scholarly journals Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches

2021 ◽  
Vol 7 (4) ◽  
pp. 153-162
Author(s):  
Pedro Henrique Borghi de Melo
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Learning Approaches ◽  
Quality Of Data ◽  
Data Flows ◽  
Processing Techniques ◽  
Data Extracting ◽  
Signal Processing Techniques

Electrocardiogram captures the electrical activity of the heart. The signal obtained can be used for various purposes such as emotion recognition, heart rate measuring and the main one, cardiac disease diagnosis. But ECG analysis and classification require experienced specialists once it presents high variability and suffers interferences from noises and artefacts. With the increase of data amount on long term records, it might lead to long term dependencies and the process become exhaustive and error prone. Automated systems associated with signal processing techniques aim to help on these tasks by improving the quality of data, extracting meaningful features, selecting the most suitable and training machine learning models to capture and generalize its behaviour. This review brings a brief stage sense of how data flows into these approaches and somewhat techniques are most used. It ends by presenting some of the countless applications that can be found in the research community.

Measuring patient engagement among cancer patients in eHealth and mHealth interventions on symptom management: an integrated review (Preprint)

2020 ◽  
Author(s):  
Juqing Zhao ◽  
Pei Chen ◽  
Guangming Wan
Keyword(s):  
Cancer Patients ◽  
Patient Engagement ◽  
Symptom Management ◽  
Subjective Experience ◽  
Participation Rate ◽  
Sensor Data ◽  
Quality Of Data ◽  
Mhealth Intervention

BACKGROUND There has been an increase number of eHealth and mHealth interventions aimed to support symptoms among cancer survivors. However, patient engagement has not been guaranteed and standardized in these interventions. OBJECTIVE The objective of this review was to address how patient engagement has been defined and measured in eHealth and mHealth interventions designed to improve symptoms and quality of life for cancer patients. METHODS Searches were performed in MEDLINE, PsychINFO, Web of Science, and Google Scholar to identify eHealth and mHealth interventions designed specifically to improve symptom management for cancer patients. Definition and measurement of engagement and engagement related outcomes of each intervention were synthesized. This integrated review was conducted using Critical Interpretive Synthesis to ensure the quality of data synthesis. RESULTS A total of 792 intervention studies were identified through the searches; 10 research papers met the inclusion criteria. Most of them (6/10) were randomized trial, 2 were one group trail, 1 was qualitative design, and 1 paper used mixed method. Majority of identified papers defined patient engagement as the usage of an eHealth and mHealth intervention by using different variables (e.g., usage time, log in times, participation rate). Engagement has also been described as subjective experience about the interaction with the intervention. The measurement of engagement is in accordance with the definition of engagement and can be categorized as objective and subjective measures. Among identified papers, 5 used system usage data, 2 used self-reported questionnaire, 1 used sensor data and 3 used qualitative method. Almost all studies reported engagement at a moment to moment level, but there is a lack of measurement of engagement for the long term. CONCLUSIONS There have been calls to develop standard definition and measurement of patient engagement in eHealth and mHealth interventions. Besides, it is important to provide cancer patients with more tailored and engaging eHealth and mHealth interventions for long term engagement.

scholarly journals By Slow Degrees: Two Centuries of Social Reproduction and Mobility in Britain

2007 ◽  
Vol 12 (1) ◽  
pp. 37-62 ◽  
Author(s):  
Paul Lambert ◽  
Kenneth Prandy ◽  
Wendy Bottero
Keyword(s):  
Social Mobility ◽  
Social Reproduction ◽  
Linear Trend ◽  
Time Frame ◽  
Quality Of Data ◽  
Term Change ◽  
Parental Background ◽  
Long Term Trends

This paper discusses long term trends in patterns of intergenerational social mobility in Britain. We argue that there is convincing empirical evidence of a small but steady linear trend towards increasing social mobility throughout the period 1800-2004. Our conclusions are based upon the construction and analysis of an extended micro-social dataset, which combines records from an historical genealogical study, with responses from 31 sample surveys conducted over the period 1963-2004. There has been much previous study of trends in social mobility, and little consensus on their nature. We argue that this dissension partly results from the very slow pace of change in mobility rates, which makes the time-frame of any comparison crucial, and raises important methodological questions about how long-term change in mobility is best measured. We highlight three methodological difficulties which arise when trying to draw conclusions over mobility trends - concerning the extent of controls for life course effects; the quality of data resources; and the measurement of stratification positions. After constructing a longitudinal dataset which attempts to confront these difficulties, our analyses provide robust evidence which challenges hitherto more popular, politicised claims of declining or unchanging mobility. By contrast, our findings suggest that Britain has moved, and continues to move, steadily towards increasing equality in the relationship between occupational attainment and parental background.

