Recorded Electrocardiogram Signals Research Articles

Ambulatory Evaluation of ECG Signals Obtained Using Washable Textile-Based Electrodes Made with Chemically Modified PEDOT:PSS.

A development of washable PEDOT:PSS (poly(3,4-ethylenedioxythiophene) polystyrene sulfonate) polyamide textile-based electrodes is an interesting alternative to the traditional Ag/AgCl disposable electrodes, usually used in clinical practice, helping to improve medical assessment and treatment before apparition or progress of patients’ cardiovascular symptoms. This study was conducted in order to determine whether physical properties of PEDOT:PSS had a significant impact on the coated electrode’s electrocardiogram (ECG) signal quality, particularly after 50 washing cycles in a domestic laundry machine. Tests performed, included the comparison of two PEDOT:PSS solutions, in term of viscosity with emphasis on wetting tests, including surface tension and contact angle measurements. In addition, polyamide textile fabrics were used as substrate to make thirty electrodes and to characterize the amount of PEDOT:PSS absorbed as a function of time. The results showed that surface tension of PEDOT:PSS had a significant impact on the wetting of polyamide textile fabric and consequently on the absorbed amount. In fact, lower values of surface tension of the solution lead to low values contact angles between PEDOT:PSS and textile fabric (good wettability). Before washing, no significant difference has been observed among signal-to-noise ratios measured (SNR) for coated electrodes by the two PEDOT:PSS solutions. However, after 50 washing cycles, SNR decreased strongly for electrodes coated by the solution that had low viscosity, since it contained less solid contents. That was confirmed by scanning electron microscopy images (SEM) and also by analyzing the color change of electrodes based on the calculation of CIELAB color space coordinates. Moreover, spectral power density of recorded ECG signals has been computed and presented. All cardiac waves were still visible in the ECG signals after 50 washing cycles. Furthermore, an experienced cardiologist considered that all the ECG signals acquired were acceptable. Accordingly, our newly developed polyamide textile-based electrodes seem to be suitable for long-term monitoring. The study also provided new insights into the better choice of PEDOT:PSS formulation as a function of a specific process in order to manufacture cheaper electrodes faster.

CMOS-Based Multifrequency Impedance Analyzer for Biomedical Applications.

In this paper, we present a monolithic microsystem, which can perform bioimpedance analysis and electroimpedance tomography measurements as well as record electrocardiogram signals. In contrast to a full analog lock-in approach, a mixed analog/digital solution is adopted. The proposed solution has been designed, implemented, and tested using a commercial 0.35-μm CMOS technology. The tuning range of the signal generator and the detector is from 10kHz to 10MHz in 1kHz steps. The circuit ensures a CMRR of 81dB@10kHz, which increases to 84dB@10MHz. The measured equivalent input noise power spectral density is en = 2.57 nV/√Hz at 10kHz in the worst case, close to the 1/f corner frequency. It decreases until en = 1.8 nV/√Hz at 1MHz and en = 1.9 nV/√Hz at 10MHz. Measurements of a reference RC network performed with the proposed monolithic solution and compared with a Keysight E4980A Precision LCR Meter shows a maximal relative error of 0.8% over the whole operating frequency range.

Comparative Study on Conductive Knitted Fabric Electrodes for Long-Term Electrocardiography Monitoring: Silver-Plated and PEDOT:PSS Coated Fabrics.

Long-term monitoring of the electrical activity of the heart helps to detect the presence of potential dysfunctions, enabling the diagnosis of a wide range of cardiac pathologies. However, standard electrodes used for electrocardiogram (ECG) acquisition are not fully integrated into garments, and generally need to be used with a gel to improve contact resistance. This article is focused on the development of washable screen-printed cotton, with and without Lycra, textile electrodes providing a medical quality ECG signal to be used for long-term electrocardiography measurements. Several samples with different Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) concentrations were investigated. Silver-plated knitted fabric electrodes were also used for comparison, within the same process of ECG signal recording. The acquisition of ECG signals carried out by a portable medical device and a low-coast Arduino-based device on one female subject in a sitting position. Three textile electrodes were placed on the right and left forearms and a ground electrode was placed on the right ankle of a healthy female subject. Plastic clamps were applied to maintain electrodes on the skin. The results obtained with PEDOT:PSS used for electrodes fabrication have been presented, considering the optimal concentration required for medical ECG quality and capacity to sustain up to 50 washing cycles. All the ECG signals acquired and recorded, using PEDOT:PSS and silver-plated electrodes, have been reviewed by a cardiologist in order to validate their quality required for accurate diagnosis.

