Tracking Tidal Volume from Holter and Wearable Armband Electrocardiogram Monitoring.

A novel method for tracking the tidal volume (TV) from electrocardiogram (ECG) is presented. The method is based on the amplitude of ECG-derived respiration (EDR) signals. Three different morphology-based EDR signals and three different amplitude estimation methods have been studied, leading to a total of 9 amplitude-EDR (AEDR) signals per ECG channel. The potential of these AEDR signals to track the changes in TV was analyzed. These methods do not need a calibration process. In addition, a personalized-calibration approach for TV estimation is proposed, based on a linear model that uses all AEDR signals from a device. All methods have been validated with two different ECG devices: a commercial Holter monitor, and a custom-made wearable armband. The lowest errors for the personalized-calibration methods, compared to a reference TV, were -3.48% [-17.41% / 12.93%] (median [first quartile / third quartile]) for the Holter monitor, and 0.28% [-10.90% / 17.15%] for the armband. On the other hand, medians of correlations to the reference TV were higher than 0.8 for uncalibrated methods, while they were higher than 0.9 for personal-calibrated methods. These results suggest that TV changes can be tracked from ECG using either a conventional (Holter) setup, or our custom-made wearable armband. These results also suggest that the methods are not as reliable in applications that induce small changes in TV, but they can be potentially useful for detecting large changes in TV, such as sleep apnea/hypopnea and/or exacerbations of a chronic respiratory disease.

Feasibility Testing of Wearable Device for Musculoskeletal Monitoring during Aquatic Therapy and Rehabilitation.

The objectives of this study were to test the feasibility of the developed waterproof wearable device with a Surface Electromyography (sEMG) sensor and Inertial Measurement Unit (IMU) sensor by (1) comparing the onset duration of sEMG recordings from maximal voluntary contractions (MVC), (2) comparing the acceleration of arm movement from IMU, and (3) observing the reproducibility of onset duration and acceleration from the developed device for bicep brachii (BB) muscle between on dry-land, and in aquatic environments. Five healthy males participated in two experimental protocols with the activity of BB muscle of the left and right arms. Using the sEMG of BB muscle, the intra-class correlation coefficient (ICC) and typical error (CV%) were calculated to determine the reproducibility and precision of onset duration and acceleration, respectively. In case of onset duration, no significant differences were observed between land and aquatic condition (p = 0.9-0.98), and high reliability (ICC = 0.93-0.98) and precision (CV% = 2.7-6.4%) were observed. In addition, acceleration data shows no significant differences between land and aquatic condition (p = 0.89-0.93), and high reliability (ICC = 0.9-0.97) and precision (CV% = 7.9-9.2%). These comparable sEMG and acceleration values in both dry-land and aquatic environment supports the suitability of the proposed wearable device for musculoskeletal monitoring during aquatic therapy and rehabilitation as the integrity of the sEMG and acceleration recordings maintained during aquatic activities.Clinical Relevance-This study and relevant experiment demonstrate the feasibility of the developed wearable device to support clinicians and therapists for musculoskeletal monitoring during aquatic therapy and rehabilitation.

Applicability of Cloud Native-based Healthcare Monitoring Platform (CN-HMP) in Older Adult Facilities.

Over the past few decades, the world has faced the huge demographic change in the aging population, which makes significant challenges in healthcare systems. The increasing older adult population along with the current health workforce shortage creates a struggling situation for current facilities and personnel to meet the demand. To tackle this situation, cloud computing is a fast-growing area in digital healthcare and it allows to settle up a modern distributed system environment, capable of scaling to tens of thousands of self healing multitenant nodes for healthcare applications. In addition, cloud native architecture is recently getting focused as an ideal structure for multi-node based healthcare monitoring system due to its high scalability, low latency, and rapid and stable maintainability. In this study, we proposed a cloud native-based rapid, robust, and productive digital healthcare platform which allows to manage and care for a large number of patient groups. To validate our platform, we simulated our Cloud Nativebased Healthcare Monitoring Platform (CN-HMP) with real-time setup and evaluated the performance in terms of request response time, data packets delivery, and end-to-end latency. We found it showing less than 0.1 ms response time in at least 92.5% of total requests up to 3K requests, and no data packet loss along with more than 28% of total data packets with no latency and only ≈ 0.6% of those with maximum latency (3 ms) in 24-hour observation. Clinical Relevance- This study and relevant experiment demonstrate the suitability of the CN-HMP to support providers and nurses for elderly patients healthcare with regular monitoring in older adult facilities.