Exploring long-term trends in marine ecosystems: machine-learning approaches to global change biology

2021 ◽  
Author(s):  
Domenico D'Alelio ◽  
Salvatore Rampone ◽  
Luigi Maria Cusano ◽  
Nadia Sanseverino ◽  
Luca Russo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Global Change ◽  
Marine Ecosystems ◽  
Learning Approaches ◽  
Global Change Biology ◽  
Long Term Trends

Machine Learning for Neurodegenerative Disorder Diagnosis — Survey of Practices and Launch of Benchmark Dataset

2018 ◽  
Vol 27 (03) ◽  
pp. 1850011 ◽  
Author(s):  
Athanasios Tagaris ◽  
Dimitrios Kollias ◽  
Andreas Stafylopatis ◽  
Georgios Tagaris ◽  
Stefanos Kollias
Keyword(s):  
Machine Learning ◽  
Neurodegenerative Disorder ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Network Architectures ◽  
Imaging Data ◽  
Learning Techniques ◽  
Medical Examinations

Neurodegenerative disorders, such as Alzheimer’s and Parkinson’s, constitute a major factor in long-term disability and are becoming more and more a serious concern in developed countries. As there are, at present, no effective therapies, early diagnosis along with avoidance of misdiagnosis seem to be critical in ensuring a good quality of life for patients. In this sense, the adoption of computer-aided-diagnosis tools can offer significant assistance to clinicians. In the present paper, we provide in the first place a comprehensive recording of medical examinations relevant to those disorders. Then, a review is conducted concerning the use of Machine Learning techniques in supporting diagnosis of neurodegenerative diseases, with reference to at times used medical datasets. Special attention has been given to the field of Deep Learning. In addition to that, we communicate the launch of a newly created dataset for Parkinson’s disease, containing epidemiological, clinical and imaging data, which will be publicly available to researchers for benchmarking purposes. To assess the potential of the new dataset, an experimental study in Parkinson’s diagnosis is carried out, based on state-of-the-art Deep Neural Network architectures and yielding very promising accuracy results.

Big Data’s Role in Health and Risk Messaging

2017 ◽  
Author(s):  
Bradford William Hesse
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Risk Communication ◽  
Large Scale ◽  
Protein Identification ◽  
Machine Learning Algorithms ◽  
National Committee ◽  
Learning Approaches ◽  
Road Map ◽  
Data Flows

The presence of large-scale data systems can be felt, consciously or not, in almost every facet of modern life, whether through the simple act of selecting travel options online, purchasing products from online retailers, or navigating through the streets of an unfamiliar neighborhood using global positioning system (GPS) mapping. These systems operate through the momentum of big data, a term introduced by data scientists to describe a data-rich environment enabled by a superconvergence of advanced computer-processing speeds and storage capacities; advanced connectivity between people and devices through the Internet; the ubiquity of smart, mobile devices and wireless sensors; and the creation of accelerated data flows among systems in the global economy. Some researchers have suggested that big data represents the so-called fourth paradigm in science, wherein the first paradigm was marked by the evolution of the experimental method, the second was brought about by the maturation of theory, the third was marked by an evolution of statistical methodology as enabled by computational technology, while the fourth extended the benefits of the first three, but also enabled the application of novel machine-learning approaches to an evidence stream that exists in high volume, high velocity, high variety, and differing levels of veracity. In public health and medicine, the emergence of big data capabilities has followed naturally from the expansion of data streams from genome sequencing, protein identification, environmental surveillance, and passive patient sensing. In 2001, the National Committee on Vital and Health Statistics published a road map for connecting these evidence streams to each other through a national health information infrastructure. Since then, the road map has spurred national investments in electronic health records (EHRs) and motivated the integration of public surveillance data into analytic platforms for health situational awareness. More recently, the boom in consumer-oriented mobile applications and wireless medical sensing devices has opened up the possibility for mining new data flows directly from altruistic patients. In the broader public communication sphere, the ability to mine the digital traces of conversation on social media presents an opportunity to apply advanced machine learning algorithms as a way of tracking the diffusion of risk communication messages. In addition to utilizing big data for improving the scientific knowledge base in risk communication, there will be a need for health communication scientists and practitioners to work as part of interdisciplinary teams to improve the interfaces to these data for professionals and the public. Too much data, presented in disorganized ways, can lead to what some have referred to as “data smog.” Much work will be needed for understanding how to turn big data into knowledge, and just as important, how to turn data-informed knowledge into action.

Fault diagnosis of various rotating equipment using machine learning approaches – A review

2020 ◽  
pp. 095440892097197
Author(s):  
S Manikandan ◽  
K Duraivelu
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Learning Strategies ◽  
Early Identification ◽  
Deep Neural Network ◽  
Fundamental Aspect ◽  
Learning Approaches ◽  
Multiple Components ◽  
Rotating Equipment

Fault diagnosis of various rotating equipment plays a significant role in industries as it guarantees safety, reliability and prevents breakdown and loss of any source of energy. Early identification is a fundamental aspect for diagnosing the faults which saves both time and costs and in fact it avoids perilous conditions. Investigations are being carried out for intelligent fault diagnosis using machine learning approaches. This article analyses various machine learning approaches used for fault diagnosis of rotating equipment. In addition to this, a detailed study of different machine learning strategies which are incorporated on various rotating equipment in the context of fault diagnosis is also carried out. Mainly, the benefits and advance patterns of deep neural network which are applied to multiple components for fault diagnosis are inspected in this study. Finally, different algorithms are proposed to propagate the quality of fault diagnosis and the conceivable research ideas of applying machine learning approaches on various rotating equipment are condensed in this article.

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