Accurate tunable-Q wavelet transform based method for QRS complex detection

The detection and delineation of QRS-complexes and T-waves in Electrocardiogram (ECG) is an important task because these features are associated with the cardiac abnormalities including ventricular arrhythmias that may lead to sudden cardiac death. In this paper, we propose a novel method for the R-peak and the T-peak detection using hierarchical clustering and Discrete Wavelet Transform (DWT) from the ECG signal. In the first step, a template of the single ECG beat is identified. Secondly, all R-peaks are detected by using hierarchical clustering. Then, each corresponding T-wave boundary is delineated based on the template morphology. Finally, the determination of T wave peaks is achieved based on the Modulus-Maxima Analysis (MMA) of the DWT coefficients. We evaluated the algorithm by using all records from the MIT-BIH arrhythmia database and QT database. The R-peak detector achieved a sensitivity of 99.89%, a positive predictivity of 99.97% and 99.83% accuracy over the validation MIT-BIH database. In addition, it shows a sensitivity of 100%, a positive predictivity of 99.83% in manually annotated QT database. It also shows 99.92% sensitivity and 99.96% positive predictivity over the automatic annotated QT database. In terms of the T-peak detection, our algorithm is verified with 99.91% sensitivity and 99.38% positive predictivity in manually annotated QT database.

/pdf/an-automatic-r-and-t-peak-detection-method-based-on-the-3minun8l9m.pdf

An Automatic R and T Peak Detection Method Based on the Combination of Hierarchical Clustering and Discrete Wavelet Transform

Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets are adopted to decompose the EEG data into five frequencies sub-bands, such as gamma, beta, alpha, theta, and delta, whose statistical features were computed and used as classification features. The grid search optimizer is used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a conventional support vector machine and a random forest classifier constructed according to previous descriptions. Multiple performance indices were used to evaluate the proposed classification scheme, which provided better classification accuracy and detection effectiveness than has been recently reported in other studies on three-class classification of EEG data.

https://www.mdpi.com/1424-8220/19/2/219/pdf

Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer

This paper presents an efficient infinite impulse response type digital fractional order differentiator (DFOD) based electrocardiogram (ECG) pre-processor to detect QRS complexes. First, an efficient optimizer namely, Antlion optimization algorithm is employed to solve the proposed DFOD design problem. Then, the designed DFOD is deployed in the pre-processing stage of a threshold independent R-peak detection technique. Finally, the proposed QRS complex detector is thoroughly assessed on the standard ECG datasets of MIT/BIH Arrhythmia, MIT/BIH ST Change, MIT/BIH Supraventricular Arrhythmia, European ST-T, QT, and T-Wave Alternans Challenge databases to show the wide sense practicability of the proposed DFOD-based QRS detector. The root-means-square magnitude error (RMSME) and the average group delay $(\tau _{DD})$ metrics of the proposed DFOD are as low as –38.17 dB and 0.04 samples, respectively. The percentage of improvement in terms of RMSME metric compared to the best-reported approach is 15%. The overall sensitivity of 99.89% and positive predictivity of 99.88% are incurred by considering all the six databases. To the best of the authors’ knowledge, it is the first time when the evolutionary algorithm based IIR-type DFOD is employed for the QRS complex detection and establishing its performance superiority. The results so obtained are compared with the results of all the recently reported QRS detectors. The proposed DFOD based ECG pre-processor has a great potential to robustly generate the feature signal related to the ECG QRS complex irrespective of the ECG morphology. Thus, the proposed DFOD based QRS detector can be employed in clinical ECG monitoring devices to augment the QRS detection performance.

An Efficient and Robust Digital Fractional Order Differentiator Based ECG Pre-Processor Design for QRS Detection

Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods.

An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter

A wearable electrocardiogram (ECG) measuring system is designed based upon capacitively coupled biopotential sensing electrodes. The sensing electrodes are made from the standard printed-circuit board. Using a reversely connected diode to provide the required high-value bias resistance, the pickup circuit for biopotential sensing is implemented by a high-input-impedance instrumentation amplifier. Two different configurations of the ECG measuring system are constructed and experimentally tested. One is a floating ground configuration in which only two sensing electrodes are used for ECG signal acquisition. Another is the passive ground configuration in which an additional grounding electrode is employed as the reference for common-mode noise suppression. The ECG signal can be obtained through thin clothes for both cases but performs lower signal-to-noise ratio. In order to improve the quality of the obtained signal, an algorithm based on a singular spectrum analysis is designed for noise reduction. A matching template is introduced to pick out the critical principal components automatically. Experimental results show that, with the aid of the proposed algorithm, satisfied ECG and respiration signals can be effectively obtained outside cotton-fiber clothing.

