> </p><p>Since the previous reporting on a low-drift relative MEMS gravimeter for multi-pixel imaging applications (Prasad, A. et al, EGU2020-18528), significant progress has been made in the development and assembly of the previously reported system. Field prototypes have been manufactured and undergone significant testing to investigate the stability and robustness of the system in preparation for the deployment of multiple devices as part of the gravity imager on Mount Etna. The device, known as Wee-g, has several key features which makes it an attractive prospect in the field of gravimetry. Examples of these features are that the Wee-g is small and portable with the ability to connect to the device remotely, can be powered through a mains connected power supply, or through portable batteries, weighs under 4kg, has a low power consumption during normal use of 5W, correct for tilt through manual adjustments or remotely through integrated stepper motors with a total tilt correction range of 5 degrees, the ability to read out tilt of the device through an inclinometer for either alignment or long term monitoring and numerous temperature sensors and heater servos to control the temperature of the MEMS to <1mK.</p><p> </p><p>This presentation aims to report on the progress that has been achieved in the development and manufacturing of the prototype devices, various testing of the devices under various laboratory conditions (such as the measurements of the Earth tides, and a relative measurement of gravity at various floor levels), as well as additional applications that are to be explored in 2021. </p>

scholarly journals Clinical Use of Tolerogenic Dendritic Cells-Harmonization Approach in European Collaborative Effort

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Anja Ten Brinke ◽  
Catharien M. U. Hilkens ◽  
Nathalie Cools ◽  
Edward K. Geissler ◽  
James A. Hutchinson ◽  
...  
Keyword(s):  
Dendritic Cells ◽  
Cost Effective ◽  
Administration Route ◽  
European Cooperation ◽  
Information Models ◽  
Tolerogenic Dendritic Cells ◽  
Specific Objective ◽  
Number Of Patients ◽  
Loss Of Tolerance

The number of patients with autoimmune diseases and severe allergies and recipients of transplants increases worldwide. Currently, these patients require lifelong administration of immunomodulatory drugs. Often, these drugs are expensive and show immediate or late-occurring severe side effects. Treatment would be greatly improved by targeting the cause of autoimmunity, that is, loss of tolerance to self-antigens. Accumulating knowledge on immune mechanisms has led to the development of tolerogenic dendritic cells (tolDC), with the specific objective to restrain unwanted immune reactions in the long term. The first clinical trials with tolDC have recently been conducted and more tolDC trials are underway. Although the safety trials have been encouraging, many questions relating to tolDC, for example, cell-manufacturing protocols, administration route, amount and frequency, or mechanism of action, remain to be answered. Aiming to join efforts in translating tolDC and other tolerogenic cellular products (e.g., Tregs and macrophages) to the clinic, a European COST (European Cooperation in Science and Technology) network has been initiated—A FACTT (action to focus and accelerate cell-based tolerance-inducing therapies). A FACTT aims to minimize overlap and maximize comparison of tolDC approaches through establishment of minimum information models and consensus monitoring parameters, ensuring that progress will be in an efficient, safe, and cost-effective way.

scholarly journals Performance of a deep learning CNN model for the automated detection of 13 common conditions on Chest X-rays

2021 ◽  
Author(s):  
Tirupathi Karthik ◽  
Vijayalakshmi Kasiraman ◽  
Bhavani Paski ◽  
Kashyap Gurram ◽  
Amit Talwar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Delayed Diagnosis ◽  
Cost Effective ◽  
Radiology Workflow ◽  
X Rays ◽  
Term Care ◽  
X Ray ◽  
Intelligence System ◽  
Deep Learning Algorithm

Background and aims: Chest X-rays are widely used, non-invasive, cost effective imaging tests. However, the complexity of interpretation and global shortage of radiologists have led to reporting backlogs, delayed diagnosis and a compromised quality of care. A fully automated, reliable artificial intelligence system that can quickly triage abnormal images for urgent radiologist review would be invaluable in the clinical setting. The aim was to develop and validate a deep learning Convoluted Neural Network algorithm to automate the detection of 13 common abnormalities found on Chest X-rays. Method: In this retrospective study, a VGG 16 deep learning model was trained on images from the Chest-ray 14, a large publicly available Chest X-ray dataset, containing over 112,120 images with annotations. Images were split into training, validation and testing sets and trained to identify 13 specific abnormalities. The primary performance measures were accuracy and precision. Results: The model demonstrated an overall accuracy of 88% in the identification of abnormal X-rays and 87% in the detection of 13 common chest conditions with no model bias. Conclusion: This study demonstrates that a well-trained deep learning algorithm can accurately identify multiple abnormalities on X-ray images. As such models get further refined, they can be used to ease radiology workflow bottlenecks and improve reporting efficiency. Napier Healthcare’s team that developed this model consists of medical IT professionals who specialize in AI and its practical application in acute & long-term care settings. This is currently being piloted in a few hospitals and diagnostic labs on a commercial basis.