Abstract 150: Impact of Epinephrine on ECG Changes During Resuscitation From Out-of-Hospital Cardiac Arrest With Initial Pulseless Electrical Activity

Introduction: Presence of electrocardiographic rhythm, documented by the electrocardiogram (ECG), in the absence of palpable pulses defines pulseless electrical activity (PEA). Our aims were to examine the development of ECG characteristics during advanced life support (ALS) from Out-of-Hospital-Cardiac-Arrest (OHCA) with initial PEA, and to explore the effects of epinephrine on these characteristics. Methods: Patients with OHCA and initial PEA in a randomized controlled trial of ALS with or without intravenous access and medications were included. QRS widths and heart-rates were measured in recorded ECG signals during pauses in compressions. Statistical analysis was carried out by multivariate regression (MANOVA). Results: Defibrillator recordings from 170 episodes of cardiac arrest were analyzed, 4840 combined measurements of QRS complex width and heart rate were made. By the multivariate regression model both whether epinephrine was administered and whether return of spontaneous circulation (ROSC) was obtained were significantly associated with changes in QRS width and heart rate. For both control and epinephrine groups, ROSC was preceded by decreasing QRS width and increasing rate, but in the epinephrine group an increase in rate without a decrease in QRS width was associated with poor outcome (fig). Conclusion: The QRS complex characteristics are affected by epinephrine administration during ALS, but still yields valuable prognostic information.

Upper envelope detection of ECG signals for baseline wander correction: a pilot study

Baseline wander (BW) is a common low frequency artifact in electrocardiogram (ECG) signals. The prime cause from which BW arises is the patient's breathing and movement. To facilitate reliable visual interpretation of the ECG and to discern particular patterns in the ECG signal, BW needs to be removed. In this paper, a novel BW removal method is presented. The hypothesis is based on the observation that ECG signal variation covaries with its BW. As such, the P, Q, R, S, and T peaks will follow the baseline drift. On this basis, the following proposition is true: a reliable approximation of the baseline drift can be obtained from the shape derived from the interpolation of one form of the ECG signal peak (peak envelope). The simulation was performed by adding artificial BW to ECG signal recordings. The signal-to-noise ratio, mean squared error, and improvement factor criteria were used to numerically evaluate the performance of the proposed approach. The technique was compared to that of the Hilbert vibration decomposition method, an empirical-mode decomposition technique and mathematical morphology. The results of the simulation indicate that the proposed technique is most effective in situations where there is a considerable distortion in the baseline wandering.

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach.

Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( and ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.

A Novel Antibacterial Membrane Electrode Based on Bacterial Cellulose/Polyaniline/AgNO3 Composite for Bio-Potential Signal Monitoring.

