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Swapna, Mudrakola, Uma Maheswari Viswanadhula, Rajanikanth Aluvalu, Vijayakumar Vardharajan, and Ketan Kotecha. "Bio-Signals in Medical Applications and Challenges Using Artificial Intelligence." Journal of Sensor and Actuator Networks 11, no. 1 (February 25, 2022): 17. http://dx.doi.org/10.3390/jsan11010017.
Повний текст джерелаАнотація:
Artificial Intelligence (AI) has broadly connected the medical field at various levels of diagnosis based on the congruous data generated. Different types of bio-signal can be used to monitor a patient’s condition and in decision making. Medical equipment uses signals to communicate information to care staff. AI algorithms and approaches will help to predict health problems and check the health status of organs, while AI prediction, classification, and regression algorithms are helping the medical industry to protect from health hazards. The early prediction and detection of health conditions will guide people to stay healthy. This paper represents the scope of bio-signals using AI in the medical area. It will illustrate possible case studies relevant to bio-signals generated through IoT sensors. The bio-signals that retrospectively occur are discussed, and the new challenges of medical diagnosis using bio-signals are identified.
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Kumar, R. Suresh, and P. Manimegalai. "Detection and Separation of Eeg Artifacts Using Wavelet Transform." International Journal of Informatics and Communication Technology (IJ-ICT) 7, no. 3 (December 1, 2018): 149. http://dx.doi.org/10.11591/ijict.v7i3.pp149-156.
Повний текст джерелаАнотація:
Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
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Gyuho Choi and Sungbum Pan. "Biometrics System Technology Trends Based on Bio-signal." Research Briefs on Information and Communication Technology Evolution 7 (November 15, 2021): 164–72. http://dx.doi.org/10.56801/rebicte.v7i.126.
Повний текст джерелаАнотація:
Biometrics is a technology that authenticates, identifies, and recognizes user using individual uniquephysical or behavioral characteristics. The scope of services is expanding with necessity and utility ina wide range of fields such as finance, security, access control, medical welfare, public service, quarantine,and entertainment. Research using bio-signal inside the body than bio-information outsidethe body is being actively conducted. In this paper, we analyze research about technologies of biometricssystems using bio-signals such as ECG, heart sound, EMG, EEG, and present the necessarytechnologies for the development direction. In the future, bio-signal based database construction incomplex conditions, deep learning network design through analyzes big data, and biometrics systemtechnologies applied in a real-time environment are expected to be studied.
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Riyadh Mahmood, Hassanein, Manaf K. Hussein, and Riyadh A. Abedraba. "Development of Low-Cost Biosignal Acquisition System for ECG, EMG, and EOG." Wasit Journal of Engineering Sciences 10, no. 3 (December 1, 2022): 191–202. http://dx.doi.org/10.31185/ejuow.vol10.iss3.352.
Повний текст джерелаАнотація:
The use of bio-signal is very crucial, providing enormous information concerning health and well-being of the individual. such signals can be measured and monitored by specialized devices to each bio-signal, for instance, the electrocardiogram (ECG), electromyography (EMG), electroencephalogram (EEG), and electrooculogram (EOG). Due to use of such devices, these signals could be utilized for several objectives. As it is observed in the devices of medical detection and Human to Machine Interactions (HCI). This paper presents a low-cost bio-signal collection device which is having the ability to record ECG, EMG, and EOG signals. Furthermore, STM32F103C8 system is used in Analog to Digital Conversion (ADC), with its particular application. An application has been developed in order to allow admins to observe and save the data signal simultaneously. This application has been developed by using C++ programming language and MATLAB’s code. The data signal is recorded in a format of mat file, which can be studied in details in the proposed system. This system is capitalized on Universal Serial Bus (USB) wired communication link, which is used to transmit the bio-signal through, that guarantees the safety ,avoid noise and interference. The system shows its compatiblity with various operating systems, such as, Windows, Linux, and Mac.
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Al-Zyoud, Izaldein, Fedwa Laamarti, Xiaocong Ma, Diana Tobón, and Abdulmotaleb El Saddik. "Towards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusion." Sensors 22, no. 24 (December 12, 2022): 9747. http://dx.doi.org/10.3390/s22249747.
Повний текст джерелаАнотація:
Human bio-signal fusion is considered a critical technological solution that needs to be advanced to enable modern and secure digital health and well-being applications in the metaverse. To support such efforts, we propose a new data-driven digital twin (DT) system to fuse three human physiological bio-signals: heart rate (HR), breathing rate (BR), and blood oxygen saturation level (SpO2). To accomplish this goal, we design a computer vision technology based on the non-invasive photoplethysmography (PPG) technique to extract raw time-series bio-signal data from facial video frames. Then, we implement machine learning (ML) technology to model and measure the bio-signals. We accurately demonstrate the digital twin capability in the modelling and measuring of three human bio-signals, HR, BR, and SpO2, and achieve strong performance compared to the ground-truth values. This research sets the foundation and the path forward for realizing a holistic human health and well-being DT model for real-world medical applications.
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Nimi W. S., P. Subha Hency Jose, and Jegan R. "Review on Reliable and Quality Wearable Healthcare Device (WHD)." International Journal of Reliable and Quality E-Healthcare 10, no. 4 (October 2021): 1–25. http://dx.doi.org/10.4018/ijrqeh.2021100101.
Повний текст джерелаАнотація:
This paper presents a brief review on present developments in wearable devices and their importance in healthcare networks. The state-of-the-art system architecture on wearable healthcare devices and their design techniques are reviewed and becomes an essential step towards developing a smart device for various biomedical applications which includes diseases classifications and detection, analyzing nature of the bio signals, vital parameters measurement, and e-health monitoring through noninvasive method. From the review on latest published research papers on medical wearable device and bio signal analysis, it can be concluded that it is more important and very essential to design and develop a smart wearable device in healthcare environment for quality signal acquisition and e-health monitoring which leads to effective measures of multiparameter extractions. This will help the medical practitioners to understand the nature of patient health condition easily by visualizing a quality signal by smart wearable devices.
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Mohanty, Mihir Narayan, and Hemanta Kumar Palo. "Machine Learning:An Effective Technique in Bio-Medical Signal Analysis and Classification." International Journal of Machine Learning and Networked Collaborative Engineering 01, no. 01 (September 30, 2017): 1–8. http://dx.doi.org/10.30991/ijmlnce.2017v01i01.001.
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Tariq, Mashal, Ayesha A. Siddiqi, Ghous Baksh Narejo, and Shehla Andleeb. "A Cross Sectional Study of Tumors Using Bio-Medical Imaging Modalities." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 1 (December 7, 2018): 66–73. http://dx.doi.org/10.2174/1573405613666170614081434.
Повний текст джерелаАнотація:
Background: Digital Signal Processing (D.S.P) is an evolutionary field. It has a vast variety of applications in all fields. Bio medical engineering has various applications of digital signal processing. Digital Image Processing is one of the branches of signal processing. Medical image modalities proved to be helpful for disease diagnosis. Higher expertise is required in image analysis by medical professional, either doctors or radiologists. Methods: Extensive research is being done and has produced remarkable results. The study is divided into three main parts. The first deals with introduction of mostly used imaging modalities such as, magnetic resonance imaging, x-rays, ultrasound, positron emission tomography and computed tomography. The next section includes explanation of the basic steps of digital image processing are also explained in the paper. Magnetic Resonance imaging modalities is selected for this research paper. Different methods are tested on MRI images. Discussion: Brain images are selected with and without tumor. Solid cum Cystic tumor is opted for the r esearch. Results are discussed and shown. The software used for digital image processing is MATLAB. It has in built functions which are used throughout the study. The study represents the importance of DIP for tumor segmentation and detection. Conclusion: This study provides an initial guideline for researchers from both fields, that is, medicine and engineering. The analyses are shown and discussed in detail through images. This paper shows the significance of image processing platform for tumor detection automation.
