No abstract

Multi magnon interaction with carriers limits the magnon lifetime in FMs compared to AFMs. The longer lifetime, double degeneracy, and higher group velocity of magnons in AFMs generally lead to higher first-order magnon-carrier drag thermopower.

The performance of a low-power single-lead armband in generating electrocardiogram (ECG) signals from the chest and left arm was validated against a BIOPAC MP160 benchtop system in real-time. The filtering performance of three adaptive filtering algorithms, namely least mean squares (LMS), recursive least squares (RLS), and extended kernel RLS (EKRLS) in removing white (W), power line interference (PLI), electrode movement (EM), muscle artifact (MA), and baseline wandering (BLW) noises from the chest and left-arm ECG was evaluated with respect to the mean squared error (MSE). Filter parameters of the used algorithms were adjusted to ensure optimal filtering performance. LMS was found to be the most effective adaptive filtering algorithm in removing all noises with minimum MSE. However, for removing PLI with a maximal signal-to-noise ratio (SNR), RLS showed lower MSE values than LMS when the step size was set to 1 × 10−5. We proposed a transformation framework to convert the denoised left-arm and chest ECG signals to their low-MSE and high-SNR surrogate chest signals. With wide applications in wearable technologies, the proposed pipeline was found to be capable of establishing a baseline for comparing left-arm signals with original chest signals, getting one step closer to making use of the left-arm ECG in clinical cardiac evaluations.

A multi-metric armband system capable of simultaneous measurement of electrocardiogram (ECG) and electrodermal activity (EDA) from left arm is presented for the assessment of sympathetic nervous response. The performance of of EDA module was validated against a BIOPAC MP160 system while a single-lead ECG module was used to capture heart rate variations simultaneously. The presented armband is anticipated to provide reliable data for the detection and prognosis of different physical and neuropsychological disorders such as autism and Alzheimer's disease. Keywords: Sympathetic Nervous System; EDA; ECG; Left Arm; I. Introduction Sympathetic nervous response (SNR) has been found to be correlated with numerous bodily and mental health disorders. Frequency analysis of heart rate variability (HRV) and electrodermal activity (EDA) are the only non-invasive methods to assess the dynamics of the autonomic nervous system. However, frequency analysis of the HRV method cannot separate the dynamics of the sympathetic and parasympathetic nervous systems. EDA is a reflection of the autonomic innervation of sweat glands resulting in the reflection of activity within the sympathetic branch of the autonomic nervous system. EDA, however, suffers from motion artifact and movement. Therefore, the simultaneous monitoring of EDA and electrocardiogram (ECG) will provide more reliable and comprehensive indices of sympathetic nerve activities. Time-domain features of EDA along with ECG has been commonly utilized to assess the overall SNR. The mere relationship between the EDA and SNR has been investigated via different approaches such as the analysis of power spectral density and time-varying analysis of EDA. The combined use of ECG/HRV and EDA has lent itself to assessing mental stress and numerous mental disorders such as schizophrenia, autism, Down syndrome. Also, it was found that EDA and SNR are heavily invested in the volume of white matter in the cingulum and inferior parietal and thus with Alzheimer’s disease. In this paper we have laid out the groundwork required for SNR evaluation, which takes advantage of the simultaneous EDA and ECG data acquisition from the left arm. II. System Design The wearable armband system, Gen2.0, is constructed with commercial off-the-shelf components (COTS) and equipped with a BLE-enabled Nordic nRF51822 microcontroller unit (MCU) and is considered low-power [1]. We custom designed the filters for the AD8232 ECG analog frontend chip so that the left arm ECG signal is clear and reliable. The analog frontend for EDA was also custom designed using LTC 6081 op-amp to achieve a sufficiently high resolution. The MCU interfaces with an ADC 1114 for ECG measurement and uses an internal ADC for interfacing the EDA signal. Under the control of an internal timer, the ADC chip examples voltage and conductance signals from ECG and EDA frontends at a specific point depending on sampling frequency. Then the signals are converted into digital data and fed into the MCU. The MCU stores it inside buffers (capacity of 64 data samples) for ECG and EDA data separately. Every time the buffer is full, data will be either stored in a flash or transmitted via BLE. Figure 1 shows the system and the optimal ECG electrodes’ positions. III. EDA Optimization And Validation The performance of our armband ECG was validated against the BIOPAC direct ECG1 system. The optimal positions for EDA electrodes were determined through a set of external physical stimuli (pinch) tests. The accuracy of the used ECG module has already been verified in our previous study [2]. Figure 2 shows the system diagram and the candidate EDA electrode positions. Both the BIOPAC and our Gen2.0 modules were assigned an equal sample rate of 10Hz. IV. Results And Discussion B. EDA Validation EDA curves were collected from a 38YO non-smoker male subject by both the BIOPAC and our proposed systems in an IRB- approved study (12418, North Carolina State University). The subject relaxed for an undisclosed amount of time (about 25s) and was then pinched in the right hand for 1s and relaxed for the rest of the test. The optimal electrodes’ positions for our EDA module were found to be positions 10&13 or 1&2 (Figure 2). Figure 3 shows that the EDA obtained by the proposed system was more stable than that of BIOPAC addressing the relaxation and tension periods more distinctively. C. Simultaneous EDA and ECG A testing protocol including resting, reading Latin, and being pinched (for 1s) and resting each for about 60s was followed. The ECG and EDA measurements were both carried out using the same armband and in real-time (Figures 4). Considering the EDA results, the nervous response to the physical stimulus (being pinched) was stronger than the cognitive stimulus (reading). HRV analysis was done in time domain by detecting RR intervals in order to capture instant changes in heart rate in the ECG data. V. Conclusions A wearable and low-power multi bio-metric armband system was proposed and validated for simultaneous monitoring of ECG and EDA. The current research lays out the groundwork for more in-depth characterization of the autonomic sympathetic nervous system and its relationship to both bodily and neurological disorders obtained from the left arm. References Nozariasbmarz et al, "Review of wearable thermoelectric energy harvesting: From body temperature to electronic systems," Applied Energy, vol. 258, pp. 114069, 2020. Mohaddes et al, "A Pipeline for Adaptive Filtering and Transformation of Noisy Left-Arm ECG to Its Surrogate Chest Signal," Electronics (Basel), vol. 9, (5), pp. 866, 2020. Figure 1

Correction for ‘Magnon-drag thermopower in antiferromagnets versus ferromagnets’ by Md. Mobarak Hossain Polash et al., J. Mater. Chem. C, 2020, 8, 4049–4057, DOI: 10.1039/C9TC06330G.