Wearable Monitoring Research Articles

Wearable Devices, Health Care Use, and Psychological Well-Being in Patients With Atrial Fibrillation.

Wearables are increasingly used by patients with atrial fibrillation (AF) for symptom monitoring and health management, but their impact on patient health care use and psychological well-being is not well understood. In this retrospective, propensity-matched study of patients with AF, survey and electronic health record data were merged to compare AF-specific health care use (outpatient/inpatient visits, rhythm-related testing, and procedures) and informal health care use (telephone calls and patient portal messages) over a 9-month period between wearable users and nonusers. We also examined the effects of wearable cardiac monitoring features (eg, heart rate alerts, irregular rhythm notification, and ECG) on patient behavior and well-being. Of 172 patients with AF in this analysis (age, 72.6±9.0 years; 42% women), 83 used a wearable. Compared with nonusers, wearable users reported higher rates of symptom monitoring and preoccupation (P=0.03) and more AF treatment concerns (P=0.02). Moreover, 20% of wearable users experienced anxiety and always contacted their doctors in response to irregular rhythm notifications. After matching, AF-specific health care use was significantly greater among wearable users compared with nonusers (P=0.04), including significantly higher rates of ECGs, echocardiograms/transesophageal echocardiogram, and ablation. Wearable users were also significantly more likely to use informal health care resources compared with nonusers (P=0.05). Wearables were associated with higher rates of symptom monitoring and preoccupation, AF treatment concerns, AF-specific health care use, and use of informal health care resources. Prospective, randomized studies are needed to understand the net effects of wearables and their alerts on patients, providers, and the health care system.

All-Fiber Flexible Electrochemical Sensor for Wearable Glucose Monitoring.

Wearable sensors, specifically microneedle sensors based on electrochemical methods, have expanded extensively with recent technological advances. Today's wearable electrochemical sensors present specific challenges: they show significant modulus disparities with skin tissue, implying possible discomfort in vivo, especially over extended wear periods or on sensitive skin areas. The sensors, primarily based on polyethylene terephthalate (PET) or polyimide (PI) substrates, might also cause pressure or unease during insertion due to the skin's irregular deformation. To address these constraints, we developed an innovative, wearable, all-fiber-structured electrochemical sensor. Our composite sensor incorporates polyurethane (PU) fibers prepared via electrospinning as electrode substrates to achieve excellent adaptability. Electrospun PU nanofiber films with gold layers shaped via thermal evaporation are used as base electrodes with exemplary conductivity and electrochemical catalytic attributes. To achieve glucose monitoring, gold nanofibers functionalized by gold nanoflakes (AuNFs) and glucose oxidase (GOx) serve as the working electrode, while Pt nanofibers and Ag/AgCl nanofibers serve as the counter and reference electrode. The acrylamide-sodium alginate double-network hydrogel synthesized on electrospun PU fibers serves as the adhesive and substance-transferring layer between the electrodes. The all-fiber electrochemical sensor is assembled layer-by-layer to form a robust structure. Given the stretchability of PU nanofibers coupled with a high specific surface area, the manufactured porous microneedle glucose sensor exhibits enhanced stretchability, superior sensitivity at 31.94 μA (lg(mM))-1 cm-2, a broad detection range (1-30 mM), and a significantly low detection limit (1 mM, S/N = 3), as well as satisfactory biocompatibility. Therefore, the novel electrochemical microneedle design is well-suited for wearable or even implantable continuous monitoring applications, thereby showing promising significant potential within the global arena of wearable medical technology.

Self-powered broadband photodetectors based on Bi2O2Se with asymmetric contact areas

