ECG Feature Extraction Based on Multiresolution Wavelet Transform

Predicting and Recognizing Drug-Induced Type I Brugada Pattern Using ECG-Based Deep Learning

BACKGROUND

Brugada syndrome (BrS) has been associated with sudden cardiac death in otherwise healthy subjects, and drug-induced BrS accounts for 55% to 70% of all patients with BrS. This study aims to develop a deep convolutional neural network and evaluate its performance in recognizing and predicting BrS diagnosis.

METHODS AND RESULTS

Consecutive patients who underwent ajmaline testing for BrS following a standardized protocol were included. ECG tracings from baseline and during ajmaline were transformed using wavelet analysis and a deep convolutional neural network was separately trained to (1) recognize and (2) predict BrS type I pattern. The resultant networks are referred to as BrS-Net. A total of 1188 patients were included, of which 361 (30.3%) patients developed BrS type I pattern during ajmaline infusion. When trained and evaluated on ECG tracings during ajmaline, BrS-Net recognized a BrS type I pattern with an AUC-ROC of 0.945 (0.921-0.969) and an AUC-PR of 0.892 (0.815-0.939). When trained and evaluated on ECG tracings at baseline, BrS-Net predicted a BrS type I pattern during ajmaline with an AUC-ROC of 0.805 (0.845-0.736) and an AUC-PR of 0.605 (0.460-0.664).

CONCLUSIONS

BrS-Net, a deep convolutional neural network, can identify BrS type I pattern with high performance. BrS-Net can predict from baseline ECG the development of a BrS type I pattern after ajmaline with good performance in an unselected population.

Heartbeat detection from the upper arm using an SWT-based zero-phase filter bank incorporated with a voting Scheme

Electrocardiogram (ECG) signal provides a graphical representation of cardiac activity and is the most commonly adopted clinical tool for cardiac abnormalities detection. Heartbeat detection, as the first step in analyzing ECG signals, is required for an accurate diagnosis. Stationary wavelet transform (SWT) as a commonly used algorithm for heartbeat detection has a disadvantage of phase shift regarding the original signal. This work addresses this issue by presenting a new method that incorporates an SWT-based zero-phase filter bank with a voting scheme. Our results indicated that a superior performance in heartbeat detection was achieved from the upper arm compared to conventional SWT with a more accurate localization. We achieved sensitivity (SE) and positive predictive value (PPV) of 0.98±0.04 and 0.95±0.09 with the most distance of 50 ms from the actual heartbeats. The SE and PPV changed to 0.75±0.15 and 0.73±0.16, respectively for the distance of 20 ms. Clinical Relevance- The proposed method can be later implemented in wearable devices for convenient cardiac activity monitoring from upper arm or other none-conventional sites.

Metaheuristic secured transmission in Telecare Medical Information System (TMIS) in the face of post-COVID-19

The outbreak of novel corona virus had led the entire world to make severe changes. A secured healthcare data transmission has been proposed through Telecare Medical Information System (TMIS) based on metaheuristic salp swarm. Patients need proper medical remote treatments in this Post-COVID-19 time from their quarantines. Secured transmission of medical data is a significant challenge of digitally overwhelmed environment. The objective is to impart the patients' data by encryption with confidentiality and integrity. Eavesdroppers can carry sniffing and spoofing in order to deluge the data. In this paper, a novel scheme on metaheuristic salp swarm based intelligence has been sculptured to encrypt electrocardiograms (ECG) for data privacy. Metaheuristic approach has been blended in cryptographic engineering to address the TMIS security issues. Session key has been derived from the weight vector of the fittest salp from the salp population. The exploration and exploitation control the movements of the salps. The proposed technique baffles the eavesdroppers by the key strength and other robustness factors. The results, thus obtained, were compared with some existing classical techniques with benchmark results. The proposed MSE and RMSE were 28,967.85, and 81.17 respectively. The time needed to decode 128 bits proposed session key was 8.66 × 1052 years. The proposed cryptographic time was 8.8 s.

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review

Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

Woven electrocardiogram (ECG) electrodes for health monitoring in operational environments

Electrical signals produced within the human body can reveal information about a wide variety of physiological processes including physical activity, cardiac health, and psychological state. The industry standard for physiological signal detection is the use of adhesive electrodes that stick onto the skin. These electrodes can irritate the skin over long periods of time and are not reusable, making them a challenge for use in operational environments. Further, these electrodes often require gel to improve signal transduction, leading to changes in signal quality as these gels dry over time. Wearable sensors for operational environments should be comfortable, unobtrusive, and non-stigmatizing while maintaining signal quality high enough to allow the detection of health states. Here, we present the development and test of a set of woven textile electrodes of 8 different sizes for chest-mounted, 3-lead electrocardiogram (ECG) monitoring. Ten male subjects were tested with each of the woven electrode sizes and with one set of adhesive electrodes. A derived performance metric and signal-to-noise ratio were calculated for each set of electrodes for comparison between them. The smallest sized electrodes were found to be least effective, while the 6^th of the 8 sizes were found to be most effective.