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Tunlasakun, Khanchai. "Heart Sound Monitor for Bio-Signal Learning." Advanced Materials Research 680 (April 2013): 644–48. http://dx.doi.org/10.4028/www.scientific.net/amr.680.644.
Повний текст джерелаАнотація:
This research presents the design and development of the heart sound monitor for bio-signal learning which can be worked with a personal computer. The prototype will receive the heart sound via the condenser microphone built-in the stethoscope. The condenser microphone will be conversed the air pressure from heart beats to electrical signal that signal will transformed to computer via sound card. The sound card will be conversed the analog signal to digital signal for process by heart sound processing program developed by LabVIEW program. The signal will be analyzed with short time Fourier transforms in heart sound processing program by graphical user interface. The user is able to select a band pass of signal for filter and choose the power spectrum of heart sound for display. The output database from this prototype is necessary for Medical Education or Clinical Practice. The prototype was tested and it worked satisfactory.
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Mantri, Prof Shamla, Dr Pankaj Agrawal, Prof Dipti Patil, and Dr V. M. Wadhai. "Depression Analysis using ECG Signal." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 7 (November 17, 2013): 2746–51. http://dx.doi.org/10.24297/ijct.v11i7.3470.
Повний текст джерелаАнотація:
ECG is a bio-medical signal which records the electrical activity of the heart versus time. They are important for diagnostic and research purposes of the human heart. In this paper we discuss a method of feature extraction which is an inevitable step in most approaches in diagnosing abnormalities in the heart. A web application is developed which extracts features of ECG signal like ST segment, QRS wave, etc. and use these features for identifying whether a person suffers from any of the four levels of stress, that is, Hyper Acute stress (Myocardial Infarction), Acute stress (Type A), Hyper Chronic stress (Ischemia) or Chronic Stress (Type B). The application is built using a decision support system formed by extensive learning of behavior of the signals of various persons.Â
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Gajare, Milind, and Shedge D.K. "CMOS Trans Conductance based Instrumentation Amplifier for Various Biomedical Signal Analysis." NeuroQuantology 20, no. 5 (April 30, 2022): 53–60. http://dx.doi.org/10.14704/nq.2022.20.5.nq22148.
Повний текст джерелаАнотація:
Feed forward design techniques for the Trans-conductance operational amplifier removes the barriers of operating frequencies. It is now possible to design amplifiers with large the Trans-conductance that operates at Giga hertz frequency range. There are several Trans-conductance amplifiers used to design a medical and Industrial application that helps in processing various bio medical signals such as Electrocardiographs, Electroencephalographs, Electromyograms and several others. The proposed paper shows the implementation of an instrumentation amplifier using CMOS based the Trans-conductance operational amplifiers also the processing of biomedical ECG, EEG and EMG signals. The CMOS process technology helps to integrate complex circuits on minimal surface area. The Trans-conductance instrumentation operational amplifiers has features includes noise reduction, low DC offset, High output impedance and Common Mode rejection Ratio values. The circuit implementation and simulations has been done on Electronic Design and Automation tool with 0.13μm CMOS process technology.
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Morimoto, Yoko, Kiyoko Yokoyama, Yasufumi Mizuno, Masanori Moyoshi, and Kazuyuki Takata. "Examination of the application of the Wavelet Transform to the Bio-medical Signal Processing." Japanese journal of ergonomics 35 (1999): 256–57. http://dx.doi.org/10.5100/jje.35.2supplement_256.
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Kyo, In Chung, and Min Byung Chan. "A Study of Detection of Drowsiness and Awakeness using Non-contact Radar Sensors." International Journal of Electrical and Electronics Research 9, no. 3 (September 30, 2021): 35–41. http://dx.doi.org/10.37391/ijeer.090303.
Повний текст джерелаАнотація:
Biometric information is used in a variety of industrial fields. Heart rate and respiration rate, in particular, are widely applied not only in medical institutions but also in life safety. However, a sensor must be worn or directly attached to the human body to obtain a bio signal, which is inconvenient and limits its application. In this study, a 24 GHz radar sensor module is developed, and an algorithm is implemented by analyzing the frequency and peak values of a human participant’s heartbeat and respiration signals in an unconstrained state. In the experiment, the existing ECG equipment (MP150) and radar sensor module are compared. The results indicate that the average value of MP150 is higher than that of the Doppler sensor in terms of all parameters; however, the deviation of the Doppler sensor is small, and the bias is low. Furthermore, it is confirmed that the HRV decreases in the drowsy state compared to that in the wakeful state in both devices. These results confirm that bio-signals change during drowsiness, and conversely, drowsiness can be detected through changes in bio-signals, which is a significant finding.
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Shinde, Ashok Naganath, Sanjay L. Lalbalwar, and Anil B. Nandgaonkar. "Modified meta-heuristic-oriented compressed sensing reconstruction algorithm for bio-signals." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 05 (September 2019): 1950031. http://dx.doi.org/10.1142/s0219691319500310.
Повний текст джерелаАнотація:
In signal processing, several applications necessitate the efficient reprocessing and representation of data. Compression is the standard approach that is used for effectively representing the signal. In modern era, many new techniques are developed for compression at the sensing level. Compressed sensing (CS) is a rising domain that is on the basis of disclosure, which is a little gathering of a sparse signal’s linear projections including adequate information for reconstruction. The sampling of the signal is permitted by the CS at a rate underneath the Nyquist sampling rate while relying on the sparsity of the signals. Additionally, the reconstruction of the original signal from some compressive measurements can be authentically exploited using the varied reconstruction algorithms of CS. This paper intends to exploit a new compressive sensing algorithm for reconstructing the signal in bio-medical data. For this purpose, the signal can be compressed by undergoing three stages: designing of stable measurement matrix, signal compression and signal reconstruction. In this, the compression stage includes a new working model that precedes three operations. They are signal transformation, evaluation of [Formula: see text] and normalization. In order to evaluate the theta ([Formula: see text]) value, this paper uses the Haar wavelet matrix function. Further, this paper ensures the betterment of the proposed work by influencing the optimization concept with the evaluation procedure. The vector coefficient of Haar wavelet function is optimally selected using a new optimization algorithm called Average Fitness-based Glowworm Swarm Optimization (AF-GSO) algorithm. Finally, the performance of the proposed model is compared over the traditional methods like Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Firefly (FF), Crow Search (CS) and Glowworm Swarm Optimization (GSO) algorithms.
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Daoui, Achraf, Mohamed Yamni, Hicham Karmouni, Mhamed Sayyouri, Hassan Qjidaa, Saad Motahhir, Ouazzani Jamil, et al. "Efficient Biomedical Signal Security Algorithm for Smart Internet of Medical Things (IoMTs) Applications." Electronics 11, no. 23 (November 23, 2022): 3867. http://dx.doi.org/10.3390/electronics11233867.
