Relevant bibliographies by topics / Biopotential detection

Academic literature on the topic 'Biopotential detection'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "Biopotential detection"

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Mahdi, A. E., and L. Faggion. "Non-contact biopotential sensor for remote human detection." Journal of Physics: Conference Series 307 (August 17, 2011): 012056. http://dx.doi.org/10.1088/1742-6596/307/1/012056.

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Noguchi, Yuta, and Akira Kawai. "Surface Stability of SU-8 Film for Accurate Biopotential Detection." Journal of Photopolymer Science and Technology 25, no. 6 (2012): 719–22. http://dx.doi.org/10.2494/photopolymer.25.719.

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Ng, E. Y. K., W. K. Ng, L. S. J. Sim, and U. Rajendra Acharya. "Numerical modelling of biopotential field for detection of breast tumour." Computers in Biology and Medicine 37, no. 8 (August 2007): 1121–32. http://dx.doi.org/10.1016/j.compbiomed.2006.10.006.

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Kareem, Ghada. "DETECTION AND CLASSIFICATION OF ABNORMALITIES OF BIO-POTENTIAL ACTIVITY OF HEART." Journal of Southwest Jiaotong University 57, no. 1 (February 28, 2022): 673–82. http://dx.doi.org/10.35741/issn.0258-2724.57.1.60.

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An electrocardiograph (ECG) is one of the essential equipment used in cardiology department research. It is used for diagnostic patients suffering from heart biopotential activity disorders. The difficulty of ECG classification is solved; the signal of ECG has some noise, so pre-processing is done at first to denoising. The signal is transformed into a small set of features simplifying the classification of the signal and diagnosing the disease. Support vector machine (SVM), K nearest neighbor (KNN), discriminant analysis (DS), random forest (RF), and Naïve Bayes (NB) classifiers are used for the classification of the signals of ECG. The proposed technique is used to detect the abnormal ECG sample and classify it into three different classes: normal sinus rhythm (NSR), arrhythmia (ARR), and congestive heart failure (CHF); it is shown that the accuracy of the technique is 98%, so the classification after feature extraction is better than classification with raw data without feature extraction. The method includes several steps: ECG signal database loading, signal pre-processing, feature extracting using wavelet transform, and displaying the classification results. Our classification and denoising technique uses discrete wavelet transform to support the diagnosis of patients with heart rate disorders, biopotential activity, and myocardial ischemia. This helps early detect heart disorders in a patient suffering from COVID-19.
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Abd-Elbaki, Mohamed K. M., Hanan A. Matar, and Naglaa R. E. Ismaeel. "Preparation and Characterization of Silk Fibroin-PANI Nanocomposites and Their Application for Electrophysiological Signals Recording." American Journal of Agricultural Science, Engineering, and Technology 6, no. 3 (December 3, 2022): 142–49. http://dx.doi.org/10.54536/ajaset.v6i3.1009.

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Wearable dry electrodes are required for long-term biopotential recordings, but their availability is limited. The diagnosis of coronary heart disease is possible with ambulatory electrocardiography (ECG). For ambulatory ECG sensing, on-skin electrodes are used by conformably contacting the skin’s moving and arbitrary formed surface. However, the low skin-adhesion of electrodes restricts their use in ambulatory ECG sensing for an extended period of time. Here, it was possible to create extremely skin-adhesive and washable on-skin electrodes by developing a new composite of Poly aniline (PANI) and silk fibroin (SF). Self-adhesive property of silk fibroin - PANI electrodes was achieved by coating with silk/Ca2+ adhesive layers. These electrodes were applied to the skin to capture high-quality ECG readings for the detection of cardiac real status in order to show how they can be used to detect precise and dependable signals. Silk fibroin - PANI electrodes showed an excellent performance for ECG signals recording over different physiological states.
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Kataoka, Hiroshi, Tsunenori Takatani, and Kazuma Sugie. "Two-Channel Portable Biopotential Recording System Can Detect REM Sleep Behavioral Disorder: Validation Study with a Comparison of Polysomnography." Parkinson's Disease 2022 (February 24, 2022): 1–5. http://dx.doi.org/10.1155/2022/1888682.

