scholarly journals Real-Time Analysis and Processing of Cardiogram Signals

2021 ◽  
pp. 108-113
Author(s):  
A. Yavorskyi
Keyword(s):  
Real Time ◽  
Early Stage ◽  
Human Life ◽  
Medical Intervention ◽  
Quality Of Data ◽  
Ecg Signals ◽  
Chronic Disability ◽  
Non Invasive ◽  
Real Time Analysis ◽  
Computationally Intensive

Analysis of Electrocardiogram (ECG) signals is an important task to save and enhance human life because a major cause of death is heart disease and the consequences. In many cases, early diagnostics of such problems can save and prolong life. In this work, we develop and present an approach to the real-time detection of Atrial Fibrillation (AF) Arrhythmia, which is a common cardiac arrhythmia affecting a large number of people. Being undetected, it develops into chronic disability or even early mortality. At the same time, This disease is hard to diagnose, especially in its early stage. A real-time automatic and non-invasive effective detection is needed to help diagnose this kind of health problem early. In-time medical intervention can save human life. ECG as a record of the heart electrical activity is widely used for detecting different heart disabilities. At the same time, AF is hard to detect due to its non-regular nature, and also because the performance of detection models depends largely on the quality of data and careful feature engineering. The research is based on the dataset from PhysioNet Computing in Cardiology Challenge 2017. It contains 8528 single-lead ECG recordings of short-term heart rhythms (9-61 sec.). Our method and the trained model reach the known state-of-the-art results in this field, but, at the same time, it is much less computationally intensive, and, thus, less power consumptive to be implemented in an embedded device.

scholarly journals A Novel Non-Invasive Algorithm Using the concept of Nail Based Anemia Disease Detection

2021 ◽  
Vol 23 (2) ◽  
Author(s):  
Saurabh Mitra ◽  
◽  
Dr. Shanti Rathore ◽  
Dr. Sanjeev Kumar Gupta ◽  
Keyword(s):  
Real Time ◽  
Human Life ◽  
Time Analysis ◽  
Non Invasive ◽  
Real Time Analysis ◽  
Deadly Disease ◽  
Analysis System ◽  
Human Nails ◽  
Learning Concept

Anemia is a danger disease for the human life. If anemia diagnosis is not found in time than its very difficult to recover the patient specially in COVID-19 time it’s a deadly disease. As we know in this era 2020 COVID is create a huge change in human life that’s why after 2019 is called New life. As we know there is lots of approaches are there to identify the anemia, but there is very few approaches are there which are non-invasive, and those approaches are not good in terms of the quality of the result and most important they are not a good real time analysis system. So, in this paper we proposed a novel non-invasive algorithm which is able to detect the anemia using the human nails. In this approach we use computer vision, machine and deep learning concept and based on that only we decide the anemia level on any particular patient. Our propose approach is complete real time and this system is able to provide result in very less time. Key Words:Invasive, Non-Invasive, SPO2, Hardware, Device.

scholarly journals The metabolomic scent of cancer disease progression in soft tissue sarcoma: A case report

2019 ◽  
Vol 34 (2) ◽  
pp. 205-209 ◽  
Author(s):  
Gianmaria Miolo ◽  
Debora Basile ◽  
Andrea Carretta ◽  
Davide Adriano Santeufemia ◽  
Agostino Steffan ◽  
...  
Keyword(s):  
Case Report ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Disease Progression ◽  
Real Time ◽  
Time Analysis ◽  
Cancer Disease ◽  
Non Invasive ◽  
Real Time Analysis ◽  
Volatile Organic