Upcycling Compact Discs for Flexible and Stretchable Bioelectronic Applications.

Electronic waste is a global issue brought about by the short lifespan of electronics. Viable methods to relieve the inundated disposal system by repurposing the enormous amount of electronic waste remain elusive. Inspired by the need for sustainable solutions, this study resulted in a multifaceted approach to upcycling compact discs. The once-ubiquitous plates can be transformed into stretchable and flexible biosensors. Our experiments and advanced prototypes show that effective, innovative biosensors can be developed at a low-cost. An affordable craft-based mechanical cutter allows pre-determined patterns to be scored on the recycled metal, an essential first step for producing stretchable, wearable electronics. The active metal harvested from the compact discs was inert, cytocompatible, and capable of vital biopotential measurements. Additional studies examined the material's resistive emittance, temperature sensing, real-time metabolite monitoring performance, and moisture-triggered transience. This sustainable approach for upcycling electronic waste provides an advantageous research-based waste stream that does not require cutting-edge microfabrication facilities, expensive materials, and high-caliber engineering skills.

Feasibility of atrial fibrillation detection from a novel wearable armband device.

Background: Atrial fibrillation (AF) is the world's most common heart rhythm disorder and even several minutes of AF episodes can contribute to risk for complications, including stroke. However, AF often goes undiagnosed owing to the fact that it can be paroxysmal, brief, and asymptomatic. Objective: To facilitate better AF monitoring, we studied the feasibility of AF detection using a continuous electrocardiogram (ECG) signal recorded from a novel wearable armband device. Methods: In our 2-step algorithm, we first calculate the R-R interval variability-based features to capture randomness that can indicate a segment of data possibly containing AF, and subsequently discriminate normal sinus rhythm from the possible AF episodes. Next, we use density Poincaré plot-derived image domain features along with a support vector machine to separate premature atrial/ventricular contraction episodes from any AF episodes. We trained and validated our model using the ECG data obtained from a subset of the MIMIC-III (Medical Information Mart for Intensive Care III) database containing 30 subjects. Results: When we tested our model using the novel wearable armband ECG dataset containing 12 subjects, the proposed method achieved sensitivity, specificity, accuracy, and F1 score of 99.89%, 99.99%, 99.98%, and 0.9989, respectively. Moreover, when compared with several existing methods with the armband data, our proposed method outperformed the others, which shows its efficacy. Conclusion: Our study suggests that the novel wearable armband device and our algorithm can be used as a potential tool for continuous AF monitoring with high accuracy.

Electrocardiogram Derived Respiratory Rate Using a Wearable Armband.

A method for deriving respiratory rate from an armband, which records three-channel electrocardiogram (ECG) using three pairs of dry (no hydrogel) electrodes, is presented. The armband device is especially convenient for long-term (months-years) monitoring because it does not use obstructive leads nor hydrogels/adhesives, which cause skin irritation even after few days. An ECG-derived respiration (EDR) based on respiration-related modulation of QRS slopes and R-wave angle approach was used. Moreover, we modified the EDR algorithm to lower the computational cost. Respiratory rates were estimated with the armband-ECG and the reference plethysmography-based respiration signals from 15 subjects who underwent breathing experiment consisting of five stages of controlled breathing (at 0.1, 0.2, 0.3, 0.4, and 0.5 Hz) and one stage of spontaneous breathing. The respiratory rates from the armband obtained a relative error with respect to the reference (respiratory rate estimated from the plethysmography-based respiration signal) that was not higher than 2.26% in median nor interquartile range (IQR) for all stages of fixed and spontaneous breathing, and not higher than 3.57% in median nor IQR in the case when the low computational cost algorithm was applied. These results demonstrate that respiration-related modulation of the ECG morphology are also present in the armband ECG device. Furthermore, these results suggest that respiration-related modulation can be exploited by the EDR method based on QRS slopes and R-wave angles to obtain respiratory rate, which may have a wide range of applications including monitoring patients with chronic respiratory diseases, epileptic seizures detection, stress assessment, and sleep studies, among others.

A robust ECG denoising technique using variable frequency complex demodulation.

BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is widely used for the detection and diagnosis of cardiac arrhythmias such as atrial fibrillation. Most of the computer-based automatic cardiac abnormality detection algorithms require accurate identification of ECG components such as QRS complexes in order to provide a reliable result. However, ECGs are often contaminated by noise and artifacts, especially if they are obtained using wearable sensors, therefore, identification of accurate QRS complexes often becomes challenging. Most of the existing denoising methods were validated using simulated noise added to a clean ECG signal and they did not consider authentically noisy ECG signals. Moreover, many of them are model-dependent and sampling-frequency dependent and require a large amount of computational time. METHODS: This paper presents a novel ECG denoising technique using the variable frequency complex demodulation (VFCDM) algorithm, which considers noises from a variety of sources. We used the sub-band decomposition of the noise-contaminated ECG signals using VFCDM to remove the noise components so that better-quality ECGs could be reconstructed. An adaptive automated masking is proposed in order to preserve the QRS complexes while removing the unnecessary noise components. Finally, the ECG was reconstructed using a dynamic reconstruction rule based on automatic identification of the severity of the noise contamination. The ECG signal quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. RESULTS: Evaluation results on the standard MIT-BIH Arrhythmia database suggest that the proposed denoising technique provides superior denoising performance compared to studies in the literature. Moreover, the proposed method was validated using real-life noise sources collected from the noise stress test database (NSTDB) and data from an armband ECG device which contains significant muscle artifacts. Results from both the wearable armband ECG data and NSTDB data suggest that the proposed denoising method provides significantly better performance in terms of accurate QRS complex detection and signal to noise ratio (SNR) improvement when compared to some of the recent existing denoising algorithms. CONCLUSIONS: The detailed qualitative and quantitative analysis demonstrated that the proposed denoising method has been robust in filtering varieties of noises present in the ECG. The QRS detection performance of the denoised armband ECG signals indicates that the proposed denoising method has the potential to increase the amount of usable armband ECG data, thus, the armband device with the proposed denoising method could be used for long term monitoring of atrial fibrillation.

Denoising Wearable Armband ECG Data Using the Variable Frequency Complex Demodulation Technique.

We propose a novel electrocardiogram (ECG) denoising technique using the variable frequency complex demodulation (VFCDM) algorithm. We used VFCDM to perform the sub-band decomposition of the noise-contaminated ECG to remove the noise components so that accurate QRS complexes could be identified. The ECG quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. The proposed method was validated on the MIT-BIH arrhythmia database (MITDB) and wearable armband ECG data. For the former, we added Gaussian white noise to the ECG signals at different signal-to-noise ratios and the denoising performance of the proposed method was compared with other denoising techniques. The proposed approach showed superior denoising performance compared to the other methods. We compared the QRS complex detection performance of the noisy to the denoised armband ECG. The performance of the proposed denoising method on the armband ECG resulted in comparable QRS complex detection as that obtained when using Holter monitor ECG signals. This demonstrates that the proposed algorithm can significantly increase the amount of usable armband ECG data, which would otherwise have been discarded due to electromyogram contamination especially during arm movements. Hence, the proposed algorithm has the potential to enable long-term monitoring of atrial fibrillation using the armband without the discomfort of skin irritation often experienced with Holter monitors.Clinical Relevance- The proposed ECG denoising method can significantly increase the ECG quality of wearable ECG devices, which are more susceptible to muscle and motion artifacts.

Electrocardiogram Derived Respiration for Tracking Changes in Tidal Volume from a Wearable Armband.

A pilot study on tracking changes in tidal volume (TV) using ECG signals acquired by a wearable armband is presented. The wearable armband provides three ECG channels by using three pairs of dry electrodes, resulting in a device that is convenient for long-term daily monitoring. An additional ECG channel was derived by computing the first principal component of the three original channels (by means of principal component analysis). Armband and spirometer signals were simultaneously recorded from five healthy subjects who were instructed to breathe with varying TV. Three electrocardiogram derived respiration (EDR) methods based on QRS complex morphology were studied: the QRS slopes range (SR), the R-wave angle (Փ), and the R-S amplitude (RS). The peak-to-peak amplitudes of these EDR signals were estimated as surrogates for TV, and their correlations with the reference TV (estimated from the spirometer signal) were computed. In addition, a multiple linear regression model was calculated for each subject, using the peak-to-peak amplitudes from the three EDR methods from the four ECG channels. Obtained correlations between TV and EDR peak-to-peak amplitude ranged from 0.0448 up to 0.8491. For every subject, a moderate correlation (>0.5) was obtained for at least one EDR method. Furthermore, the correlations obtained for the subject-specific multiple linear regression model ranged from 0.8234 up to 0.9154, and the goodness of fit was 0.73±0.07 (median ± standard deviation). These results suggest that the peak-to-peak amplitudes of the EDR methods are linearly related to the TV. opening the possibility of estimating TV directly from an armband ECG device.Clinical Relevance- This opens the door to possible continuous monitoring of TV from the armband by using EDR.

