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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. 21, Pages 6636: The Stumblemeter: Design and Validation of a System That Detects and Classifies Stumbles during Gait
Smit Stumbling during gait is commonly encountered in patients who suffer from mild to serious walking problems, e.g., after stroke, in osteoarthritis, or amputees using a lower leg prosthesis. Instead of self-reporting, an objective assessment of the number of stumbles in daily life would inform clinicians more accurately and enable the evaluation of treatments that aim to achieve a safer walking pattern. An easy-to-use wearable might fulfill this need. The goal of the present study was to investigate whether a single inertial measurement unit (IMU) placed at the shank and machine learning algorithms could be used t...
Source: Sensors - October 6, 2021 Category: Biotechnology Authors: Hartog Harlaar Smit Tags: Article Source Type: research

Sensors, Vol. 22, Pages 96: Artifacts in EEG-Based BCI Therapies: Friend or Foe?
In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While inform...
Source: Sensors - December 24, 2021 Category: Biotechnology Authors: Eric James McDermott Philipp Raggam Sven Kirsch Paolo Belardinelli Ulf Ziemann Christoph Zrenner 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 6960: Using Deep Learning to Predict Minimum Foot & ndash;Ground Clearance Event from Toe-Off Kinematics
Sensors, Vol. 22, Pages 6960: Using Deep Learning to Predict Minimum Foot–Ground Clearance Event from Toe-Off Kinematics Sensors doi: 10.3390/s22186960 Authors: Clement Ogugua Asogwa Hanatsu Nagano Kai Wang Rezaul Begg Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading cause of falls, and the swing-phase event Minimum Foot Clearance (MFC) is recognised as ...
Source: Sensors - September 14, 2022 Category: Biotechnology Authors: Clement Ogugua Asogwa Hanatsu Nagano Kai Wang Rezaul Begg 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. 22, Pages 9347: Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification
Elgendy An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel based on convolutional neural networks (CNNs) that can transfer knowledge from many lightweight deep learning models and decant it into one model to aid in the diagnosis of arrhythmia by using electrocardiogram (ECG) signals. Thus, we gained a multimodel able to classify arrhythmia from ECG signals. Our system&a...
Source: Sensors - December 1, 2022 Category: Biotechnology Authors: Mohamed Hammad Souham Meshoul Piotr Dziwi ński Pawe ł Pławiak Ibrahim A. Elgendy Tags: Article Source Type: research

Sensors, Vol. 22, Pages 9480: An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals
ammad Usman Akram Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated auton...
Source: Sensors - December 4, 2022 Category: Biotechnology Authors: Amna Waheed Awan Syed Muhammad Usman Shehzad Khalid Aamir Anwar Roobaea Alroobaea Saddam Hussain Jasem Almotiri Syed Sajid Ullah Muhammad Usman Akram Tags: Article Source Type: research

Sensors, Vol. 22, Pages 9891: Improving Inertial Sensor-Based Activity Recognition in Neurological Populations
This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations. The framework produces images from numerical inertial time-series data (initial state) and then artificially augments the number of produced images (enhanced state) to achieve a larger dataset. Here, we used convolutional neural network (CNN) architectures by utilizing image input. In addition, CNN enables transfer learning which enables limited datasets to benefit from models that are trained with big data. Initially, two benchmarked public datasets were used to verify the framework. A...
Source: Sensors - December 15, 2022 Category: Biotechnology Authors: Celik Aslan Sabanci Stuart Woo Godfrey Tags: Article Source Type: research

Sensors, Vol. 23, Pages 643: An Effective Framework for Deep-Learning-Enhanced Quantitative Microwave Imaging and Its Potential for Medical Applications
rocco Microwave imaging is emerging as an alternative modality to conventional medical diagnostics technologies. However, its adoption is hindered by the intrinsic difficulties faced in the solution of the underlying inverse scattering problem, namely non-linearity and ill-posedness. In this paper, an innovative approach for a reliable and automated solution of the inverse scattering problem is presented, which combines a qualitative imaging technique and deep learning in a two-step framework. In the first step, the orthogonality sampling method is employed to process measurements of the scattered field into an image, ...
Source: Sensors - January 6, 2023 Category: Biotechnology Authors: Álvaro Yago Ruiz Marta Cavagnaro Lorenzo Crocco Tags: Article Source Type: research

Sensors, Vol. 23, Pages 857: A Comparative Investigation of Automatic Speech Recognition Platforms for Aphasia Assessment Batteries
Qiang Fang The rehabilitation of aphasics is fundamentally based on the assessment of speech impairment. Developing methods for assessing speech impairment automatically is important due to the growing number of stroke cases each year. Traditionally, aphasia is assessed manually using one of the well-known assessment batteries, such as the Western Aphasia Battery (WAB), the Chinese Rehabilitation Research Center Aphasia Examination (CRRCAE), and the Boston Diagnostic Aphasia Examination (BDAE). In aphasia testing, a speech-language pathologist (SLP) administers multiple subtests to assess people with aphasia (PWA). Th...
Source: Sensors - January 11, 2023 Category: Biotechnology Authors: Seedahmed S. Mahmoud Raphael F. Pallaud Akshay Kumar Serri Faisal Yin Wang Qiang Fang 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 4042: Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot
Changcheng Shi Robot-assisted rehabilitation therapy has been proven to effectively improve upper-limb motor function in stroke patients. However, most current rehabilitation robotic controllers will provide too much assistance force and focus only on the patient’s position tracking performance while ignoring the patient’s interactive force situation, resulting in the inability to accurately assess the patient’s true motor intention and difficulty stimulating the patient’s initiative, thus negatively affecting the patient’s rehabilitation outcome....
Source: Sensors - April 17, 2023 Category: Biotechnology Authors: Yang Hu Jingyan Meng Guoning Li Dazheng Zhao Guang Feng Guokun Zuo Yunfeng Liu Jiaji Zhang Changcheng Shi 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

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