We propose a flexible, dry, and antibacterial electrode with a low and stable skin electrode contact impedance for bio-potential signal monitoring. We fabricated a bacterial cellulose/polyaniline/AgNO3 nanocomposite membrane (BC/PANI/AgNO3) and used it for bio-potential signal monitoring. The bacterial cellulose (BC) provides a 3-D nanoporous network structure, and it was used as a substrate material in the BC/PANI/AgNO3 nanocomposite membrane. Polyaniline (PANI) and AgNO3, acting as conductive and antibacterial components, respectively, were polymerized and deposited on the surfaces of BC nanofibers to produce uniform thin film membrane with flexible, antibacterial, and conductive properties. Various measurements were conducted, in terms of antibacterial activity, skin electrode contact impedance, and qualitative analysis of ECG signal recordings. The BC/PANI/AgNO3 membrane revealed 100% antibacterial activities against both the Staphylococcus aureus and Escherichia coli bacteria. The skin electrode contact impedance of the proposed BC/PANI/AgNO3 electrode is lower than that of the Ag/AgCl gel electrode, with the same active area. In addition, the electrocardiogram (ECG) signals acquired with the proposed electrodes have stable characteristic waveforms, and they are not contaminated by noise. The waveform fidelity of the BC/PANI/AgNO3 membrane electrodes over 800 ECG cardiac cycles is 99.49%, and after the electrodes were worn for 24 hours, a fidelity of 98.40% was recorded over the same number of cardiac cycles. With the low and stable skin electrode contact impedance, the proposed dry BC/PANI/AgNO3 membrane electrode provided high fidelity for ECG signal recordings, thus offering a potential approach for bio-potential signal monitoring. With the above benefits, the novel flexible and dry BC/PANI/AgNO3 electrode has a significant antibacterial. Most of all, it is the first research to develop antibacterial in the electrode design.

An automated ECG signal quality assessment method for unsupervised diagnostic systems

In this paper, the authors present an automated method for quality assessment of electrocardiogram (ECG) signal. Our proposed method not only detects and classifies the ECG noises but also localizes the ECG noises which can play a crucial role in extracting reliable clinical features for ECG analysis systems. The proposed method is based on three stages: Wavelet decomposition of ECG signal into sub-bands; simultaneous ECG signal and noise reconstruction; extraction of temporal features such as maximum absolute amplitude, zerocrossings, kurtosis and autocorrelation function for detection, localization and classification of ECG noises including flat line (FL), time-varying noise or pause (TVN), baseline wander (BW), abrupt change (AB), power line interference (PLI), muscle artifacts (MA) and additive white Gaussian noise (AWGN). The proposed method is tested and validated against manually annotated ECG signals corrupted with aforementioned noises taken from MIT-BIH arrhythmia database, Physionet challenge database, and real-time recorded ECG signals. Comparative detection and classification results depict the superior performance of the proposed method over state of art methods. Detection results show that our method can achieve an average sensitivity (Se), average specificity (Sp) and accuracy (A) of 99.61%, 98.51%, 99.49% respectively. Also, the method achieves a Se of 98.18%, and Sp of 94.97% for real-time recorded ECG signals. The method has an average timing error of 0.14s in localizing the noise segments. Further, classification results demonstrate that the proposed method achieves an average sensitivity (Se), average positive predictivity (PP) and classification accuracy (Ac) of 98.53%, 98.89%, 97.50% respectively.

Matched Filtering for Heart Rate Estimation on Compressive Sensing ECG Measurements.

Compressive sensing (CS) has recently been applied as a low-complexity compression framework for long-term monitoring of electrocardiogram (ECG) signals using wireless body sensor networks. Long-term recording of ECG signals can be useful for diagnostic purposes and to monitor the evolution of several widespread diseases. In particular, beat-to-beat intervals provide important clinical information, and these can be derived from the ECG signal by computing the distance between QRS complexes (R-peaks). Numerous methods for R-peak detection are available for uncompressed ECG. However, in the case of compressed sensed data, signal reconstruction can be performed with relatively complex optimization algorithms, which may require significant energy consumption. This paper addresses the problem of heart rate estimation from CS ECG recordings, avoiding the reconstruction of the entire signal. We consider a framework, where the ECG signals are represented under the form of CS linear measurements. The QRS locations are estimated in the compressed domain by computing the correlation of the compressed ECG and a known QRS template. Experiments on actual ECG signals show that our novel solution is competitive with methods applied to the reconstructed signals. Avoiding the reconstruction procedure, the proposed method proves to be very convenient for real-time low-power applications.

