Impedance Cardiography Signals Research Articles

ICG signal denoising based on ICEEMDAN and PSO-VMD methods.

Impedance cardiography (ICG) plays a crucial role in clinically evaluating cardiac systolic and diastolic functions, along with various other cardiac parameters. However, its accuracy heavily depends on precisely identifying feature points reflecting cardiac function. Moreover, traditional signal processing techniques used to mitigate random noise and breathing artifacts may inadvertently distort the amplitude and temporal characteristics of ICG signals. To address this issue, this study investigates a noise and artifact elimination method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Particle Swarm Optimization-based Variational Mode Decomposition Algorithm (PSO-VMD). The goal is to preserve the amplitude and temporal features of ICG signals to ensure accurate feature point extraction and computation of associated cardiac parameters. Comparative analysis with signal processing methods employing various wavelet families and Ensemble Empirical Mode Decomposition (EEMD) in ICG signal processing applications reveals that the proposed method achieves superior signal-to-noise ratio (SNR) and lower root-mean-square error (RMSE), while demonstrating enhanced correlation and waveform consistency with the original signal.

Motion impedance cardiography denoising method based on canonical correlation analysis and coherence analysis

Impedance cardiography (ICG) is an attractive noninvasive method for measuring stroke volume and cardiac output. However, it is easily disturbed by the artifacts such as respiration and body shaking. Considering the correlation between physiological and motion signals, and the synchronization relationship among the physiological signals, this paper introduces an ICG denoising method based on canonical correlation analysis (CCA) and coherence analysis to remove the artifacts during the measurement. First, the CCA extracts the shared components between the electrocardiogram (ECG) and the motion signal, as well as the shared components between ICG and the motion signal. Then, set those shared components to zero to suppress the primary motion artifacts. Next, the coherence analysis was used to calculate the synchronization relationship between obtained ECG and ICG components. Finally, the components with strong synchronization relationships were used to reconstruct the ICG signal. The denoising method was evaluated for 54 subjects during lying and walking. Experimental results show that after removing the artifacts, the signal quality index beat contribution factor (BCF) was increased from the original 78.1% to 97.8%, and the physiological parameters measured based on the proposed method were in good agreement with those measured by the standard instrument. The proposed denoising method effectively improves the reliability of analysis and diagnosis on cardiovascular diseases relying on ICG signals.

Robust Arm Impedocardiography Signal Quality Enhancement Using Recursive Signal Averaging and Multi-Stage Wavelet Denoising Methods for Long-Term Cardiac Contractility Monitoring Armbands.

Impedance cardiography (ICG) is a low-cost, non-invasive technique that enables the clinical assessment of haemodynamic parameters, such as cardiac output and stroke volume (SV). Conventional ICG recordings are taken from the patient's thorax. However, access to ICG vital signs from the upper-arm brachial artery (as an associated surrogate) can enable user-convenient wearable armband sensor devices to provide an attractive option for gathering ICG trend-based indicators of general health, which offers particular advantages in ambulatory long-term monitoring settings. This study considered the upper arm ICG and control Thorax-ICG recordings data from 15 healthy subject cases. A prefiltering stage included a third-order Savitzky-Golay finite impulse response (FIR) filter, which was applied to the raw ICG signals. Then, a multi-stage wavelet-based denoising strategy on a beat-by-beat (BbyB) basis, which was supported by a recursive signal-averaging optimal thresholding adaptation algorithm for Arm-ICG signals, was investigated for robust signal quality enhancement. The performance of the BbyB ICG denoising was evaluated for each case using a 700 ms frame centred on the heartbeat ICG pulse. This frame was extracted from a 600-beat ensemble signal-averaged ICG and was used as the noiseless signal reference vector (gold standard frame). Furthermore, in each subject case, enhanced Arm-ICG and Thorax-ICG above a threshold of correlation of 0.95 with the noiseless vector enabled the analysis of beat inclusion rate (BIR%), yielding an average of 80.9% for Arm-ICG and 100% for Thorax-ICG, and BbyB values of the ICG waveform feature metrics A, B, C and VET accuracy and precision, yielding respective error rates (ER%) of 0.83%, 11.1%, 3.99% and 5.2% for Arm-IG, and 0.41%, 3.82%, 1.66% and 1.25% for Thorax-ICG, respectively. Hence, the functional relationship between ICG metrics within and between the arm and thorax recording modes could be characterised and the linear regression (Arm-ICG vs. Thorax-ICG) trends could be analysed. Overall, it was found in this study that recursive averaging, set with a 36 ICG beats buffer size, was the best Arm-ICG BbyB denoising process, with an average of less than 3.3% in the Arm-ICG time metrics error rate. It was also found that the arm SV versus thorax SV had a linear regression coefficient of determination (R2) of 0.84.