Self-powered broadband photodetectors are widely used in military reconnaissance, outdoor environmental sensing, wearable medical monitoring. Current commercial broadband photodetectors based on conventional materials are structurally complex, high cost, and external power consumption. 2D materials emerge as promising candidates for the development of novel photodetectors with superior performances. The construction of self-powered photodetectors predominantly employs the heterostructures or metal–semiconductor–metal (MSM) configurations, which still suffer from either multiple steps of material growth, transferring, alignment or various photolithography, film deposition techniques. Herein, a simple and effective asymmetric contact strategy is proposed to fabricate Bi2O2Se-based self-powered broadband photodetectors with asymmetric Au contact areas. The photodetector exhibits excellent performance under 350–1150 nm illumination. The responsivity is as high as 1.38, 4.76, and 0.63 A/W under 350, 550, and 1050 nm, respectively, which are much higher than those of the previously reported self-powered photodetectors based on Bi2O2Se nanosheets. Meanwhile, the self-powered photodetector has a high external quantum efficiency of 1073.16 % at 550 nm. In addition, the photodetector has excellent stability in the air and enables both imaging and optical communication. Owing to the simple architecture, cost-effectiveness, straightforward manufacturing process, and impressive performance, the proposed self-powered photodetectors hold considerable promise for future applications.

Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study.

Abdominal auscultation (i.e., listening to bowel sounds (BSs)) can be used to analyze digestion. An automated retrieval of BS would be beneficial to assess gastrointestinal disorders noninvasively. This study aims to develop a multiscale spotting model to detect BSs in continuous audio data from a wearable monitoring system. We designed a spotting model based on the Efficient-U-Net (EffUNet) architecture to analyze 10-second audio segments at a time and spot BSs with a temporal resolution of 25 ms. Evaluation data were collected across different digestive phases from 18 healthy participants and 9 patients with inflammatory bowel disease (IBD). Audio data were recorded in a daytime setting with a smart T-Shirt that embeds digital microphones. The data set was annotated by independent raters with substantial agreement (Cohen κ between 0.70 and 0.75), resulting in 136 hours of labeled data. In total, 11,482 BSs were analyzed, with a BS duration ranging between 18 ms and 6.3 seconds. The share of BSs in the data set (BS ratio) was 0.0089. We analyzed the performance depending on noise level, BS duration, and BS event rate. We also report spotting timing errors. Leave-one-participant-out cross-validation of BS event spotting yielded a median F1-score of 0.73 for both healthy volunteers and patients with IBD. EffUNet detected BSs under different noise conditions with 0.73 recall and 0.72 precision. In particular, for a signal-to-noise ratio over 4 dB, more than 83% of BSs were recognized, with precision of 0.77 or more. EffUNet recall dropped below 0.60 for BS duration of 1.5 seconds or less. At a BS ratio greater than 0.05, the precision of our model was over 0.83. For both healthy participants and patients with IBD, insertion and deletion timing errors were the largest, with a total of 15.54 minutes of insertion errors and 13.08 minutes of deletion errors over the total audio data set. On our data set, EffUNet outperformed existing BS spotting models that provide similar temporal resolution. The EffUNet spotter is robust against background noise and can retrieve BSs with varying duration. EffUNet outperforms previous BS detection approaches in unmodified audio data, containing highly sparse BS events.

Highly Efficient Back-End-of-Line Compatible Flexible Si-Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision

HighlightsA novel optoelectronic synapse compatible with existing chip technology is demonstrated. This device excels in mimicking memory and learning functions using light, making it ideal for future neuromorphic computing in biomedicine.Our design surpasses previous models with its ability to switch between short-term and long-term memory states using light pulses.Experiments on real-world biomedical data (electroencephalogram, electromyography, electrocardiogram) showed significant improvement in classification accuracy. This highlights the device's potential for advanced physiological signal processing and wearable health monitoring systems.

Inductive Paper-Based Flexible Contact Force Sensor Utilizing Natural Micro-Nanostructures of Paper: Simplicity, Economy, and Eco-Friendliness.

Inductive contact force sensors, known for their high precision and anti-interference capabilities, hold significant potential applications in fields such as wearable and medical monitoring devices. Most of the current research on inductive contact force sensors employed novel nanomaterials as sensitive elements to enhance their sensitivity and other performance characteristics. However, sensors developed through such methods typically involve complex preparation processes, high costs, and difficulty in biodegradation, which limit their further development. This article introduces a new flexible inductive contact force sensor using paper as a sensitive element. Paper inherently possesses micro- and nanostructures on its surface and interior, enabling it to sensitively convert changes in contact force into changes in displacement, making it suitable for use as the sensor's sensitive element. Additionally, the advantages of paper also include its great flexibility, low cost, wide availability, and biodegradability. Performance testing on this flexible sensor showed good repeatability, hysteresis, sensitivity, and consistency. When used in experiments for monitoring human motion and respiration, this sensor also exhibited great detection performance. The proposed inductive paper-based flexible contact force sensor, with its simple structure, easy manufacturing process, cost-effectiveness, eco-friendliness, and good sensing performance, provides new insights into research for contact force sensors.

