Electrocardiography (ECG) is the most common and extensively used vital sign monitoring method in modern healthcare systems. Different designs of ambulatory ECG systems were developed as alternatives to the commonly used 12-lead clinical ECG systems. These designs primarily focus on portability and user convenience, while maintaining signal integrity and lowering power consumption. Here, a wireless ECG monitoring system is developed using flexible and dry capacitive electrodes for long-term monitoring of cardiovascular health. Our capacitive-coupled dry electrodes can measure ECG signals over a textile-based interface material between the skin and electrodes. The electrodes are connected to a data acquisition system that receives the raw ECG signals from the electrodes and transmits the data using Bluetooth to a computer. A software application was developed to process, store, and display the ECG signal in real time. ECG measurements were obtained over different types of textile materials and in the presence of body movements. Our experimental results show that the performance of our ECG system is comparable to other reported ECG monitoring systems. In addition, to put this research into perspective, recent ambulatory ECG monitoring systems, ECG systems-on-chip, commercial ECG monitoring systems, and different state-of-the-art ECG systems are reviewed, compared, and critically discussed.

Noncontact Wearable Wireless ECG Systems for Long-Term Monitoring

Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.

/pdf/robust-and-accurate-anomaly-detection-in-ecg-artifacts-using-53bivgsp0s.pdf

Robust and accurate anomaly detection in ECG artifacts using time series motif discovery.

In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms that can be used to improve the interpretability of data into clinically relevant information to facilitate diagnostics. A total of 25 corneal epithelium images without the application of eye staining were used. Three image feature extraction approaches were applied for image classification: (i) image feature classification from histogram using a support vector machine with a Gaussian radial basis function (SVM-GRBF); (ii) physical image feature classification using deep-learning Convolutional Neural Networks (CNNs) only; and (iii) the combined classification of CNNs and SVM-Linear. The performance results indicate that our chosen image features from the histogram and length-scale parameter were able to classify with up to 100% accuracy; particularly, at CNNs and CNNs-SVM, by employing 80% of the data sample for training and 20% for testing. Thus, in the assessment of corneal epithelium injuries, HSI has high potential as a method that could surpass current technologies regarding speed, objectivity, and reliability.

Hyperspectral Image Enhancement and Mixture Deep-Learning Classification of Corneal Epithelium Injuries.

We present a method for the removal of movement artifacts from the recordings of electroencephalography (EEG) signals in the context of sports health. We use a smart wearable Internet of Things-based signal recording system to record physiological human signals [EEG, electrocardiography (ECG)] in real time. Then, the movement artifacts are removed using ECG as a reference signal and the baseline estimation and denoising with sparsity (BEADS) filter algorithm for trend removal. The parameters (cut-off frequency) of the BEADS filter are optimized with respect to the number of QRS complexes detected in the reference ECG signal. Next, surrogate movement signals are generated using a linear combination of intrinsic mode functions derived from the sample movement signals by the application of empirical mode decomposition. Surrogate signals are used to test the efficiency of the BEADS method for filtering the movement-contaminated EEG signals. We provide an analysis of the efficiency of the method, extracted movement artifacts and detrended EEG signals.

/pdf/removal-of-movement-artefact-for-mobile-eeg-analysis-in-3sai8rmvwj.pdf

Removal of Movement Artefact for Mobile EEG Analysis in Sports Exercises

Noisy ECG signals contain variations in the amplitudes or in the time intervals which represents the abnormalities associated with the heart; thereby making visual diagnosis difficult for cardiovascular diseases. Hence, to facilitate proper analysis of ECG; this paper presents a combination of wavelets analysis and morphological filtering as an approach for noise removal in ECG signals. The proposed algorithm involves sub-band decomposition of ECG signal using bi-orthogonal wavelet family. The wavelet detail coefficients containing the noisy components are then processed by morphological operators using linear structuring elements. The morphological filter processes only the corrupted bands without affecting the signal parameters. Simulation results of the proposed algorithm show noteworthy suppression of noise in terms of higher signal-to-noise ratio preserving the ST segment and R wave of ECG.

