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

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

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

Using the redundant convolutional encoder–decoder to denoise QRS complexes in ECG signals recorded with an armband wearable device

Long-term electrocardiogram (ECG) recordings while performing normal daily routines are often corrupted with motion artifacts, which in turn, can result in the incorrect calculation of heart rates. Heart rates are important clinical information, as they can be used for analysis of heart-rate variability and detection of cardiac arrhythmias. In this study, we present an algorithm for denoising ECG signals acquired with a wearable armband device. The armband was worn on the upper left arm by one male participant, and we simultaneously recorded three ECG channels for 24 h. We extracted 10-s sequences from armband recordings corrupted with added noise and motion artifacts. Denoising was performed using the redundant convolutional encoder–decoder (R-CED), a fully convolutional network. We measured the performance by detecting R-peaks in clean, noisy, and denoised sequences and by calculating signal quality indices: signal-to-noise ratio (SNR), ratio of power, and cross-correlation with respect to the clean sequences. The percent of correctly detected R-peaks in denoised sequences was higher than in sequences corrupted with either added noise (70–100% vs. 34–97%) or motion artifacts (91.86% vs. 61.16%). There was notable improvement in SNR values after denoising for signals with noise added (7–19 dB), and when sequences were corrupted with motion artifacts (0.39 dB). The ratio of power for noisy sequences was significantly lower when compared to both clean and denoised sequences. Similarly, cross-correlation between noisy and clean sequences was significantly lower than between denoised and clean sequences. Moreover, we tested our denoising algorithm on 60-s sequences extracted from recordings from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and obtained improvement in SNR values of 7.08 ± 0.25 dB (mean ± standard deviation (sd)). These results from a diverse set of data suggest that the proposed denoising algorithm improves the quality of the signal and can potentially be applied to most ECG measurement devices

Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia

Identifying trauma patients at risk of imminent hemorrhagic shock is a challenging task in intraoperative and battlefield settings given the variability of traditional vital signs, such as heart rate and blood pressure, and their inability to detect blood loss at an early stage. To this end, we acquired N = 58 photoplethysmographic (PPG) recordings from both trauma patients with suspected hemorrhage admitted to the hospital, and healthy volunteers subjected to blood withdrawal of 0.9 L. We propose four features to characterize each recording: goodness of fit (r2), the slope of the trend line, percentage change, and the absolute change between amplitude estimates in the heart rate frequency range at the first and last time points. Also, we propose a machine learning algorithm to distinguish between blood loss and no blood loss. The optimal overall accuracy of discriminating between hypovolemia and euvolemia was 88.38%, while sensitivity and specificity were 88.86% and 87.90%, respectively. In addition, the proposed features and algorithm performed well even when moderate blood volume was withdrawn. The results suggest that the proposed features and algorithm are suitable for the automatic discrimination between hypovolemia and euvolemia, and can be beneficial and applicable in both intraoperative/emergency and combat casualty care

Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study

BACKGROUND: Accumulation of excess body fluid and autonomic dysregulation are clinically important characteristics of acute decompensated heart failure. We hypothesized that transthoracic bioimpedance, a noninvasive, simple method for measuring fluid retention in lungs, and heart rate variability, an assessment of autonomic function, can be used for detection of fluid accumulation in patients with acute decompensated heart failure. OBJECTIVE: We aimed to evaluate the performance of transthoracic bioimpedance and heart rate variability parameters obtained using a fluid accumulation vest with carbon black-polydimethylsiloxane dry electrodes in a prospective clinical study (System for Heart Failure Identification Using an External Lung Fluid Device; SHIELD). METHODS: We computed 15 parameters: 8 were calculated from the model to fit Cole-Cole plots from transthoracic bioimpedance measurements (extracellular, intracellular, intracellular-extracellular difference, and intracellular-extracellular parallel circuit resistances as well as fitting error, resonance frequency, tissue heterogeneity, and cellular membrane capacitance), and 7 were based on linear (mean heart rate, low-frequency components of heart rate variability, high-frequency components of heart rate variability, normalized low-frequency components of heart rate variability, normalized high-frequency components of heart rate variability) and nonlinear (principal dynamic mode index of sympathetic function, and principal dynamic mode index of parasympathetic function) analysis of heart rate variability. We compared the values of these parameters between 3 participant data sets: control (n=32, patients who did not have heart failure), baseline (n=23, patients with acute decompensated heart failure taken at the time of admittance to the hospital), and discharge (n=17, patients with acute decompensated heart failure taken at the time of discharge from hospital). We used several machine learning approaches to classify participants with fluid accumulation (baseline) and without fluid accumulation (control and discharge), termed with fluid and without fluid groups, respectively. RESULTS: Among the 15 parameters, 3 transthoracic bioimpedance (extracellular resistance, R0; difference in extracellular-intracellular resistance, R0 - Rinfinity, and tissue heterogeneity, alpha) and 3 heart rate variability (high-frequency, normalized low-frequency, and normalized high-frequency components) parameters were found to be the most discriminatory between groups (patients with and patients without heart failure). R0 and R0 - Rinfinity had significantly lower values for patients with heart failure than for those without heart failure (R0: P=.006; R0 - Rinfinity: P=.001), indicating that a higher volume of fluids accumulated in the lungs of patients with heart failure. A cubic support vector machine model using the 5 parameters achieved an accuracy of 92% for with fluid and without fluid group classification. The transthoracic bioimpedance parameters were related to intra- and extracellular fluid, whereas the heart rate variability parameters were mostly related to sympathetic activation. CONCLUSIONS: This is useful, for instance, for an in-home diagnostic wearable to detect fluid accumulation. Results suggest that fluid accumulation, and subsequently acute decompensated heart failure detection, could be performed using transthoracic bioimpedance and heart rate variability measurements acquired with a wearable vest. Emily Ensom, Eric Ding, Anna Hayes, Jarno Riistama, Chad Darling, David McManus, Ki H. Chon. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 27.08.2020