A Novel Instruction Driven 1-D CNN Processor for ECG Classification.

Electrocardiography (ECG) has emerged as a ubiquitous diagnostic tool for the identification and characterization of diverse cardiovascular pathologies. Wearable health monitoring devices, equipped with on-device biomedical artificial intelligence (AI) processors, have revolutionized the acquisition, analysis, and interpretation of ECG data. However, these systems necessitate AI processors that exhibit flexible configuration, facilitate portability, and demonstrate optimal performance in terms of power consumption and latency for the realization of various functionalities. To address these challenges, this study proposes an instruction-driven convolutional neural network (CNN) processor. This processor incorporates three key features: (1) An instruction-driven CNN processor to support versatile ECG-based application. (2) A Processing element (PE) array design that simultaneously considers parallelism and data reuse. (3) An activation unit based on the CORDIC algorithm, supporting both Tanh and Sigmoid computations. The design has been implemented using 110 nm CMOS process technology, occupying a die area of 1.35 mm2 with 12.94 µW power consumption. It has been demonstrated with two typical ECG AI applications, including two-class (i.e., normal/abnormal) classification and five-class classification. The proposed 1-D CNN algorithm performs with a 97.95% accuracy for the two-class classification and 97.9% for the five-class classification, respectively.

Bearing witness: Early detection of cardiac arrhythmias using a modified wearable ECG monitor

Bearing witness: Early detection of cardiac arrhythmias using a modified wearable ECG monitor

Study on the clinical value of the wearable foetal electrocardiogram monitoring system.

Electronic foetal monitoring (EFM), a method to monitor foetal intrauterine conditions and foetal reserve capacity, is the most extensively used intrauterine monitoring technology in obstetrics. This study aims to compare the Thoth wearable foetal electrocardiogram (foetal ECG [FECG]) monitoring system with a traditional Doppler foetal heart monitoring system before labour to investigate their respective values in clinical application. A total of 393 pregnant women admitted to our hospital between 2020 and 2022 participated in this study. They were recruited using the convenience sampling method. We employed a paired design to assess the confusion rate, trend overlap, and foetal heart rate/ECG monitoring consistency, whereas a completely randomised design was used to measure pregnancy outcome indicators. The participants were divided into two groups using a random number table: the Thoth group (n= 196) and the traditional Doppler group (n= 197). Each group was monitored using the corresponding system. The Thoth monitor demonstrated a lower confusion rate compared with the traditional Doppler monitor (0.25% vs 2.04%; χ2= 5.508, P= 0.019). The trend overlap in foetal heart rates was consistently 100%, with 91.2% of readings showing a consistency rate of ⩾ 95%. Additionally, the Thoth monitor recorded a higher cumulative interruption time in the foetal heart rate curve (12.13 ± 2.22 vs 21.02 ± 2.34; t= 18.471, P< 0.001) and more abnormal ECGs (21.21 ± 4.32 vs 18.21 ± 2.91; t= 7.582, P< 0.001) than the traditional Doppler system. The Thoth wearable FECG monitor offers several advantages over the traditional Doppler foetal heart monitoring system. These include a reduced confusion rate, more accurate data collection, a lower rate of clinical misjudgement, reduced workload for medical staff, and enhanced comfort during vaginal delivery. The rates of emergency caesarean sections and neonatal asphyxia in the Thoth group were marginally lower than those in the Doppler group, which may be attributed to issues such as ECG disconnection or interference from the maternal heart rate.

