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Equivalent circuit diagram for (a) wet-contact Ag/AgCl electrode, (b) typical capacitive electrode, and (c) negative impedance capacitive electrode.
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Many applications utilize flexible capacitive electrode implementations and analog front-end optimizations for improving noncontact electrocardiogram (ECG) measurements. However, the influence of the negative impedance in the circuit front-end on noncontact measurements has not been investigated. The objective of this study was to develop a negativ...
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Context 1
... this section, an equivalent model for the negative impedance capacitive electrode is presented and compared with the wet-contact Ag/AgCl electrode and a typical capacitive electrode. Fig. 1(a) and (b) shows the equivalent circuit diagram of the wet-contact Ag/AgCl electrode and a typical capacitive electrode. The dermis layer is equivalent to the resistance R d ; the epidermis layer is considered to have a resistance R e and capacitance C e in parallel. In Fig. 1(b), the cloth layer was considered as a resistance R E in ...
Context 2
... the wet-contact Ag/AgCl electrode and a typical capacitive electrode. Fig. 1(a) and (b) shows the equivalent circuit diagram of the wet-contact Ag/AgCl electrode and a typical capacitive electrode. The dermis layer is equivalent to the resistance R d ; the epidermis layer is considered to have a resistance R e and capacitance C e in parallel. In Fig. 1(b), the cloth layer was considered as a resistance R E in parallel with a capacitor C E , where R E is the resistance of the fabric surface and C E is the skin-electrode capacitance. The skin, cloth layer, and electrode sensing surface can be considered as a capacitor. The skin-electrode capacitance was determined from the cloth ...
Context 3
... area between the electrode surface and cloth, according to (1), where ε 0 is the permittivity of vacuum, ε r is the relative permittivity of the cloth, A represents the area of capacitive coupling between the electrode and body, and d is the thickness of the cloth layer. In this study, the negative impedance capacitive electrode shown in Fig. 1(c) is proposed Fig. 2 shows a circuit schematic of the negative impedance capacitive electrode, where Z skin , Z cloth , Z neg , and Z in are the skin, cloth, negative, and preamplifier input impedances, respectively, given by the following ...
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Automated bioelectric signal analysis has an important application in the wisdom medical care. In this work, we focus on ECG-signal and address a novel approach for cardiac arrhythmia diseases classification. We designed a novel analysis framework which extract different feature transformations from ECG signals. And we trained the ANN model for mul...
Citations
... This gel-free technique has been further developed in recent years for ambulatory ECG monitoring [9], [10], [11], [12]. Many studies have been conducted to develop capacitive ECG monitoring systems, including the design of the electrode, e.g., size, material, and recording circuits [13], the development of noises reduction algorithms [14], [15], and their application to assess cardiac health [11], [9]. ...
... Due to the high sensitivity of capacitive electrode to noise and the lack of effective noise-reduction algorithms, almost all the existing capacitive ECG systems can merely be employed for heart rate monitoring [24], [13], [25], [26]. Accurate morphological analysis using capacitive ECG has seldom been reported, hampering the application of capacitive ECG systems in clinical diagnosis. ...
... However, the capacitive electrode is more vulnerable to various kinds of noises as compared to the wet electrode. Hence, most of previously proposed capacitive systems were only verified in terms of RR interval [24], [13], [25], [26]. In the present study, we try to explore the feasibility of using capacitive ECG for morphological analysis, and thus to extract clinical meaningful parameters for diagnosing purpose. ...