Tidal Volume Estimation Using the Blanket Fractal Dimension of the Tracheal Sounds Acquired by Smartphone

In this paper, we propose the use of blanket fractal dimension (BFD) to estimate the tidal volume from smartphone-acquired tracheal sounds. We collected tracheal sounds with a Samsung Galaxy S4 smartphone, from five (N = 5) healthy volunteers. Each volunteer performed the experiment six times; first to obtain linear and exponential fitting models, and then to fit new data onto the existing models. Thus, the total number of recordings was 30. The estimated volumes were compared to the true values, obtained with a Respitrace system, which was considered as a reference. Since Shannon entropy (SE) is frequently used as a feature in tracheal sound analyses, we estimated the tidal volume from the same sounds by using SE as well. The evaluation of the performed estimation, using BFD and SE methods, was quantified by the normalized root-mean-squared error (NRMSE). The results show that the BFD outperformed the SE (at least twice smaller NRMSE was obtained). The smallest NRMSE error of 15.877% ± 9.246% (mean ± standard deviation) was obtained with the BFD and exponential model. In addition, it was shown that the fitting curves calculated during the first day of experiments could be successfully used for at least the five following days

Electrodermal Activity and Electrocardiogram during submaximal exercise test

<p>Subjects for whom exercise represents a low risk level, based on standardized guidelines from the American College of Sports Medicine (ACSM) [20], were asked to participate in the study. Eighteen healthy subjects, 11 males and 7 females, age 21 ± 3 years were enrolled. Participants were asked to avoid caffeine and alcohol during the 48 hours preceding the test, and were instructed to fast (water only) for at least 3 h before testing. The study was conducted in a quiet, comfortable room (ambient temperature, 18-20 °C, and relative humidity between 30-50%). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. This protocol was approved by the Institutional Review Board of the University of Connecticut. </p> <p>Before the exercise test began, the subjects were asked to lay in the supine position for 5 min to procure hemodynamic stabilization prior to 5 minutes of data collection in this position. ECG and EDA were measured simultaneously for each subject throughout the entire experiment. The ECG signal was used to monitor subjects’ HR throughout the experiment. An HP ECG monitor (HP 78354A) and GSR ADInstruments module were used. Three hydrogel Ag-AgCl electrodes were used for ECG signal collection. The electrodes were placed on the shoulders and lower left rib. In addition, a pair of stainless steel electrodes were placed on index and middle fingers of the right hand to collect the EDA signal. Subjects were instructed to keep their right hand stable, raised at chest height. The skin was cleaned with alcohol before placing the ECG and EDA electrodes. The leads were taped to the subject’s skin using latex-free tape, to avoid movement of the cables, which can corrupt the signals. All signals were acquired through the ADInstruments analog-to-digital converter, and compatible PowerLab software, while the sampling frequency was fixed to 400 Hz for all signals. Participants were asked to wear their own active wear/gym clothes during the protocol with the shirt covering the electrodes and cables during the experiment.</p><p>Subjects were first monitored for 5 min at rest (supine, without any movement or talking) to measure resting HR and EDA. The subjects then performed the incremental test on a motorized treadmill (Life Fitness F3). 85% HRmax was calculated from the equation HRmax = 206.9-(0.67*age).<br></p><p></p><p>The incremental running began with an initial warm-up, followed by walking at 3mi/h (~ 4.82 km/h). The speed was increased to 5 mi/h (~ 8 km/h) and increased 0.6 mi/h (about 1 km/h) every subsequent minute until the subjects reached 85% of their HRmax. When a subject reached 85% of HRmax within 2 min of running, the data were excluded because at least 2 minutes of data are required for processing. The 18 subjects enrolled for this study represents those who were able to provide at least 2 minutes of data prior to reaching 85% of HRmax. After subjects reached 85% of their HRmax, treadmill speed was reduced to 5 mi/h (~ 8 km/h) for another 4 min to start the recovery phase, followed by walking at 3 mi/h (about 4.82 km/h) for 5 minutes. A final 10 min period (or more if needed to achieve baseline HR) in the supine position was utilized to allow HR to return to baseline. The duration of the experiment was approximately one hour.</p><p></p

Tracheal Sounds Acquisition Using Smartphones

Tracheal sounds have received a lot of attention for estimating ventilation parameters in a non-invasive way. The aim of this work was to examine the feasibility of extracting accurate airflow, and automating the detection of breath-phase onset and respiratory rates all directly from tracheal sounds acquired from an acoustic microphone connected to a smartphone. We employed the Samsung Galaxy S4 and iPhone 4s smartphones to acquire tracheal sounds from N = 9 healthy volunteers at airflows ranging from 0.5 to 2.5 L/s. We found that the amplitude of the smartphone-acquired sounds was highly correlated with the airflow from a spirometer, and similar to previously-published studies, we found that the increasing tracheal sounds’ amplitude as flow increases follows a power law relationship. Acquired tracheal sounds were used for breath-phase onset detection and their onsets differed by only 52 ± 51 ms (mean ± SD) for Galaxy S4, and 51 ± 48 ms for iPhone 4s, when compared to those detected from the reference signal via the spirometer. Moreover, it was found that accurate respiratory rates (RR) can be obtained from tracheal sounds. The correlation index, bias and limits of agreement were r2 = 0.9693, 0.11 (−1.41 to 1.63) breaths-per-minute (bpm) for Galaxy S4, and r2 = 0.9672, 0.097 (–1.38 to 1.57) bpm for iPhone 4s, when compared to RR estimated from spirometry. Both smartphone devices performed similarly, as no statistically-significant differences were found