Portable Monitoring Research Articles

Recent Advances and Challenges in Textile Electrodes for Wearable Biopotential Signal Monitoring: A Comprehensive Review.

The technology of wearable medical equipment has advanced to the point where it is now possible to monitor the electrocardiogram and electromyogram comfortably at home. The transition from wet Ag/AgCl electrodes to various types of gel-free dry electrodes has made it possible to continuously and accurately monitor the biopotential signals. Fabrics or textiles, which were once meant to protect the human body, have undergone significant development and are now employed as intelligent textile materials for healthcare monitoring. The conductive textile electrodes provide the benefit of being breathable and comfortable. In recent years, there has been a significant advancement in the fabrication of wearable conductive textile electrodes for monitoring biopotential signals. This review paper provides a comprehensive overview of the advances in wearable conductive textile electrodes for biopotential signal monitoring. The paper covers various aspects of the technology, including the electrode design, various manufacturing techniques utilised to fabricate wearable smart fabrics, and performance characteristics. The advantages and limitations of various types of textile electrodes are discussed, and key challenges and future research directions are identified. This will allow them to be used to their fullest potential for signal gathering during physical activities such as running, swimming, and other exercises while being linked into wireless portable health monitoring systems.

Polyethyleneimine/polyethylene-glycol/Ti3C2Tx composites for ultrasensitive room-temperature CO2 sensing

Bestowed with abundant surface terminations and good electrical conductivity, transition-metal carbides/nitrides (MXene) have potential applications as gas sensors. Here, Ti3C2Tx-HF and Ti3C2Tx-HCl MXenes were functionalized with polyethyleneimine/polyethylene-glycol (PEI/PEG) to form PEI/PEG/Ti3C2Tx-HF (P/P/Ti-1) and PEI/PEG/Ti3C2Tx-HCl (P/P/Ti-2) composites for gaseous CO2 detection at room temperature. The morphologies and elemental compositions indicated that Ti3C2Tx-HCl more easily combined with PEI/PEG than did Ti3C2Tx-HF. The response of the P/P/Ti-2 sensor to 2000 ppm CO2 was six-fold better than that of the P/P/Ti-1 sensor. In addition, the P/P/Ti-2 sensor exhibited higher selectivity and good stability. The enhanced P/P/Ti-2 sensing was attributed to heterojunctions formed at the interfaces between Ti3C2Tx-HCl and PEI/PEG because of the thinly and fully covered PEI/PEG layer on the Ti3C2Tx-HCl via abundant -OH functional groups. This was confirmed by current-voltage curves, and good Ti3C2Tx conductivity. A portable wireless CO2 indoor monitoring system based on the P/P/Ti-2 sensor was demonstrated. These results provide a better understanding of the physical and gas-sensing properties of Ti3C2Tx materials.

A 0.053 mm2 10-bit 10-ks/s 40-nW SAR ADC with pseudo single ended switching procedure for bio-related applications

This paper presents a power and area efficient pseudo single ended switching ADC for portable biomedical signal monitoring applications. The switching method features an 8-bit binary-weighted DAC array with a novel layout method called limited bottom-plate routing (LBPR) maintaining both feasibility and symmetry, a dynamic biased comparator and dynamic control logic. As a result, the proposed ADC saves 96.7% power consumption and 87.5% area occupation compared with conventional SAR ADC. Also, a novel DAC array layout method is promoted maintaining both feasibility and symmetry. A prototyped chip including a proposed ADC is fabricated in a 0.13 μm CMOS process. On condition of 0.6 V supply voltage and 10 kS/s sampling rate, the measured signal-to-noise-distortion (SNDR) and spurious free dynamic range (SFDR) are 57.8 dB and 64.3 dB, respectively. Besides, the ADC consumes only 40 nW power and 0.053 mm2 area achieving a figure-of-merit (FoM) of 6.26 fJ/conversion-step. Low power and low area occupation make it a good candidate for biomedical signal monitoring applications.