Objective: Capacitive electrocardiography (cECG) has been proposed for ambulatory cardiac health monitoring. However, due to the high sensitivity of capacitive electrodes to various noise sources, particularly the power-line interference (PLI) and motion artifacts (MA), the existing capacitive systems are only verified in terms of RR interval. The aim of the present study is to explore the feasibility of using cECG for morphological analysis, and thus to extract clinical meaningful parameters. Methods: A capacitive electrode with active guarding is realized. A phase-locked-loop (PLL) based adaptive canceller is employed to remove PLI from the cECG. Wavelet analysis is adopted to cancel other noises. The developed capacitive system and algorithms are evaluated by real ECG measurements on 7 volunteers using 3-lead configuration. The correlation coefficient (CC) between the processed cECG and the wet ECG is calculated in two different conditions: with and without the QRS complex. Several frequently used diagnostic parameters, i.e., RR interval, QRS interval, P segment, T segment, ST segment, are extracted and compared with that obtained from the wet ECG. Results: High CCs are observed between the cECG and the wet ECG in both conditions, i.e.,
$0.97\pm 0.03$
with the QRS complex and
$0.92\pm 0.07$
without the QRS complex. Besides, RR interval extracted from the two different ECG signals are identical for each subject. Other diagnostic parameters are quite similar. Significance: Our results suggest cECG to be reliable for ambulatory heart rate monitoring. The results also indicate the feasibility of using cECG for clinical diagnosis.
... The relation between I 1 and U 1 can be expressed as Eq. (21), according to Eqs. (18) and (20). ...
... (37). Where ε, E, and D are permittivity, electric field intensity, electric flux density, respectively [17][18][19]. ...
Wireless power transmission (WPT) can provide an alternative for wireless power in implantable medical devices (IMDs). The WPT in implantable medical devices will involve many emerging biomedical topics, such as implantable pacemakers , optogenetic devices, and bio-impedance sensors. To this end, this chapter comprehensively reviews the recent WPT studies for those mentioned above emerging biomedical applications. The specific key components are carried out for those applications. Besides, the operation principle and system design are presented. In conclusion, this chapter's significance can help evolve reliable implantable device development in the future.
... There is much-unexplored ground to explore in the subject area of non-physiological artifacts removal from EEG especially motion artifacts, which provides excellent future scope for utilizing machine learning methods in the domain of EEG signal denoising. The scope of this method is not limited to EEG signal only, it can be applied in removal of motion artifacts present in other biological signals such as electrocardiogram (ECG) [36], [37], electromyogram (EMG) [2], Plethysmogram (PPG) [38]- [40] etc. ...
Motion artifacts are one of the most challenging non-physiological noise sources present in the biomedical signal, which can hinder the true performance of EEG-based neuro-engineering applications. Therefore, motion artifact removal from EEG signals can be potentially the utmost protuberant research topic. To solve this issue, a hybrid signal denoising framework which comprises of modified empirical mode decomposition (EMD) and an optimized Laplacian of Gaussian (LoG) filter is proposed for suppression of motion artifacts from EEG signals. The modified-EMD decomposes the single-channel noisy EEG signal into a set of the optimal number of intrinsic mode functions (IMFs). Further, the optimized LoG filter has been applied to the motion artifact intermixed EEG signal which is considered as low-frequency noise likely to present at low-frequency IMFs. This filter performs smoothing of the signal and removes background noises or artifacts from the EEG signal. The denoised signal is reconstructed by adding filtered output signal and high-frequency IMFs. The robustness of the proposed method is demonstrated through simulated and real EEG data with artifacts. The experimental values of the different sets of performance assessment metrics reveal that the proposed algorithm outperforms the existing state-of-the-art EEG signal denoising algorithms for suppression of motion artifacts from EEG signals.
... In practical operation, the thickness and humidity are the main factors affecting the cotton's biological dielectric properties. In terms of fabric thickness, the cECG signal can be obtained from cotton fabric with a thickness of less than 1.8 mm [28]- [30]. However, due to the influence of fabric humidity, even if a similar circuit topology is used, the signal quality seems to be inconsistent. ...