A New Method of Identifying Characteristic Points in the Impedance Cardiography Signal Based on Empirical Mode Decomposition

The accurate detection of fiducial points in the impedance cardiography signal (ICG) has a decisive impact on the proper estimation of diagnostic parameters such as stroke volume or cardiac output. It is, therefore, necessary to find an algorithm that is able to assess their positions with great precision. The solution to this problem is, however, quite challenging with regard to the high sensitivity of the ICG technique to the noise and varying morphology of the acquired signals. The aim of this study is to propose a novel method that allows us to overcome these limitations. The developed algorithm is based on Empirical Mode Decomposition (EMD)-an effective technique for processing and analyzing various types of non-stationary signals. We find high correlations between the results obtained from the algorithm and annotated by an expert. This, in turn, implies that the difference in estimation of the diagnostic-relevant parameters is small, which suggests that the method can automatically provide precise clinical information.

Ejection Fraction Measurement Based on Impedance Cardiography

Ejection fraction (EF) is an important indicator of cardiac function, especially in clinical conditions. Unlike imaging technology such as echocardiography, impedance cardiography (ICG) is a low-cost, non-invasive and continuous method of monitoring EF. However, the accuracy of the ICG method to evaluate the EF value needs to be further improved. This paper uses a simplified ventricular model to simplify the complex relationship between the EF value and the relevant ICG parameters, and proposes a method for calculating the EF value through these relevant ICG parameters. We tested this method with 52 subjects. Experimental results show that compared with the traditional ICG method, the proposed method has a higher correlation and closer agreement with the EF measurement by echocardiography. The proposed scheme effectively improves the reliability of the measurement of the EF value relying on the ICG signal.

Monitoring of false lumen thrombosis in type B aortic dissection by impedance cardiography - A multiphysics simulation study.

Aortic dissection is caused by a tear on the aortic wall that allows blood to flow through the wall layers. Usually, this tear involves the intimal and partly the medial layer of the aortic wall. As a result, a new false lumen develops besides the original aorta, denoted then as the true lumen. The local hemodynamic conditions such as flow disturbances, recirculations and low wall shear stress may cause thrombus formation and growth in the false lumen. Since the false lumen status is a significant predictor for late-dissection-related deaths, it is of great importance in the medical management of patients with aortic dissection. The hemodynamic changes in the aorta also alter the electrical conductivity of blood. Since the blood is much more conductive than other tissues in the body, such changes can be identified with non-invasive methods such as impedance cardiography. Therefore, in this study, the capability of impedance cardiography in monitoring thrombosis in the false lumen is studied by multiphysics simulations to assist clinicians in the medical management of patients under treatment. To tackle this problem, a 3D computational fluid dynamics simulation has been set up to model thrombosis in the false lumen and its impact on the blood flow-induced conductivity changes. The electrical conductivity changes of blood have been assigned as material properties of the blood-filled aorta in a 3D finite element electric simulation model to investigate the impact of conductivity changes on the measured impedance from the body's surface. The results show remarkable changes in the electrical conductivity distribution in the measurement region due to thrombosis in the false lumen, which significantly impacts the morphology of the impedance cardiogram. Thus, frequent monitoring of impedance cardiography signals may allow tracking the thrombus formation and growth in the false lumen.

Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique.