Deep Learning-Based Portable Image Analysis System for Real-Time Detection of Vespa velutina

Honeybees pollinate over 75% of the total food resources produced annually, and they produce valuable hive products, such as bee pollen, propolis, and royal jelly. However, species such as the Asian hornet (Vespa velutina) feed on more than 85% of honeybees, causing a decline in their population and considerable damage to beekeepers in Korea. To prevent damage to honeybees, a portable real-time monitoring system was developed that detects V. velutina individuals and notifies users of their presence. This system was designed with a focus on portability and ease of installation, as V. velutina can be found in various areas of apiary sites. To detect V. velutina, an improved convolutional neural network YOLOv5s was trained on 1960 high-resolution (3840×2160) image data. At the confidence threshold of ≥0.600 and intersection over the union of ≥0.500, the performance of the system in terms of detection accuracy, precision, recall, F1 score, and mean average precision was high. A distance-based performance comparison showed that the system was able to detect V. velutina individuals while monitoring three beehives. During a field test of monitoring three beehives, the system could detect 83.3% of V. velutina during their hunting activities and send alarms to registered mobile application users.

Polyaniline/Prussian blue nanolayer enhanced electrochemical sensing of H2O2 in EBC using an integrated condensation facemask

The low concentration of respiratory H2O2 requires high detection sensitivity and stability. In this work, a Polyaniline/Prussian Blue Nanolayer (nano-PANI/PB) coated electrode using a 30 nm porous alumina template was fabricated to enhance the roughness of the electrode surface to improve the sensitivity of H2O2 detection. The limit of detection of 2.52 μM was realized within a detection range of 0–1 mM. In addition, a condensing device that uses microchannel with a secondary flow structure was designed with computational fluid dynamics to downsize the exhaled breath condensing device and improve the condensing efficiency. Increasing the contact area between exhaled breath and condensing materials in the microchannel can obviously increase the amount of condensate. The performance of the condensing device was visualized through the condensing of sodium fluorescein solution. A multichannel condensing device was integrated with screen printing electrode, three-electrode detection circuit and display module into a N95 mask to realize portable Exhaled breath condensate (EBC) detection and showed a favorable feasibility. The application of this portable exhaled breath measurement device could be expanded through modifying the specific biomarker probes on electrode, and may provide some inspirations for the development of wearable and portable non-invasive healthy monitoring devices.

Lensfree Air-Quality Monitoring of Fine and Ultrafine Particulate Matter Using Vapor-Condensed Nanolenses.

Current commercial air-quality monitoring devices lack a large dynamic range, especially at the small, ultrafine size scale. Furthermore, there is a low density of air-quality monitoring stations, reducing the precision with which local particulate matter hazards can be tracked. Here, we show a low-cost, lensfree, and portable air-quality monitoring device (LPAQD) that can detect and measure micron-sized particles down to 100 nm-sized particles, with the capability to track and measure particles in real time throughout a day and the ability to accurately measure particulate matter densities as low as 3 μg m-3. A vapor-condensed film is deposited onto the coverslip used to collect particles before the LPAQD is deployed at outdoor monitoring sites. The vapor-condensed film increases the scattering cross section of particles smaller than the pixel size, enabling the sub-pixel and sub-diffraction-limit-sized particles to be detected. The high dynamic range, low cost, and portability of this device can enable citizens to monitor their own air quality to hopefully impact user decisions that reduce the risk for particulate matter-related diseases.

Inter- and intrarater reliability of short-term measurement of heart rate variability on rest in chronic obstructive pulmonary disease (COPD).

Individuals with COPD have an imbalance of cardiac autonomic control. In this context, HRV is considered an important tool for assessing cardiac sympathetic and parasympathetic balance, however it is a dependent evaluator measure and subject to methodological biases that may compromise the interpretation of results. This study examines the inter- and intrarater reliability of HRV parameters derived from short-term recordings in individuals with COPD. Fifty-one individuals of both genders with COPD clinical diagnosis confirmed by the pulmonary function test and aged ≥50 years were included. The RR interval (RRi) were recorded during a 10 min period on supine position using a portable heart rate monitor (Polar® H10 model). The data were transferred into Kubios® HRV Standard analysis software and analyzed within the stable sessions containing 256 sequential RRi. The intraclass correlation coefficient (ICC) ranged from 0.942 to 1.000 according to the intrarater analysis by Researcher 01 and 0.915 to 0.998 to the intrarater by Researcher 02. The interrater ICC ranged from 0.921 to 0.998. The coefficient of variation was up to 8.28 for Researcher 01 intrarater analysis, 9.06 for Researcher 02 intrarater analysis and 13.07 for interrater analysis. The measurement of HRV using a portable heart rate device in individuals with COPD present acceptable values of intra- and interrater reliability, supporting the use of HRV in the clinical and scientific scenario. Furthermore, it is important that the data analysis be performed by the same experienced evaluator.

