A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor.

Metabolic syndrome poses a significant health challenge worldwide, prompting the need for comprehensive strategies integrating physical activity monitoring and energy expenditure. Wearable sensor devices have been used both for energy intake and energy expenditure (EE) estimation. Traditionally, sensors are attached to the hip or wrist. The primary aim of this research is to investigate the use of an eyeglass-mounted wearable energy intake sensor (Automatic Ingestion Monitor v2, AIM-2) for simultaneous recognition of physical activity (PAR) and estimation of steady-state EE as compared to a traditional hip-worn device. Study data were collected from six participants performing six structured activities, with the reference EE measured using indirect calorimetry (COSMED K5) and reported as metabolic equivalents of tasks (METs). Next, a novel deep convolutional neural network-based multitasking model (Multitasking-CNN) was developed for PAR and EE estimation. The Multitasking-CNN was trained with a two-step progressive training approach for higher accuracy, where in the first step the model for PAR was trained, and in the second step the model was fine-tuned for EE estimation. Finally, the performance of Multitasking-CNN on AIM-2 attached to eyeglasses was compared to the ActiGraph GT9X (AG) attached to the right hip. On the AIM-2 data, Multitasking-CNN achieved a maximum of 95% testing accuracy of PAR, a minimum of 0.59 METs mean square error (MSE), and 11% mean absolute percentage error (MAPE) in EE estimation. Conversely, on AG data, the Multitasking-CNN model achieved a maximum of 82% testing accuracy in PAR, a minimum of 0.73 METs MSE, and 13% MAPE in EE estimation. These results suggest the feasibility of using an eyeglass-mounted sensor for both PAR and EE estimation.

Corporate capital structure effects on corporate performance pursuing a strategy of innovation in manufacturing companies.

Within the sphere of finance, the concept of capital structure has long been a subject of intense debate, serving as a quantitative depiction of the balance between debt, preference shares, and common stock within a company. This structure serves a crucial role in optimizing the utilization of a company's existing resources while simultaneously elevating the revenue streams for stakeholders. This particular study delves into the intricate relationship between corporate performance and capital structure, focusing on 78 publicly listed firms within the Dhaka Stock Exchange (DSE). Bangladesh holds the 29th position globally in terms of purchasing power, lending significant weight to this investigation. To comprehensively analyze this correlation, panel data encompassing the span from 2017 to 2021 was collected for these 78 sample companies operating within the DSE. Several key determinants of capital structure were considered in this analysis, namely the debt-to-equity ratio, short-term leverage ratio, long-term leverage ratio, and total debt ratio. Meanwhile, the performance of these firms was gauged using key metrics such as Return on Assets (ROA), Return on Equity (ROE), and Earnings Per Share (EPS). To ensure a robust analysis, factors such as inflation, liquidity, growth rate, tax rate, and firm size were meticulously controlled for. The findings unveiled a compelling narrative: all forms of debt ratios-be it short-term, long-term, or the total debt ratio-exhibited a substantial negative impact on ROA at a significant level of 1 %. Conversely, specific debt ratios, like the short-term total debt and the total debt-to-total asset ratio, displayed a notable positive correlation with ROE at a 1 % significance level. Intriguingly, the long-term total debt ratio yielded a negative and insignificant effect on ROE. Moreover, within the spectrum of predictors influencing a firm's performance, the liquidity ratio emerged as a non-significant factor-a notable discovery that highlights the nuanced nature of the interplay between capital structure and performance within these companies.

Corporate social Responsibility's impact on passenger loyalty and satisfaction in the Chinese airport industry: The moderating role of green HRM.

Corporate social responsibility has been extensively discussed and linked to the firm performance by the researchers. However, a significant research gap remains unexplored and that is measuring the association between corporate social responsibility, passenger satisfaction, and loyalty in the context of two international airports in China. This research also measures the moderating impact of green human resources management on the relationship between CSR, passengers' satisfaction, and loyalty. Data from two international airports in China were collected through a questionnaire. A total of 269 questionnaires were used for statistical analysis using Smart PLS 3.3. The findings from the statistical analysis revealed that corporate social responsibility in the airport affected passenger satisfaction and loyalty positively and significantly. Moreover, green human resource management in an airport plays a moderating role between corporate social responsibility, passengers' satisfaction, and loyalty. Overall, the study's findings enrich the literature on CSR, customer satisfaction, and loyalty, portray GHRM's role in the airport setting, and suggest practical indications for services industries. Discussions, limitations, and future recommendations are also given.

Green human resource management practices to accomplish green competitive advantage: A moderated mediation model.