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Background. Sleep disorders are frequent nonmotor symptoms of Parkinson’s disease (PD). Polysomnography (PSG) has been the gold standard for its assessment. However, it requires patients to stay overnight in a hospital or sleep center. The mobile two-channel electroencephalography (EEG)/electrooculography (EOG) recording system is a self-applicable and affordable method to objectively assess sleep at home. We aimed at evaluating patients with PD to confirm the difference in sleep parameters between the portable recording system and PSG. Methods. PSG and the portable recording system were simultaneously performed on a similar night in eight patients with PD. We compared the difference in sleep parameters between them using nonparametric tests. Results. All patients displayed a score of both PDSS −2 ≥ 15 and PSQI ≥ 5, respectively, which revealed poor sleep quality. There was no difference in the sleep parameters between the portable recording system and PSG, except for the percentage of sleep stage N3. Regarding the detection of REM sleep without atonia, we observed accordance between the portable recording system and PSG in six patients ( P = 0.686 ). Conclusions. The portable EEG/EOG recording system may gain an advantage from home-based evaluations for habitual sleep at home. Our study on device validation may contribute to measuring natural sleep, including rapid eye movement (REM) sleep behavioral disorder (RBD), in an outpatient care setting.
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Golparvar, Ata Jedari, and Murat Kaya Yapici. "Toward graphene textiles in wearable eye tracking systems for human–machine interaction." Beilstein Journal of Nanotechnology 12 (February 11, 2021): 180–89. http://dx.doi.org/10.3762/bjnano.12.14.

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The study of eye movements and the measurement of the resulting biopotential, referred to as electrooculography (EOG), may find increasing use in applications within the domain of activity recognition, context awareness, mobile human–computer and human–machine interaction (HCI/HMI), and personal medical devices; provided that, seamless sensing of eye activity and processing thereof is achieved by a truly wearable, low-cost, and accessible technology. The present study demonstrates an alternative to the bulky and expensive camera-based eye tracking systems and reports the development of a graphene textile-based personal assistive device for the first time. This self-contained wearable prototype comprises a headband with soft graphene textile electrodes that overcome the limitations of conventional “wet” electrodes, along with miniaturized, portable readout electronics with real-time signal processing capability that can stream data to a remote device over Bluetooth. The potential of graphene textiles in wearable eye tracking and eye-operated remote object interaction is demonstrated by controlling a mouse cursor on screen for typing with a virtual keyboard and enabling navigation of a four-wheeled robot in a maze, all utilizing five different eye motions initiated with a single channel EOG acquisition. Typing speeds of up to six characters per minute without prediction algorithms and guidance of the robot in a maze with four 180° turns were successfully achieved with perfect pattern detection accuracies of 100% and 98%, respectively.
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Donisi, Leandro, Giuseppe Cesarelli, Noemi Pisani, Alfonso Maria Ponsiglione, Carlo Ricciardi, and Edda Capodaglio. "Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature." Diagnostics 12, no. 12 (December 5, 2022): 3048. http://dx.doi.org/10.3390/diagnostics12123048.