The machine learning (ML)-based classification models are widely utilized for the automated detection of heart diseases (HDs) using various physiological signals such as electrocardiogram (ECG), magnetocardiography (MCG), heart sound (HS), and impedance cardiography (ICG) signals. However, ECG-based HD identification is the most common one used by clinicians. In the current investigation, the ECG records or subjects have been sampled and are used as inputs to the classification model to distinguish between normal and abnormal patients. The study has employed an imbalanced number of ECG samples for training the various classification models. Few ML methods such as support vector machine (SVM), logistic regression (LR), and adaptive boosting (AdaBoost) which have been rarely used for HD detection have been selected. The performance of the developed model has been evaluated in terms of accuracy, F1-score, and area under curve (AUC) values using ECG signals of subjects given in publicly available (PTB-ECG, MIT-BIH) datasets. Ranking of the models has been assigned based on these performance metrics and it is found that the AdaBoost and LR classifiers stand in first and second positions. These two models have been ensembled based on the majority voting principle and the performance measure of this ensemble model has also been determined. It is, in general, observed that the proposed ensemble model demonstrates the best HD detection performance of 0.946, 0.949, and 0.951 for the PTB-ECG dataset and 0.921, 0.926, and 0.950 for the MIT-BIH dataset in terms of accuracy, F1-score, and AUC, respectively. The proposed methodology can also be employed for the classification of HD using ICG, MCG, and HS signals as inputs. Further, the proposed methodology can also be applied to the detection of other diseases.

Impedance Cardiography Signal Improvement Through Sign Related Adaptive Removers for Remote Health Care

The impedance cardiography (ICG) shows the impedance caused in the heart. The ICG gives a clear and accurate results and the applied techniques reduce artifacts and also mathematical calculations. The ICG signal shows various physiological and non-physiological situations, and can calculate the heart volume. The computational complexity is also a main part for remote health care. The adaptive techniques used in this project reduces or removes the noise in the signal, Improve the signal. Mainly to take over the applied signal, convergence rate, and to reduce computations of the proposed theory, we added the sign and normalized adaptive algorithms to take out the realistic wave. In final, the results are taken out from the outcomes by additionally relating it with the adaptive filter to situate it in the proper format.

Non-invasive cuffless blood pressure and heart rate monitoring using impedance cardiography

BackgroundContinuous blood pressure (BP) monitoring provides additional information about how changes in BP may correlate with daily activities and sleep patterns. Recommendations from the American Heart Association and American College of Cardiology strongly suggest confirming a diagnosis of hypertension with continuous BP monitoring. Non-invasive and non-intrusive detection of haemodynamic parameters is emerging as a norm, based on self-monitoring wearable medical devices. Researchers have carried out several studies using non-invasive and continuous BP measurements as an alternative to conventional cuff-based measurements. In this work, we proposed a novel method for cuffless estimation of BP using impedance cardiography (ICG). MethodsWe conducted a single-centre, cross-sectional study of 104 subjects (of whom 30 were categorized as controls and the remaining 74 as the disease group) at the Medical College and Hospital, Kolkata. The disease group consisted of patients with confirmed coronary artery disease, while the individuals in the control group were deemed to be healthy. All subjects underwent electrocardiogram recording by on-duty doctors in order to determine their health status. A custom-made device based on the principle of impedance plethysmography was designed to record impedance changes due to subjects’ peripheral blood flow. The device was used to record ICG signals. In this study, we developed a novel auto-adaptive algorithm based on ICG signals for non-invasive, cuffless, continuous monitoring of BP and heart rate. Separate mathematical models were developed for all the estimated parameters (BP and heart rate) for both the study groups (control and disease). The developed models were auto-adaptive and did not require subject-specific calibration. Performance indicators including, r2, error percentage, standard deviation, and mean difference were used to quantify the performance of the models. ResultsThe ICG signal recorded by the device was used to extract features and compute the augmentation index. The calculated augmentation index values showed strong correlations with systolic BP (r=0.99, P<0.05), diastolic BP (r=0.95, P<0.05), and heart rate (r=0.78, P<0.05). The models were also shown to have a high degree of accuracy for systolic and diastolic BP. Error margins were in the range ±2.33 and ±1.79 mmHg for systolic BP in disease and control subjects, respectively, and ±3.60 and ±1.82 mmHg for diastolic BP. However, the accuracy was lower in the prediction of heart rate in disease subjects, with a reported r2 value of 0.72 and an error margin of ±2.88 beats per min; for healthy subjects, the results were marginally better, with an error margin of ±1.82 beats per min. All statistical analyses were performed using MATLAB (R2017a, MathWorksⓇ, USA). ConclusionIn this study, we developed a non-invasive cuffless approach for estimation of systemic peripheral BP and heart rate using ICG. The proposed methodology eliminated any discomfort to patients caused by inflation of the cuff (in the case of cuff-based BP monitoring) or the need to constantly wear a fingertip photoplethysmography device (in the case of cuffless BP monitoring). The results obtained appeared promising and increased the potential scope of ICG for monitoring other haemodynamic parameters related to heart function.

