Wearable sensor array for biopotential measurements

Rymarczyk, Tomasz; Stanikowski, Andrzej; Nita, Pawel

doi:10.23919/ptze.2019.8781712

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…"Reviews" was the next single largest group with eight (29% studies [15,17,18,23,29,30,35,38]. "Wearables" was the smallest single group with fou (14%) studies [20,25,28,37]. The final six (21%) unassigned studies [4,19,24,33,36,39] wer Of the 49 studies that remained, 1 study was excluded from the review due to issues with accessing the full manuscript, leaving 48 studies to be included for full-text readings and to form the dataset for this review.…”

Section: Study Subgroupsmentioning

confidence: 99%

“…"Reviews" was the next single largest group with eight (29%) studies [15,17,18,23,29,30,35,38]. "Wearables" was the smallest single group with four (14%) studies [20,25,28,37]. The final six (21%) unassigned studies [4,19,24,33,36,39] were placed in the "Others" group as they did not meet the inclusion criteria for the previous groups.…”

Section: Study Subgroupsmentioning

confidence: 99%

“…Similar to the aforementioned studies, Castillo-Atoche et al [37] described the development of a new wearable ECG with a dynamic power management strategy that then automatically passes the collected data to an ML model to detect arrhythmias in real time. Unlike Adetiba et al [25] and Hussain et al [20], the final study in this group by Rymarczyk et al [28] concentrated exclusively on the development of a new type of electrode for physiological signal sensing as an alternative to a conventional gelled electrode.…”

Section: Wearablesmentioning

confidence: 99%

See 2 more Smart Citations

The Athlete’s Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research

Bellfield

Ortega‐Martorell

Lip

et al. 2022

JCDD

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Background: Intense training exercise regimes cause physiological changes within the heart to help cope with the increased stress, known as the “athlete’s heart”. These changes can mask pathological changes, making them harder to diagnose and increasing the risk of an adverse cardiac outcome. Aim: This paper reviews which machine learning techniques (ML) are being used within athlete’s heart research and how they are being implemented, as well as assesses the uptake of these techniques within this area of research. Methods: Searches were carried out on the Scopus and PubMed online datasets and a scoping review was conducted on the studies which were identified. Results: Twenty-eight studies were included within the review, with ML being directly referenced within 16 (57%). A total of 12 different techniques were used, with the most popular being artificial neural networks and the most common implementation being to perform classification tasks. The review also highlighted the subgroups of interest: predictive modelling, reviews, and wearables, with most of the studies being attributed to the predictive modelling subgroup. The most common type of data used was the electrocardiogram (ECG), with echocardiograms being used the second most often. Conclusion: The results show that over the last 11 years, there has been a growing desire of leveraging ML techniques to help further the understanding of the athlete’s heart, whether it be by expanding the knowledge of the physiological changes or by improving the accuracies of models to help improve the treatments and disease management.

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Section: Study Subgroupsmentioning

confidence: 99%

Section: Study Subgroupsmentioning

confidence: 99%

Section: Wearablesmentioning

confidence: 99%

See 1 more Smart Citation

The Athlete’s Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research

Bellfield

Ortega‐Martorell

Lip

et al. 2022

JCDD

View full text Add to dashboard Cite

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Metal oxide-based gas sensor array for VOCs determination in complex mixtures using machine learning

Singh,

Varma

et al. 2024

Microchim Acta

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Detection of volatile organic compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array of 3 component metal oxides that give maximal cross-sensitivity and can successfully use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array comprises NiO-Au (ohmic), CuO-Au (Schottky), and ZnO–Au (Schottky) sensors made by the DC reactive sputtering method and having a film thickness of 80–100 nm. The NiO and CuO films have ultrafine particle sizes of < 50 nm and rough surface texture, while ZnO films consist of nanoscale platelets. This array was subjected to various VOC concentrations, including ethanol, acetone, toluene, and chloroform, one by one and in a pair/mix of gases. Thus, the response values show severe interference and departure from commonly observed power law behavior. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with an R2 value of more than 0.99 and LOD of 0.012 ppm, 0.015 ppm, 0.014 ppm, and 0.025 ppm for predicting the concentrations of acetone, toluene, ethanol, and chloroform, respectively, in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring. Graphical Abstract

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Measurement channels’ information relationships in body surface potential mapping

Przysucha,

Kowalski,

Oleszek

et al. 2024

JoMS

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Body Surface Potential Mappings (BSPM) measurements allow heart rate monitoring. Based on measurements obtained from BSPM, defects and heart rhythm disorders can be detected. Due to the multitude of data obtained from BSPM measurements, the crucial aspect in the potential test signals analysis on the human body is the automation of the disturbance detection process. This automation process involves the use of advanced algorithms and machine learning techniques to analyze the recorded data and identify patterns indicative of heart rhythm disorders. In the article, the information dependencies in the measurement channels were analyzed and presented. PCA analysis was used to determine informational relationships between the channels. The study was carried out on the data from simulations made for the BSPM measuring vest on a simulation system created for this purpose and based on the ECG signal generator. As a result of the conducted research indicated informational relationships between the channels for signals with disturbances: Atrial fibrillation, Brachycardia, Normal signal, PVC, Tachycardia, Ventricular Fibrillation, and Ventricular Tachycardia.

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Wearable sensor array for biopotential measurements

Cited by 4 publications

References 28 publications

The Athlete’s Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research

The Athlete’s Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research

Metal oxide-based gas sensor array for VOCs determination in complex mixtures using machine learning

Measurement channels’ information relationships in body surface potential mapping

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