Повний текст джерелаАнотація:
Due to the rapid development of information and emerging communication technologies, developing and implementing solutions in the Internet of Medical Things (IoMTs) field have become relevant. This work developed a novel data security algorithm for deployment in emerging wireless biomedical sensor network (WBSN) and IoMTs applications while exchanging electronic patient folders (EPFs) over unsecured communication channels. These EPF data are collected using wireless biomedical sensors implemented in WBSN and IoMTs applications. Our algorithm is designed to ensure a high level of security for confidential patient information and verify the copyrights of bio-signal records included in the EPFs. The proposed scheme involves the use of Hahn’s discrete orthogonal moments for bio-signal feature vector extraction. Next, confidential patient information with the extracted feature vectors is converted into a QR code. The latter is then encrypted based on a proposed two-dimensional version of the modified chaotic logistic map. To demonstrate the feasibility of our scheme in IoMTs, it was implemented on a low-cost hardware board, namely Raspberry Pi, where the quad-core processors of this board are exploited using parallel computing. The conducted numerical experiments showed, on the one hand, that our scheme is highly secure and provides excellent robustness against common signal-processing attacks (noise, filtering, geometric transformations, compression, etc.). On the other hand, the obtained results demonstrated the fast running of our scheme when it is implemented on the Raspberry Pi board based on parallel computing. Furthermore, the results of the conducted comparisons reflect the superiority of our algorithm in terms of robustness when compared to recent bio-signal copyright protection schemes.
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Kim, Seonho, Jungjoon Kim, and Hong-Woo Chun. "Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease." International Journal of Environmental Research and Public Health 15, no. 8 (August 15, 2018): 1750. http://dx.doi.org/10.3390/ijerph15081750.
Повний текст джерелаАнотація:
Interest in research involving health-medical information analysis based on artificial intelligence, especially for deep learning techniques, has recently been increasing. Most of the research in this field has been focused on searching for new knowledge for predicting and diagnosing disease by revealing the relation between disease and various information features of data. These features are extracted by analyzing various clinical pathology data, such as EHR (electronic health records), and academic literature using the techniques of data analysis, natural language processing, etc. However, still needed are more research and interest in applying the latest advanced artificial intelligence-based data analysis technique to bio-signal data, which are continuous physiological records, such as EEG (electroencephalography) and ECG (electrocardiogram). Unlike the other types of data, applying deep learning to bio-signal data, which is in the form of time series of real numbers, has many issues that need to be resolved in preprocessing, learning, and analysis. Such issues include leaving feature selection, learning parts that are black boxes, difficulties in recognizing and identifying effective features, high computational complexities, etc. In this paper, to solve these issues, we provide an encoding-based Wave2vec time series classifier model, which combines signal-processing and deep learning-based natural language processing techniques. To demonstrate its advantages, we provide the results of three experiments conducted with EEG data of the University of California Irvine, which are a real-world benchmark bio-signal dataset. After converting the bio-signals (in the form of waves), which are a real number time series, into a sequence of symbols or a sequence of wavelet patterns that are converted into symbols, through encoding, the proposed model vectorizes the symbols by learning the sequence using deep learning-based natural language processing. The models of each class can be constructed through learning from the vectorized wavelet patterns and training data. The implemented models can be used for prediction and diagnosis of diseases by classifying the new data. The proposed method enhanced data readability and intuition of feature selection and learning processes by converting the time series of real number data into sequences of symbols. In addition, it facilitates intuitive and easy recognition, and identification of influential patterns. Furthermore, real-time large-capacity data analysis is facilitated, which is essential in the development of real-time analysis diagnosis systems, by drastically reducing the complexity of calculation without deterioration of analysis performance by data simplification through the encoding process.
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Jeon, Gwanggil, Awais Ahmad, Salvatore Cuomo, and Wei Wu. "Special issue on bio-medical signal processing for smarter mobile healthcare using big data analytics." Journal of Ambient Intelligence and Humanized Computing 10, no. 10 (August 24, 2019): 3739–45. http://dx.doi.org/10.1007/s12652-019-01425-9.
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Shiksha Jain & Raj Kumar Tiwari. "Ultra Low Frequency Wide Band Low Pass Active Filter for Bio-Medical Applications Simulated on Cadence tool." International Journal for Modern Trends in Science and Technology 7, no. 05 (May 27, 2021): 84–88. http://dx.doi.org/10.46501/ijmtst0705013.
Повний текст джерелаАнотація:
An ultra low frequency wide band low pass active filter is designed and simulated on 180nm cadence virtuoso tool for biomedical applications in this paper. This proposed designed low pass active filter is being able to stop low frequency signal of µHz and can pass up to Hz using the CMOS nanotechnology. This is a second order low pass active filter. It can be useful to identify the human disease by detecting ultra low frequency bio-signal. The simulated result shows ultra low power consumption of 200pW with high bandwidth at 1mV input supply.
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V, Dhananjaya, and Geetha M. "Effective Analysis and Accurate Detection of Common Diseases in ECG Signals and Classifications and Monitoring Through Cloud Computing Technology." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 19 (May 7, 2022): 107–17. http://dx.doi.org/10.37394/23208.2022.19.13.
Повний текст джерелаАнотація:
In the modern age of technology and advancements, the world is shrinking and accessible to everyone, in all ways including the medical field at the outset to save the lives. As the various experts are spread over the globe with their expertise, even the human dis-orders are also growing equally with the same speed. Hence it requires an assimilation of various sensors, embedded systems and some regularized protocol for hassle free interaction over the finger tips. In the proposed work we are attempting to interface few biosensors to capture ECG signals and to perform parametric estimation on breath-rate, heart beat rate, systolic pulse and other required parameters to classify the signal into any disease oriented or normal human being and uploaded the results along with type of disease. From the cloud, the concern medical experts can access and can treat the patient for further diagnosis. In this paper we are considering Epilepsy, heart beat rate, systolic pulse compared with normal condition of the human being. To classify the recorded ECG signals to discriminate among above condition we make use of Artificial Neural Network model and the entire processing of bio-medical signals are done on Matlab Platform. The processing of database is selected from the standard universally available database MIMIC II. The Matlab processed signals are again processed over a common protocol to communicate to the expert for exact diagnosis of the patient conditions over the captured bio-signals, the entire system we make use of modified LED algorithm and we term it as Health-Raid algorithm.
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Bala, Ștefan-Radu, Ionuț Banu, Tania-Ioana Cîmpeanu, and Bogdan-Gabriel Duță. "BIOSIGNALS-BASED AIR TREATMENT CONTROL SYSTEM FOR NEUROVEGETATIVE PATIENTS." Romanian Journal of Petroleum & Gas Technology 2(73), no. 2 (2021): 46–55. http://dx.doi.org/10.51865/jpgt.2021.02.06.
Повний текст джерелаАнотація:
The global epidemic of COVID-19 has highlighted a not obvious weakness of the medical system, namely establishing a favorable environment that can also contribute to the recovery process. This has led to many medical complications and the gravity of this situation could be mitigated by simply implementing an automatic system for monitoring and adjusting environmental parameters. The paper highlights how the bio signals sampled by the sensors will be used for the calibration of the heating ventilation air conditioning control system and maintain the optimal parameters both in terms of temperature and humidity, which will prevent the development and transmission of microbes, while the ECG device and the EMG signal amplifier will help with the monitoring of the neurovegetative patients.
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Liu, Yang, Chengdong Lin, and Zhenjiang Li. "WR-Hand." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 3 (September 9, 2021): 1–27. http://dx.doi.org/10.1145/3478112.
Повний текст джерелаАнотація:
This paper presents WR-Hand, a wearable-based system tracking 3D hand pose of 14 hand skeleton points over time using Electromyography (EMG) and gyroscope sensor data from commercial armband. This system provides a significant leap in wearable sensing and enables new application potentials in medical care, human-computer interaction, etc. A challenge is the armband EMG sensors inevitably collect mixed EMG signals from multiple forearm muscles because of the fixed sensor positions on the device, while prior bio-medical models for hand pose tracking are built on isolated EMG signal inputs from isolated forearm spots for different muscles. In this paper, we leverage the recent success of neural networks to enhance the existing bio-medical model using the armband's EMG data and visualize our design to understand why our solution is effective. Moreover, we propose solutions to place the constructed hand pose reliably in a global coordinate system, and address two practical issues by providing a general plug-and-play version for new users without training and compensating for the position difference in how users wear their armbands. We implement a prototype using different commercial armbands, which is lightweight to execute on user's phone in real-time. Extensive evaluation shows the efficacy of the WR-Hand design.