In present high-tech era, firms need to possess a variety of capabilities and resources to attain and sustain a competitive position in the market. The motivation for this study was to understand green competitive advantage through the application of Ability-Motivation-Opportunity theory and Natural-Resource-Based view. In a time-lagging longitudinal online survey related to small-and-medium-sized manufacturing enterprises, 223 professionals provided data according to their opinions. The structural and measurement model were designed for analyses. The results supported the model and verify the Green human resource management practices' influence on green competitive advantage, with partial mediation of green knowledge sharing and green innovation (green product innovation and green process innovation). The analyses revealed the positive highly significant moderation of green human capital, which is the novelty of the study. Green human capital is important to develop sustainable workforce who act as catalyst in achieving sustainable development goals. The report offers practical advice for small-medium manufacturing enterprises (SMMEs) aiming to attain a green competitive advantage. With the help of a green competitive advantage, the recommendations in this study can benefit SMMEs to develop a green human capital and to create innovative knowledge. As a result, it is a futuristic approach to dealing with the improved environmental conditions and developed a green human capital in this industrialized age.

Green shared vision: A bridge between responsible leadership and green behavior under individual green values.

The pressure on businesses to be environmentally conscious and focus on sustainable development is accruing due to environmental challenges. Companies are adopting ecological practices and policies to improve their environmentally friendly performance. To achieve this, organizations must substantiate and change the behavior of workers to align their behavior with the organization's ecological objectives. The study endeavors to integrate research on the responsible style of leaders and green behaviors of employees (in-role and extra-role green behaviors) through the mediation of green shared vision and analyze the moderation mechanism of individual green values. For collecting the data, a questionnaire-based survey was conducted among MBA executive program students with at least a year of experience in manufacturing. Out of the 450 questionnaires distributed, only 307 useful responses were obtained. The collected data has been analyzed using SPSS and AMOS. Ethical standards were followed, and participants were assured that their responses would be confidential. The study found that responsible leadership positively impacts green behaviors among employees. This means that when leaders within an organization demonstrate responsible and environmentally conscious behavior, it tends to encourage employees to engage in green behaviors. The study also discovered that a "green shared vision" partially mediates the relationship between responsible leadership and in-role green behavior. In contrast, green shared vision does not mediate the relation between responsible leadership and extra role green behavior. Moreover, this study also finds that the relationship between green shared vision and in-role and extra-role green behavior is strengthened when individual green values moderate it. The study highlights the importance of responsible leadership and the role of green shared values and individual green values in promoting environmentally friendly behavior in the workplace.

Parameters optimization of Fe3O4 NPs synthesis by Tamarindus indica leaf extract possessing both peroxidase as well as excellent dye removal activity.

This study reports optimized conditions for the green synthesis of iron (II,III) oxide nanoparticles (Fe3O4 NPs) from Tamarindus indica (T. indica) leaf extract. The synthetic parameters like concentration of leaf extract, solvent system, buffer, electrolyte, pH, and time were optimized for Fe3O4 NPs synthesis. Fe3O4 NPs were obtained from the synthesis protocol by measuring size (80 ± 3 nm approx.), characteristics color changes, and an absorption peak between 270 nm and 280 nm using a UV-visible spectrophotometer, scanning electron microscope (SEM), and an energy dispersive X-ray spectrometer (EDS) study. Peroxidase activity was tested with 3,3,5,5-Tetramethylbenzidine (TMB) oxidation in the presence of hydrogen peroxide and dye removal activity was tested with malachite green (MG). The results indicated that the successful synthesis of Fe3O4 nanoparticles using aqueous leaf extract of T. indica is a practical alternative for biomedical applications due to its potent peroxidase activity and high dye removal capacity (about 93% with UV light and 55% with room light).

Eco-innovation influence on business performance in Jordanian micro, small and medium enterprises operating in the food processing sector.

Innovative performance is a fundamental asset for building competitive advantage of micro, small and medium enterprises MSMEs. This research empirically examines the direct and indirect relationship between eco-innovation and business performance in Jordanian MSMEs enterprises working in the food processing sector. This research draws on the resource-based view theory to investigate the inter-relationships among three types of eco-innovation (process, product, organizational) and their relative impact on business performance. Furthermore, the researchers used structural equation modelling of 86 samples collected from Jordanian MSMEs operating in the food processing sector. The major contribution of this research is providing a holistic view that explains the inter-relationship among eco-process, eco-product, and eco-organizational innovation. The research reveals the impact of eco-innovation variables on business performance. The greatest is the impact of eco-process on business performance followed by eco-product and eco-organizational respectively. Regarding the effect of eco-organizational and eco-process innovation on eco-product, the findings of the study showed that the greatest is the effect of eco-organizational followed by eco-process. According to the post hoc, the mean differences show that there is statically significant difference in the responses of the respondents towards eco- process regarding different organization age.

