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

Motion Artifact Processing Techniques for Physiological Signals

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an ageing population and this is placing an ever-increasing burden on our healthcare systems. The urgent need to address this so called healthcare \time bomb" has led to a rapid growth in research into ubiquitous, pervasive and distributed healthcare technologies where recent advances in signal acquisition, data storage and communication are helping such systems become a reality. However, similar to recordings performed in the hospital environment, artifacts continue to be a major issue for these systems. The magnitude and frequency of artifacts can vary signicantly depending on the recording environment with one of the major contributions due to the motion of the subject or the recording transducer. As such, this thesis addresses the challenges of the removal of this motion artifact removal from various physiological signals. The preliminary investigations focus on artifact identication and the tagging of physiological signals streams with measures of signal quality. A new method for quantifying signal quality is developed based on the use of inexpensive accelerometers which facilitates the appropriate use of artifact processing methods as needed. These artifact processing methods are thoroughly examined as part of a comprehensive review of the most commonly applicable methods. This review forms the basis for the comparative studies subsequently presented. Then, a simple but novel experimental methodology for the comparison of artifact processing techniques is proposed, designed and tested for algorithm evaluation. The method is demonstrated to be highly eective for the type of artifact challenges common in a connected health setting, particularly those concerned with brain activity monitoring. This research primarily focuses on applying the techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) data due to their high susceptibility to contamination by subject motion related artifact. Using the novel experimental methodology, complemented with simulated data, a comprehensive comparison of a range of artifact processing methods is conducted, allowing the identication of the set of the best performing methods. A novel artifact removal technique is also developed, namely ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA), which provides the best results when applied on fNIRS data under particular conditions. Four of the best performing techniques were then tested on real ambulatory EEG data contaminated with movement artifacts comparable to those observed during in-home monitoring. It was determined that when analysing EEG data, the Wiener lter is consistently the best performing artifact removal technique. However, when employing the fNIRS data, the best technique depends on a number of factors including: 1) the availability of a reference signal and 2) whether or not the form of the artifact is known. It is envisaged that the use of physiological signal monitoring for patient healthcare will grow signicantly over the next number of decades and it is hoped that this thesis will aid in the progression and development of artifact removal techniques capable of supporting this growth

Liikeartefaktat elektrokardiografiassa

Movement of the patient during electrocardiograph (ECG) recording is a severe source of artifacts. Recent technical developments have enabled ECG recording without continuous supervision by experts. However, ECG recording outside of hospitals is prone to poor quality and movement artifacts. Therefore, it is important to study how and how much ECG recordings are affected by movement. Movement artifacts can hide signal components or mimic them, which causes false negative or false positive detections. Methods to manage movement artifacts include both computational and non-computational approaches. Computational approaches include, for example, adaptive filtering and machine learning methods. Additional variables that correlate with the artifact sources can be utilized in artifact recognition. For example, acceleration, impedance, and pressure signals have been studied as possible movement references. These additional signals are recorded by sensors that are placed on the ECG electrodes or on the patient’s body. In this thesis, the effect of movement artifacts is quantified using a simulation. The simulation makes use of open ECG databases. This study investigates how automated ECG analysis is affected by incremental increase in the movement artifact level. According to the results QRS detection statistics worsen with increased artifact levels. Capturing a movement reference for ECG is studied by experimental research. ECG and inertial measurement unit signals were recorded during different movements in order to analyze the creation of movement artifacts and movement reference signals. According to the results, placement of the movement reference signal sensor has a significant effect on the results. Different movements are captured better by different sensors and affect different ECG leads with different strengths.Potilaan liike sydänsähkökäyrämittauksen (EKG) aikana on merkittävä artefaktien lähde. Viimeaikainen teknologinen kehitys on mahdollistanut EKG-mittauksen ilman asiantuntijoiden jatkuvaa valvontaa. EKG-mittaukset sairaalaolosuhteiden ulkopuolella ovat kuitenkin erityisen alttiita huonolle signaalilaadulle ja liikeartefaktoille. Tämän vuoksi on tärkeää tutkia, miten ja kuinka paljon liike vaikuttaa EKG-mittauksiin. Liikeartefaktat voivat joko peittää tai jäljitellä EKG-signaalin eri osia, aiheuttaen vääriä negatiivisia tai vääriä positiivisia havaintoja. Liikeartefaktojen vaikutusta voidaan vähentää sekä laskennallisten että muiden menetelmien avulla. Laskennallisia menetelmiä ovat esimerkiksi adaptiivinen suodatus ja koneoppimismenetelmät. Artefaktojen lähteen kanssa korreloivia muuttujia mittaamalla voidaan edistää artefaktojen tunnistusta EKG-signaalista. Esimerkiksi kiihtyvyys-, impedanssi- ja painesignaalien käyttöä liikereferensseinä on tutkittu. Kyseisiä referenssisignaaleja voidaan mitata EKG-elektrodeihin tai potilaan kehoon kiinnitettävillä sensoreilla. Liikeartefaktojen vaikutuksen suuruutta tutkitaan tässä työssä simulaation avulla. Simulaatiossa hyödynnetään avoimia EKG-tietokantoja. Tutkimuksessa tarkastellaan sitä, miten vähittäinen liikeartefaktatason kasvu vaikuttaa automaattiseen EKG-analyysiin. Tulosten mukaan QRS-detektioon liittyvät tilastot huononevat artefaktatason kasvaessa. Liikereferenssin luomista tarkastellaan kokeellisen tutkimuksen avulla. EKG- ja inertiamittausyksikkö-signaaleja mitattiin erilaisten liikkeiden aikana, jotta voitaisiin havainnoida liikeartefaktojen ja liikesignaalin syntymistä. Tulosten mukaan liikereferenssiä mittaavan sensorin sijoituspaikalla on merkittävä vaikutus tuloksiin. Tietyt liikkeet saadaan paremmin mitattua eri tavoin sijoitettujen sensorien avulla. Lisäksi liikkeet vaikuttavat eri vahvuuksilla eri EKG-kytkentöihin