Wearable Armband Device for Daily Life Electrocardiogram Monitoring.

A wearable armband electrocardiogram (ECG) monitor has been used for daily life monitoring. The armband records three ECG channels, one electromyogram (EMG) channel, and tri-axial accelerometer signals. Contrary to conventional Holter monitors, the armband-based ECG device is convenient for long-term daily life monitoring because it uses no obstructive leads and has dry electrodes (no hydrogels), which do not cause skin irritation even after a few days. Principal component analysis (PCA) and normalized least mean squares (NLMS) adaptive filtering were used to reduce the EMG noise from the ECG channels. An artifact detector and an optimal channel selector were developed based on a support vector machine (SVM) classifier with a radial basis function (RBF) kernel using features that are related to the ECG signal quality. Mean HR was estimated from the 24-hour armband recordings from 16 volunteers in segments of 10 seconds each. In addition, four classical HR variability (HRV) parameters (SDNN, RMSSD, and powers at low and high frequency bands) were computed. For comparison purposes, the same parameters were estimated also for data from a commercial Holter monitor. The armband provided usable data (difference less than 10% from Holter-estimated mean HR) during 75.25%/11.02% (inter-subject median/interquartile range) of segments when the user was not in bed, and during 98.49%/0.79% of the bed segments. The automatic artifact detector found 53.85%/17.09% of the data to be usable during the non-bed time, and 95.00%/2.35% to be usable during the time in bed. The HRV analysis obtained a relative error with respect to the Holter data not higher than 1.37% (inter-subject median/interquartile range). Although further studies have to be conducted for specific applications, results suggest that the armband device has a good potential for daily life HR monitoring, especially for applications such as arrhythmia or seizure detection, stress assessment, or sleep studies.

Graphene and Poly(3,4-ethylene dioxythiophene):Poly(4-styrenesulfonate) on Nonwoven Fabric as a Room Temperature Metal and Its Application as Dry Electrodes for Electrocardiography.

Highly conductive, metal-like poly(ethylene terephthalate) (PET) nonwoven fabric was prepared by coating poly(3,4-ethylenedioxythiophene):poly(4-styrenesulfonate) (PEDOT:PSS) containing dimethyl sulfoxide (DMSO) onto PET nonwoven fabric previously coated with graphene/graphite. The sheet resistance of the original nonwoven fabric decreases from >80 MΩ□-1 to 1.1 Ω□-1 after coating with 10.7 wt % graphene and 5.48 wt % PEDOT:PSS with a maximum current at breakdown of 4 A. This sheet resistance is lower than previously reported sheet resistances of fabrics coated with graphene films, PEDOT:PSS films, or PEDOT:PSS coated fabrics from the literature. The effect of temperature on the resistance of graphene/PEDOT:PSS coated fabric has revealed that the resistance decreases with increasing temperature, analogous to semiconductors, with a clear semiconductor-metal transition occurring at 290 K. Finally, a coating of 18 wt % graphene/graphite and 2.5 wt % PEDOT:PSS (Rs = 5.5 Ω□-1) screen printed on the nonwoven fabric was shown to function as an electrode for electrocardiography without any hydrogel and with dry skin conditions. This composite coating finds application in wearable electronics for military and consumer sectors.

Feasibility of Long-Term Daily Life Electrocardiogram Monitoring Based on a Wearable Armband Device.

A study on the feasibility of obtaining usable electrocardiogram (ECG) signals from a wearable armband during 24-hour continuous monitoring is presented. The wearable armband records 3-channel ECG and, unlike the conventional Holter monitors, it is convenient for long-term daily life monitoring because it uses no obstructive leads and it is based on dry (no gels) electrodes, which do not cause skin irritation. An optimal channel selector is presented, based on a linear classifier using features that are related to the ECG signal quality. In addition, this linear classifier is also used for artifact detection. The developed optimal channel selector and artifact detector are applied to 24-hour armband ECG recordings from 5 subjects. For reference comparison, the subjects also wore a Holter device. The armband obtained usable data during 51.07±13.54% (inter-subject mean ± standard deviation) of the non-bed recording time, and the mean heart rate was accurately (relative error with respect to the Holter less than 10%) estimated from the armband selected ECG channel from 94.39±3.41% of the usable data. During the bed recording time, the percentage of usable data was 93.54±2.92%, and mean heart rate was estimated accurately from 97.01±1.80% of those data. These results suggest that the armband device is potentially feasible for a long-term daily life heart rate monitoring based on the presented channel selector and artifact detector, especially during the bed time.