Background: The purpose of this case report is to describe the potential that metabolomics breath analysis may have in cancer disease monitoring. The advances in mass spectrometry instrumentation allow the accurate real-time analysis of volatile metabolites exhaled in the breath. The application of such non-invasive devices may provide innovative and complementary monitoring of the physio-pathological conditions of cancer patients. Case presentation: A 59-year-old Caucasian woman with spindle cell malignant mesenchymal sarcoma of the presacral region started a first-line therapy with non-pegylated liposomal doxorubicin and ifosfamide associated with pelvic radiant treatment. After two cycles of chemotherapy plus radiotherapy, a significant pulmonary disease progression was reported. Thus, a second-line therapy with trabectedin was administered. However, after only two cycles of treatment a re-staging computed tomography scan reported further cancer disease progression of the target pulmonary lesions as well as occurrence of new satellite bilateral nodules. Real-time analysis of breath exhaled volatile organic compounds, performed by select ion flow tube mass spectrometry (SIFT-MS) during the follow-up of the patient, showed a specific metabolic pattern not observed in the breath of other soft tissue sarcoma patients who achieved clinical benefit from the treatments. Conclusions: This case report revealed the importance of the non-invasive real-time volatile organic compounds breath analysis to distinguish individual specific chemo-resistance phenotypes among soft tissue sarcoma patients. Such observation seems to suggest that breath metabolomics may be particularly useful for monitoring cancer disease progression in soft tissue sarcoma patients where only cost-effective diagnostic tools, such as positron emission tomography and computed tomography, are available.

scholarly journals Non-invasive long-term and real-time analysis of endocrine cells on micro-electrode arrays

2012 ◽  
Vol 590 (5) ◽  
pp. 1085-1091 ◽  
Author(s):  
Matthieu Raoux ◽  
Yannick Bornat ◽  
Adam Quotb ◽  
Bogdan Catargi ◽  
Sylvie Renaud ◽  
...  
Keyword(s):  
Real Time ◽  
Endocrine Cells ◽  
Electrode Arrays ◽  
Time Analysis ◽  
Non Invasive ◽  
Real Time Analysis ◽  
Micro Electrode Arrays

Non-invasive spatial visualization system of exhaled ethanol for real-time analysis of ALDH2 related alcohol metabolism

The Analyst ◽  
2011 ◽  
Vol 136 (18) ◽  
pp. 3680 ◽  
Author(s):  
Xin Wang ◽  
Eri Ando ◽  
Daishi Takahashi ◽  
Takahiro Arakawa ◽  
Hiroyuki Kudo ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Visualization ◽  
Alcohol Metabolism ◽  
Time Analysis ◽  
Visualization System ◽  
Non Invasive ◽  
Real Time Analysis

scholarly journals Machine Learning for Healthcare Diagnostics

2019 ◽  
Vol 8 (6S2) ◽  
pp. 999-1001
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Genetic Disease ◽  
World Wide ◽  
Early Stage ◽  
Human Life ◽  
The Real ◽  
Initial Stage

Presently machine learning and artificial intelligence is playing one of the most important role in diagnose many genetic and non genetic disease. So that the rapid inventions in machine learning can save thousands of life’s as it can diagnose the early stage of many serious diseases. In this research the datasets for such diseases is studied and it will be analyzed that how such deep machine learning will impact to a human life. The problem with such methodology is that it is not possible to get accurate results in the initial stage of research. The reason is every human have different immunity power and stamina. There are many diagnostics center who are fully dependent on the equipments which are fully based on machine learning. In order to boost this process it is necessary to collect the real time patient’s data from different hospitals, states and countries. So that it will be beneficial for world wide.

scholarly journals A Fetal ECG Monitoring System Based on the Android Smartphone

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 446 ◽  
Author(s):  
Li Yuan ◽  
Yanchao Yuan ◽  
Zhuhuang Zhou ◽  
Yanping Bai ◽  
Shuicai Wu
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Sample Entropy ◽  
Ecg Monitoring ◽  
Fixed Point Algorithm ◽  
Ecg Signals ◽  
Non Invasive ◽  
Fetal Ecg ◽  
Fastica Algorithm ◽  
Electrocardiogram Ecg