Motion Artifact Reduction in Impedance Plethysmography Signal

The research related to designing portable monitoring devices for physiological signals has been at its peak in the last decade or two. One of the main obstacles in building such devices is the effect of the subject\u27s movements on the quality of the signal. There have been numerous studies addressing the problem of removing motion artifact from the electrocardiogram (ECG) and photoplethysmography (PPG) signals in the past few years. However, no such study exists for the Impedance Plethysmography (IP) signal. The IP signal can be used to monitor respiration in mobile devices. However, it is very susceptible to motion artifact. The main aim of this dissertation is to develop adaptive and non-adaptive filtering algorithms to address the problem of motion artifact reduction from the IP signal

ASCNet-ECG: Deep Autoencoder based Attention aware Skip Connection network for ECG filtering

Currently, the telehealth monitoring field has gained huge attention due to its noteworthy use in day-to-day life. This advancement has led to an increase in the data collection of electrophysiological signals. Due to this advancement, electrocardiogram (ECG) signal monitoring has become a leading task in the medical field. ECG plays an important role in the medical field by analysing cardiac physiology and abnormalities. However, these signals are affected due to numerous varieties of noises, such as electrode motion, baseline wander and white noise etc., which affects the diagnosis accuracy. Therefore, filtering ECG signals became an important task. Currently, deep learning schemes are widely employed in signal-filtering tasks due to their efficient architecture of feature learning. This work presents a deep learning-based scheme for ECG signal filtering, which is based on the deep autoencoder module. According to this scheme, the data is processed through the encoder and decoder layer to reconstruct by eliminating noises. The proposed deep learning architecture uses a modified ReLU function to improve the learning of attributes because standard ReLU cannot adapt to huge variations. Further, a skip connection is also incorporated in the proposed architecture, which retains the key feature of the encoder layer while mapping these features to the decoder layer. Similarly, an attention model is also included, which performs channel and spatial attention, which generates the robust map by using channel and average pooling operations, resulting in improving the learning performance. The proposed approach is tested on a publicly available MIT-BIH dataset where different types of noise, such as electrode motion, baseline water and motion artifacts, are added to the original signal at varied SNR levels

Removal of artifacts from electrocardiogram

The electrocardiogram is the recording of the electrical potential of heart versus time. The analysis of ECG signal has great importance in the detection of cardiac abnormalities. The electrocardiographic signals are often contaminated by noise from diverse sources. Noises that commonly disturb the basic electrocardiogram are power line interference, instrumentation noise, external electromagnetic field interference, noise due to random body movements and respirational movements. These noises can be classified according to their frequency content. It is essential to reduce these disturbances in ECG signal to improve accuracy and reliability. Different types of adaptive and non-adaptive digital filters have been proposed to remove these noises. In this thesis, window based FIR filters, adaptive filters and wavelet filter bank are applied to remove the noises. Performances of the filters are compared based on the PSNR values. It is difficult to apply filters with fixed filter coefficients to reduce the instrumentation noise, because the time varying behaviour of this noise is not exactly known. Adaptive filter technique is required to overcome this problem, as the filter coefficients can be varied to track the dynamic variations of the signals. In wavelet transform, a signal is analyzed and expressed as a linear combination of the summation of the product of the wavelet coefficients and mother wavelet. The wavelet decomposition offers an excellent resolution both in time and frequency domain. Better estimation of the amplitudes is also obtained in wavelet based denoising

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

Adsorption study of textile dye effluent using mixture of nanocrystalline cellulose and eggshell powder adsorbents

Today, textile industry produces dye wastewater which cause huge wellbeing risks to living organisms and degradation of the environment. The aim of this research was to study the capability of adsorption technique for dye removal from textile industry by using the mixture of nanocrystalline cellulose and eggshell powder (NCC+ESP) adsorbents. In this study, NCC+ESP adsorbents found to have high surface area and porosity from the characterization study which prove that they are suitable for adsorption. Adsorption studies were also carried out to determine the effect of adsorbent dose (8 g NCC+2-8 g of ESP), contact time (5-60 minutes), initial dye concentration (100-300 ADMI) and pH (2-10). Result from the determination of adsorbent dosage have shown that 8 g of NCC+5 g of ESP was the optimum amount for adsorption of dye which achieved 82.667% of dye removal. Equilibrium was attained at 30 minutes which reflects the maximum adsorption capacity. The extent of dye removal decreased from 90% to 84% with increase in the initial concentration of the dye. The adsorption capacities were shown to be favored at pH 6 which achieved 82.667% of dye removal. Lastly, this study also investigate the stability of NCC+ESP adsorbents after multiple batch adsorption cycles and the result found out that it can be considered reused effectively as the percentage of removal dye just a slightly decreased from 84% to 74%. In conclusion, this research indicated that NCC+ESP adsorbents could be efficiently and economically applied in removing dyes as well as reusing of these adsorbents could be employed as an alternative for dye remediation in the textile industrial

Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power—50 Hz, EMG, and base line wander – were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Mean square error between clean and filtered ECGs was used as filtering performance indexes. Results showed that high noise reduction is the major advantage of the EEMD based filter, especially on arrhythmia ECGs

Advanced bioelectrical signal processing methods: Past, present and future approach - Part III: Other biosignals

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).Web of Science2118art. no. 606