scholarly journals Real Time LDR Data Prediction using IoT and Deep Learning Algorithm

2020 ◽  
pp. 158-161
Author(s):  
Chandraprabha S ◽  
Pradeepkumar G ◽  
Dineshkumar Ponnusamy ◽  
Saranya M D ◽  
Satheesh Kumar S ◽  
...  
Keyword(s):  
Deep Learning ◽  
Light Intensity ◽  
Real Time ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Time Interval ◽  
Deep Learning Algorithm ◽  
Data Prediction ◽  
Solar Plant

This paper outfits artificial intelligence based real time LDR data which is implemented in various applications like indoor lightning, and places where enormous amount of heat is produced, agriculture to increase the crop yield, Solar plant for solar irradiance Tracking. For forecasting the LDR information. The system uses a sensor that can measure the light intensity by means of LDR. The data acquired from sensors are posted in an Adafruit cloud for every two seconds time interval using Node MCU ESP8266 module. The data is also presented on adafruit dashboard for observing sensor variables. A Long short-term memory is used for setting up the deep learning. LSTM module uses the recorded historical data from adafruit cloud which is paired with Node MCU in order to obtain the real-time long-term time series sensor variables that is measured in terms of light intensity. Data is extracted from the cloud for processing the data analytics later the deep learning model is implemented in order to predict future light intensity values.

scholarly journals An Automated Heart Rate-based Algorithm for Sleep Stage Classification: Validation Using Conventional PSG and Innovative Wearable ECG Device

2021 ◽  
Author(s):  
Nicolò Pini ◽  
Ju Lynn Ong ◽  
Gizem Yilmaz ◽  
Nicholas I. Y. N. Chee ◽  
Zhao Siting ◽  
...  
Keyword(s):  
New York ◽  
Learning Algorithm ◽  
Sleep Stage ◽  
Cost Effective ◽  
Deep Sleep ◽  
Sleep Staging ◽  
Deep Learning Algorithm ◽  
Best Value ◽  
Sleep Stage Classification

Study Objectives: Validate a HR-based deep-learning algorithm for sleep staging named Neurobit-HRV (Neurobit Inc., New York, USA). Methods: The algorithm can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-second epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n=994 participants) and a proprietary dataset (Z3Pulse, n=52 participants), composed of HR recordings collected with a chest-worn, wireless sensor. A simultaneous PSG was collected using SOMNOtouch. We evaluated the performance of the models in both datasets using Accuracy (A), Cohen's kappa (K), Sensitivity (SE), Specificity (SP). Results: CinC - The highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect sleep scoring, while a significant decrease of performance by age was reported across the models. Z3Pulse - The highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment. Conclusions: Results demonstrate the feasibility of accurate HR-based sleep staging. The combination of the illustrated sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution easily deployable in the home.

scholarly journals Contrast Induced Nephropathy and its predictors after Primary Percutaneous Intervention

2021 ◽  
Vol 18 (2) ◽  
pp. 27-31
Author(s):  
Krishna Chandra Adhikari ◽  
Rabi Malla ◽  
Arun Maskey ◽  
Sujeeb Rajbhandari ◽  
Bishow Raj Baral ◽  
...  
Keyword(s):  
Contrast Media ◽  
Primary Pci ◽  
Contrast Induced Nephropathy ◽  
Cross Sectional ◽  
Number Of Patients ◽  
Contrast Volume ◽  
Percutaneous Coronary ◽  
Life Threatening ◽  
Short And Long Term

Background and Aims: Worldwide many patients are receiving intravascular contrast media (CM) during interventional procedures. Contrast media are used to enhance visualization and guide percutaneous coronary interventions (PCI).1 However, the use of CM also carries the risk of complications and it is important to be aware of these complications. Complications with CM range from mild symptoms to life-threatening conditions like anaphylaxis, hypotension and renal dysfunction and contrast-induced nephropathy (CIN) is one of them which can have both short and long term consequences.2 This study aimed to know the incidence of CIN in our center and possible predictors associated with it. Methods: This is the single hospital based cross sectional observational study. Patients undergoing primary PCI were enrolled in the study. All the patients underwent thorough history taking and physical examination. Baseline required laboratory investigations were sent. Electrocardiogram and echocardiography screening was done before taking patient to primary PCI as per the protocol of the hospital. Results: The number of patients enrolled in the study was 83 out of which 65(78.2%) were males and mean age was 59.7±13.2. Mean Arterial Pressure (MAP) among the patients was 103.8±21.3. Almost 2/3rd of the population received intravenous fluids. Minimum contrast volume used was 50ml and maximum was 270. When absolute rise in creatinine was considered 12 (14.5%) had CIN and when percent rise was also considered total 28 (33.7%) had CIN. While evaluating the predictors of CIN, higher mean age (p=0.01), hypotension with mean MAP <60 mmhg (p=0.04)) and higher contrast volume >100ml (p=0.04) was found to be significant. Conclusion: The incidence of CIN in patients undergoing PPCI was similar to the studies done in other parts of the world. Evaluating the predictors of CIN, higher mean age, hypotension and higher contrast volume was the significant predictor.