Cross-Entropy Learning for Aortic Pathology Classification of Artificial Multi-Sensor Impedance Cardiography Signals.

An aortic dissection, a particular aortic pathology, occurs when blood pushes through a tear between the layers of the aorta and forms a so-called false lumen. Aortic dissection has a low incidence compared to other diseases, but a relatively high mortality that increases with disease progression. An early identification and treatment increases patients’ chances of survival. State-of-the-art medical imaging techniques have several disadvantages; therefore, we propose the detection of aortic dissections through their signatures in impedance cardiography signals. These signatures arise due to pathological blood flow characteristics and a blood conductivity that strongly depends on the flow field, i.e., the proposed method is, in principle, applicable to any aortic pathology that changes the blood flow characteristics. For the signal classification, we trained a convolutional neural network (CNN) with artificial impedance cardiography data based on a simulation model for a healthy virtual patient and a virtual patient with an aortic dissection. The network architecture was tailored to a multi-sensor, multi-channel time-series classification with a categorical cross-entropy loss function as the training objective. The trained network typically yielded a specificity of and a sensitivity of . A study of the accuracy as a function of the size of an aortic dissection yielded better results for a small false lumen with larger noise, which emphasizes the question of the feasibility of detecting aortic dissections in an early state.

A method for suppressing respiratory noise in impedance cardiography and comprehensive assessment of noise reduction performance

Cardiac Output (CO) is a significant hemodynamic index for the diagnosis and treatment of cardiovascular diseases. Impedance cardiography (ICG) is one of the non-invasive methods widely investigated for its simplicity, cost-effectiveness and ability to measure cardiac output continuously. However, measured ICG signals are contaminated by the respiratory artefact leading to difficulties in determining characteristic points in the signal waveform, thereby lowering the accuracy of measurement results. Thus, suppressing this artefact plays an important role in ICG signal processing. This paper aims to propose a method of noise filtering to improve the quality of the signal as well as a model to evaluate the noise filtering efficiency of the method. The proposed algorithm showed promising results with the output SNR values of 21.99 ± 3.20 dB, 20.40 ± 2.88 dB, 15.57 ± 4.79 dB for normal breathing, forced breathing, and rapid breathing respectively. The root mean square percentage error (RMSPE) values of the output signals processed by the proposed algorithm compared to the standard ICG signal source for normal breathing, forced breathing, and rapid breathing are 24.13 ± 22.66%, 18.09 ± 12.98%, 32.13 ± 20.40% respectively. The quality-enhanced ICG signal could be effective tool for assisting doctors to detect cardiac abnormalities via evaluating the morphology of ICG signal waveforms as well as improving accuracy in calculating beat-to-beat and averaged hemodynamic parameters.

Study of Electrode Locations for Joint Acquisition of Impedance- and Electro-cardiography Signals.

ICG (impedance cardiography) and ECG (electrocardiography) provide important indications about functioning of the heart and of overall cardiovascular system. Measuring ICG along with ECG using wearable devices will improve the quality of health monitoring, as ICG points to important hemodynamic parameters (such as time intervals, stroke volume, cardiac output, and their variability). In this work, various electrode locations (12 different setups) have been tested for possible joint ECG & ICG data acquisition (using the same electrodes) and signal quality has been evaluated for every setup. It is shown that, while typically ICG is acquired over the whole thorax, a wrist-based joint acquisition of ECG & ICG signals can achieve acceptable signal quality and therefore can be considered in wearable sensing.