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Physical ergonomics has established itself as a valid strategy for monitoring potential disorders related, for example, to working activities. Recently, in the field of physical ergonomics, several studies have also shown potential for improvement in experimental methods of ergonomic analysis, through the combined use of artificial intelligence, and wearable sensors. In this regard, this review intends to provide a first account of the investigations carried out using these combined methods, considering the period up to 2021. The method that combines the information obtained on the worker through physical sensors (IMU, accelerometer, gyroscope, etc.) or biopotential sensors (EMG, EEG, EKG/ECG), with the analysis through artificial intelligence systems (machine learning or deep learning), offers interesting perspectives from both diagnostic, prognostic, and preventive points of view. In particular, the signals, obtained from wearable sensors for the recognition and categorization of the postural and biomechanical load of the worker, can be processed to formulate interesting algorithms for applications in the preventive field (especially with respect to musculoskeletal disorders), and with high statistical power. For Ergonomics, but also for Occupational Medicine, these applications improve the knowledge of the limits of the human organism, helping in the definition of sustainability thresholds, and in the ergonomic design of environments, tools, and work organization. The growth prospects for this research area are the refinement of the procedures for the detection and processing of signals; the expansion of the study to assisted working methods (assistive robots, exoskeletons), and to categories of workers suffering from pathologies or disabilities; as well as the development of risk assessment systems that exceed those currently used in ergonomics in precision and agility.
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Cao, Tianao, Dan Liu, Qisong Wang, Ou Bai, and Jinwei Sun. "Surface Electromyography-Based Action Recognition and Manipulator Control." Applied Sciences 10, no. 17 (August 22, 2020): 5823. http://dx.doi.org/10.3390/app10175823.

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To improve the quality of lives of disabled people, the application of intelligent prosthesis was presented and investigated. In particular, surface Electromyography (sEMG) signals succeeded in controlling the manipulator in human–machine interface, due to the fact that EMG activity belongs to one of the most widely utilized biosignals and can reflect the straightforward motion intention of humans. However, the accuracy of real-time action recognition is usually low and there is usually obvious delay in a controlling manipulator, as a result of which the task of tracking human movement precisely, cannot be guaranteed. Therefore, this study proposes a method of action recognition and manipulator control. We built a multifunctional sEMG detection and action recognition system that integrated all discrete components. A biopotential measurement analog-to-digital converter with a high signal–noise rate (SNR) was chosen to ensure the high quality of the acquired sEMG signals. The acquired data were divided into sliding windows for processing in a shorter time. Mean Absolute Value (MAV), Waveform Length (WL), and Root Mean Square (RMS) were finally extracted and we found that compared to the Genetic-Algorithm-based Support Vector Machine (GA–SVM), the back propagation (BP) neural network performed better in joint action classification. The results showed that the average accuracy of judging the 5 actions (fist clenching, hand opening, wrist flexion, wrist extension, and calling me) was up to 93.2% and the response time was within 200 ms, which achieved a simultaneous control of the manipulator. Our work took into account the action recognition accuracy and real-time performance, and realized the sEMG-based manipulator control eventually, which made it easier for people with arm disabilities to communicate better with the outside world.
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Vajravelu, Ashok, Muhammad Mahadi Bin Abdul Jamil, Mohd Helmy Bin Abd Wahab, Wan Suhaimizan Bin Wan Zaki, Vibin Mammen Vinod, Karthik Ramasamy Palanisamy, and Gousineyah Nageswara Rao. "Nanocomposite-Based Electrode Structures for EEG Signal Acquisition." Crystals 12, no. 11 (October 27, 2022): 1526. http://dx.doi.org/10.3390/cryst12111526.