Incidence, severity and detection of blood pressure and heart rate perturbations in postoperative ward patients after noncardiac surgery

Study objectiveWe sought to determine changes in continuous mean and systolic blood pressure and heart rate in a cohort of non-cardiac surgical patients recovering on the postoperative ward. Furthermore, we estimated the proportion of vital signs changes that would remain undetected with intermittent vital signs checks. DesignRetrospective cohort. SettingPost-operative general ward. Patients14,623 adults recovering from non-cardiac surgical procedures. Interventions & measurementsUsing a wireless, noninvasive monitor, we recorded postoperative blood pressure and heart rate at 15-s intervals and encouraged nursing intervention as clinically indicated. Main results7% of our cohort of 14,623 patients spent >15 sustained minutes with a MAP <65 mmHg, and 23% had MAP <75 mmHg for 15 sustained minutes. Hypertension was more common, with 67% of patients spending at least 60 sustained minutes with MAP >110 mmHg. Systolic pressures <90 mmHg were present for 15 sustained minutes in about a fifth of all patients, and 40% of patients had pressures >160 mmHg sustained for 30 min. 40% of patients were tachycardic with heart rates >100 beats/min for at least continuous 15 min and 15% of patients were bradycardic at a threshold of <50 beats/min for 5 sustained minutes. Conventional vital sign assessments at 4-h intervals would have missed 54% of mean pressure episodes <65 mmHg sustained >15 min, 20% of episodes of mean pressures >130 mmHg sustained >30 min, 36% of episodes of heart rate > 120 beats/min sustained <10 min, and 68% of episodes of heart rate sustained <40 beats per minute for >3 min. ConclusionsSubstantial hemodynamic disturbances persisted despite implementing continuous portable ward monitoring coupled with nursing alarms and interventions. A significant proportion of these changes would have gone undetected using traditional intermittent monitoring. Better understanding of effective responses to alarms and appropriate interventions on hospital wards remains necessary.

Nonlinear features of photoplethysmography signals for Non-invasive blood pressure estimation

ObjectiveContinuous monitoring of blood pressure (BP) plays an essential role in the prognosis and prevention of hypertension and related cardiovascular diseases. Moreover, the ever-increasing demand for portable continuous health monitoring systems coupled with promising capabilities of photoplethysmography (PPG) sensors for developing easy-to-use, portable wearable devices have motivated many researchers toward applying PPG signals for non-invasive health monitoring. Nonlinear nature of BP and proven capability of the Poincaré plot for analyzing dynamic behavior of nonlinear systems motivated us to use this powerful tool for BP estimation. This study aims to explore the dynamical behavior of PPG signals to assist feature extraction for machine-learning (ML) algorithms to estimate BP. We proposed a Poincaré-based feature extraction method for BP estimation with no need to extract precise local points on the PPG signal. MethodsFirst, a Poincaré plot of 10-s segments of the PPG signal was prepared. Then, three distinct time series were retrieved from Poincaré mapping of PPG signals; following this, numerous indices were extracted from the three time series. The F-Test feature selection method was applied to pick the most effective features. Finally, the picked features were fed into different ML algorithms for the estimation of BP. ResultsThe proposed method was validated using a subset of the Medical Information Mart for Intensive Care II (MIMIC-II) database containing ambulatory blood pressure (ABP) and PPG records. The performance evaluation was carried out in terms of mean absolute error (MAE), standard deviation (STD), and Pearson’s correlation coefficient. According to the results, application of the Gaussian process regression (GPR) led to the best performance for both systolic and diastolic BP (0.79 ± 3.08 and 1.38 ± 4.53 mmHg, respectively). Satisfying the criteria for Advancement of Medical Instrumentation (AAMI), it was rated as Grade A for systolic and diastolic BP measurements under terms of British Hypertension Society (BHS) standards, which confirms the efficiency of the Poincaré-based features for BP estimation. ConclusionThe results justify the usefulness of the proposed Poincaré-based features as a powerful tool in BP estimation scenarios.