ReBeatICG: Real-time Low-Complexity Beat-to-beat Impedance Cardiogram Delineation Algorithm.

This work presents ReBeatICG, a real-time, low-complexity beat-to-beat impedance cardiography (ICG) delineation algorithm that allows hemodynamic parameters monitoring. The proposed procedure relies only on the ICG signal compared to most algorithms found in the literature that rely on synchronous electrocardiogram signal (ECG) recordings. ReBeatICG was designed with implementation on an ultra-low-power microcontroller (MCU) in mind. The detection accuracy of the developed algorithm is tested against points manually labeled by cardiologists. It achieves a detection Gmean accuracy of 94.9%, 98.6%, 90.3%, and 84.3% for the B, C, X, and O characteristic points, respectively. Furthermore, several hemodynamic parameters were calculated based on annotated characteristic points and compared with values generated from the cardiologists' annotations. ReBeatICG achieved mean error rates of 0.11 ms, 9.72 ms, 8.32 ms, and 3.97% for HR, LVET, IVRT, and relative C-point amplitude, respectively.

Motion robust ICG measurements using a two-step spectrum denoising method

Objective. Impedance cardiography (ICG) is a noninvasive and continuous method for evaluating stroke volume and cardiac output. However, the ICG measurement is easily interfered due to respiration and body movements. Taking into consideration about the spectral correlations between the simultaneously collected ICG, electrocardiogram (ECG), and acceleration signals, this paper introduces a two-step spectrum denoising method to remove motion artifacts of ICG measurements in both resting and exercising scenarios. Approach. First, the major motion artifacts of ECG and ICG are separately suppressed by the spectral subtraction with respect to acceleration signals. The obtained ECG and ICG are further decomposed into two sets of intrinsic mode functions (IMFs) through the ensemble empirical mode decomposition. We then extract the shared spectral information between the two sets of IMFs using the canonical correlation analysis in a spectral domain. Finally, the ICG signal is reconstructed using those canonical variates with largest spectral correlations with ECG IMFs. Main results. The denoising method was evaluated for 30 subjects under both resting and cycling scenarios. Experimental results show that the beat contribution factor of ICG signals increases from its original 80.1%–97.4% after removing the motion artifacts. Significance. The proposed denoising scheme effectively improves the reliability of diagnosis and analysis on cardiovascular diseases relying on ICG signals.

Investigating the estimation of cardiac time intervals using gyrocardiography

Objective: Assessment of cardiac time intervals (CTIs) is essential for monitoring cardiac performance. Recently, gyrocardiography (GCG) has been introduced as a non-invasive technology for cardiac monitoring. GCG measures the chest’s angular precordial vibrations caused by myocardium wall motion using a gyroscope sensor attached to the sternum. In this study, we investigated the accuracy and reproducibility of estimating CTIs from the GCG recordings of 50 adults. Approach: We proposed five fiducial points for the GCG waveforms associated with the opening and closure of aortic and mitral valves. Two annotators annotated the suggested points on each cardiac cycle. The points were compared to the corresponding opening and closing of cardiac valves delineated on Tissue Doppler imaging (TDI) recordings. The fiducial points were annotated on seismocardiography (SCG) and impedance cardiography (ICG) signals recorded simultaneously. Main results: For estimating the timing of mitral valve closure, aortic valve opening, aortic valve closure, and mitral valve opening, 40%, 67%, 75%, and 70% of GCG annotations fell in the corresponding echocardiography ranges, respectively. The results showed moderate-to-excellent (r = 0.4-0.92; p-value < 0.01) correlation between the measured and the reference CTls. A myocardial performance index (Tei index) adapted using joint GCG and SCG resulted in a moderate correlation (r = 0.4; p-value < 0.001). Significance: The findings showed that the CTIs can be easily measured using GCG. Also, we found that using SCG and GCG recordings together could provide an opportunity to estimate CTIs more accurately, and make it possible to calculate the Tei index as an indicator of myocardial performance.