From Pacemaker to Wearable: Techniques for ECG Detection Systems

With the alarming rise in the deaths due to cardiovascular diseases (CVD), present medical research scenario places notable importance on techniques and methods to detect CVDs. As adduced by world health organization, technological proceeds in the field of cardiac function assessment have become the nucleus and heart of all leading research studies in CVDs in which electrocardiogram (ECG) analysis is the most functional and convenient tool used to test the range of heart-related irregularities. Most of the approaches present in the literature of ECG signal analysis consider noise removal, rhythm-based analysis, and heartbeat detection to improve the performance of a cardiac pacemaker. Advancements achieved in the field of ECG segments detection and beat classification have a limited evaluation and still require clinical approvals. In this paper, approaches on techniques to implement on-chip ECG detector for a cardiac pacemaker system are discussed. Moreover, different challenges regarding the ECG signal morphology analysis deriving from medical literature is extensively reviewed. It is found that robustness to noise, wavelet parameter choice, numerical efficiency, and detection performance are essential performance indicators required by a state-of-the-art ECG detector. Furthermore, many algorithms described in the existing literature are not verified using ECG data from the standard databases. Some ECG detection algorithms show very high detection performance with the total number of detected QRS complexes. However, the high detection performance of the algorithm is verified using only a few datasets. Finally, gaps in current advancements and testing are identified, and the primary challenge remains to be implementing bullseye test for morphology analysis evaluation.

A wavelet transform based feature extraction and classification of cardiac disorder

This paper approaches an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. This method is based on using Symlet Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these particular parameters were used as input of ANFIS classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre-Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU) Myocardial Ischemia. The inclusion of ANFIS in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical engineering. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies. The results give importance to that the proposed ANFIS model illustrates potential advantage in classifying the ECG signals. The classification accuracy of 98.24 % is achieved.

A low-complexity ECG feature extraction algorithm for mobile healthcare applications

This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the Discrete Wavelet Transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time and frequency domain signal processing. Feature extraction results from 27 ECG signals from QTDB, were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2:423N + 214 additions and 1:093N + 12 multiplications for N 861 or 2:553N + 102 additions and 1:093N +10 multiplications for N > 861 (N being the number of input samples), reveals that the proposed method achieves an ideal trade-off between computational complexity and performance, a key requirement in remote CVD monitoring systems.

Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

Background

Myocardial ischemia can be developed into more serious diseases. Early Detection of the ischemic syndrome in electrocardiogram (ECG) more accurately and automatically can prevent it from developing into a catastrophic disease. To this end, we propose a new method, which employs wavelets and simple feature selection.

Methods

For training and testing, the European ST-T database is used, which is comprised of 367 ischemic ST episodes in 90 records. We first remove baseline wandering, and detect time positions of QRS complexes by a method based on the discrete wavelet transform. Next, for each heart beat, we extract three features which can be used for differentiating ST episodes from normal: 1) the area between QRS offset and T-peak points, 2) the normalized and signed sum from QRS offset to effective zero voltage point, and 3) the slope from QRS onset to offset point. We average the feature values for successive five beats to reduce effects of outliers. Finally we apply classifiers to those features.

Results

We evaluated the algorithm by kernel density estimation (KDE) and support vector machine (SVM) methods. Sensitivity and specificity for KDE were 0.939 and 0.912, respectively. The KDE classifier detects 349 ischemic ST episodes out of total 367 ST episodes. Sensitivity and specificity of SVM were 0.941 and 0.923, respectively. The SVM classifier detects 355 ischemic ST episodes.

Conclusions

We proposed a new method for detecting ischemia in ECG. It contains signal processing techniques of removing baseline wandering and detecting time positions of QRS complexes by discrete wavelet transform, and feature extraction from morphology of ECG waveforms explicitly. It was shown that the number of selected features were sufficient to discriminate ischemic ST episodes from the normal ones. We also showed how the proposed KDE classifier can automatically select kernel bandwidths, meaning that the algorithm does not require any numerical values of the parameters to be supplied in advance. In the case of the SVM classifier, one has to select a single parameter.

FPGA-core defibrillator using wavelet-fuzzy ECG arrhythmia classification

An electrocardiogram (ECG) feature extraction and classification system has been developed and evaluated using Quartus II 7.1 belong to Altera Ltd. In wavelet domain QRS complexes were detected and each complex was used to locate the peaks of the individual waves. Then, fuzzy classifier block used these features to classify ECG beats. Three types of arrhythmias and abnormalities were detected using the procedure. The completed algorithm was embedded into Field Programmable Gate Array (FPGA). The completed prototype was tested through software-generated signals, in which test scenarios covering several kinds of ECG signals on MIT-BIH Database. For the purpose of feeding signals into the FPGA, a software was designed to read signal files and import them to the LPT port of computer that was connected to FPGA. From the results, it was achieved that the proposed prototype could do real time monitoring of ECG signal for arrhythmia detection. We also implemented algorithm in a sequential structure device like AVR microcontroller with 16 MHZ clock for the same purpose. External clock of FPGA is 50 MHZ and by utilizing of Phase Lock Loop (PLL) component inside device, it was possible to increase the clock up to 1.2 GHZ in internal blocks. Final results compare speed and cost of resource usage in both devices. It shows that in cost of more resource usage, FPGA provides higher speed of computation; because FPGA makes the algorithm able to compute most parts in parallel manner.

Model-based feature extraction of electrocardiogram using mean shift

Feature extraction of electrocardiogram (ECG) is the fundamental work of further automatic diagnosis. However, suffered from various kinds of noises with white, pink, and other colors, feature extraction is not a straightforward work but requires necessary signal processing techniques. In this paper, we propose an accurate and robust ECG feature extraction method based on mean shift algorithm which has the ability to remove noise involved in input signal by taking advantage of its embedded Gaussian filter and locate extremes of input signal using gradient optimization based on self-adaptive search steps. To demonstrate the availability and efficacy of the proposed method, we conduct experiments on signals contaminated by noises of white, pink and brown colors from 5dB to 15dB signal-noise ratios. Clean signals are produced by ECG synthesizer (ECGSyn) so that we can obtain the real features and quantitatively calculate feature extraction errors of the proposed method. Experiment results verify that our method can handle various kinds of noises and achieve satisfactory feature extraction performance.