The relationship between vitamin D and short-term blood pressure variability.

This study aimed to evaluate the relationships between serum levels of 25-hydroxyvitamin D (25(OH)D) and short-term blood pressure variability (BPV) in newly diagnosed hypertensive patients. One hundred newly diagnosed patients with stage one essential hypertension were included and divided into two groups, the deficient and non-deficient groups, according to their 25(OH)D level. The blood pressure was recorded automatically by a portable ambulatory blood pressure monitor for 24 h. In the present study, there was no significant relationship between vitamin D levels and short-term BPV or other parameters derived from ambulatory blood pressure monitoring (ABPM) (P > 0.05). Age (r = 0.260, P = 0.009), serum phosphorus (r = 0.271, P = 0.007), and cholesterol levels (r = 0.310, P = 0.011) were positively correlated with 25(OH)D levels, while glomerular filtration rate (r = -0.232, P = 0.021) negatively correlated with vitamin D levels. There was no crude or adjusted relationship between the levels of 25(OH)D and any parameters of ABPM in multiple linear regression analysis. Although the relationship between vitamin D levels and cardiovascular diseases has been confirmed, vitamin D deficiency does not cause an increase in cardiovascular risk by influencing the short-term BPV or other parameters derived from ABPM.

Identification of major depression patients using machine learning models based on heart rate variability during sleep stages for pre-hospital screening

With the COVID-19 pandemic causing challenges in hospital admissions globally, the role of home health monitoring in aiding the diagnosis of mental health disorders has become increasingly important. This paper proposes an interpretable machine learning solution to optimise initial screening for major depressive disorder (MDD) in both male and female patients. The data is from the Stanford Technical Analysis and Sleep Genome Study (STAGES). We analyzed 5-min short-term electrocardiogram (ECG) signals during nighttime sleep stages of 40 MDD patients and 40 healthy controls, with a 1:1 gender ratio. After preprocessing, we calculated the time-frequency parameters of heart rate variability (HRV) based on the ECG signals and used common machine learning algorithms for classification, along with feature importance analysis for global decision analysis. Ultimately, the Bayesian optimised extremely randomized trees classifier (BO-ERTC) showed the best performance on this dataset (accuracy 86.32%, specificity 86.49%, sensitivity 85.85%, F1-score 0.86). By using feature importance analysis on the cases confirmed by BO-ERTC, we found that gender is one of the most important factors affecting the prediction of the model, which should not be overlooked in our assisted diagnosis. This method can be embedded in portable ECG monitoring systems and is consistent with the literature results.

A Financial Incentives Program to Promote Smoking Cessation Among Recently Hospitalized Individuals: Feasibility and Acceptability Study

Hospitalization is an opportunity to engage underserved individuals in tobacco treatment who may not otherwise have access to it. Tobacco treatment interventions that begin during hospitalization and continue for at least 1 postdischarge month are effective in promoting smoking cessation. However, there is low usage of postdischarge tobacco treatment services. Financial incentives for smoking cessation are an intervention in which participants receive incentives, such as cash payments or vouchers for goods, to encourage individuals to stop smoking or to reward individuals for maintaining abstinence. We sought to determine the feasibility and acceptability of a novel postdischarge financial incentive intervention that uses a smartphone application paired to measurements of exhaled carbon monoxide (CO) concentration levels to promote smoking cessation in individuals who smoke cigarettes. We collaborated with Vincere Health, Inc. to tailor their mobile application that uses facial recognition features, a portable breath test CO monitor, and smartphone technology to deliver financial incentives to a participant's digital wallet after the completion of each CO test. The program includes 3 racks. Track 1: Noncontingent incentives for conducting CO tests. Track 2: Combination of noncontingent and contingent incentives for CO levels <10 parts per million (ppm). Track 3: Contingent incentives only for CO levels <10 ppm. After obtaining informed consent, we pilot-tested the program from September to November 2020 with a convenience sample of 33 hospitalized individuals at Boston Medical Center, a large safety-net hospital in New England. Participants received text reminders to conduct CO tests twice daily for 30 days postdischarge. We collected data on engagement, CO levels, and incentives earned. We measured feasibility and acceptability quantitatively and qualitatively at 2 and 4 weeks. Seventy-six percent (25/33) completed the program and 61% (20/33) conducted at least 1 breath test each week. Seven patients had consecutive CO levels <10 ppm during the last 7 days of the program. Engagement with the financial incentive intervention as well as in-treatment abstinence was highest in Track 3 that delivered financial incentives contingent on CO levels <10 ppm. Participants reported high program satisfaction and that the intervention helped motivate smoking cessation. Participants suggested increasing program duration to at least 3 months and adding supplemental text messaging to increase motivation to stop smoking. Financial incentives paired to measurements of exhaled CO concentration levels is a novel smartphone-based tobacco cessation approach that is feasible and acceptable. Future studies should examine the efficacy of the intervention after it is refined to add a counseling or text-messaging component.