Increased Conductivity and Reduced Settling Time of Carbon-Based Electrodes By Addition of Sea Salt for Wearable Application.

A carbon-based dry electrode is designed to measure bio-potential from skin surface without hydrogel. Consequently, unlike Ag/AgCl electrodes, the carbon-based electrodes require some settling time before a high-fidelity signal is obtained due to the process for impedance matching among skin surface, electrode and amplifiers in biometric system. Besides, especially, when electrocardiogram (ECG) is measured at some distance away from the chest using carbon-based electrodes for wearable application, the settling time could be a critical concern for immediate data collection due to the smaller bio-potential and bigger motion artifact noises. The settling time was defined as the time it takes for the carbon-based electrodes to have the same impedance as that of Ag/AgCl electrodes at a particular frequency (< 1 kHz) for bio-signals. In this study, we investigated the characteristics of the skin contact impedance as a function of time using carbon-based electrodes with and without sea salt and different thickness. Specifically, sea salt was added to the carbon black (SCB)/polydimethlysiloxane (PDMS) electrode to examine the level of enhanced conductivity and reduction of settling time. We used SCB/PDMS and CB/PDMS electrodes with thickness of 1.0 mm and 1.5 mm, examined their electrode and skin contact impedance values and compared them to Ag/AgCl electrodes. We collected impedance data from seven subjects using both SCB and CB/PDMS electrodes every 10 minutes for 50 minutes. A SCB/PDMS electrode showed lower impedance than a CB/PDMS electrode, and for both types of electrodes, higher thickness resulted in lower impedance. The same results were found for skin contact impedance. The settling times of the SCB/PDMS electrodes were found to be $20 \pm 10$ minutes and $40 \pm 10$ minutes for widths of 1.0 mm and 1.5 mm, respectively. The settling time for CB/PDMS without sea salt resulted in significantly higher settling time (> 50 minutes) when compared to SCB/PDMS electrodes. In summary, when carbon-based electrodes are used to measure bio-signals from skin surface for wearable application, its settling time can be partially offset by adding sea salt to CB/PDMS electrode and by making it thinner.

Analysis of Consistency of Transthoracic Bioimpedance Measurements Acquired with Dry Carbon Black PDMS Electrodes, Adhesive Electrodes, and Wet Textile Electrodes.

The detection of intrathoracic volume retention could be crucial to the early detection of decompensated heart failure (HF). Transthoracic Bioimpedance (TBI) measurement is an indirect, promising approach to assessing intrathoracic fluid volume. Gel-based adhesive electrodes can produce skin irritation, as the patient needs to place them daily in the same spots. Textile electrodes can reduce skin irritation; however, they inconveniently require wetting before each use and provide poor adherence to the skin. Previously, we developed waterproof reusable dry carbon black polydimethylsiloxane (CB/PDMS) electrodes that exhibited a good response to motion artifacts. We examined whether these CB/PDMS electrodes were suitable sensing components to be embedded into a monitoring vest for measuring TBI and the electrocardiogram (ECG). We recruited N = 20 subjects to collect TBI and ECG data. The TBI parameters were different between the various types of electrodes. Inter-subject variability for copper-mesh CB/PDMS electrodes and Ag/AgCl electrodes was lower compared to textile electrodes, and the intra-subject variability was similar between the copper-mesh CB/PDMS and Ag/AgCl. We concluded that the copper mesh CB/PDMS (CM/CB/PDMS) electrodes are a suitable alternative for textile electrodes for TBI measurements, but with the benefit of better skin adherence and without the requirement of wetting the electrodes, which can often be forgotten by the stressed HF subjects.

Feasibility Testing of Hydrophobic Carbon Electrodes for Acquisition of Underwater Surface Electromyography Data.