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P. Dhivya, T. Kumaresan, P. Subramanian, K. Gunasekaran, and G. Sathish Kumar. "HYBRID FIREFLY META OPTIMIZATION FOR BIO MEDICAL IMAGE PROCESSING USING DEEP LEARNING." Journal of Pharmaceutical Negative Results 13, no. 4 (November 10, 2022): 1199–209. http://dx.doi.org/10.47750/pnr.2022.13.04.169.
Повний текст джерелаАнотація:
Signal and image processing is a part of biomedical science. In that, Biomedical image processing have a great value such as recognition, segmentation and classification for disease diagnosis. In one part of biomedical science, brain tumor classification is considered with Magnetic Resonance Images (MRI) images using state of art models. Initially, the Convolutional Neural Network (CNN), Fast Convolutional Neural Network (FCNN), U-Net and M-Net model was considered for classification. Further, the Hybrid Firefly Meta Optimization (HFMO) is proposed for the better prediction purpose. The proposed work has three phases like normalization with augmentation, deep attention segmentation and classification. In the first phase, data augmentation is applied to increase the scope of the dataset. In the second phase, a deep attention technique is applied to concentrate on hotspot in the image during segmentation. Finally, a hybrid firefly optimization is applied to enhance the performance of the model in convolution neural network by backtracking the process. The measuring parameters like Dice coefficient, Jaccard index, Positive Projected Value (PPV), True Positive Rate and False Positive Rate were evaluated. The comparative analysis of various state of art models with proposed classifier were demonstrated. Thus the proposed technique produces the training accuracy as 98.62%, testing accuracy as 95.31 % and 1 % of loss.
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Kim, Dong-Sun, and Jin-San Kwon. "A Lossless Multichannel Bio-Signal Compression Based on Low-Complexity Joint Coding Scheme for Portable Medical Devices." Sensors 14, no. 9 (September 18, 2014): 17516–29. http://dx.doi.org/10.3390/s140917516.
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Woo, Seong-Tak, Ji-Wan Ha, Sungdae Na, Hyunjoo Choi, and Sung-Bom Pyun. "Design and Evaluation of Korean Electropalatography (K-EPG)." Sensors 21, no. 11 (May 31, 2021): 3802. http://dx.doi.org/10.3390/s21113802.
Повний текст джерелаАнотація:
Recently, the development of medical rehabilitation technology has resulted in an increased interest in speech therapy equipment. In particular, research on articulation therapy for communication disorders is being actively conducted. The existing methods for the diagnosis and treatment of speech disorders, such as traditional tactile perception tests and methods based on the empirical judgment of speech therapists, have many limitations. Moreover, the position and contact force of the tongue are key factors in speech disorders with regards to articulation. This is a very important factor in the distinction of Korean characters such as lax, tense and aspirated consonants. In this study, we proposed a Korean-electropalatography (EPG) system to easily measure and monitor the position and contact force of the tongue during articulation treatment and diagnosis. In our proposed K-EPG system, a sensor was fabricated using an AgCl electrode and biocompatible silicon. Furthermore, the measured signal was analyzed by implementing a bio-signal processing module and monitoring program. In particular, the bio-signal was measured by inserting the device into the palate of an experimental healthy test group (four subjects). Through these experiments, we confirmed that our K-EPG system could be applied to clinical treatment in speech therapy.
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Nissen, Nina. "An assemblage of everyday technologies in the practice of western herbal medicine - a photo essay." Outlines. Critical Practice Studies 23, no. 1 (October 18, 2022): 50–73. http://dx.doi.org/10.7146/ocps.v23i1.128782.
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Small, mundane technologies, such as stethoscopes, medicinal bottles, labels, cleaning and dispensing equipment, are integral to the practice of western herbal medicine (WHM) in the UK. A focus on such technologies reveals the dynamic character and porousness of medical systems and allows us to identify cultural interactions. In this photo essay, based on long-term anthropological research, I explore an assemblage of everyday technologies used by WHM practitioners and the ways in which these technologies contribute to shaping diagnostic stories, to performing (bio)medical legitimacy and invoking herbal traditions. The biomedical, herbal and domestic technologies-in-use come into view as vibrant and dynamic objects with highly contingent meanings and identities. Their absorption into this non-biomedical therapy supports the performance of (bio)medical legitimacy, authority, tradition and professionalism, while the use of everyday domestic objects may signal female-coded practices of care. This demonstrates the adaptability of a medical practice situated at the margin of mainstream healthcare and subject to ongoing technological and ideological influence. The strategic integration of this assemblage of everyday technologies into WHM contributes, I suggest, to evoking a competent, trustworthy and time-honoured medical practice, which is simultaneously inscribed with multiple tensions, ambiguities and contestations.
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Sattigeri, Prasanna, Jayaraman Thiagarajan, Karthikeyan Ramamurthy, Andreas Spanias, Mahesh Banavar, Abhinav Dixit, Jie Fan, et al. "Instruction Tools for Signal Processing and Machine Learning for Ion-Channel Sensors." International Journal of Virtual and Personal Learning Environments 12, no. 1 (January 2022): 1–17. http://dx.doi.org/10.4018/ijvple.285601.
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Ion Channel sensors have several applications including DNA sequencing, biothreat detection, and medical applications. Ion-channel sensors mimic the selective transport mechanism of cell membranes and can detect a wide range of analytes at the molecule level. Analytes are sensed through changes in signal patterns. Papers in the literature have described different methods for ion channel signal analysis. In this paper, we describe a series of new graphical tools for ion channel signal analysis which can be used for research and education. The paper focuses on the utility of this tools in biosensor classes. Teaching signal processing and machine learning for ion channel sensors is challenging because of the multidisciplinary content and student backgrounds which include physics, chemistry, biology and engineering. The paper describes graphical ion channel analysis tools developed for an on-line simulation environment called J-DSP. The tools are integrated and assessed in a graduate bio-sensor course through computer laboratory exercises.
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Bilbao, Emanuel, Octavio Garate, Theo Rodríguez Campos, Mariano Roberti, Mijal Mass, Alex Lozano, Gloria Longinotti, Leandro Monsalve, and Gabriel Ybarra. "Electrochemical Sweat Sensors." Chemosensors 11, no. 4 (April 14, 2023): 244. http://dx.doi.org/10.3390/chemosensors11040244.
Повний текст джерелаАнотація:
Sweat analysis by means of minimally invasive wearable sensors is considered a potentially disruptive method for assessing clinical parameters, with exciting applications in early medical diagnostics and high-performance sports. Electrochemical sensors and biosensors are especially attractive because of the possibility of the electronic integration of wearable devices. In this article, we review several aspects regarding the potentialities and present limitations of electrochemical sweat (bio)sensors, including: the main target analytes and their relationships with clinical conditions; most usual electrochemical techniques of transduction used according to the nature of the target analytes; issues connected to the collection of representative sweat samples; aspects regarding the associated, miniaturized electronic instrumentation used for signal processing and communication; and signal processing by machine learning.
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HEGDE, VEENA N., RAVISHANKAR DEEKSHIT, and P. S. SATYANARAYANA. "RANDOM NOISE CANCELLATION IN BIOMEDICAL SIGNALS USING VARIABLE STEP SIZE GRIFFITH LMS ADAPTIVE LINE ENHANCER." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1240020. http://dx.doi.org/10.1142/s0219519412400209.