Stretchable Thermoelectric Generators for Self‐Powered Wearable Health Monitoring

AbstractAs continuous wearable physiological monitoring systems become more ubiquitous in healthcare, there is an increasing need for power sources that can sustainably power wireless sensors and electronics for long durations. Wearable energy harvesting with thermoelectric generators (TEGs), in which body heat is converted to electrical energy, presents a promising way to prolong wireless operation and address battery life concerns. In this work, high performance TEGs are introduced that combine 3D printed elastomers with liquid metal epoxy polymer composites and thermoelectric semiconductors to achieve elastic compliance and mechanical compatibility with the body. The thermoelectric properties are characterized in both energy harvesting (Seebeck) and active heating/cooling (Peltier) modes, and examine the performance of wearable energy harvesting under various conditions such as sitting, walking, and running. When worn on a user's forearm while walking outside, the TEG arrays are able to power circuitry to collect photoplethysmography (PPG) waveform data with a photonic sensor and wirelessly transmit the data to an external PC using an on‐board Bluetooth Low Energy (BLE) radio. This represents a significant step forward on the path to sustainable body‐worn smart electronics.

Wearable Voice Dosimetry System

The objective quantification of voice acoustic parameters is used for the diagnosis, monitoring, and treatment of voice disorders. Such assessments are carried out with specialised equipment within the doctor’s office. The controlled conditions employed are usually not those of the real environment of the patient. The results, although very informative, are specific to those measurement conditions and to the time when they were performed. A wearable voice monitoring system, based on an accelerometer to ensure the message, can overcome these limitations. We present a miniaturised, low-power, and low-cost wearable system to estimate and record voice fundamental frequency (F0), intensity and phonation time for long intervals in the everyday environment of the patient. It was tested on two subjects for up to two weeks of recording time. It was possible to identify distinct periods in vocal activity, such as normal, professional, demanding or hyperfunctional. It provided information on the workload that the vocal cords needed to cope with over time and when and to what extent that workload was concentrated. The proposed voice dosimetry system enables the extraction and recording of voice parameters for long periods of time in the everyday environment of the patient, allowing the objectification of vocal risk situations and personalised treatment and monitoring.

Prediction of Health Status of Small-Tailed Cold Sheep Based on Improved BP Neural Network

According to related research, different body temperatures, heart rates, and locomotor behaviors of small-tailed cold goats can represent the physical condition of the goats themselves and are used as direct evidence for evaluating the physical health status of small-tailed cold goats. In this paper, we designed and tested a system for predicting the health status of small-tailed cold sheep based on wearable information monitoring technology. To test the system, sheep wearable devices were worn on 36 small-tailed cold sheep of different ages and inconsistent health conditions at different time points from May to October. A SLBAS-BP neural network model for predicting the health condition of small-tailed cold sheep was established using the collected and processed data, which overcame the problem that the traditional gradient descent method in the BP neural network is prone to fall into local optimization leading to insufficient prediction ability. The correct prediction rates of the improved BP neural network for the four health conditions of healthy, sub-healthy, fever, and disease were 98.4%, 94.5%, 90.4%, and 98.7%, respectively, and the average correct prediction rate of the four conditions was 5.8% higher than that before the improvement, reaching 95.2%.

Curvature-Adaptive Compact Triple-Band Metamaterial Uniplanar Compact Electromagnetic Bandgap-Based Printed Antenna for Wearable Wireless and Medical Body Area Network Applications

A novel, compact, monopole apple-shaped, triple-band metamaterial-printed wearable antenna backed by a uniplanar compact electromagnetic bandgap (UC-EBG) structure is introduced in this paper for wearable wireless and medical body area network (WBAN/MBAN) applications. A tri-band UC-EBG structure has been utilized as a ground plane to minimize the impact of antenna radiation on the human body and improve antenna performance for the proposed wearable antenna. Metamaterial triangular complementary split ring resonators (TCSRRs) are incorporated into the antenna and UC-EBG structure, resulting in a compact UC-EBG-backed antenna with an overall size of 39 × 39 × 2.84 mm3 (0.41 λg × 0.41 λg × 0.029 λg). The printed textile antenna operates at 2.45 GHz for the wireless local area network (WLAN), 3.5 GHz for 5G new radio (NR), and 5.8 GHz for the industrial, scientific, and medical (ISM) bands with improved gain and high-efficiency values. Furthermore, the performance of the antenna is analyzed on the human body, where three models of curved body parts are considered: a child’s arm (worst case) with a 40 mm radius, an adult’s arm with a 60 mm radius, and an adult’s leg with a 70 mm radius. The results demonstrate that the proposed antenna is an attractive candidate for wearable healthcare and fitness monitoring devices and other WBAN/MBAN applications due to its compact size, high performance, and low SAR values.