Impedance Cardiography Signal Enhancement through Block Based Adaptive Cancellers for Distant Medical Care

The impedance Cardiography (ICG) assists the impedance occurred in the heart. The ICG also known as Thoracic Electro Bio-Impedance (TEB).This projects various flexible and mathematical reduced adaptive methods to visualize the extreme clear TEB modules. In the medical premises, TEB wave notifies the several physiological and non-physiological incidents, that covers small characteristics which are predominant for finding the volume of the stroke. In addition, mathematical difficulty is a significant constraint to novel healthcare observing tool. Therefore, the paper project novel wave working methods for TEB improvement in isolated arrangements. So that we selected higher preference adaptive eliminator as a fundamental constituent in the procedure. To recover the original signal, convergence rate, to minimize mathematical difficulty of the wave working method, we relate the data normalization to extract the ideal wave. The projected realizations are simulated with practical TEB waves. At last, outcomes proves projected extracted normalized higher order filter is appropriate to realistic medical arrangement.

Automatic diagnosis of valvular heart diseases by impedance cardiography signal processing

Valvular heart diseases (VHDs) are an abnormal activity of the heart caused by a damage of one of the four heart valves. The impedance cardiography (ICG) is a non-invasive method employed to identify and classify the heart abnormalities. Despite its importance, there are not many works in scientific literature that use the ICG method in order to diagnose VHDs. Therefore, this paper deals with the ICG signal processing for the classification of normal (N) and various VHDs classes of heartbeats. In this work, six types of heartbeat classes of VHD are used, namely: aortic insufficiency (AOI), aortic stenosis (AOS), aortic disease (AOD), mitral disease (MD), mitral-aortic heart disease (MAOHD) and tricuspid insufficiency + mitral disease (TI + MD). The classification of these heartbeat classes is performed using a combination among statistical, morphological and spectral features. For each ICG heartbeat, the statistical features (median, mean, standard deviation, kurtosis, skewness, central moment and Shannon entropy) are computed from the first four intrinsic mode functions (IMFs) calculated using the empirical mode decomposition (EMD) technique. These statistical features are subjected to principal component analysis (PCA) to reduce the dimensionality. Then, the morphological features are extracted by calculating maximums and minimums of the peaks existing in the ICG heartbeat signal and determining the intervals which separate these peaks. Besides, the spectral features are calculated from the first three harmonics of each heartbeat ICG spectrum. From these three types of features, we selected the most significant of them by applying the analysis of variance (ANOVA) test. In order to obtain the best classification performance, three combination of the selected features are investigated and tested using the decision tree (DT), the random forest (RF) and the support vector machine (SVM) classifiers: i) spectral + morphological, ii) statistical + morphological, iii) statistical + morphological + spectral. The achieved results showed that the use of the third combination of features coupled with the RF classifier gave the highest overall accuracy of 96.34 %, average class-accuracy of 98.95 %, average specificity of 99.38 %, average positive predictivity of 96.73 % and average negative predictivity of 99.44 % using 10-fold cross validation. Thus, our developed method seems robust and very efficient for automatic detection of normal and VHDs ICG classes.

Adaptive Noise Cancellation Techniques for Impedance Cardiography Signal Analysis

Impedance Cardiography (ICG) evaluation facilitates the volume of heart stroke in the sudden cardiac arrest. It is a noninvasive method for measurement of stroke volume, cardiac output monitoring and observing the hemodynamic parameters by changes in the body blood volume. Bloodvolume changes caused due to various physiological processes is extracted in the form of the variations in the impedance of the body segment. In the real time clinical environment during the extraction the ICG signals are influenced with several artifacts.As these artifacts are not stationary in nature, we can’t predict their characteristics. Hence,we developed several hybrid adaptive filtering mechanisms to improve the ICG signals resolution. Least mean square (LMS) algorithm is the basic enhancement technique in the adaptive filtering. However, in the non-stationery situation the LMS algorithm suffers with low rate of convergence and weight drift problems. In this paper we developed some hybrid variantsof LMS algorithm those are Leaky LMS (LLMS) for ICG signal enhancement. More over to progress the convergence rate, filtering capability and to reduce the computational complexity we also developed various sign versions of LLMS algorithms. The sign variants of LLMS algorithms are sign regressor LLMS (SRLLMS), Sign LLMS (SLLMS), and Sign Sign LLMS (SSLLMS). Severaladaptive signal enhancement units (ASEUs) are developed based on adaptive algorithms and performance is evaluated on the real ICG signal taken from MIT-BIT database. To ensure the efficiency of these algorithms, four experiments were performed to eliminate the various artifacts such as sinusoidal artifacts (SA), respiration artifacts (RA), muscle artifacts (MA) and electrode artifacts (EA). Among these techniques, the ASEU associated with SRLLMS performs better in the artifacts filtering process. The signal to noise ratio improvement (SNRI) for this algorithm is calculated as 9.3388 dBs, 5.7514 dBs, 8.4449 dBs and 8.7358 dBs respectively for SA, RA, MA and EA. Hence, the SRLLMS based ASEUs are more suitable in ICG signal filtering in real time health care sensing systems.

