QRS Complex Detection based on Multilevel Thresholding and Peak-to-Peak Interval Statistics

Heart beats are important aspects of the study of heart diseases in medical science as they provide vital information on heart disorders and diseases or abnormalities in the heart rhythm. Each heart beat provides a QRS complex in the electrocardiogram (ECG) which is centered at the R-peak. The analysis of ECG is hindered by low-frequency noises, high-frequency noise, interference from P and T waves, and change in QRS morphology. Therefore, it is a major challenge to detect the QRS complexes using automatic detection algorithms.This thesis aims to present three new peak detection algorithms based on a statistical analysis of the ECG signal. In the first algorithm, a novel method of segmentation and statistical false peak elimination is proposed. The second algorithm uses different levels of adaptive thresholds to detect true peaks while the third algorithm combines and modifies the two proposed algorithms to provide better efficiency and accuracy in QRS complex detection. The proposed algorithms are tested on the MIT-BIH arrhythmia and provides better detection accuracy in comparison to several state-of-the-art methods in the field. To evaluate the performance of the proposed method, the merits of evaluation consider the number of false positives and negatives. A false positive (FP) is the result of a noise peak being detected and a false negative (FN) occurs when a beat is not detected at all. The methods emphasize better detection algorithms that detect peaks efficiently and automatically without eliminating the high-frequency noise completely and hence reduces the overall computational time

Cardiac Health Diagnosis Using Higher Order Spectra and Support Vector Machine

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification

Recognition and Evaluation of Heart Arrhythmias via a General Sparse Neural Network

In clinical use, an electrocardiogram (ECG) is an essential medical tool for assessing heart arrhythmias. Thousands of human beings worldwide are affected by different cardiac problems nowadays. As a consequence, studying the features of the ECG pattern is critical for detecting a wide range of cardiac diseases. The ECG is a test which assesses the intensity of the electrical impulses in the circulatory system. In the present investigation, detection and examination of arrhythmias in the heart on the  system using GSNNs (General sparsed neural network classifier) can be carried out[1]. In this paper, the methodologies of support vector regression(SVR), neural mode decomposition(NMD), Artificial Neural Network (ANN), Support Vector Machine(SVM) and are examined. To assess the suggested structure, three distinct ECG waveform situations are chosen from the MIT-BIH arrhythmia collection. The main objective of this assignment is to create a simple, accurate, and simply adaptable approach for classifying the three distinct heart diseases chosen. The wavelet transform Db4 is used in the present paper to obtain several features from an ECG signal. The suggested setup was created using the MATLAB programme. The algorithms suggested are 98% accurate for forecasting cardiac arrhythmias, which is greater than prior techniques

Identification of cardiac signals in ambulatory ECG data

The Electrocardiogram (ECG) is the primary tool for monitoring heart function. ECG signals contain vital information about the heart which informs diagnosis and treatment of cardiac conditions. The diagnosis of many cardiac arrhythmias require long term and continuous ECG data, often while the participant engages in activity. Wearable ambulatory ECG (AECG) systems, such as the common Holter system, allow heart monitoring for hours or days. The technological trajectory of AECG systems aims towards continuous monitoring during a wide range of activities with data processed locally in real time and transmitted to a monitoring centre for further analysis. Furthermore, hierarchical decision systems will allow wearable systems to produce alerts or even interventions. These functions could be integrated into smartphones.A fundamental limitation of this technology is the ability to identify heart signal characteristics in ECG signals contaminated with high amplitude and non-stationary noise. Noise processing become more severe as activity levels increase, and this is also when many heart problems are present.This thesis focuses on the identification of heart signals in AECG data recorded during participant activity. In particular, it explored ECG filters to identify major heart conditions in noisy AECG data. Gold standard methods use Extended Kalman filters with extrapolation based on sum of Gaussian models. New methods are developed using linear Kalman filtering and extrapolation based on a sum of Principal Component basis signals. Unlike the gold standard methods, extrapolation is heartcycle by heartcycle. Several variants are explored where basic signals span one or two heartcycles, and applied to single or multi-channel ECG data.The proposed methods are extensively tested against standard databases or normal and abnormal ECG data and the performance is compared to gold standard methods. Two performance metrics are used: improvement in signal to noise ratio and the observability of clinically important features in the heart signal. In all tests the proposed method performs better, and often significantly better, than the gold standard methods. It is demonstrated that abnormal ECG signals can be identified in noisy AECG data

Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter

The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects

Comparison of Low-Complexity Algorithms for Real-Time QRS Detection using Standard ECG Database

Today, thanks to the development of advanced wearable devices, it is possible to track patient conditions outside hospital setting for several days. One of the most important bio-signals used for health analysis is the electrocardiographic (ECG) signal. It provides information about the heart rate, rhythm, and morphology of heart. Many algorithms are proposed over years for automated ECG analysis. Due to their computational complexity, not all these techniques can be implemented on wearable devices for real-time ECG detection. In this frame, a particular interest is toward the algorithms for automatic QRS detection. Different algorithms have been presented in the literature. Among all, more suitable class for the implementation on embedded systems is based on the use of signal derivatives and thresholds. These algorithms are composed by pre-processing stage, for the noise removal, and decision stage for the QRS detection. In literature, the different algorithms were discriminated only with respect to their pre-processing stages. Furthermore, not all algorithms were tested with standard database: this makes the results difficult to compare and evaluate. Moreover, the algorithms performance in case of pathological behaviours was not compared. This paper is motivated by the need to perform a comparison of the whole algorithms, both pre-processing and decision stages, under a standard database (MIT-BIH ECG database of Physionet), either for non-pathological and pathological signals. The results confirm that the Pan & Tompkins algorithm has the best performance in terms of QRS complex detection. However, in some cases, its performance is comparable with the other algorithms ones. For this reason, in the applications in which the reduced of computational complexity is an important constraint, it is possible to implemented algorithms with comparable performance but with lesser complexity with respect to P&T algorithm

ASCNet-ECG: Deep Autoencoder based Attention aware Skip Connection network for ECG filtering

Currently, the telehealth monitoring field has gained huge attention due to its noteworthy use in day-to-day life. This advancement has led to an increase in the data collection of electrophysiological signals. Due to this advancement, electrocardiogram (ECG) signal monitoring has become a leading task in the medical field. ECG plays an important role in the medical field by analysing cardiac physiology and abnormalities. However, these signals are affected due to numerous varieties of noises, such as electrode motion, baseline wander and white noise etc., which affects the diagnosis accuracy. Therefore, filtering ECG signals became an important task. Currently, deep learning schemes are widely employed in signal-filtering tasks due to their efficient architecture of feature learning. This work presents a deep learning-based scheme for ECG signal filtering, which is based on the deep autoencoder module. According to this scheme, the data is processed through the encoder and decoder layer to reconstruct by eliminating noises. The proposed deep learning architecture uses a modified ReLU function to improve the learning of attributes because standard ReLU cannot adapt to huge variations. Further, a skip connection is also incorporated in the proposed architecture, which retains the key feature of the encoder layer while mapping these features to the decoder layer. Similarly, an attention model is also included, which performs channel and spatial attention, which generates the robust map by using channel and average pooling operations, resulting in improving the learning performance. The proposed approach is tested on a publicly available MIT-BIH dataset where different types of noise, such as electrode motion, baseline water and motion artifacts, are added to the original signal at varied SNR levels

Noise Reduction of 15-lead Electrocardiogram Signals Using Signal Processing Algorithms

In the research, a signal processing algorithm is proposed to reduce the noise and baseline wander in ECG spectra. The ECG spectra are first processed by symmetrical triangle smooth (can also be considered as a low pass filter) to reduce the noise, then a FIR high pass filter is applied to decrease the baseline wander. The ROC curves of the same classification test for myocardial infarction using ECG spectra processed by the standard and proposed method are plotted. The area under the curve can be improved up to 29.3%, which indicates the proposed ECG spectra can provide a more accuracy diagnostic result for myocardial infarction.Chemistry Departmen