/pdf/a-composite-wavelets-and-morphology-approach-for-ecg-noise-41zzop89t1.pdf

A Composite Wavelets and Morphology Approach for ECG Noise Filtering

Pre-Processing of Electrocardiographic (ECG) signals involves the baseline wander elimination and impulse noise filtering to facilitate automated analysis. In this paper a new morphological filtering algorithm using combinations of flat (two dimensional) structuring elements is proposed for pre-processing of ECG signals. Usage of two dimensional structuring elements, (over single dimension) aids in controlling effective inhibition of noise, leading to reconstruction with minimal signal distortion. Signal to noise ratio (SNR) and Root Mean Squared Error (RMSE) are used as quantitative evaluation measures for optimizing the selection of size of the structuring elements. Experimental results show that the proposed algorithm yields effective pre-processing of ECG signals, thereby eliminating the discussed artifacts.

A New Morphological Filtering Algorithm for Pre-Processing of Electrocardiographic Signals

One of the major problems encountered in recording ECG is the appearance of unwanted distortions induced by power line interference in the electrocardiogram. In addition, infections due to impulse noise leads to variations in the amplitudes which represent the abnormalities associated with the heart. This paper proposes a novel approach for addressing both the aforementioned issues in ECG signals employing sub-band decomposition using wavelets analysis. Morphological filtering is applied to the detail sub-bands for removal of impulse noise using one-dimensional structuring element. Further, the powerline interference is removed using IIR Butterworth filter providing significant reduction in power spectral density levels between 50 to 60 Hz. The finally reconstructed ECG signal yields reasonably good impulse noise as well as powerline suppression using the proposed approach.

A novel approach for suppression of powerline interference and impulse noise in ECG signals

Electrocardiogram contains a wealth of diagnostic information normally used to guide clinical decision making for proper diagnosis of cardiovascular diseases disorders. ECG is often contaminated by noises and artifacts that can be within the frequency band of interest and can manifest with similar morphologies as the ECG signal itself. Baseline correction and noise suppression are the two important pre-requisites for conditioning of the ECG signal. This paper presents a novel morphological filtering technique for removing baseline drift using non-flat structuring element. Further, to achieve noise suppression an improved median filtering technique is applied using mask of variable sizes. Depending upon the degree of impulse noise contamination the mask size may vary to a maximum of 1×11. The residual noise left after this stage is then filtered out using morphological filtering. Simulation results show noteworthy improvement in baseline correction and noise filtering in comparison to other proposed morphological filtering based approaches in the literature.

An integration of improved median and morphological filtering techniques for electrocardiogram signal processing

Analysis of the Electrocardiogram ECG signals is the pre-requisite for the clinical diagnosis of cardiovascular diseases. ECG signal is degraded by artifacts such as baseline drift and noises which appear during the acquisition phase. The effect of impulse and Gaussian noises is randomly distributed whereas baseline drift generally affects the baseline of the ECG signal; these artifacts induce interference in the diagnosis of cardio diseases. The influence of these artifacts on the ECG signals needs to be removed by suitable ECG signal processing scheme. This paper proposes combination of non linear morphological operators for the noise and baseline drift removal. Non flat structuring elements of varying dimensions are employed with morphological filtering to achieve low distortion as well as good noise removal. Simulation outcomes illustrate noteworthy improvement in baseline drift yielding lower values of MSE and PRD; on the other hand high signal to noise ratios depicts suppression of impulse and Gaussian noises.

Rishendra Verma

Papers

A Composite Wavelets and Morphology Approach for ECG Noise Filtering

A New Morphological Filtering Algorithm for Pre-Processing of Electrocardiographic Signals

A novel approach for suppression of powerline interference and impulse noise in ECG signals

An integration of improved median and morphological filtering techniques for electrocardiogram signal processing

A Non-Linear Approach to ECG Signal Processing using Morphological Filters