Noncontact capacitive coupling electrocardiogram (cECG) is considered to have a broad application prospect for its comfort and noninvasive. As an important part of cECG transmission path, the characteristics of dielectric materials significantly affect the signal quality. This article studies the influence on the dielectric properties of coupling capacitance, human body–electrode impedance, and cECG signal quality from the perspectives of cotton fabric humidity and thickness and gives suggestions on the application of cECG. First, the effects of fabric humidity and thickness on human body–electrode impedance are analyzed theoretically. Then, the human body–electrode impedance and cECG signal quality index (SQI) are recorded and calculated to evaluate how thickness and humidity affect the sensing performance of cECG. The results show that influenced by the fabric thickness and humidity, the impedance values reveal observable changes varying from 5.6
$\text{G}\Omega $
(thickness: 1.00 mm and moisture regain: 3.2%) to 50.4
$\text{M}\Omega $
(thickness: 0.18 mm and moisture regain: 23.2%) at 0.5 Hz, and the impedance change will cause the impedance mismatch between the two electrodes, resulting in poor signal quality. According to the trend of SQI, the recommended moisture regain under different thicknesses is given. When the fabric thickness is 0.18, 0.35, and 1.00 mm, the moisture regain should be higher than 8.5%, 14.6%, and 23.2%, respectively, so as to obtain a relatively high-quality cECG signal. The design parameters discussed in this article could provide a supporting evidence for the design of efficient noncontact cECG systems in the future.
... The filter implementation is also a strategy for EMI elimination in medical devices [24][25][26][27]. For example, the typical ranges of P, R, T waves in ECG are 20 to 40 Hz, 18 to 50 Hz, and 0 to 10 Hz [28], as shown in Table 2. ...
Electromagnetic compatibility (EMC) in biomedical applications is a significant
issue related to the user's life safety, especially in implantable medical devices.
Cardiovascular diseases and neurodegenerative disorders are the main chronic disease
worldwide that rely on implantable treatment devices such as cardiac pacemakers
and vagus nerve stimulators. Both devices must have high EMC to avoid
electromagnetic interference-induced health risks, even death during the treatment.
Thus, it is important to understand how EMI can affect implantable devices and
proactively protect devices from electromagnetic interference, providing reliable
and safe implantable device therapy. To this end, this chapter comprehensively
introduces the clinical issues and provides EMC requirements for the implantable
device such as a cardiac pacemaker and vagus nerve stimulator. The significance of
this chapter is to present the EMC important issues in medical engineering that can
help to evolve reliable and secure implantable device development in the future.
... Difference in textile requires varied separation between electrode and body. Thus there are works which focused on the circuitry improvement and signal processing to compensate these factors for enabling contactless ECG acquisition [9,10,11,12]. However, analysis of existing research indicates that it is possible to implement it for monitoring and screening purposes. ...
Contactless sensors have brought a new way of patient monitoring for healthcare applications. The patient is only required to be in the vicinity of the sensor for health monitoring. Also, there is opportunity for a contactless sensing system to monitor multiple patients simultaneously, which is not possible with wearable contact-based sensors. As a result, a contactless monitoring system would require fewer resources and can be cost effective. In this chapter, different contactless physiological monitoring is discussed. Contactless cardiac and blood related monitoring is discussed including Electrocardiogram (ECG), heart rate, blood pressure etc. Respiratory monitoring is another important area which is included. Monitoring of neurological diseases is reviewed according to different applications. A limited analysis of blood glucose monitoring in diabetes is also provided. Majority of these works involve video camera, motion sensors and Doppler radar as a contactless sensor. In this work, it is intended to analyze the pros and cons of different contactless monitoring and their feasibility to use in various healthcare requirements such as hospitals, nursing homes and homes.
Continuous monitoring of heart rate (HR) and respiratory rate (RR) plays a pivotal role in evaluating cardiopulmonary coordination (CRC). Wearable technologies have empowered long-term monitoring of cardiopulmonary parameters across a spectrum of activities, including physical exercise, mental states, dietary habits, and the effects of pharmaceutical interventions. This study presents a wearable pocket-sized cardiopulmonary sensor based on biomedical eddy current sensing technology. The novel sensor utilizes the magnetic coupling between AC coil-produced magnetic fields and cardiopulmonary eddy current-induced counteracting magnetic fields to measure resonant frequency variation in response to synchronized cardiac and lung activities without skin contact. The proposed pocket-sized sensor is characterized by its compact form with a diameter of 4.53 cm and supporting wireless data transmission. Importantly, the influence of resonant frequencies on cardiopulmonary signals was investigated through capacitor adjustments, resulting in personalized optimization of measurement conditions. In practical applications, the effect of wide fabric thicknesses on measurement performance was assessed, demonstrating identifiable signals for allowing the determination of HR and RR. In real-life scenarios, HR and RR were measured during both resting and physical exercise conditions, effectively showcasing the potential of a single compact sensor to capture essential CRC indicators. These findings provide strong validation for the novel sensor’s promising potential in the realm of wearable, long-term, all-in-one cardiopulmonary healthcare.