Engineered Lysozyme: An Eco‐Friendly Bio‐Mechanical Energy Harvester

Eco‐friendly and antimicrobial globular protein lysozyme is widely produced for several commercial applications. Interestingly, it can also be able to convert mechanical and thermal energy into electricity due to its piezo‐ and pyroelectric nature. Here, we demonstrate engineering of lysozyme into piezoelectric devices that can exploit the potential of lysozyme as environmentally friendly, biocompatible material for mechanical energy harvesting and sensorics, especially in micropowered electronic applications. Noteworthy that this flexible, shape adaptive devices made of crystalline lysozyme obtained from hen egg white exhibited a longitudinal piezoelectric charge coefficient (d ~ 2.7 pC N−1) and piezoelectric voltage coefficient (g ~ 76.24 mV m N−1) which are comparable to those of quartz (~2.3 pC N−1 and 50 mV m N−1). Simple finger tapping on bio‐organic energy harvester (BEH) made of lysozyme produced up to 350 mV peak‐to‐peak voltage, and a maximum instantaneous power output of 2.2 nW cm−2. We also demonstrated that the BEH could be used for self‐powered motion sensing for real‐time monitoring of different body functions. These results pave the way toward self‐powered, autonomous, environmental‐friendly bio‐organic devices for flexible energy harvesting, storage, and in wearable healthcare monitoring.

Piezoresistive Pressure Sensors with Composite Film of Indium Tin Oxide Nanocrystals Dispersed in Poly(vinyl alcohol)

Flexible pressure sensors have attracted much attention because of their application prospects in wearable devices, electronic skin, and health monitoring. However, it is still difficult to obtain pressure sensors with excellent performance simply and efficiently. Indium tin oxide (ITO) is a widely used transparent conductive material, and polyvinyl alcohol (PVA) is an elastic insulating material with the advantages of easy processing and stable mechanical properties. ITO nano‐crystalline particles are dispersed into PVA polymers to form an ITO nanocrystalline‐PVA composite film with good electrical conductivity as a piezorestoresistive material, fabricating a pressure sensor with excellent performance. The pressure sensor possesses superior sensitivity (641.15 kPa−1), wide detection range (0–80 kPa), fast response time (10.93 ms), and recovery time (8.05 ms), and excellent stability (more than 1500 cycles). The pressure sensor shows excellent performance in a variety of applications, such as pulse testing, speech recognition, and detection of various joint movements of the human body (such as knees, fingers, elbows, etc.). The sensor has application prospects in health monitoring and motion perception.

Effect of Nanostructure Morphology and Concentration on the Piezoelectric Performance of Flexible Pressure Sensor based on PVDFTrFE/ Nano-ZnO Composite Thin Film

Background: The development of high-performance piezoelectric pressure sensors with outstanding sensitivity, good linearity, flexibility, durability, and biocompatibility is of great significance for smart robotics, human healthcare devices, smart sensors, and electronic skin. Thus, considerable progress has been achieved in enhancing the piezoelectric property of PVDF-TrFEbased composite pressure sensors by adding various ZnO nanostructures in PVDF-TrFE polymer acting as a nucleating agent and dielectric material. Aims: In this work, flexible pressure sensors with a sandwich structure based on PVDFTrFE/ nano-ZnO composite sensing film were fabricated using a simple spin-coating method and post-annealing process, while electrospinning and high-voltage polarization processes were not adopted. Methods: Poly (vinylidene fluoride-trifluoroethylene) (PVDF-TrFE)/nano-ZnO composite films were prepared via spin coating to fabricate flexible piezoelectric pressure sensors. ZnO nanoparticles (ZnO NPs), tetrapod ZnO (T-ZnO) and ZnO nanorods (ZnO NRs) were used as nano-fillers for piezoelectric PVDF-TrFE, to enhance the beta-crystal ratio as well as the crystallinity of PVDF-TrFE. The structural and surface morphologies of the composite films were investigated using Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD) and scanning electron microscopy (SEM). Results: Among three different types of ZnO nanostructures with a concentration range (0-7.5 wt%), the sensor with 0.75 wt% ZnO NRs nanofiller exhibits a maximum output voltage of 1.73 V under an external pressure of 3 N and a maximum sensitivity of 586.3 mV/N at the range of 0-3 N. Further, the sensor can generate a clear piezoelectric voltage under bending and twisting deformation as well as compression and tensile deformation. Conclusion: To summarize, the addition of different concentrations of nano-ZnO can remarkably improve the piezoelectric performance of the composite sensor, and ZnO NRs can achieve better piezoelectric properties of the sensor as compared to ZnO NPs and T-ZnO. In addition, the sensor with 0.75 wt% ZnO NRs as nanofiller has the highest piezoelectric response, which is about 2.4 times that of the pure PVDF-TrFE sensor. It is demonstrated that the sensor has great potential applications in wearable health monitoring systems and mechanical stress measurement electronics.