Detecting Autism Spectrum Disorder Using Spectral Analysis of Electroretinogram and Machine Learning: Preliminary results.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that impacts language, communication and social interactions. The current diagnostic process for ASD is based upon a detailed multidisciplinary assessment. Currently no clinical biomarker exists to help in the diagnosis and monitoring of this condition that has a prevalence of approximately 1%. The electroretinogram (ERG), is a clinical test that records the electrical response of the retina to light. The ERG is a promising way to study different neurodevelopmental and neurodegenerative disorders, including ASD. In this study, we have proposed a machine learning based method to detect ASD from control subjects using the ERG waveform. We collected ERG signals from 47 control (CO) and 96 ASD individuals. We analyzed ERG signals both in the time and the spectral domain to gain insight into the statistically significant discriminating features between CO and ASD individuals. We evaluated the machine learning (ML) models using a subject independent cross validation-based approach. Time-domain features were able to detect ASD with a maximum 65% accuracy. The classification accuracy of our best ML model using time-domain and spectral features was 86%, with 98% sensitivity. Our preliminary results indicate that spectral analysis of ERG provides helpful information for the classification of ASD.

Improving the Accuracy of R-Peak Detection in a Wearable Armband Device for Daily Life Electrocardiogram Monitoring Using a Deep Convolutional Denoising Encoder-Decoder Network.

Continuous long-term heart rate (HR) monitoring using wearable devices is desirable to aid in the diagnosis of many health-related conditions. Recently, we have developed an armband device that does not use obstructive leads, has dry electrodes which are convenient for long-term electrocardiogram (ECG) recording, and has been shown to be an effective alternate approach for continuous ECG monitoring. However, motion artifacts (MA) due to electromyogram (EMG) contractions are acknowledged as the major challenge of an armband. In this study, we used a deep convolutional neural network denoising encoder-decoder (CNNDED) to enhance the accuracy of R-peak detection in MA-corrupted ECG recordings obtained by an armband device. We collected simultaneous 24-hour ECG recordings using both the armband device and a Holter monitor on 10 subjects. Each 10-sec ECG segment was converted to a time-frequency representation and subsequently used as the input to CNNDED. During the training process, the model learned to accentuate the location of R peaks by amplifying their values in each ECG beat and suppressing the remaining waveforms. For the training output, the model used the R-peak location information from the simultaneously collected Holter ECG data, which were considered as the reference. The performance of CNNDED was evaluated on an independent test data set using the standard performance metrics. The mean relative error of the estimated HR with respect to the Holter data was 17.5 and 7.3 beats/min, pre- and post-CNNDED, respectively. The mean relative difference of the root mean square of successive difference values were 0.23 and 0.06 before and after applying CNNDED, respectively. Although further study is needed, the current preliminary results suggest that CNNDED can improve detection of R peaks even when they are completely buried in the presence of EMG artifacts.

A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in Electrodermal Activity Signals: A Preliminary Study.

Automatic motion artifact (MA) removal in electrodermal activity (EDA) signals is a major challenge because of the aperiodic and irregular characteristics of EDA. Given the lack of a suitable MA removal algorithm, a substantial amount of EDA data is typically discarded, especially during ambulatory monitoring. Current methods for MA removal in EDA are feasible when data are corrupted with low magnitude artifacts. In this study, we propose a more data-driven deep convolutional autoencoder (DCAE) for automated motion artifact removal in EDA signals. The DCAE was trained using several publicly available datasets. We used both Gaussian white noise (GWN) and real-life induced MA data records collected in a laboratory setting to corrupt the clean EDA signals. We compared the performance of our DCAE network with three state-of-the-art methods using the performance metrics the signal-to-noise ratio (SNR) improvement (SNRimp), and the mean squared error (MSE). The proposed DCAE provided significantly higher SNRimpand lower MSE compared to three other methods for both synthetically and real-life induced MA. While the work is preliminary, this work illustrates a promising approach which can potentially be used to remove many different types of MA.

Partnerships in the introduction of new routine vaccines in Bangladesh: evidence from a prospective process evaluation.