In this paper, a fetal electrocardiogram (ECG) monitoring system based on the Android smartphone was proposed. We designed a portable low-power fetal ECG collector, which collected maternal abdominal ECG signals in real time. The ECG data were sent to a smartphone client via Bluetooth. Smartphone app software was developed based on the Android system. The app integrated the fast fixed-point algorithm for independent component analysis (FastICA) and the sample entropy algorithm, for the sake of real-time extraction of fetal ECG signals from the maternal abdominal ECG signals. The fetal heart rate was computed using the extracted fetal ECG signals. Experimental results showed that the FastICA algorithm can extract a clear fetal ECG, and the sample entropy can correctly determine the channel where the fetal ECG is located. The proposed fetal ECG monitoring system may be feasible for non-invasive, real-time monitoring of fetal ECGs.

scholarly journals cAMP Biosensors Based on Genetically Encoded Fluorescent/Luminescent Proteins

Biosensors ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 39
Author(s):  
Namdoo Kim ◽  
Seunghan Shin ◽  
Se Won Bae
Keyword(s):  
Energy Transfer ◽  
Real Time ◽  
Intracellular Signaling ◽  
Fluorescent Protein ◽  
Fluorescent Proteins ◽  
Early Stage ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Adenosine Monophosphate ◽  
Non Invasive

Cyclic adenosine monophosphate (cAMP) plays a key role in signal transduction pathways as a second messenger. Studies on the cAMP dynamics provided useful scientific insights for drug development and treatment of cAMP-related diseases such as some cancers and prefrontal cortex disorders. For example, modulation of cAMP-mediated intracellular signaling pathways by anti-tumor drugs could reduce tumor growth. However, most early stage tools used for measuring the cAMP level in living organisms require cell disruption, which is not appropriate for live cell imaging or animal imaging. Thus, in the last decades, tools were developed for real-time monitoring of cAMP distribution or signaling dynamics in a non-invasive manner. Genetically-encoded sensors based on fluorescent proteins and luciferases could be powerful tools to overcome these drawbacks. In this review, we discuss the recent genetically-encoded cAMP sensors advances, based on single fluorescent protein (FP), Föster resonance energy transfer (FRET), single luciferase, and bioluminescence resonance energy transfer (BRET) for real-time non-invasive imaging.

scholarly journals MS-Based Analytical Techniques: Advances in Spray-Based Methods and EI-LC-MS Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-24 ◽  
Author(s):  
Federica Bianchi ◽  
Nicolò Riboni ◽  
Veronica Termopoli ◽  
Lucia Mendez ◽  
Isabel Medina ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Real Time ◽  
Early Stage ◽  
Simultaneous Detection ◽  
Analytical Techniques ◽  
Real Time Analysis ◽  
Detection And Identification ◽  
Safety Risks ◽  
Structural Details ◽  
Key Factor

Mass spectrometry is the most powerful technique for the detection and identification of organic compounds. It can provide molecular weight information and a wealth of structural details that give a unique fingerprint for each analyte. Due to these characteristics, mass spectrometry-based analytical methods are showing an increasing interest in the scientific community, especially in food safety, environmental, and forensic investigation areas where the simultaneous detection of targeted and nontargeted compounds represents a key factor. In addition, safety risks can be identified at the early stage through online and real-time analytical methodologies. In this context, several efforts have been made to achieve analytical instrumentation able to perform real-time analysis in the native environment of samples and to generate highly informative spectra. This review article provides a survey of some instrumental innovations and their applications with particular attention to spray-based MS methods and food analysis issues. The survey will attempt to cover the state of the art from 2012 up to 2017.

scholarly journals Fluorescent small organic probes for biosensing

2021 ◽  
Author(s):  
Xue Tian ◽  
Lloyd C. Murfin ◽  
Luling Wu ◽  
Simon E. Lewis ◽  
Tony D. James
Keyword(s):  
Real Time ◽  
Small Molecule ◽  
Fluorescent Probes ◽  
High Selectivity ◽  
Signaling Molecules ◽  
Living Systems ◽  
Time Analysis ◽  
Non Invasive ◽  
Real Time Analysis

Small-molecule based fluorescent probes are increasingly important for the detection and imaging of biological signaling molecules due to their simplicity, high selectivity and sensitivity, whilst being non-invasive, and suitable for real-time analysis of living systems.

Sign in / Sign up

Export Citation Format

Share Document