Capacitively Coupled Electrocardiogram Measuring System and Noise Reduction by Singular Spectrum Analysis

In order to detect the QRS complexes locally in the microcontroller-based embedded system, a novel algorithm with lower computation burden is proposed by phase space reconstruction and box-scoring calculation. The method depends on the geometrical property of the ECG signal phase portraits. According to the time-delay method, one segment of the sampled 1-D ECG signal is embedded into a 2-D phase space. The phase space is divided into $2^{M}\times 2^{M}$ grids, and each grid is called a box. The box gridding is implemented by numerically truncating the high $M$ -bits from the digital data. The phase portraits of the sampled ECG signal in the phase space are therefore divided by these boxes. Since the trajectory points corresponding to the R-waves are located relatively far away from the diagonal line, boxes that crossed by the trajectory are flagged and valued according to its distance to the diagonal line. Correspondingly, the box-scoring of the ECG segment is obtained by summarizing the values of the flagged boxes. The location of the R-waves can then be found by finding peak values in the box-scoring time series. The proposed algorithm is suitable for real-time QRS complex detection in microcontroller-based wearable ECG acquisition devices.

A Real-Time QRS Detection Method Based on Phase Portraits and Box-Scoring Calculation

A microprocessor measurement system is developed for simultaneously capturing electrocardiography (ECG) and impedance plethysmogram (IPG) signals. All the measurement electrodes are coupled with human skin in a non-contact capacitive way. A capacitive coupled pickup circuit is used for obtaining the original ECG and IPG signals. Three differences of electrode configurations are experimentally tested for picking up the ECG signals, i.e., from the plantar area of the feet, from the instep area of the feet, and from a single hand and a single foot. Two different configurations (i.e., two-electrode IPG and four-electrode IPG) are tested to get IPG signals. A singular spectrum analysis (SSA)-based algorithm is embedded in a microprocessor system for signal processing. It is found that the original signals, especially the IPG signals, are quite noisy and can only be used after SSA processing. When both the ECG and IPG signals are measured simultaneously, the ECG signal will be severely ruined by the interference from the excitation current signal of IPG measurement. Relatively satisfied signals can only be simultaneously obtained when one ECG electrode is placed in a hand. The time difference between the ECG R-peak and IPG C-peak (the peak point in IPG) is experimentally tested for its potential application in blood pressure measurement.

Simultaneously Capturing Electrocardiography and Impedance Plethysmogram Signals From Human Feet by Capacitive Coupled Electrode System

A portable tester for accessing human skin status quantitatively at home is reported. An interdigital electrode (IDE) that contacts directly with human skin is used as the sensing element. The electrode-skin model is simplified as a resistance and a capacitance in series. Using square wave signal for excitation, impedance of the IDE is measured in a charging/discharging way. During one measurement cycle, the response voltages at two excitation frequencies, i.e., 10 kHz and 600 Hz, are measured successively. Equivalent resistance and capacitance of skin are then resolved from the measured values. Both the numerical and experimental simulation results indicate that the proposed circuit and corresponding algorithms work well for resistors and capacitors. The performance of the tester is experimentally tested with human skin in cases of different status. Obvious relationship between the measured values and the skin status is observed. Moreover, distinctive results are obtained between hydrating and emollient cosmetics smeared skins. The tester can be potentially used to give a quantitative guidance for individual skin care, such as cosmetics selection.

A Portable Dual-Parameter Tester for Assessing Electrical Properties of Human Skin Surface

Biopotential signals captured by non-contact capacitively coupled biopotential sensing electrodes are easily polluted by different kinds of noises, especially power line noises. Singular spectrum analysis (SSA) algorithm is utilized for processing capacitively captured biopotential signals. By analyzing the spectrum characteristics of the circulant matrix and Toeplitz matrix, it has been found that the window length of SSA is strongly related to the frequency width corresponding to each principal component. A criterion for window length selection is, therefore, proposed and numerically verified by simulated electrocardiography signals. A capacitive-coupled biopotential signal measurement system is constructed to obtain original biopotential signals from human body. Experimental results show that, with the aid of the criterion, satisfied biopotential signals can be effectively obtained by SSA.

Singular Spectrum Analysis Window Length Selection in Processing Capacitive Captured Biopotential Signals

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