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This paper presents a new method of random noise cancellation for removing artefacts from biomedical signals using an adaptive line enhancer (ALE). The ALE is implemented using proposed time domain variable step size Griffith least mean square (VSGLMS) algorithm. The technique is based on the adaptation of the gradient of the error surface. The method makes both the step size and the gradient free from observation noise and reduces the gradient mis-adjustment error. Here, both the gradient and the scale factor for the step size are free from the input noise effects, which makes the algorithm robust to both stationary and non-stationary observation noise. Further the additional computational load involved for this is marginal. The VSGLMS adaptive filter technique for ALE is tested on noise cancellation of two types of bio-medical signals — separation of electro cardiogram (ECG) signal from a background of electro myogram (EMG) and heart sound signal (HSS) from lung sound signal (LSS). Application of VSGLAM–ALE for the separation of HSS from LSS and ECG from EMG has been demonstrated using synthetic White Gaussian noise (WGN). It is found that VSGLMS–ALE can separate the desired signals like ECG or HSS at an input SNR of -5 dB to 27 dB. The performance of VSGLMS is compared with state-of-the-art least mean square LMS–ALE and normalised LMS–ALE. The results of PSDs, time domain waveforms, and mean square error (MSE) have proven that VSGLMS performs better than advanced techniques.
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Basaeri, Hamid, David B. Christensen, and Shad Roundy. "A review of acoustic power transfer for bio-medical implants." Smart Materials and Structures 25, no. 12 (November 10, 2016): 123001. http://dx.doi.org/10.1088/0964-1726/25/12/123001.
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Koegl, Matthias, Christoph Weiß, and Lars Zigan. "Fluorescence Spectroscopy for Studying Evaporating Droplets Using the Dye Eosin-Y." Sensors 20, no. 21 (October 22, 2020): 5985. http://dx.doi.org/10.3390/s20215985.
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Laser-induced fluorescence (LIF) spectroscopy using dyes is frequently applied for characterization of liquids and two-phase flows. The technique is utilized e.g., for mixing studies, thermometry, or droplet sizing. One major application of the LIF technique combined with Mie-scattering is the planar measurement of droplet sizes in spray systems. However, its uncertainty is determined, among others, by varying dye concentration and temperature changes occurring during mixing and droplet evaporation. Systematic experimental investigations are necessary to determine the influence of dye enrichment effects on the LIF-signal of single droplets. For these investigations, the fluorescence dye Eosin-Y is dissolved in water and ethanol, which are typical solvents and working fluids in bio-medical applications and power engineering. A photo-physical characterization of the mixtures under various conditions was conducted using a spectrometric LIF setup and a micro cell. For ethanol, a small temperature dependency of the Eosin-Y LIF signal is observed up to 373 K. Photo-dissociation of Eosin-Y is negligible for solution in ethanol while it is distinct in water. The LIF signals of the single droplets are studied with an acoustic levitator. Effects of droplet evaporation, droplet deformation and varying dye concentration on the LIF-signal are studied. The single droplet measurements revealed a complex change of the fluorescence signal with reduced droplet size. This is due to droplet deformations leading to variations in the internal illumination field as well as dye enrichment during evaporation.
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Kim, Seong-Hoon, Zong Woo Geem, and Gi-Tae Han. "A Novel Human Respiration Pattern Recognition Using Signals of Ultra-Wideband Radar Sensor." Sensors 19, no. 15 (July 30, 2019): 3340. http://dx.doi.org/10.3390/s19153340.
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Recently, various studies have been conducted on the quality of sleep in medical and health care fields. Sleep analysis in these areas is typically performed through polysomnography. However, since polysomnography involves attaching sensor devices to the body, accurate sleep measurements may be difficult due to the inconvenience and sensitivity of physical contact. In recent years, research has been focused on using sensors such as Ultra-wideband Radar, which can acquire bio-signals even in a non-contact environment, to solve these problems. In this paper, we have acquired respiratory signal data using Ultra-wideband Radar and proposed 1D CNN (1-Dimension Convolutional Neural Network) model that can classify and recognize five respiration patterns (Eupnea, Bradypnea, Tachypnea, Apnea, and Motion) from the signal data. Also, in the proposed model, we find the optimum parameter range through the recognition rate experiment on the combination of parameters (layer depth, size of kernel, and number of kernels). The average recognition rate of five breathing patterns experimented by applying the proposed method was 93.9%, which is about 3%~13% higher than that of conventional methods (LDA, SVM, and MLP).
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Fakhari, Pegah, Ehsan Vahedi, and Caro Lucas. "Protecting patient privacy from unauthorized release of medical images using a bio-inspired wavelet-based watermarking approach." Digital Signal Processing 21, no. 3 (May 2011): 433–46. http://dx.doi.org/10.1016/j.dsp.2011.01.014.
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Ruppert-Stroescu, Mary, and Mahendran Balasubramanian. "Effects of stitch classes on the electrical properties of conductive threads." Textile Research Journal 88, no. 21 (August 21, 2017): 2454–63. http://dx.doi.org/10.1177/0040517517725116.
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Bio-physical signal measurement tools embedded in clothing are becoming a viable alternative in mobile health monitoring systems, particularly Wearable Electronic Textile-based Systems (WETS). To assure clinical viability, utilizing flexible and inconspicuous conductive media that can acquire and transmit reliable signals while assuring signal durability and biocompatibility are particularly important when developing WETS for medical applications. To accomplish this task, conductive threads are emerging as an appropriate electrical medium for health monitoring garments. However, little has been studied on the behavior of these conductive threads under various conditions. We report here the electrical conductive properties of specific conductive threads under two conditions: (i) as sewn configurations onto a textile substrate with different stitch types and (ii) as independent strands under controlled extension independent from a sewing machine. Statistical results showed that the stitch class and thread location significantly influenced the electrical resistance of the conductive thread, revealing the chain stitch to provide resistance even lower than the un-stitched conductive thread. In addition, under controlled extension all three of the conductive threads exhibited both a hysteresis and a stress-relaxation effect. These are important phenomena to examine when conductive threads are incorporated into WETS because the choice of stitch type will influence the strength of the signals received and transmitted, while the wearers’ body movements will cause the threads to encounter multi-axial stretch. Knowing the influence of stitch type, stretch, and relaxation on conductive thread resistance will inform objective design and manufacturing decisions for developing clinical-grade textile-based electrical circuits for medical applications.
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B.M., Manjula, and Chirag Sharma. "BCG Artifact Removal Using Improved Independent Component Analysis Approach." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (January 1, 2017): 130. http://dx.doi.org/10.11591/ijeecs.v5.i1.pp130-138.
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<p>Recent advancement in bio-medical field has attracted researchers toward BCG signal processing for monitoring the health activities. There have been various techniques for monitoring physical activities such as (SCG) Seismocardiography, Electrocardiography (ECG) etc. BCG signal is a measurement of reaction force applied for cardiac ejection of blood. Various measurement schemes and systems have been developed for BCG detection and measurement such as tables, beds, weighing scale and chairs. Weighing scales have been promising method for measurement of BCG signal because of less cost of implementation, smaller size etc. but these devices still suffer from the artifact which are induced due to subject movement or motion during signal acquisition or it can be caused due to floor vibrations. Artifact removal is necessary for efficient analysis and health monitoring. In this work we address the issue of artifact removal in BCG signal by proposing a novel method of signal processing. According to proposed approach raw signal is pre-processed and parsed to independent component analysis which provides the decomposed components and later k-means is applied to detect the components which are responsible for artifact and removed. Proposed approach is compared with existing method and shows better performance in terms of artifact removal.</p>
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Chomba, Haji, Ting Pan, Xinyue Zhuo, Lan Zhao, Yulin Wang, Zhao Huang, Haikael Martin, Dongchu Chen, and Libo Nie. "A Bio-Barcode Electrochemical DNA Biosensor Based on Poly T30 Copper Nanoparticle Signaling." Science of Advanced Materials 13, no. 1 (January 1, 2021): 73–79. http://dx.doi.org/10.1166/sam.2021.3837.