Improved Pre-Ejection Period Estimation From Ballistocardiogram and Electrocardiogram Signals by Fusing Multiple Timing Interval Features

Sympathetic nervous system (SNS) activity plays a significant role in cardiovascular control. Preejection period (PEP) is a noninvasive biomarker that reflects SNS activity. In this paper, unobtrusive estimation of PEP of the heart using ballistocardiogram (BCG) and electrocardiogram (ECG) signals is investigated. Although previous work has shown that the time intervals from ECG R-peak to BCG I and J peaks are correlated with PEP, relying on a single BCG beat can be prone to errors. An approach is proposed based on multiple regression and use of initial training data sets with a reference standard, impedance cardiography (ICG). For evaluation, healthy subjects were asked to stand on a force plate to record BCG and ECG signals. Regression coefficients were obtained using leave-one-out cross-validation and the true PEP values were obtained using ECG and ICG. Regression coefficients were averaged over two different recordings from the same subjects. The estimation performance was evaluated based on the data, via leave-one-out cross-validation. Multiple regression is shown to reduce the mean absolute error and the root mean square error, and has a reduced confidence interval compared with the models based on only a single feature. This paper shows that the fusion of multiple timing intervals can be useful for improved PEP estimation.

Efficient Skin Temperature Sensor and Stable Gel-Less Sticky ECG Sensor for a Wearable Flexible Healthcare Patch.

Wearable, flexible healthcare devices, which can monitor health data to predict and diagnose disease in advance, benefit society. Toward this future, various flexible and stretchable sensors as well as other components are demonstrated by arranging materials, structures, and processes. Although there are many sensor demonstrations, the fundamental characteristics such as the dependence of a temperature sensor on film thickness and the impact of adhesive for an electrocardiogram (ECG) sensor are yet to be explored in detail. In this study, the effect of film thickness for skin temperature measurements, adhesive force, and reliability of gel-less ECG sensors as well as an integrated real-time demonstration is reported. Depending on the ambient conditions, film thickness strongly affects the precision of skin temperature measurements, resulting in a thin flexible film suitable for a temperature sensor in wearable device applications. Furthermore, by arranging the material composition, stable gel-less sticky ECG electrodes are realized. Finally, real-time simultaneous skin temperature and ECG signal recordings are demonstrated by attaching an optimized device onto a volunteer's chest.

Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities

Analysis of electrocardiogram (ECG) signal provides valuable information about the heart conditions of the patient to the clinicians. The wavelet transform is an effective tool for extracting discriminative features in ECG signal classification for automatic diagnosis of cardiac arrhythmia. In this paper we proposed an improved algorithm to detect QRS complex features based on the multiresolution wavelet transform to classify four types of ECG beats. Extracted features are used for classifying cardiac abnormalities: normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), Paced beats (P) using neural network (NN) and support vector machines (SVM) classifier. The performance of the method is evaluated in terms of sensitivity, specificity, accuracy for 48 recorded ECG signals obtained from the MIT–BIH arrhythmia database. The proposed process achieved high detection performance with less error rate of 0.42% in detecting QRS complex. The classifier confirmed its superiority with an average accuracy of 96.67% and 98.39% in NN and SVM respectively. The classification accuracy of SVM approach proves superior for the proposed method to that of the NN classifier with extracted parameter in detecting ECG arrhythmia beats.

Carbon nanotube-based self-adhesive polymer electrodes for wireless long-term recording of electrocardiogram signals

In this study, the concept of polymer electrodes integrated with a wireless electrocardiogram (ECG) system was described. Polymer electrodes for long-term ECG measurements were fabricated by loading high content of carbon nanotubes (CNTs) in polydimethylsiloxane. Silver nanoparticles (Ag NPs) were added to increase the flexibility of the polymer and the conductivity of the electrode. An ECG electrode patch was fabricated by integrating the electrodes with an adhesive polydimethylsiloxane (aPDMS) layer. Holes in the electrode filled with aPDMS can enable robust contact between the electrode and skin, reducing motion artifacts. A wireless ECG measurement system was developed and adapted to the polymer electrodes. The polymer electrodes combined with the measurement system were successfully applied in wireless, long-term recording of ECG signals. An eleven-day continuous test showed that the ECG signal did not degrade over time. The results of attach/detach tests demonstrated that the ECG signal was affected by motion artifacts after six attach/detach cycles. The electrodes produced are flexible and exhibit good ECG performance, and therefore can be used in wearable medical monitoring systems. The approach proposed in this study holds significant promise for commercial application in medical fields.