Wearable Alcohol Monitoring Device for the Data-Driven Transcutaneous Alcohol Diffusion Model.

Wearable alcohol monitoring devices demand noninvasive, real-time measurement of blood alcohol content (BAC) reliably and continuously. A few commercial devices are available to determine BAC noninvasively by detecting transcutaneous diffused alcohol. However, they suffer from a lack of accuracy and reliability in the determination of BAC in real time due to the complex scenario of the human skin for transcutaneous alcohol diffusion and numerous factors (e.g., skin thickness, kinetics of alcohol, body weight, age, sex, metabolism rate, etc.). In this work, a transcutaneous alcohol diffusion model has been developed from real-time captured data from human wrists to better understand the kinetics of diffused alcohol from blood to different skin epidermis layers. Such a model will be a footprint to determine a base computational model in larger studies. Eight anonymous volunteers participated in this pilot study. A laboratory-built wearable blood alcohol content (BAC) monitoring device collected all the data to develop this diffusion model. The proton exchange membrane fuel cell (PEMFC) sensor was fabricated and integrated with an nRF51822 microcontroller, LMP91000 miniaturized potentiostat, 2.4 GHz transceiver supporting Bluetooth low energy (BLE), and all the necessary electronic components to build this wearable BAC monitoring device. The %BAC data in real time were collected using this device from these volunteers' wrists and stored in the end device (e.g., smartphone). From the captured data, we demonstrate how the volatile alcohol concentration on the skin varies over time by comparing the alcohol concentration in the initial stage (= 10 min) and later time (= 100 min). We also compare the experimental results with the outputs of three different input profiles: piecewise linear, exponential linear, and Hoerl, to optimize the developed diffusion model. Our results demonstrate that the exponential linear function best fits the experimental data compared to the piecewise linear and Hoerl functions. Moreover, we have studied the impact of skin epidermis thickness within ±20% and demonstrate that a 20% decrease in this thickness results in faster dynamics compared to thicker skin. The model clearly shows how the diffusion front changes within a skin epidermis layer with time. We further verified that 60 min was roughly the time to reach the maximum concentration, Cmax, in the stratum corneum from the transient analysis. Lastly, we found that a more significant time difference between BACmax and Cmax was due to greater alcohol consumption for a fixed absorption time.

Cardiorespiratory disturbances in focal impaired awareness seizures: Insights from wearable ECG monitoring