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Simple, cost effective, high sensitive and selective detection strategies for disease related DNA sequences in clinical diagnostics and research purposes are still on demand. Detection of DNA specific sequences of particular biomedical importance, based on electrochemical signaling, has been reported as a promising analytical approach for medical diagnostics due to its simplicity, cost effective, sensitivity, selectivity and rapidity. Herein, a simple and cost effective DNA biosensor based on poly T30 Copper Nanoparticle Signaling and biobarcode technique is presented for the first time. In this design, complementary sequence places the poly T30 modified bio-barcode probe (P2-SiO2-T30) on the sensor interface. Upon copper reduction reaction, copper nanoparticles (CuNPs) are clustered along the poly T30 modified bio-barcode probe (P2-SiO2-T30-CuNPs). During electrochemical measurements copper nanoparticles (CuNPs) are oxidized to give current signal. This detection strategy has a detection limit of 10 pM.
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Noh, Younghee, Taehoon Kim, and Woojung Kwak. "A Study of Establishing an Academic Classification System on New Technology Field." Korean Society of Culture and Convergence 45, no. 2 (February 28, 2023): 801–22. http://dx.doi.org/10.33645/cnc.2023.02.45.02.801.
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In this study, to propose a classification system for new technology academic fields, the current state of domestic and foreign new technology academic classification system was analyzed and implications were derived. Based on this, the establishment (draft) of the academic classification system for 21 new technology fields was proposed. As a result, the artificial intelligence application in the field of artificial intelligence and the artificial intelligence platform part were integrated, and the items were adjusted by deleting the subcategories for similar items. Second, in the case of big data, a big data classification system was proposed by reflecting the opinion that service and academic perspectives are mixed. Third, in the case of next-generation semiconductors, a new technology classification system was proposed based on the common recognition of those working in the industry, academia and research institutes. Fourth, in the case of customized health care, health care software, digital treatment study, dormant health (bio living body), medical device development study using bio-signal measurement and information, bio-health regulation and licensing, etc. were included. Lastly, the classification of the new energy industry was readjusted by referring to the National Science and Technology Standard Classification System.
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Rundo, Francesco, Alessandro Ortis, Sebastiano Battiato, and Sabrina Conoci. "Advanced Bio-Inspired System for Noninvasive Cuff-Less Blood Pressure Estimation from Physiological Signal Analysis." Computation 6, no. 3 (August 28, 2018): 46. http://dx.doi.org/10.3390/computation6030046.
Повний текст джерелаАнотація:
Blood Pressure (BP) is one of the most important physiological indicators that provides useful information in the field of health-care monitoring. Blood pressure may be measured by both invasive and non-invasive methods. A novel algorithmic approach is presented to estimate systolic and diastolic blood pressure accurately in a way that does not require any explicit user calibration, i.e., it is non-invasive and cuff-less. The approach herein described can be applied in a medical device, as well as in commercial mobile smartphones by an ad hoc developed software based on the proposed algorithm. The authors propose a system suitable for blood pressure estimation based on the PhotoPlethysmoGraphy (PPG) physiological signal sampling time-series. Photoplethysmography is a simple optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is non-invasive since it takes measurements at the skin surface. In this paper, the authors present an easy and smart method to measure BP through careful neural and mathematical analysis of the PPG signals. The PPG data are processed with an ad hoc bio-inspired mathematical model that estimates systolic and diastolic pressure values through an innovative analysis of the collected physiological data. We compared our results with those measured using a classical cuff-based blood pressure measuring device with encouraging results of about 97% accuracy.
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Chen, Xingchi, Fa Zhu, and Hai Zhao. "Edge-Enabled Heart Rate Estimation from Multisensor PPG Signals." Journal of Healthcare Engineering 2023 (February 22, 2023): 1–18. http://dx.doi.org/10.1155/2023/4682760.
Повний текст джерелаАнотація:
Heart rate (HR) estimation from multisensor PPG signals suffers from the dilemma of inconsistent computation results, due to the prevalence of bio-artifacts (BAs). Furthermore, advancements in edge computing have shown promising results from capturing and processing diversified types of sensing signals using the devices of Internet of Medical Things (IoMT). In this paper, an edge-enabled method is proposed to estimate HRs accurately and with low latency from multisensor PPG signals captured by bilateral IoMT devices. First, we design a real-world edge network with several resource-constrained devices, divided into collection edge nodes and computing edge nodes. Second, a self-iteration RR interval calculation method, at the collection edge nodes, is proposed leveraging the inherent frequency spectrum feature of PPG signals and preliminarily eliminating the influence of BAs on HR estimation. Meanwhile, this part also reduces the volume of sent data from IoMT devices to compute edge nodes. Afterward, at the computing edge nodes, a heart rate pool with an unsupervised abnormal detection method is proposed to estimate the average HR. Experimental results show that the proposed method outperforms traditional approaches which rely on a single PPG signal, attaining better results in terms of the consistency and accuracy for HR estimation. Furthermore, at the designed edge network, our proposed method processes a 30 s PPG signal to obtain an HR, consuming only 4.24 s of computation time. Hence, the proposed method is of significant value for the low-latency applications in the field of IoMT healthcare and fitness management.
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Ahmad, Shafiq, Zia ur Rehman, Saud Altaf, Mazen Zaindin, Shamsul Huda, Muhammad Haroon, and Sofia Iqbal. "Dynamic Key Extraction Technique Using Pulse Signal and Lightweight Cryptographic Authentication Scheme for WBAN." Sustainability 14, no. 21 (November 7, 2022): 14625. http://dx.doi.org/10.3390/su142114625.
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As a key component of ubiquitous computing, the wireless body area network (WBAN) can be used in a variety of disciplines, including health monitoring. Our everyday routines have been transformed by wearable technology, which has changed the medical industry and made our lives more convenient. However, the openness of the wireless network has raised concerns about the privacy and security of patient’s data because of the latent threat imposed by attackers. Patients’ sensitive data are safeguarded with authentication schemes against a variety of cyberattacks. Using pulse signals and a lightweight cryptographic approach, we propose a hybrid, anonymous, authentication scheme by extracting the binarized stream (bio-key) from pulse signal. We acquired 20 different sample signals to verify the unpredictability and randomness of keys, which were further utilized in an authentication algorithm. Formal proof of mutual authentication and key agreement was provided by the widely known BAN logic, and informal verification was provided by the Automated Validation of Internet Security Protocol and Applications (AVISPA) tool. The performance results depicted that storage cost on the sensor side was only 640 b, whereas communication cost was 512 b. Similarly, the computation time and energy consumption requirements were 0.005 ms and 0.55 µJ, respectively. Hence, it could be asserted that the proposed authentication scheme provided sustainable communication cost along with efficient computation, energy, and storage overheads as compared to peer work.
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Gautam, Divya, Kavita Khare, and Bhavana P. Shrivastava. "A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising." Applied Sciences 13, no. 12 (June 11, 2023): 7032. http://dx.doi.org/10.3390/app13127032.