scholarly journals Cost-effective autonomous sensor for the long-term monitoring of water electrical conductivity of crop fields

2019 ◽  
Vol 165 ◽  
pp. 104940 ◽  
Author(s):  
Ernesto Serrano-Finetti ◽  
Carles Aliau-Bonet ◽  
Oscar López-Lapeña ◽  
Ramon Pallàs-Areny
Keyword(s):  
Electrical Conductivity ◽  
Cost Effective ◽  
Crop Fields ◽  
Autonomous Sensor ◽  
Long Term Monitoring ◽  
Term Monitoring

An Efficient Approach to Predict COVID-19 Infected Patient Applying Deep learning Algorithm on Chest X-ray Images with Analyzing the Patient Symptoms (Preprint)

2020 ◽  
Author(s):  
Mohammad Helal Uddin ◽  
Mohammad Nahid Hossain ◽  
K. Thapa ◽  
S.-H Yang
Keyword(s):  
Learning Algorithm ◽  
Early Stage ◽  
Low Cost ◽  
Research Work ◽  
Lung Infection ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
Proposed Model ◽  
Life Threatening ◽  
Chest X Ray

BACKGROUND COVID-19 is a life-threatening infectious disease that has become a pandemic for the time being. The virus grows within the lower respiratory tract where early-stage symptoms(like- cough, fever, sore throat, etc.) develop and then it causes lung infection(pneumonia) OBJECTIVE This paper proposed a new methodology of artificial testing whether a patient has been infected by COVID-19 or not METHODS We have presented a prediction model based on, Convolutional Neural Networks(CNN) and our own developed mathematical equation based algorithm named SymptomNet. The CNN algorithm classifies the lung infection(pneumonia) from frontal chest X-ray images, while the symptoms analyzing algorithm(SymptomNet) predicts the possibility of COVID-19 infection from developed symptoms in a patient RESULTS The model has the accuracy of 96% while predicting COVID-19 patients. In this Model, the CNN classifier has the accuracy of around 96% and the SymptomNet algorithm has the accuracy of 97%. CONCLUSIONS This research work obtained a promising accuracy while predicting COVID-19 infected patients. The proposed model can be ubiquitously used at a low cost with high accuracy.

scholarly journals A Cost-Effective Trace Gas Measurement Program for Long-Term Monitoring of the Stratospheric Circulation

2014 ◽  
Vol 95 (1) ◽  
pp. 147-155 ◽  
Author(s):  
Fred L. Moore ◽  
Eric A. Ray ◽  
Karen H. Rosenlof ◽  
James W. Elkins ◽  
Pieter Tans ◽  
...  
Keyword(s):  
Cost Effective ◽  
Trace Gas ◽  
Gas Measurement ◽  
Stratospheric Circulation ◽  
Long Term Monitoring ◽  
Term Monitoring

The Effectiveness of Three Psychotherapies of Different type and Length in the Treatment of Patients Suffering from Anxiety Disorder

2017 ◽  
Vol 41 (S1) ◽  
pp. s777-s777
Author(s):  
P. Knekt ◽  
O. Lindfors ◽  
T. Maljanen
Keyword(s):  
Anxiety Disorder ◽  
Work Ability ◽  
Psychodynamic Psychotherapy ◽  
Cost Effective ◽  
Term Therapy ◽  
Short Term ◽  
Number Of Patients ◽  
Axis I

IntroductionData on the comparative effect of short and long-term psychotherapy in anxiety disorder is scarce.AimTo compare the effectiveness of two short-term therapies and one long-term psychotherapy in the treatment of patients with anxiety disorder.MethodsAltogether 50 outpatients with anxiety disorder as the only axis I diagnosis, were randomly assigned to long-term psychodynamic psychotherapy (LPP), short-term psychodynamic psychotherapy (SPP), and solution-focused therapy (SFT) and were followed for 5 years. The outcome measures were psychiatric symptoms, working ability, need for psychiatric treatment, remission, and cost-effectiveness.ResultsDuring the first year of follow-up, no significant differences in the effectiveness between the therapies were noted. During the following 3 years, LPP and SFT more effectively reduced symptoms, improved work ability, and elevated the remission rate than SPP. No significant differences between LPP and SFT were seen. At the end of the follow-up, the use of auxiliary treatment was lowest in the SFT group whereas remission rates or changes in psychiatric symptom or work ability did not differ between the groups. The average total direct costs were about three times higher in the LPP group than in the short-term therapy groups.ConclusionsThe difference in effectiveness of LPP and SFT was negligible, whereas SPP appeared less effective. Thus, the resource-oriented SFT may be a cost-effective option in this selected patient group, while unconsidered allocation of patients to LPP does not appear to be cost-effective. Given the small number of patients, no firm conclusions should, however be drawn based on this study.Disclosure of interestThe authors have not supplied their declaration of competing interest.

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