ECG Triggering in Ultra-High Field Cardiovascular MRI

Cardiac magnetic resonance imaging at ultra-high field (B0 ≥ 7 T) potentially provides improved resolution and new opportunities for tissue characterization. Although an accurate synchronization of the acquisition to the cardiac cycle is essential, electrocardiogram (ECG) triggering at ultra-high field can be significantly impacted by the magnetohydrodynamic (MHD) effect. Blood flow within a static magnetic field induces a voltage, which superimposes the ECG and often affects the recognition of the R-wave. The MHD effect scales with B0 and is particularly pronounced at ultra-high field creating triggering-related image artifacts. Here, we investigated the performance of a conventional 3-lead ECG trigger device and a state-of-the-art trigger algorithm for cardiac ECG synchronization at 7 T. We show that by appropriate subject preparation and by including a learning phase for the R-wave detection outside of the magnetic field, reliable ECG triggering is feasible in healthy subjects at 7 T without additional equipment. Ultra-high field cardiac imaging was performed with the ECG signal and the trigger events recorded in 8 healthy subjects. Despite severe ECG signal distortions, synchronized imaging was successfully performed. Recorded ECG signals, vectorcardiograms, and large consistency in trigger event spacing indicate high accuracy for R-wave detection.

Development of data acquisition components for simultaneous recording of 3D epicardial and surface ECG signals in the langendorff perfusion apparatus.

Sudden Cardiac Death (SCD) claims 7 million lives per year. The importance of myocardial electrogram (EGM) repolarization alternans and surface electrocardiogram (ECG) T-wave alternans is gaining traction for understanding the underlying SCD mechanisms. However, the relationship between the 3D spatial distribution of myocardial EGMs and surface ECG with respect to SCD has yet to be investigated. To make this happen, a modified data acquisition system has been developed and fabricated in conjunction with the Langendorff perfusion system to enable simultaneous recording and analysis of the 3D spatial distribution of myocardial EGMs and the surface ECG. Two devices have been fabricated: a basket catheter, which obtains 3D EGM data; and an ECG chamber, capable of keeping the constraints of the Langendorff system. Noise analysis confirmed, for all devices, a signal to noise ratio (SNR) of median (μ) >= 51.7dB and standard deviation (σ) <;= 2.1dB. A Langendorff rat heart experiment further confirmed successful recording of 3D EGM and surface ECG data with an acceptable SNR. The developed system can be used to study the relationship between 3D EGM and surface ECG data, which can be utilized to understand the mechanisms of SCD.

A TASER conducted electrical weapon with cardiac biomonitoring capability: Proof of concept and initial human trial

IntroductionDespite research demonstrating the overall safety of Conducted Electrical Weapons (CEWs), commonly known by the brand name TASER®, concerns remain regarding cardiac safety. The addition of cardiac biomonitoring capability to a CEW could prove useful and even lifesaving in the rare event of a medical crisis by detecting and analyzing cardiac rhythms during the period immediately after CEW discharge. ObjectiveTo combine an electrocardiogram (ECG) device with a CEW to detect and store ECG signals while still allowing the CEW to perform its primary function of delivering an incapacitating electrical discharge. MethodsThis work was performed in three phases. In Phase 1 standard law enforcement issue CEW cartridges were modified to demonstrate transmission of ECG signals. In Phase 2, a miniaturized ECG recorder was combined with a standard issue CEW and tested. In Phase 3, a prototype CEW with on-board cardiac biomonitoring was tested on human volunteers to assess its ability to perform its primary function of electrical incapacitation. ResultsBench testing demonstrated that slightly modified CEW cartridge wires transmitted simulated ECG signals produced by an ECG rhythm generator and from a human volunteer. Ultimately, a modified CEW incorporating ECG monitoring successfully delivered incapacitating current to human volunteers and successfully recorded ECG signals from subcutaneous CEW probes after firing. ConclusionAn ECG recording device was successfully incorporated into a standard issue CEW without impeding the functioning of the device. This serves as proof-of-concept that safety measures such as cardiac biomonitoring can be incorporated into CEWs and possibly other law enforcement devices.