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Medical image denoising is a crucial pre-processing task in the medical field to ensure accurate analysis of anomalies or sicknesses in the human body. Digital filters are popular for reducing undesired noise as they provide reliability, high accuracy, and reduced sensitivity to component tolerances compared to analog filters. However, conventional digital filter design approaches lack efficiency in achieving global optimization robustness. To overcome these incapabilities, this paper adopted bio-inspired optimization algorithms to offer viable digital filter designing tools because of their simple implementation and requirement of a few parameters to control their convergence. This research article explores a hybrid strategy that combines a novel guided decimation box filter (GDBF) with a hybrid cuckoo particle swarm optimization (HCPSO) algorithm to design a denoising filter for medical images. It is the first time a decimation box filter has been used for denoising, leading to novelty. The HCPSO algorithm is applied to obtain the filter parameters optimally. Medical images mostly suffer from four types of noises. The performance of the proposed filter is analyzed for these types of noise. To highlight the importance of parameter selection, the results of the proposed method are compared with other recently utilized bio-inspired genetic algorithms, such as PSO (particle swarm optimization), CS (cuckoo search), and FF (firefly). The superiority (potency) of the proposed method has been established by calculating the improvement in quality parameters such as the peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM). The proposed filter achieved the highest PSNR (~35.7 dB), SSIM (~0.95), and FSIM (~0.92) and proved its numerical and visual quality efficacy over state-of-the-art models.
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Lin, Shiyong, Yuan-Shin Lee, and Roger J. Narayan. "Snapping algorithm and heterogeneous bio-tissues modeling for medical surgical simulation and product prototyping." Virtual and Physical Prototyping 2, no. 2 (June 2007): 89–101. http://dx.doi.org/10.1080/17452750701487941.
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Raghavendra, V., N. Vinay kumar, and Manish Kumar. "Latest advancement in image processing techniques." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 390. http://dx.doi.org/10.14419/ijet.v7i2.12.11357.
Повний текст джерелаАнотація:
Image processing is method of performing some operations on an image, for enhancing the image or for getting some information from that image, or for some other applications is nothing but Image Processing [1]. Image processing is one sort of signal processing, where input is an image and output may be an image, characteristics of that image or some features that image [1]. Image will be taken as a two dimensional signal and signal processing techniques will be applied to that two dimensional image. Image processing is one of the growing technologies [1]. In many real time applications image processing is widely used. In the field of bio technology, computer science, in medical field, envi-ronmental areas etc., image processing is being used for mankind benefits. The following steps are the basics of image processing:Image is taken as an inputImage will be processed (manipulation, analyzing the image, or as per requirement)Altered image will be the outputImage processing is of two typesAnalog Image Processing:As the name implies, analog image processing is applied on analog signals. Television image is best example of analog signal processing [1].(DIP) Digital Image Processing:DIP techniques are used on images, which are in the format of digital for processing them, and get the required output as per the application. Operations were applied on the digital images for processing [1].In this paper, we will discuss about the technologies or tools for image processing especially by using Open CV. With the help of Open CV image processing will be very easy and efficient. When Open CV is collaborated or integrated with python the results are mind blowing. We will discuss about the process of using python and Open CV.
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Fritea, Luminita, Florin Banica, Traian Costea, Liviu Moldovan, Luciana Dobjanschi, Mariana Muresan, and Simona Cavalu. "Metal Nanoparticles and Carbon-Based Nanomaterials for Improved Performances of Electrochemical (Bio)Sensors with Biomedical Applications." Materials 14, no. 21 (October 22, 2021): 6319. http://dx.doi.org/10.3390/ma14216319.
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Monitoring human health for early detection of disease conditions or health disorders is of major clinical importance for maintaining a healthy life. Sensors are small devices employed for qualitative and quantitative determination of various analytes by monitoring their properties using a certain transduction method. A “real-time” biosensor includes a biological recognition receptor (such as an antibody, enzyme, nucleic acid or whole cell) and a transducer to convert the biological binding event to a detectable signal, which is read out indicating both the presence and concentration of the analyte molecule. A wide range of specific analytes with biomedical significance at ultralow concentration can be sensitively detected. In nano(bio)sensors, nanoparticles (NPs) are incorporated into the (bio)sensor design by attachment to the suitably modified platforms. For this purpose, metal nanoparticles have many advantageous properties making them useful in the transducer component of the (bio)sensors. Gold, silver and platinum NPs have been the most popular ones, each form of these metallic NPs exhibiting special surface and interface features, which significantly improve the biocompatibility and transduction of the (bio)sensor compared to the same process in the absence of these NPs. This comprehensive review is focused on the main types of NPs used for electrochemical (bio)sensors design, especially screen-printed electrodes, with their specific medical application due to their improved analytical performances and miniaturized form. Other advantages such as supporting real-time decision and rapid manipulation are pointed out. A special attention is paid to carbon-based nanomaterials (especially carbon nanotubes and graphene), used by themselves or decorated with metal nanoparticles, with excellent features such as high surface area, excellent conductivity, effective catalytic properties and biocompatibility, which confer to these hybrid nanocomposites a wide biomedical applicability.
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Seok, Seonho. "Polymer-Based Biocompatible Packaging for Implantable Devices: Packaging Method, Materials, and Reliability Simulation." Micromachines 12, no. 9 (August 27, 2021): 1020. http://dx.doi.org/10.3390/mi12091020.
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Polymer materials attract more and more interests for a biocompatible package of novel implantable medical devices. Medical implants need to be packaged in a biocompatible way to minimize FBR (Foreign Body Reaction) of the implant. One of the most advanced implantable devices is neural prosthesis device, which consists of polymeric neural electrode and silicon neural signal processing integrated circuit (IC). The overall neural interface system should be packaged in a biocompatible way to be implanted in a patient. The biocompatible packaging is being mainly achieved in two approaches; (1) polymer encapsulation of conventional package based on die attach, wire bond, solder bump, etc. (2) chip-level integrated interconnect, which integrates Si chip with metal thin film deposition through sacrificial release technique. The polymer encapsulation must cover different materials, creating a multitude of interface, which is of much importance in long-term reliability of the implanted biocompatible package. Another failure mode is bio-fluid penetration through the polymer encapsulation layer. To prevent bio-fluid leakage, a diffusion barrier is frequently added to the polymer packaging layer. Such a diffusion barrier is also used in polymer-based neural electrodes. This review paper presents the summary of biocompatible packaging techniques, packaging materials focusing on encapsulation polymer materials and diffusion barrier, and a FEM-based modeling and simulation to study the biocompatible package reliability.
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MUTASHAR, SAAD, M. A. HANNAN, S. A. SAMAD, and A. HUSSAIN. "DEVELOPMENT OF BIO-IMPLANTED MICRO-SYSTEM WITH SELF-RECOVERY ASK DEMODULATOR FOR TRANSCUTANEOUS APPLICATIONS." Journal of Mechanics in Medicine and Biology 14, no. 04 (July 3, 2014): 1450062. http://dx.doi.org/10.1142/s0219519414500626.
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This paper deals with the development of bio-implanted micro-system with low-power and high data rate based on amplitude shift keying (ASK) modulation technique to stimulate nerves and muscles. The modified system is operated by a low-frequency band 13.56 MHz according to the industrial-scientific-medical (ISM) bands to avoid the biological tissue damage. The data rate on the demodulator side is from 1 Mb/s and up to 1.5 Mb/s depending of generating binary signal (T BIT = 1 μs or 0.5 μs) with modulation index of 13% and modulation rate 7.3%, 9% and 11%, respectively. The proposed inductive coupling link achieves 73% of link efficiency. The modified rectifier with self-threshold voltage cancellation techniques and voltage regulator without thermal protection circuit and without passive elements occupies small area that is modified to generate adequate and stable DC voltages of 1.8 V. A new ASK demodulator structure based on two comparators is developed to extract a synchronized demodulated signal with minimum error. Thereby no need for clock recovery circuit and delay-locked loops (DLL) circuits for data synchronization at 1 Mb/s and 1.250 Mb/s of speed. The system designed using OrCAD Pspice 16.2 is based on 0.35 μm technologies.