Underwater surface electromyography (sEMG) signals are especially of interest for rehabilitation and sports medicine applications. Silver/silver chloride (Ag/AgCl) hydrogel electrodes, although the gold standard for sEMG data collection, require waterproofing for underwater applications. Having to apply waterproof tape over electrodes impedes the deployment of sEMG in immersed conditions. As a better alternative for underwater applications, we have developed carbon black/polydimethylsiloxane (CB/PDMS) electrodes for collecting sEMG signals under water. We recruited twenty subjects to collect simultaneous recordings of sEMG signals using Ag/AgCl and CB/PDMS electrodes on biceps brachii, triceps brachii, and tibial anterior muscles. The Ag/AgCL electrodes were covered in waterproof tape, and the CB/PDMS electrodes were not. We found no differences in sEMG signal amplitudes between both sensors, for the three muscles. Moderate mean correlation between Ag/AgCl and CB/PDMS electrodes was found on the linear envelopes (≥ 0.7); correlation was higher for power spectral densities (≥ 0.84). Ag/AgCl electrodes performed better in response to noise, whilst the CB/PDMS electrodes were more sensitive to myoelectric activity in triceps and tibialis, and exhibited better response to motion artifacts in the measurements on the triceps and tibialis. Results suggest that sEMG signal collection is possible under water using CB/PDMS electrodes without requiring any waterproof or adhesive tape.

Effect of Shallow and Deep SCUBA Dives on Heart Rate Variability.

Prolonged and high pressure diving may lead to various physiological changes including significant alterations of autonomic nervous system (ANS) activity that may be associated with altered physical performance, decompression sickness, or central nervous system oxygen toxicity. Ideally, researchers could elucidate ANS function before, during, and after dives that are most associated with altered function and adverse outcomes. However, we have a limited understanding of the activities of the ANS especially during deeper prolonged SCUBA diving because there has never been a convenient way to collect physiological data during deep dives. This work is one of the first studies which was able to collect electrocardiogram (ECG) data from SCUBA divers at various depths (33, 66, 99, 150, and 200 ftsw; equivalent to 10.05, 20.10, 30.17, 45.72, and 60.96 m of salt water, respectively) breathing different gas mixtures (air, nitrox and trimix). The aim of this study was to shed light on cardiac ANS behavior during dives, including deep dives. With the aid of dry suits, a Holter monitor that could handle the pressure of a 200 ft. dive, and a novel algorithm that can provide a useful assessment of the ANS from the ECG signal, we investigated the effects of SCUBA dives with different time durations, depths and gas mixtures on the ANS. Principal dynamic mode (PDM) analysis of the ECG, which has been shown to provide accurate separation of the sympathetic and parasympathetic dynamics, was employed to assess the difference of ANS behavior between baseline and diving conditions of varying depths and gas mixtures consisting of air, nitrox and trimix. For all depths and gas mixtures, we found consistent dominance in the parasympathetic activity and a concomitant increase of the parasympathetic dynamics with increasing diving duration and depth. For 33 and 66 ft. dives, we consistently found significant decreases in heart rates (HR) and concomitant increases in parasympathetic activities as estimated via the PDM and root mean square of successive differences (RMSSD) for all time intervals (from the first 5 min to the last 30 min) at the bottom depth when compared to the baseline depth at sea level. The sympathetic dynamics did not change with dive duration or gas mixtures, but at the 150 and 200 ft. dives, we found a significant increase in the sympathetic dynamics in addition to the elevated parasympathetic dynamics when compared to baseline The power spectral density (PSD) measures such as the low frequency (LF), high frequency (HF) and its ratio, and approximate entropy (ApEn) indices were not as consistent when compared to PDM-derived parasympathetic dynamics and RMSSD index.

Screen-Printed PEDOT:PSS Electrodes on Commercial Finished Textiles for Electrocardiography.

Electrocardiography (ECG) is an essential technique for analyzing and monitoring cardiovascular physiological conditions such as arrhythmia. This article demonstrates the integration of screen-printed ECG circuitry from a commercially available conducting polymer, PEDOT:PSS, onto commercially available finished textiles. ECG signals were recorded in dry skin conditions due to the ability of PEDOT:PSS to function as both ionic and electronic conductors. The signal amplifies when the skin transpires water vapor or by applying a common lotion on the skin. Finally, PEDOT:PSS wires connected to PEDOT:PSS electrodes have been shown to record ECG signals comparable to Ag/AgCl connected to copper wires.