Artifact Elimination in Impedance Cardiography using Gradient based Adaptive Signal Enhancement Techniques

Impedance Cardiography (ICG) is a noninvasive method for indirect measurement of stroke volume, monitoring the cardiac output and observing the other hemodynamic parameters by the blood volume changes in the body. The blood volume changes inside a certain body segment due to a number of physiological processes are extracted in the form of the impedance variations of the body segment. The ICG analysis provides the heart stroke volume in sudden cardiac arrest. In the clinical environment desired ICG signals are influenced by several physiological and non-physiological artifacts.As these artifacts are not stationary in nature, we proposed adaptive filtering techniques to eliminate the artifacts. In this paper we used Least Mean Square (LMS), Least Mean Fourth (LMF), Median LMS (MLMS), Leaky LMS (LLMS), and Dead Zone (DZLMS) adaptive techniques to eliminate artifacts from the desired signals. Several adaptive signal enhancement units (ASEUs) are developed based on these adaptive techniques, and evaluated on the real ICG signal components. The ability of these algorithms is evaluated by performing the experiments to eliminate the various artifacts such as sinusoidal artifacts (SA), respiration artifacts (RA), muscle artifacts (MA) and electrode artifacts (EA). Among these techniques, the DZLMS based ASEU performs better in the filtering process. The signal to noise ratio improvement (SNRI) for this algorithm is calculated as 11.9140 dB, 7.3657 dB, 10.4060 dB and 10.5125 dB respectively for SA, RA, MA and EA. Hence, the DZLMS based ASEUs are well suitable for ICG filtering in the real time health care monitoring systems.

Estimation of echocardiogram parameters with the aid of impedance cardiography and artificial neural networks.

The advent of cardiovascular diseases as a disease of mass catastrophy, in recent years is alarming. It is expected to spread as an epidemic by 2030. Present methods of determining the health of one's heart include doppler based echocardiogram, MDCT (Multi Detector Computed Tomography), among various other invasive and non-invasive hemodynamic monitoring techniques. These methods require expert supervision and costly clinical set-ups, and cannot be employed by a common individual to perform a self diagnosis of one's cardiac health, unassisted. In this work, the authors propose a novel methodology using impedance cardiography (ICG), for the determination of a person's cardio-vascular health. The recorded ICG signal helps in extraction of features which are used for estimating parameters for cardiac health monitoring. The proposed methodology with the aid of artificial neural network is able to determine Stroke Volume (SV), Left Ventricular End Systolic Volume (LVESV), Left Ventricular End Diastolic Volume (LVEDV), Left Ventricular Ejection Fraction (LVEF), Iso Volumetric Contraction Time (IVCT), Iso Volumetric Relaxation Time (IVRT), Left Ventricular Ejection Time (LVET), Total Systolic Time (TST), Total Diastolic Time (TDT), and Myocardial Performance Index (MPI), with error margins of ±8.9%, ±3.8%, ±1.4%, ±7.8%, ±16.0%, ±9.0%, ±9.7%, ±6.9%, ±6.2%, and ±0.9%, respectively. The proposed methodology could be used in screening of precursors to cardiac ailments, and to keep a check on the cardio-vascular health.