OBJECTIVE: To assess the contribution of partners in the introduction of two new vaccines concurrently: pneumococcal 10-valent conjugate vaccine (PCV-10) and inactivated polio vaccine (IPV) into the routine Expanded Programme on Immunization (EPI) in Bangladesh. DESIGN: We conducted a prospective process evaluation that included the theory of change development, root cause analysis and in-depth investigation. As part of process tracking, we reviewed relevant documents, observed trainers' and vaccinators' training and key stakeholder meetings. We analysed the data thematically. SETTING: We purposively selected eight Upazila (subdistrict) and one city corporation covering nine districts and seven administrative divisions of Bangladesh. PARTICIPANTS: Nineteen national key informants were interviewed and 16 frontline health workers were invited to the group discussions considering their involvement in the vaccine introduction process. RESULTS: The EPI experienced several challenges during the joint introduction of PCV-10 and IPV, such as frequent changes in the vaccine introduction schedule, delays in budget allocation, vaccine supply shortage and higher wastage rates of IPV. EPI addressed these challenges in collaboration with its partners, that is, the World Health Organization (WHO) and United Nations Children's Fund (UNICEF), who provided technical assistance to develop a training curriculum and communication materials and enhanced demand generation at the community level. In addition, the WHO conducted a country readiness assessment for PCV-10, and UNICEF supported vaccine shipment. Other government ministries, City Corporations and municipalities also supported the EPI. CONCLUSIONS: The partnership among the EPI stakeholders effectively addressed various operational challenges during the joint introduction of PCV-10 and IPV helped strengthen Bangladesh's immunisation systems. These accomplishments are attributed to several factors that should be supported and strengthened for future vaccine introductions in Bangladesh and other low and-middle countries.

A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in Electrodermal Activity.

OBJECTIVE: This study aimed to develop a robust and data driven automatic motion artifacts (MA) removal technique from electrodermal activity (EDA) signal. METHODS: we proposed a deep convolutional autoencoder (DCAE) approach for automatic MA removal in EDA signals. Our model was trained using several publicly available datasets that were collected using a wide variety of stimuli to cause EDA reactions; the sample size was large ([Formula: see text]). We trained and validated our DCAE network using both Gaussian white noise (GWN) and realistic MA data records collected using a novel circuitry in our lab. We further evaluated and compared the performance of our DCAE model with the existing methods on two independent and unseen datasets called Chon lab motion artifact dataset II (CMAD II) and central nervous system oxygen toxicity dataset (CNS-OT). RESULTS: Our DCAE model showed significantly higher signal-to-noise-power-ratio improvement ( SNRimp) and lower mean squared error ( MSE) when compared with that of the three previous methods (averaged [Formula: see text], and MSE = 0.028 on the MA-corrupted data). Moreover, the reconstructed EDAs from the CMAD II dataset had a mean correlation value of 0.78 (statistically significantly higher when compared with other methods) with the reference clean data from the motionless hand, whereas the raw MA-corrupted data had a correlation value of only 0.68. CONCLUSION: The results presented in the paper indicates that our DCAE can remove MAs with higher intensity where the existing methods fails. SIGNIFICANCE: Proposed DCAE model can be used to recover a significant amount of otherwise discarded EDA data.

Comparison of Electrodermal Activity from Multiple Body Locations Based on Standard EDA Indices' Quality and Robustness against Motion Artifact.

The most traditional sites for electrodermal activity (EDA) data collection, palmar locations such as fingers or palms, are not usually recommended for ambulatory monitoring given that subjects have to use their hands regularly during their daily activities, and therefore, alternative sites are often sought for EDA data collection. In this study, we collected EDA signals (n = 23 subjects, 19 male) from four measurement sites (forehead, back of neck, finger, and inner edge of foot) during cognitive stress and induction of mild motion artifacts by walking and one-handed weightlifting. Furthermore, we computed several EDA indices from the EDA signals obtained from different sites and evaluated their efficiency to classify cognitive stress from the baseline state. We found a high within-subject correlation between the EDA signals obtained from the finger and the feet. Consistently high correlation was also found between the finger and the foot EDA in both the phasic and tonic components. Statistically significant differences were obtained between the baseline and cognitive stress stage only for the EDA indices computed from the finger and the foot EDA. Moreover, the receiver operating characteristic curve for cognitive stress detection showed a higher area-under-the-curve for the EDA indices computed from the finger and foot EDA. We also evaluated the robustness of the different body sites against motion artifacts and found that the foot EDA location was the best alternative to other sites.

A Preliminary Study on Automatic Motion Artifact Detection in Electrodermal Activity Data Using Machine Learning.