Unconstrained vital sign monitoring technology has been a hot topic of research in recent years as people increasingly focus on their health status and sleep quality. Conventional contact electrocardiogram (ECG) measurement technology has difficulties in meeting the application needs of sleep scenarios due to short validity period and low comfort. To handle this problem, ECG measurement using non-contact capacitively coupled sensing technology, which do not require direct skin contact for ECG signal acquisition, could be an alternative. However, this method requires fixing the ECG measurement electrodes in two specific positions, which makes it difficult to meet the needs for body differences and multiple sleep postures. This study presents a non-contact sleep ECG measurement system based on a flexible electrode array, which can acquire ECG signals through pajamas and bed sheets using the principle of capacitive coupling. The experiment results showed that the average signal-to-noise ratio (SNR) of the data measured in the supine and lateral postures of subjects was above 30 dB, and the respiratory waves (RW) with the same frequency as contact ECG measurement system could be extracted. We also used this system to acquire ECG signals from multiple points on the dorsal of four subjects and analyzed their variation patterns, and the experimental results were in high agreement with the ECG potential employment recorded by 32-lead body surface maps. Meanwhile, the T-wave, R-wave, S-wave peaks and the ratio of T and R waves of the collected ECG signals were extracted as features. Based on this, the action classification model was trained using long short-term memory network (LSTM) to classify the postures of four subjects in supine, left lateral and right lateral positions, and the classification accuracies were 95.83%, 98.94%, 98.80% and 98.30%, respectively. Although our prototype system must be further improvement and development, these results provide a strong basis for future realization of electrode array-based ECG signal selection algorithms. The proposed system has great potential value in detecting abnormalities of ECG, heart rate (HR), respiration and other vital indicators during human sleep and realizing personalized medical treatment.
Sensing and computing based on intelligent fabrics can meet the ultra-reliable and low-latency communication (URLLC) needs of sixth-generation wireless (6G) by integrating sensing units into fabric fibers to perceive user data. Although some researchers have designed sensing or computing solutions, such solutions have not been well explored. In this paper, we consider the joint sensing adaptation and model placement in a 6G fabric space. We first propose an intelligent-fiber-driven 6G fabric computing network to minimize acquisition latency so as to ensure accuracy. Then, we formulate an optimization model that takes the fabric sampling rate, sampling density, and model placement as variables. To solve the model, we propose an effective learning algorithm based on deep reinforcement learning. That is, by transforming the optimization problem into a state space, action space, and reward function, we design an optimal policy. The simulation results show that our proposed scheme can achieve optimal sensing and computing compared with several baseline algorithms.
Capacitive electrocardiogram (cECG) systems are increasingly used for the monitoring of cardiac activity. They can operate within the presence of a small layer of air, hair or cloth and do not require a qualified technician. They can be integrated into wearables, clothing or objects of daily life, such as beds or chairs. While they offer many advantages over conventional electrocardiogram systems (ECG) that rely on wet electrodes, they are more prone to be affected by motion artifacts (MAs). These effects, which are due to the relative movement of the electrode in relation to the skin, are several orders of magnitude higher than ECG signal amplitudes, they occur in frequencies that might overlap with the ECG signal, and they may saturate the electronics in the most severe cases. In this paper, we provide a detailed description of MA mechanisms that translate into capacitance variations due to electrode-skin geometric changes or into triboelectric effects due to electrostatic charge redistribution. A state-of-the-art overview of the different approaches based on materials and construction, analog circuits and digital signal processing is provided as well as the trade-offs to be made using these techniques, to mitigate MAs efficiently.