PurposeSeizures are characterized by periictal autonomic changes. Wearable devices could help improve our understanding of these phenomena through long-term monitoring. In this study, we used wearable electrocardiogram (ECG) data to evaluate differences between temporal and extratemporal focal impaired awareness (FIA) seizures monitored in the hospital and at home. We assessed periictal heart rate, respiratory rate, heart rate variability (HRV), and respiratory sinus arrhythmia (RSA). MethodsWe extracted ECG signals across three time points — five minutes baseline and preictal, ten minutes postictal — and the seizure duration. After automatic Rpeak selection, we calculated the heart rate and estimated the respiratory rate using the ECG-derived respiration methodology. HRV was calculated in both time and frequency domains. To evaluate the influence of other modulators on the HRV after removing the respiratory influences, we recalculated the residual power in the high-frequency (HF) and low-frequency (LF) bands using orthogonal subspace projections. Finally, 5-minute and 30-second (ultra-short) ECG segments were used to calculate RSA using three different methods. Seizures from temporal and extratemporal origins were compared using mixed-effects models and estimated marginal means. ResultsThe mean preictal heart rate was 69.95 bpm (95 % CI 65.6 – 74.3), and it increased to 82 bpm, 95 % CI (77.51 – 86.47) and 84.11 bpm, 95 % CI (76.9 – 89.5) during the ictal and postictal periods. Preictal, ictal and postictal respiratory rates were 16.1 (95 % CI 15.2 – 17.1), 14.8 (95 % CI 13.4 – 16.2) and 15.1 (95 % CI 14 – 16.2), showing not statistically significant bradypnea. HRV analysis found a higher baseline power in the LF band, which was still significantly higher after removing the respiratory influences. Postictally, we found decreased power in the HF band and the respiratory influences in both frequency bands. The RSA analysis with the new methods confirmed the lower cardiorespiratory interaction during the postictal period. Additionally, using ultra-short ECG segments, we found that RSA decreases before the electroclinical seizure onset. No differences were observed in the studied parameters between temporal and extratemporal seizures. ConclusionsWe found significant increases in the ictal and postictal heart rates and lower respiratory rates. Isolating the respiratory influences on the HRV showed a postictal reduction of respiratory modulations on both LF and HF bands, suggesting a central role of respiratory influences in the periictal HRV, unlike the baseline measurements. We found a reduced cardiorespiratory interaction during the periictal period using other RSA methods, suggesting a blockade in vagal efferences before the electroclinical onset. These findings highlight the importance of respiratory influences in cardiac dynamics during seizures and emphasize the need to longitudinally assess HRV and RSA to gain insights into long-term autonomic dysregulation.

Spiking neural networks for physiological and speech signals: a review.

The integration of Spiking Neural Networks (SNNs) into the analysis and interpretation of physiological and speech signals has emerged as a groundbreaking approach, offering enhanced performance and deeper insights into the underlying biological processes. This review aims to summarize key advances, methodologies, and applications of SNNs within these domains, highlighting their unique ability to mimic the temporal dynamics and efficiency of the human brain. We dive into the core principles of SNNs, their neurobiological underpinnings, and the computational advantages they bring to signal processing, particularly in handling the temporal and spatial complexities inherent in physiological and speech data. Comparative analyses with conventional neural network models are presented to underscore the superior efficiency, lower power consumption, and higher temporal resolution of SNNs. The review further explores challenges and future prospects, highlighting the potential of SNNs to revolutionize wearable healthcare monitoring systems, neuroprosthetic devices, and natural language processing technologies. By providing a comprehensive overview of current strategies, this review aims to inspire innovative approaches in the field, fostering advances in real-time and energy-efficient processing of complex biological signals.

Design, Fabrication, and Evaluation of 3D Biopotential Electrodes and Intelligent Garment System for Sports Monitoring.

This study presents the development and evaluation of an innovative intelligent garment system, incorporating 3D knitted silver biopotential electrodes, designed for long-term sports monitoring. By integrating advanced textile engineering with wearable monitoring technologies, we introduce a novel approach to real-time physiological signal acquisition, focusing on enhancing athletic performance analysis and fatigue detection. Utilizing low-resistance silver fibers, our electrodes demonstrate significantly reduced skin-to-electrode impedance, facilitating improved signal quality and reliability, especially during physical activities. The garment system, embedded with these electrodes, offers a non-invasive, comfortable solution for continuous ECG and EMG monitoring, addressing the limitations of traditional Ag/AgCl electrodes, such as skin irritation and signal degradation over time. Through various experimentation, including impedance measurements and biosignal acquisition during cycling activities, we validate the system's effectiveness in capturing high-quality physiological data. Our findings illustrate the electrodes' superior performance in both dry and wet conditions. This study not only advances the field of intelligent garments and biopotential monitoring, but also provides valuable insights for the application of intelligent sports wearables in the future.