The electrodermal activity (EDA) signal is a sensitive and non-invasive surrogate measure of sympathetic function. Use of EDA has increased in popularity in recent years for such applications as emotion and stress recognition; assessment of pain, fatigue, and sleepiness; diagnosis of depression and epilepsy; and other uses. Recently, there have been several studies using ambulatory EDA recordings, which are often quite useful for analysis of many physiological conditions. Because ambulatory monitoring uses wearable devices, EDA signals are often affected by noise and motion artifacts. An automated noise and motion artifact detection algorithm is therefore of utmost importance for accurate analysis and evaluation of EDA signals. In this paper, we present machine learning-based algorithms for motion artifact detection in EDA signals. With ten subjects, we collected two simultaneous EDA signals from the right and left hands, while instructing the subjects to move only the right hand. Using these data, we proposed a cross-correlation-based approach for non-biased labeling of EDA data segments. A set of statistical, spectral and model-based features were calculated which were then subjected to a feature selection algorithm. Finally, we trained and validated several machine learning methods using a leave-one-subject-out approach. The classification accuracy of the developed model was 83.85% with a standard deviation of 4.91%, which was better than a recent standard method that we considered for comparison to our algorithm.

Validation of Spectral Indices of Electrodermal Activity with a Wearable Device.

Electrodermal activity (EDA) has been found to be a highly sensitive, accurate and non-invasive measure of the sympathetic nervous system's activity and has been used to extract biomarkers of various pathophysiological conditions including stress, fatigue, epilepsy, and chronic pain. Recently, various robust signal processing techniques have been developed to obtain more reliable and accurate indices that capture the meaningful characteristics of the EDA using data collected from laboratory-scale devices. However, EDA also has the potential to monitor such physiological conditions in active ambulatory settings, for which the developed tools must be deployed in wearable devices. In this paper, we studied the feasibility of obtaining the highly-sensitive spectral indices of EDA using a wearable device. EDA signals were collected from left hand fingers using a wearable device and a laboratory-scale reference device, while N=18 subjects underwent the Head up Tilt test and the Stroop test to stimulate orthostatic and cognitive stress, respectively. We computed two time-domain indices, the skin conductance level (SCL) and nonspecific skin conductance responses (NS.SCRs), and two spectral indices, the normalized sympathetic components of the EDA (EDASympn), and the time-varying EDA index of sympathetic control (TVSymp). The results showed similar performances for EDASympn and TVSymp indices across both devices. While spectral indices obtained from both devices performed similarly in response to orthostatic and cognitive stress, time-domain exhibited large variation when obtained by the wearable device. Further research is required to develop and refine such devices, as well as the indices used to analyze EDA results.Clinical Relevance- This study proves the feasibility of obtaining spectral indices of EDA using a wearable device, which can be used to develop wearable tools to detect pain, stress, fatigue, between others.

Development and Validation of an Automated Algorithm to Detect Atrial Fibrillation Within Stored Intensive Care Unit Continuous Electrocardiographic Data: Observational Study.

BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia during critical illness, representing a sepsis-defining cardiac dysfunction associated with adverse outcomes. Large burdens of premature beats and noisy signal during sepsis may pose unique challenges to automated AF detection. OBJECTIVE: The objective of this study is to develop and validate an automated algorithm to accurately identify AF within electronic health care data among critically ill patients with sepsis. METHODS: This is a retrospective cohort study of patients hospitalized with sepsis identified from Medical Information Mart for Intensive Care (MIMIC III) electronic health data with linked electrocardiographic (ECG) telemetry waveforms. Within 3 separate cohorts of 50 patients, we iteratively developed and validated an automated algorithm that identifies ECG signals, removes noise, and identifies irregular rhythm and premature beats in order to identify AF. We compared the automated algorithm to current methods of AF identification in large databases, including ICD-9 (International Classification of Diseases, 9th edition) codes and hourly nurse annotation of heart rhythm. Methods of AF identification were tested against gold-standard manual ECG review. RESULTS: AF detection algorithms that did not differentiate AF from premature atrial and ventricular beats performed modestly, with 76% (95% CI 61%-87%) accuracy. Performance improved (P=.02) with the addition of premature beat detection (validation set accuracy: 94% [95% CI 83%-99%]). Median time between automated and manual detection of AF onset was 30 minutes (25th-75th percentile 0-208 minutes). The accuracy of ICD-9 codes (68%; P=.002 vs automated algorithm) and nurse charting (80%; P=.02 vs algorithm) was lower than that of the automated algorithm. CONCLUSIONS: An automated algorithm using telemetry ECG data can feasibly and accurately detect AF among critically ill patients with sepsis, and represents an improvement in AF detection within large databases.

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.

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.

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.