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Markovics, Zigurds, Juris Lauznis, Matiss Erins, Olesja Minejeva, and Raivis Kivlenieks. "Testing and Analysis of the HRV Signals from Wearable Smart HRV Sensors." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 1211. http://dx.doi.org/10.14419/ijet.v7i4.36.28191.
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The objective of the test procedure is to obtain bio signals from Photoplethysmograph and Electrocardiograph sensors on selected consumer devices and to statistically validate the data for use with a drowsiness estimation method.The method selected for validation uses LF/HF ratio calculated by a set of R-R interval data to estimate drowsiness state of a human. The value LF to HF ratio calculates balance between sympathetic and parasympathetic activity that can be measured from HRV (Heart rate variability) signals. The statistical data collected are processed by using Fast Fourier Transform and HRV frequency domain analysis on a set of test participants.There is a correlation between medical ECG equipment control output and Matlab tool’s HRVAS (Burg) output of data processed from ECG based wearable smart sensor when the LF/HF ratio is calculated in all observed volunteer data. The results for Photoplethysmograph sensors of this test correlate with other tested tools but level of the values is lower, and data from optical biosensor devices which are designed to measure HRV time-domain properties as pulse did not confirm with ECG equipment results for frequency-domain analysis required for use with selected drowsiness estimation method. The result affecting factors are sensor placement, motion artefacts and discrete vendor-specific signal pre-processing of wearable device output data.The following results confirm the use of consumer grade biosensor that produces discretely pre-processed R-R interval data for the frequency based HRV method and application validation against directly processed ECG data from certified medical equipment.
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Markovics, Zigurds, Juris Lauznis, Matiss Erins, Olesja Minejeva, and Raivis Kivlenieks. "Testing and Analysis of the HRV Signals from Wearable Smart HRV Sensors." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 1211. http://dx.doi.org/10.14419/ijet.v7i4.36.28214.
Повний текст джерелаАнотація:
The objective of the test procedure is to obtain bio signals from Photoplethysmograph and Electrocardiograph sensors on selected consumer devices and to statistically validate the data for use with a drowsiness estimation method.The method selected for validation uses LF/HF ratio calculated by a set of R-R interval data to estimate drowsiness state of a human. The value LF to HF ratio calculates balance between sympathetic and parasympathetic activity that can be measured from HRV (Heart rate variability) signals. The statistical data collected are processed by using Fast Fourier Transform and HRV frequency domain analysis on a set of test participants.There is a correlation between medical ECG equipment control output and Matlab tool’s HRVAS (Burg) output of data processed from ECG based wearable smart sensor when the LF/HF ratio is calculated in all observed volunteer data. The results for Photoplethysmograph sensors of this test correlate with other tested tools but level of the values is lower, and data from optical biosensor devices which are designed to measure HRV time-domain properties as pulse did not confirm with ECG equipment results for frequency-domain analysis required for use with selected drowsiness estimation method. The result affecting factors are sensor placement, motion artefacts and discrete vendor-specific signal pre-processing of wearable device output data.The following results confirm the use of consumer grade biosensor that produces discretely pre-processed R-R interval data for the frequency based HRV method and application validation against directly processed ECG data from certified medical equipment.
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Devashena, Thangaiyan, and K. Dhanalakshmi. "Electromagnetic Characteristics of Shape Memory Spring." Materials Science Forum 978 (February 2020): 421–27. http://dx.doi.org/10.4028/www.scientific.net/msf.978.421.
Повний текст джерелаАнотація:
Electric impedance is widely used in imaging and detection techniques. The applications range from non-destructive testing, structural health monitoring, and geophysical imaging to medical imaging. The frequency of the signal used for the measurement ranges from less than 1 Hz to about 1 GHz. This paper addresses the measurement and evaluation of the phase dependent electrical resistance, inductance, capacitance, and impedance of a shape memory alloy (SMA) spring (BMX 150, Toki Corporation). The material characteristics can be obtained by means of their electromechanical impedance. Experimental procedures are implemented and the electrical characteristics are obtained for a wide range of frequency. The electrical resistance, inductance, impedances of the austenite and martensite phase are determined, also the quality factor of the Bio Metal coil to be (9.465 – 9.95) Ω and (10.358 – 10.8) Ω, (0.458 – 0.38) μH and (0.458 – 0.36) μH and, (9.47 – 10.24) Ω and (10.36 – 11.11) Ω respectively for the frequency range of 100 kHz - 1MHz. The quality factor of the Bio Metal ranges between 0.03 and 0.2 during heating and, 0.028 and 0.022 during the cooling phase. The experimental results herein show that an equivalent circuit of the SMA spring is a series resistor-inductor circuit with a parasitic capacitance effect. The electromagnetic behaviour of SMA is determined using a finite element tool.
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AbuShawish, Israa Y., and Soliman A. Mahmoud. "A programmable gain and bandwidth amplifier based on tunable UGBW rail-to-rail CMOS op-amps suitable for different bio-medical signal detection systems." AEU - International Journal of Electronics and Communications 141 (November 2021): 153952. http://dx.doi.org/10.1016/j.aeue.2021.153952.
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Devasena, D., M. Jagadeeswari, and K. Srinivasan. "Development of Optimized Algorithm and Field Programmable Gate Array Implementation for Bio Medical Image Denoising for Health Informatics Applications." Journal of Medical Imaging and Health Informatics 11, no. 10 (October 1, 2021): 2626–38. http://dx.doi.org/10.1166/jmihi.2021.3851.
Повний текст джерелаАнотація:
Denoising images is a most difficult task in applications for image processing. The image specifics are preserved and the additional sounds found in the images are removed. It is also a challenge to remove noise from medical and satellite images. It improves the diagnostic capacity of medical images and satellite images visual clarity. The noise in the images varies and its density varies depending on imaging techniques. The algorithms in the literature were suggested based on the noise density and the forms of noise. The aim of this paper is to eliminate the noise from ultrasound, magnetic resonance images and satellite images using an effective denoisation algorithm Hybrid Wiener Adaptive Weighted Median filter (HWAWMF) which is the combination of Wiener and Adaptive Centre Pixel Weighted Median Filter (ACPWMEF). In terms of performance parameters with an improved Peak to Signal Noise Ratio(PSNR), the hybrid filter shows better results than ACPWMEF. The Vienna filter takes out the additional noises in the images thus blurs the image’s optical perception. And also uses optimization approaches to enhance the image consistency. This paper proposes HWAWMF (PSO HWAWMF) based on particle swarm optimization and HWAWMF based on dragonfly optimization algorithms (DOAF HWAWMF). Visual vision and PSNR also have been improved by using the optimising algorithm at an average of 3.18 db, 4.83 db, and 3.14 db for lower noise (0.0% to 30%), medium noise (40% to 60%) as well as high noise density (70% to 90%). The efficacy of the algorithm using MATLAB R2013 is verified through both medical images, simulated and actual. In order to assess the computer complexity of the Altera algorithm for location, power and time using Cyclone II EP2C35F672C6, Cyclone II and Stratix III EP3SL150F1152C2, this algorithm is also implemented in the Altera Field Programable Gate Array (FPGA).