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Sensors, Vol. 21, Pages 5302: Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques
uang Zhou Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection ...
Source: Sensors - August 5, 2021 Category: Biotechnology Authors: Yaru Yue Chengdong Chen Pengkun Liu Ying Xing Xiaoguang Zhou Tags: Article Source Type: research

Sensors, Vol. 22, Pages 1776: Compressed Deep Learning to Classify Arrhythmia in an Embedded Wearable Device
In conclusion, Mobilenet would be a more efficient model than Resnet to classify arrhythmia in an embedded wearable device.
Source: Sensors - February 24, 2022 Category: Biotechnology Authors: Kwang-Sig Lee Hyun-Joon Park Ji Eon Kim Hee Jung Kim Sangil Chon Sangkyu Kim Jaesung Jang Jin-Kook Kim Seongbin Jang Yeongjoon Gil Ho Sung Son Tags: Article Source Type: research

Sensors, Vol. 22, Pages 4310: Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning
In this study, we aimed at diagnosing CVD using a novel approach integrating information from retinal images and DXA data. We considered an adult Qatari cohort of 500 participants from Qatar Biobank (QBB) with an equal number of participants from the CVD and the control groups. We designed a case-control study with a novel multi-modal (combining data from multiple modalities—DXA and retinal images)—to propose a deep learning (DL)-based technique to distinguish the CVD group from the control group. Uni-modal models based on retinal images and DXA data achieved 75.6% and 77.4% accuracy, respective...
Source: Sensors - June 7, 2022 Category: Biotechnology Authors: Hamada R. H. Al-Absi Mohammad Tariqul Islam Mahmoud Ahmed Refaee Muhammad E. H. Chowdhury Tanvir Alam Tags: Article Source Type: research

Sensors, Vol. 22, Pages 8615: A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions
is Kyriazis Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain’s requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario’s requirements and datasets, for efficient...
Source: Sensors - November 8, 2022 Category: Biotechnology Authors: Argyro Mavrogiorgou Athanasios Kiourtis Spyridon Kleftakis Konstantinos Mavrogiorgos Nikolaos Zafeiropoulos Dimosthenis Kyriazis Tags: Article Source Type: research

Sensors, Vol. 23, Pages 1161: Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction
In this study, a supervised ML-based methodology is presented through which we aim to design efficient prediction models for CVD manifestation, highlighting the SMOTE technique’s superiority. Detailed analysis and understanding of risk factors are shown to explore their importance and contribution to CVD prediction. These factors are fed as input features to a plethora of ML models, which are trained and tested to identify the most appropriate for our objective under a binary classification problem with a uniform class probability distribution. Various ML models were evaluated after the use or non-use of Synt...
Source: Sensors - January 19, 2023 Category: Biotechnology Authors: Elias Dritsas Maria Trigka Tags: Article Source Type: research

Sensors, Vol. 23, Pages 5618: Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation
s D. Zink Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insu...
Source: Sensors - June 15, 2023 Category: Biotechnology Authors: Markus Lueken Michael Gramlich Steffen Leonhardt Nikolaus Marx Matthias D. Zink Tags: Article Source Type: research