0189 Particulate Matter 2.5: Examining How Its Exposure Affects Perceived Sleep Quality and Electroencephalography Based Sleep Metrics

Abstract Introduction Individuals are exposed to various air pollutants every day, both when they are inside and when they are outside. Poor air quality can negatively impact an individual’s sleep quality, especially when the individual has been exposed to Particulate Matter 2.5 (PM2.5) which can adversely affect human health due to its ability to penetrate into a human body easily. Methods We examined how PM2.5 exposure impacts perceived sleep quality and sleep metrics. 11 individuals (7 Female, 4 Male) with an average age 34.18±10.24 years participated where each participant lived in a simulated one-bedroom apartment unit located in Rochester, MN for 20 nights in total over 4 weeks. Participants wore an EEG-based sleep headband (Dreem) to measure sleep metrics and completed surveys to assess perceived sleep quality and fatigue level each weekday after they woke up. Air quality including PM2.5 was monitored in the units from sensors and from the participants outside the units using a portable research grade air quality monitor paired with GPS. Total PM2.5 exposure was calculated each day by multiplying the PM2.5 concentration by 6 liter/min of inhalation rate and duration (min). We excluded nights with less than 5 hours of sleep duration. One participant who reported poor sleep quality over 70% of the time was also excluded. Results Participants had an average of 58.2μg±63.8 PM2.5 exposure during a day until they went to bed and 0.8μg±2.6 PM2.5 exposure during sleep. Because there was minimal night PM2.5 exposure, only the day exposure was correlated to sleep measures. There was no significant effect of PM2.5 exposure on sleep efficiency, sleep onset latency, wake after sleep onset, and total sleep time as measured from the Dreem headband. There was a modest but significant effect of PM2.5 on perceived sleep quality, as participants rated poorer sleep quality (r(141)=.17, p=.042) and higher fatigue levels (r(141)=.26, p=.002) the next morning. Conclusion These findings suggest acute PM2.5 exposure levels were not high enough to affect sleep metrics but did affect one’s perceived sleep. Future studies should examine longer period to assess whether there is a chronic effect of PM2.5 exposure on sleep. Support (if any)

Mapping of outdoor gamma radiation and consequential health risk assessment in north-eastern regions of Rajasthan, India

Living beings get inevitable exposure to radiation of which approximately half of the dose is due to natural background radiation. In the present study, an extensive and systematic survey of outdoor GDR (GDR) was carried out in Churu and Jhunjhunu districts of Rajasthan. A total of 687 locations were covered to quantify the outdoor GDR. The portable radiation monitor, based on the Geiger-Muller counter, was used to monitor the outdoor gamma dose, and its associated health risk was estimated. Outdoor GDRs were found to vary from 32 nSv/h to 231 nSv/h and 75 nSv/h to 188 nSv/h in Churu and Jhunjhunu districts, respectively. The annual effective dose at most locations of the study area was greater than 0.07 nSv/h but less than the 1 mSv/y permissible limit recommended by the ICRP. The excess lifetime cancer risk in Churu district was estimated and found to range from 0.147 × 10−3 to 1.063 × 10−3, while in Jhunjhunu district it was 0.345 × 10−3 to 0.865 × 10−3