A Robust Motion Artifact Detection Algorithm for Accurate Detection of Heart Rates From Photoplethysmographic Signals Using Time-Frequency Spectral Features.

Motion and noise artifacts (MNAs) impose limits on the usability of the photoplethysmogram (PPG), particularly in the context of ambulatory monitoring. MNAs can distort PPG, causing erroneous estimation of physiological parameters such as heart rate (HR) and arterial oxygen saturation (SpO2). In this study, we present a novel approach, "TifMA," based on using the time-frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the nonusable part of the corrupted data. The term "nonusable" refers to segments of PPG data from which the HR signal cannot be recovered accurately. Two sequential classification procedures were included in the TifMA algorithm. The first classifier distinguishes between MNA-corrupted and MNA-free PPG data. Once a segment of data is deemed MNA-corrupted, the next classifier determines whether the HR can be recovered from the corrupted segment or not. A support vector machine (SVM) classifier was used to build a decision boundary for the first classification task using data segments from a training dataset. Features from time-frequency spectra of PPG were extracted to build the detection model. Five datasets were considered for evaluating TifMA performance: (1) and (2) were laboratory-controlled PPG recordings from forehead and finger pulse oximeter sensors with subjects making random movements, (3) and (4) were actual patient PPG recordings from UMass Memorial Medical Center with random free movements and (5) was a laboratory-controlled PPG recording dataset measured at the forehead while the subjects ran on a treadmill. The first dataset was used to analyze the noise sensitivity of the algorithm. Datasets 2-4 were used to evaluate the MNA detection phase of the algorithm. The results from the first phase of the algorithm (MNA detection) were compared to results from three existing MNA detection algorithms: the Hjorth, kurtosis-Shannon entropy, and time-domain variability-SVM approaches. This last is an approach recently developed in our laboratory. The proposed TifMA algorithm consistently provided higher detection rates than the other three methods, with accuracies greater than 95% for all data. Moreover, our algorithm was able to pinpoint the start and end times of the MNA with an error of less than 1 s in duration, whereas the next-best algorithm had a detection error of more than 2.2 s. The final, most challenging, dataset was collected to verify the performance of the algorithm in discriminating between corrupted data that were usable for accurate HR estimations and data that were nonusable. It was found that on average 48% of the data segments were found to have MNA, and of these, 38% could be used to provide reliable HR estimation.

Novel Conductive Carbon Black and Polydimethlysiloxane ECG Electrode: A Comparison with Commercial Electrodes in Fresh, Chlorinated, and Salt Water.

In this study, we evaluated the performance of two novel conductive carbon black (CB) and polydimethlysiloxane (PDMS) bio-potential electrodes, with and without an integrated flexible copper mesh, against commercially available electrodes (Polar(®) textile, Silver-coated textile, and carbon rubber). The electrodes were tested in three types of water (fresh/unfiltered, chlorinated, and salt water). Our testing revealed that our CB/PDMS electrode with integrated copper mesh provided a high-fidelity ECG signal morphologies without any amplitude degradation in all of the types of water tested (N = 10). The non-meshed CB/PDMS electrodes were also subjected to a long-term durability test by the US Navy SCUBA divers during which the electrodes maintained ECG signal quality for a 6 h period of continuous use. The results of a material degradation analysis revealed the CB/PDMS composite material does not exhibit significant changes in physical integrity after prolonged exposure to the test conditions. The newly developed meshed CB/PDMS electrodes have the potential to be used in a wide variety of both dry and wet environments including the challenge of obtaining ECG signals in salt water environments.

An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation.

In this paper, an improved algorithm for the extraction of respiration signal from the electrocardiogram (ECG) in home healthcare is proposed. The whole system consists of two-lead electrocardiogram acquisition using conductive textile electrodes located in bed, baseline fluctuation elimination, R-wave detection, adjustment of sudden change in R-wave area using moving average, and optimal lead selection. In order to solve the problems of previous algorithms for the ECG-derived respiration (EDR) signal acquisition, we are proposing a method for the optimal lead selection. An optimal EDR signal among the three EDR signals derived from each lead (and arctangent of their ratio) is selected by estimating the instantaneous frequency using the Hilbert transform, and then choosing the signal with minimum variation of the instantaneous frequency. The proposed algorithm was tested on 15 male subjects, and we obtained satisfactory respiration signals that showed high correlation (r(2) > 0.8) with the signal acquired from the chest-belt respiration sensor.