Wearable Devices Combined with Artificial Intelligence—A Future Technology for Atrial Fibrillation Detection?

Mäkynen, M.; Schlindwein, Fernando S.

doi:10.3390/s22228588

Cited by 10 publications

(7 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, other wearable devices can use another connected device or the cloud for the required computing power. Hence, resource-intensive AI algorithms can be seamlessly integrated into a wearable device [14][15][16]. If effectively used, wearable AI can greatly help in the accurate diagnosis and prediction of anxiety as well as the management of several ADs.…”

Section: Introductionmentioning

confidence: 99%

Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis

Abd-alrazaq,

AlSaad,

Harfouche

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

Background Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based assessment methods conducted by clinicians, which can be subjective, time-consuming, and challenging to repeat. Therefore, there is an increasing demand for using technologies capable of providing objective and early detection of anxiety. Wearable artificial intelligence (AI), the combination of AI technology and wearable devices, has been widely used to detect and predict anxiety disorders automatically, objectively, and more efficiently. Objective This systematic review and meta-analysis aims to assess the performance of wearable AI in detecting and predicting anxiety. Methods Relevant studies were retrieved by searching 8 electronic databases and backward and forward reference list checking. In total, 2 reviewers independently carried out study selection, data extraction, and risk-of-bias assessment. The included studies were assessed for risk of bias using a modified version of the Quality Assessment of Diagnostic Accuracy Studies–Revised. Evidence was synthesized using a narrative (ie, text and tables) and statistical (ie, meta-analysis) approach as appropriate. Results Of the 918 records identified, 21 (2.3%) were included in this review. A meta-analysis of results from 81% (17/21) of the studies revealed a pooled mean accuracy of 0.82 (95% CI 0.71-0.89). Meta-analyses of results from 48% (10/21) of the studies showed a pooled mean sensitivity of 0.79 (95% CI 0.57-0.91) and a pooled mean specificity of 0.92 (95% CI 0.68-0.98). Subgroup analyses demonstrated that the performance of wearable AI was not moderated by algorithms, aims of AI, wearable devices used, status of wearable devices, data types, data sources, reference standards, and validation methods. Conclusions Although wearable AI has the potential to detect anxiety, it is not yet advanced enough for clinical use. Until further evidence shows an ideal performance of wearable AI, it should be used along with other clinical assessments. Wearable device companies need to develop devices that can promptly detect anxiety and identify specific time points during the day when anxiety levels are high. Further research is needed to differentiate types of anxiety, compare the performance of different wearable devices, and investigate the impact of the combination of wearable device data and neuroimaging data on the performance of wearable AI. Trial Registration PROSPERO CRD42023387560; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387560

show abstract

Section: Introductionmentioning

confidence: 99%

Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis

Abd-alrazaq,

AlSaad,

Harfouche

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…According to Vijayan et al (2021), these sensors offer a "wear-and-forget" feature despite compromising the user's convenience or interfering with daily routines. Figure 1 depicts the different wearable devices developed in biomedical (Mäkynen et al 2022).…”

Section: Biomedical Engineeringmentioning

confidence: 99%

An examination of wearable technology in the field biomedical engineering: A review

Awasthi,

Bhatt,

Rajendran

et al. 2024

Multidiscip. Rev.

View full text Add to dashboard Cite

A wearable device is a piece of technology that can be worn on the body, designed to be portable, lightweight and equipped with various sensors or features for specific functions. As a game-changer in the field of biomedical engineering, wearable technology provides innovative approaches to illness management, individualized healthcare and health monitoring. This review looks at wearable technology as it stands in the field of biomedical engineering, emphasizing its uses, difficulties and potential. The research examines a wide variety of wearable wireless biosensors, sensor patches, wristwatches, wearable face masks and multi-sensor smart clothing, emphasizing their potential to measure physiological parameters, including blood pressure, heart rate and sleep patterns. This study demonstrates the need for intelligent wearable devices for high-quality signal capture, processing and continuous monitoring of critical parameters. The creation of smart wearables that meet the specified design standards enables the creator of a wearable with reduced power consumption that can be used to monitor the health of patients. This study offers a thorough analysis of wearable technology's present state, obstacles and potential applications in the field of biomedical engineering. It will be of great use to investigators, medical professionals and developers of technology.

show abstract

“…Personal ECG devices such as the AliveCor Kardia, Apple Watch, or Samsung Galaxy Watch allow one to detect various arrhythmias on a larger scale than ever before and in a non-ambulatory environment [12]. The work of Mäkynen et al [13] discussed the possibility of applying wearable devices for AF detection, focusing on the benefits and limitations of pairing wearable devices with machine learning/deep learning algorithms.…”

Section: Review Of Existing Literaturementioning

confidence: 99%

An Improved Approach for Atrial Fibrillation Detection in Long-Term ECG Using Decomposition Transforms and Least-Squares Support Vector Machine

Pander

2023

Applied Sciences

View full text Add to dashboard Cite

Atrial fibrillation is a common heart rhythm disorder that is now becoming a significant healthcare challenge as it affects more and more people in developed countries. This paper proposes a novel approach for detecting this disease. For this purpose, we examined the ECG signal by detecting QRS complexes and then selecting 30 successive R-peaks and analyzing the atrial activity segment with a variety of indices, including the entropy change, the variance of the wavelet transform indices, and the distribution of energy in bands determined by the dual-Q tunable Q-factor wavelet transform and coefficients of the Hilbert transform of ensemble empirical mode decomposition. These transformations provided a vector of 21 features that characterized the relevant part of the electrocardiography signal. The MIT-BIH Atrial Fibrillation Database was used to evaluate the proposed method. Then, using the K-fold cross-validation method, the sets of features were fed into the LS-SVM and SVM classifiers and a trilayered neural network classifier. Training and test subsets were set up to avoid sampling from a single participant and to maintain the balance between classes. In addition, individual classification quality scores were analyzed for each signal to determine the dependencies of the classification quality on the subject. The results obtained during the testing procedure showed a sensitivity of 98.86%, a positive predictive value of 99.04%, and a classification accuracy of 98.95%.

show abstract

Wearable Devices Combined with Artificial Intelligence—A Future Technology for Atrial Fibrillation Detection?

Cited by 10 publications

References 44 publications

Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis

Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis

An examination of wearable technology in the field biomedical engineering: A review

An Improved Approach for Atrial Fibrillation Detection in Long-Term ECG Using Decomposition Transforms and Least-Squares Support Vector Machine

Contact Info

Product

Resources

About