Silver nanowire-composite electrodes for long-term electrocardiogram measurements

Composite electrodes are fabricated for long-term electrocardiogram (ECG) measurements. The electrode consists of a polydimethylsiloxane base, silver nanowire layer, and adhesive layer. The adhesive layer is fabricated by loading carbon nanotubes into adhesive polydimethylsiloxane. An electrode patch consisting of three composite electrodes and an adhesive polydimethylsiloxane layer is fabricated and combined with a wireless acquisition system to obtain ECG measurements. The composite electrode patch is self-adhesive and can conform to the skin, achieving robust contact between electrode and skin and thus decreasing motion artifacts. The composite electrodes combined with the measurement system are successfully used for wireless long-term recording of ECG signals. Continuous testing for 8 days shows that the ECG signal amplitude decreases slightly after wearing for 6 days and can be largely recovered by cleaning with ethanol. The composite electrodes are flexible and exhibit good ECG performance, and therefore can be used in wearable medical monitoring systems.

Robust cardiac event change detection method for long-term healthcare monitoring applications.

A long-term continuous cardiac health monitoring system highly demands more battery power for real-time transmission of electrocardiogram (ECG) signals and increases bandwidth, treatment costs and traffic load of the diagnostic server. In this Letter, the authors present an automated low-complexity robust cardiac event change detection (CECD) method that can continuously detect specific changes in PQRST morphological patterns and heart rhythms and then enable transmission/storing of the recorded ECG signals. The proposed CECD method consists of four stages: ECG signal quality assessment, R-peak detection and beat waveform extraction, temporal and RR interval feature extraction and cardiac event change decision. The proposed method is tested and validated using both normal and abnormal ECG signals including different types of arrhythmia beats, heart rates and signal quality. Results show that the method achieves an average sensitivity of 99.76%, positive predictivity of 94.58% and overall accuracy of 94.32% in determining the changes in heartbeat waveforms of the ECG signals.

Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals

Nowadays, doctors use electrocardiogram (ECG) to diagnose heart diseases commonly. However, some nonideal effects are often distributed in ECG. Discrete wavelet transform (DWT) is efficient for nonstationary signal analysis. In this paper, the Symlets sym5 is chosen as the wavelet function to decompose recorded ECG signals for noise removal. Soft-thresholding method is then applied for feature detection. To detect ECG features, R peak of each heart beat is first detected, and the onset and offset of the QRS complex are then detected. Finally, the signal is reconstructed to remove high frequency interferences and applied with adaptive searching window and threshold to detect P and T waves. We use the MIT-BIH arrhythmia database for algorithm verification. For noise reduction, the SNR improvement is achieved at least 10 dB at SNR 5 dB, and most of the improvement SNR are better than other methods at least 1 dB at different SNR. When applying to the real portable ECG device, all R peaks can be detected when patients walk, run, or move at the speed below 9 km/h. The performance of delineation on database shows in our algorithm can achieve high sensitivity in detecting ECG features. The QRS detector attains a sensitivity over 99.94%, while detectors of P and T waves achieve 99.75% and 99.7%, respectively.

Denoising Baseline Wander Noise from Electrocardiogram Signal using Fast ICA with Multiple Adjustments

The electrocardiogram (ECG) is widely utilitarian for prognostic of heart diseases. Quality and utilization of ECG signal is affected by different noises and hence it is very difficult to measure important parameter to know the exact condition of heart. Baseline wander is one type of noise which is normally seen in ECG signal. This artifact severally limits the usefulness of recorded ECG signals and thus need to be removed for better clinical appraisal. Independent component analysis (ICA) is a statistical technique for estimating a multidimensional random vector into components that are statistically not dependent from each other. This paper proposed the implementation of fast ICA with multiple adjustments for removing baseline wander noise effect from ECG. Simulation results demonstrate that the proposed method is better in denoised the baseline wander noise from ECG signal. General Terms ECG, ECG Denoising, Baseline Wader Noise, Independent Component Analysis