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Objective: To fabricate a lightweight, breathable, comfortable, and able to contour to the curvilinear body shape, electrodes built on a flexible substrate are a significant growth in wearable health monitoring. This research aims to create a GNP/FE electrode-based EEG signal acquisition system that is both efficient and inexpensive. Methodology: Three distinct electrode concentrations were developed for EEG signal acquisition, three distinct electrode concentrations (1.5:1.5, 2:1, and 3:0). The high strength-to-weight ratio to form the tribofilm in the fabrication of the electrode will provide good efficiency. The EEG signal is first subjected to a wavelet transform, which serves as a preliminary analysis. The use of biopotential signals in wearable systems as biofeedback or control commands is expected to substantially impact point-of-care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMI), and brain–computer interfaces (BCIs). The graphene oxide (GO), glycerol (GL), and polyvinyl alcohol (PVA) GO/GL/PVA plastic electrodes were measured and compared to that of a commercially available electrode using the biopic equipment. The GO/GL/PVA plastic electrode was able to detect EEG signals satisfactorily after being used for two months, demonstrating good conductivity and lower noise than the commercial electrode. The GO/GL/PVA nanocomposite mixture was put into the electrode mold as soon as it was ready and then rapidly chilled. Results: The quality of an acquired EEG signal could be measured in several ways including by its error percentage, correlation coefficient, and signal-to-noise ratio (SNR). The fabricated electrode yield detection ranged from 0.81 kPa−1 % to 34.90 kPa−1%. The performance was estimated up to the response of 54 ms. Linear heating at the rate of 40 °C per minute was implemented on the sample ranges from 0 °C to 240 °C. During the sample electrode testing in EEG signal analysis, it obtained low impedance with a good quality of signal acquisition when compared to a conventional wet type of electrode. Conclusions: A large database was frequently built from all of the simulated signals in MATLAB code. Through the experiment, all of the required data were collected, checked against all other signals, and proven that they were accurate representations of the intended database. Evidence suggests that graphene nanoplatelets (GNP) hematite (FE2O3) polyvinylidene fluoride (PVDF) GNP/FE2O3@PVDF electrodes with a 3:0 concentration yielded the best outcomes.
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Dissertations / Theses on the topic "Biopotential detection"

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Chen, Chang Hao. "Instrumentation amplifier and filter design for biopotential acquisition system." Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2182898.

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Ma, Chon Teng. "Biopotential readout front-end circuits using frequency-translation filtering techniques." Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2182904.

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Zhang, Tan Tan. "Sub-nano-watt subthreshold-biased source-follower-based LPF for biopotential signal acquisition systems." Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2182899.

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Butler, Nickolas Andrew. "Development of a Myoelectric Detection Circuit Platform for Computer Interface Applications." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/1981.

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Personal computers and portable electronics continue to rapidly advance and integrate into our lives as tools that facilitate efficient communication and interaction with the outside world. Now with a multitude of different devices available, personal computers are accessible to a wider audience than ever before. To continue to expand and reach new users, novel user interface technologies have been developed, such as touch input and gyroscopic motion, in which enhanced control fidelity can be achieved. For users with limited-to-no use of their hands, or for those who seek additional means to intuitively use and command a computer, novel sensory systems can be employed that interpret the natural electric signals produced by the human body as command inputs. One of these novel sensor systems is the myoelectric detection circuit, which can measure electromyographic (EMG) signals produced by contracting muscles through specialized electrodes, and convert the signals into a usable form through an analog circuit. With the goal of making a general-purpose myoelectric detection circuit platform for computer interface applications, several electrical circuit designs were iterated using OrCAD software, manufactured using PCB fabrication techniques, and tested with electrical measurement equipment and in a computer simulation. The analog circuit design culminated in a 1.35” x 0.8” manufactured analog myoelectric detection circuit unit that successfully converts a measured EMG input signal from surface skin electrodes to a clean and usable 0-5 V DC output that seamlessly interfaces with an Arduino Leonardo microcontroller for further signal processing and logic operations. Multiple input channels were combined with a microcontroller to create an EMG interface device that was used to interface with a PC, where simulated mouse cursor movement was controlled through the voluntary EMG signals provided by a user. Functional testing of the interface device was performed, which showed a long battery life of 44.6 hours, and effectiveness in using a PC to type with an on-screen keyboard.
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Li, Jin Tao. "A novel readout front-end circuit topology for flexible biopotential signal acquisition system = 一種適用於靈活採集生物電信號的新型前端電路結構." Thesis, University of Macau, 2009. http://umaclib3.umac.mo/record=b2144082.

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Nonclercq, Antoine. "New strategies of acquisition and processing of encephalographic biopotentials." Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210711.