Effect of obstructive sleep apnea on prognosis in patients with acute coronary syndromes with varying numbers of standard modifiable risk factors: insight from the OSA-ACS study

BackgroundStandard modifiable risk factors (SMuRFs) increase the risk of cardiovascular events in patients with acute coronary syndrome (ACS) and are also strongly associated with obstructive sleep apnea (OSA) in a bidirectional relationship. However, the association of OSA with recurrent cardiovascular events in ACS patients based on the number of SMuRFs remains unclear. Hence, we aimed to elucidate the prognostic implication of OSA in ACS patients stratified by the number of SMuRFs.MethodsThis was a post hoc analysis of the OSA-ACS study (NCT03362385), including 1927 patients admitted for ACS and undergoing portable sleep monitoring. OSA was defined as an apnea hypopnea index ≥ 15 events/h. The primary endpoint was major adverse cardiovascular and cerebrovascular event (MACCE) including cardiovascular death, myocardial infarction, stroke, hospitalization for unstable angina or heart failure, and ischemia-driven revascularization. Cox proportional hazards model and Kaplan-Meier analysis were used to investigated the relationship between OSA and subsequent cardiovascular events after patients were stratified by the number of SMuRFs.ResultsAmong 1927 patients enrolled, 130 (6.7%) had no SMuRF, 1264 (65.6%) exhibited 1–2 SMuRFs and 533 (27.7%) presented 3–4 SMuRFs. With the increase of the number of SMuRFs, the proportion of OSA in ACS patients tended to increase (47.7% vs. 51.5% vs. 56.6%), but there was no significant difference between them (P = 0.08). After the stratification of ACS patients via SMuRF numbers and adjustment for confounding factors, fully adjusted Cox regression indicated that OSA increased the risk of MACCE (adjusted HR, 1.65; 95%CI, 1.06–2.57; P = 0.026) and ischemia-driven revascularization (adjusted HR, 2.18; 95%CI, 1.03–4.65; P = 0.042) in ACS patients with 3–4 SMuRFs.ConclusionsIn hospitalized ACS patients, OSA is associated with an increased risk of MACCE and ischemia-driven revascularization among patients with 3–4 SMuRFs. Therefore, screening for OSA should be emphasized in ACS patients with 3–4 SMuRFs, and intervention trials should be prioritized in these high-risk patients.

At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea

Although many people suffer from sleep disorders, most are undiagnosed, leading to impairments in health. The existing polysomnography method is not easily accessible; it's costly, burdensome to patients, and requires specialized facilities and personnel. Here, we report an at-home portable system that includes wireless sleep sensors and wearable electronics with embedded machine learning. We also show its application for assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system using numerous bulky sensors, the soft, all-integrated wearable platform offers natural sleep wherever the user prefers. In a clinical study, the face-mounted patches that detect brain, eye, and muscle signals show comparable performance with polysomnography. When comparing healthy controls to sleep apnea patients, the wearable system can detect obstructive sleep apnea with an accuracy of 88.5%. Furthermore, deep learning offers automated sleep scoring, demonstrating portability, and point-of-care usability. At-home wearable electronics could ensure a promising future supporting portable sleep monitoring and home healthcare.

Smartphone-assisted colorimetric sensing platform based on molybdenum-doped carbon dots nanozyme for visual monitoring of ampicillin

Monitoring and control of ampicillin (AMP) residues in environment are of great significance for reducing bacterial resistance, maintaining microecological balance, and protecting human health. In this work, an innovative molybdenum-doped carbon dots (Mo-CDs) nanozyme was designed and synthesized via one-step hydrothermal synthesis. Due to the suitable selection of precursors, the carbon dots were doped with variable valence Mo, resulting in excellent peroxide-like activity to catalyze H2O2 to 1O2 (Km = 0.176 mM of H2O2). Based on the considerable reactivity between AMP and generated 1O2 of the catalytic system, a colorimetric sensor for AMP detection was constructed with a good linear relationship from 0.0500 to 100 μg/mL, and detection limit down to 12 ng/mL. Furthermore, the visual sensing platform of colorimetric test strips integrated with smartphone were established and successfully realized rapid, portable, and sensitive visual monitoring of AMP in water samples. This work has not only developed a new and efficient carbon dots nanozyme and constructed a smartphone-assisted colorimetric sensor, but also proposed a novel design strategy for developing a low-cost and easy-to-operate antibiotic detection tool with good specificity and sensitivity.