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Electroencephalography is a medical diagnosis technique. It consists in measuring the biopotentials produced by the upper layers of the brain at various standardized places on the skull.

Since the biopotentials produced by the upper parts of the brain have an amplitude of about one microvolt, the measurements performed by an EEG are exposed to many risks.

Moreover, since the present tendency is measure those signals over periods of several hours, or even several days, human analysis of the recording becomes extremely long and difficult. The use of signal analysis techniques for the help of paroxysm detection with clinical interest within the electroencephalogram becomes therefore almost essential. However the performance of many automatic detection algorithms becomes significantly degraded by the presence of interference: the quality of the recordings is therefore fundamental.

This thesis explores the benefits that electronics and signal processing could bring to electroencephalography, aiming at improving the signal quality and semi-automating the data processing.

These two aspects are interdependent because the performance of any semi-automation of the data processing depends on the quality of the acquired signal. Special attention is focused on the interaction between these two goals and attaining the optimal hardware/software pair.

This thesis offers an overview of the medical electroencephalographic acquisition chain and also of its possible improvements.

The conclusions of this work may be extended to some other cases of biological signal amplification such as the electrocardiogram (ECG) and the electromyogram (EMG). Moreover, such a generalization would be easier, because their signals have a wider amplitude and are therefore more resistant toward interference.


Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished

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Book chapters on the topic "Biopotential detection"

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Supriya, Supriya, Siuly Siuly, Hua Wang, and Yanchun Zhang. "Weighted complex network based framework for epilepsy detection from EEG signals." In Modelling and Analysis of Active Biopotential Signals in Healthcare, Volume 1. IOP Publishing, 2020. http://dx.doi.org/10.1088/978-0-7503-3279-8ch3.

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Khare, Smith K., Varun Bajaj, and G. R. Sinha. "Automatic drowsiness detection based on variational non-linear chirp mode decomposition using electroencephalogram signals." In Modelling and Analysis of Active Biopotential Signals in Healthcare, Volume 1. IOP Publishing, 2020. http://dx.doi.org/10.1088/978-0-7503-3279-8ch5.

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Subasi, Abdulhamit, Turker Tuncer, Sengul Dogan, and Dahiru Tanko. "Local binary pattern based feature extraction and machine learning for epileptic seizure prediction and detection." In Modelling and Analysis of Active Biopotential Signals in Healthcare, Volume 2. IOP Publishing, 2020. http://dx.doi.org/10.1088/978-0-7503-3411-2ch6.

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Conference papers on the topic "Biopotential detection"

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Lin, Feiyan, Michael McKnight, James Dieffenderfer, Eric Whitmire, Tushar Ghosh, and Alper Bozkurt. "Microfabricated impedance sensors for concurrent tactile, biopotential, and wetness detection." In 2014 IEEE Sensors. IEEE, 2014. http://dx.doi.org/10.1109/icsens.2014.6985252.

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Gagnon-Turcotte, G., C. O. Dufresne Camaro, and B. Gosselin. "Comparison of low-power biopotential processors for on-the-fly spike detection." In 2015 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2015. http://dx.doi.org/10.1109/iscas.2015.7168755.

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Diab, Maha S., and Soliman A. Mahmoud. "14:5nW; 30 dB Analog Front-End in 90-nm Technology for Biopotential Signal Detection." In 2020 43rd International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2020. http://dx.doi.org/10.1109/tsp49548.2020.9163572.

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Shokouhmand, Arash, Haoran Wen, Samiha Khan, Joseph A. Puma, Amisha Patel, Philip Green, Farrokh Ayazi, and Negar Tavassolian. "Detection of Left Ventricular Ejection Fraction Abnormality Using Fusion of Acoustic and Biopotential Characteristics of Precordium." In 2022 IEEE Sensors. IEEE, 2022. http://dx.doi.org/10.1109/sensors52175.2022.9967355.

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