Multiangle, self-powered sensor array for monitoring head impacts.

Mild concussions occur frequently and may come with long-term cognitive, affective, and physical sequelae. However, the diagnosis of mild concussions lacks objective assessment and portable monitoring techniques. Here, we propose a multiangle self-powered sensor array for real-time monitoring of head impact to further assist in clinical analysis and prevention of mild concussions. The array uses triboelectric nanogenerator technology, which converts impact force from multiple directions into electrical signals. With an average sensitivity of 0.214 volts per kilopascal, a response time of 30 milliseconds, and a minimum resolution of 1.415 kilopascals, the sensors exhibit excellent sensing capability over a range of 0 to 200 kilopascals. Furthermore, the array enables reconstructed head impact mapping and injury grade assessment via a prewarning system. By gathering standardized data, we expect to build a big data platform that will permit in-depth research of the direct and indirect effects between head impacts and mild concussions in the future.

Constructed a self-powered sensing platform based on nitrogen-doped hollow carbon nanospheres for ultra-sensitive detection and real-time tracking of double markers

Acute myocardial infarction (AMI) is acute necrosis of a portion of the myocardium caused by myocardial ischemia, which seriously threatens people's health and life safety. Its early diagnosis is a difficult problem in clinical medicine. Research has found that the abnormal expression of microRNA-199a (miR-199a) and microRNA-499 (miR-499) was closely related to AMI disease. In this work, we took advantage of the structural advantages of nitrogen-doped hollow carbon nanospheres (N-HCNSs) to design an ultra-sensitive, portable real-time monitoring visual self-powered biosensor system, which based on dual-target miRNAs triggered catalytic hairpin assembly (CHA) for sensitive detection of miR-199a and miR-499. In addition, the capacitor and the smartphone are introduced into the system to realize the secondary improvement of system sensitivity and portable real-time visual monitoring. Under optimized conditions, in the linear range of 0.1–100000 aM, the detection limits of miR-199a and miR-499 are 0.031 and 0.027 aM, respectively. At the same time, the ultra-sensitive detection of miRNAs is realized in the serum sample, and the recovery rate of miR-199a and miR-499 are 98.0–106.0% (RSD: 0.6–8.1%) and 94.0–109.7% (RSD: 1.8–7.7%), respectively. The method is simple, sensitive and can be used for real-time tracking and portable monitoring of related diseases.

Automatic Seizure Detection and Prediction Based on Brain Connectivity Features and a CNNs Meet Transformers Classifier.

(1) Background: Epilepsy is a neurological disorder that causes repeated seizures. Since electroencephalogram (EEG) patterns differ in different states (inter-ictal, pre-ictal, and ictal), a seizure can be detected and predicted by extracting various features. However, the brain connectivity network, a two-dimensional feature, is rarely studied. We aim to investigate its effectiveness for seizure detection and prediction. (2) Methods: Two time-window lengths, five frequency bands, and five connectivity measures were used to extract image-like features, which were fed into a support vector machine for the subject-specific model (SSM) and a convolutional neural networks meet transformers (CMT) classifier for the subject-independent model (SIM) and cross-subject model (CSM). Finally, feature selection and efficiency analyses were conducted. (3) Results: The classification results on the CHB-MIT dataset showed that a long window indicated better performance. The best detection accuracies of SSM, SIM, and CSM were 100.00, 99.98, and 99.27%, respectively. The highest prediction accuracies were 99.72, 99.38, and 86.17%, respectively. In addition, Pearson Correlation Coefficient and Phase Lock Value connectivity in the β and γ bands showed good performance and high efficiency. (4) Conclusions: The proposed brain connectivity features showed good reliability and practical value for automatic seizure detection and prediction, which expects to develop portable real-time monitoring equipment.