Segmentation of the ECG Signal by Means of a Linear Regression Algorithm

The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.

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“…In this study,

components are extracted manually. However, this problem can be solved by the method presented in [ 53 , 54 , 55 ].…”

Section: Discussionmentioning

confidence: 99%

Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias

Awal

Mostafa²,

Ahmad

et al. 2021

Sensors

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“…This segmentation is necessary in HRV computation since the analysis requires to know with precision the moment (occurrence) of each R-peak or QRS-complex ( 116 ). The element most used for detecting R-peaks is perhaps the Pan-Tompkins algorithm ( 117 ), other methods being linear regression algorithm ( 118 ), adaptive Hermite functions ( 119 ), adaptive bandpass filters and wavelet analysis ( 120 ), and CNN ( 121 , 122 ). Once the R-peaks or QRS complexes are segmented, it is possible to compute the HRV signal.…”

Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

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“…The ECG signal contains five significant peaks, known as Fiducial points, shown by letters P, Q, R, S, T [25], as seen in Fig. 15.…”

Section: ) R-peak Detectionmentioning

confidence: 99%

Design and Implementation of an Ultra-Low-Power ECG Patch and Smart Cloud-Based Platform

Baraeinejad¹,

Shayan²,

Vazifeh³

et al. 2021

Preprint

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This paper reports a new device for electrocardiogram (ECG) signal monitoring and software for signal analysis and artificial intelligence (AI) assisted diagnosis. The hardware mitigates the signal loss common in previous products by enhancing the ergonomy, flexibility, and battery life. The power efficiency is optimized by design using switching converters, ultra-low-power components, and efficient signal processing. It enables 14-day of uninterrupted ECG monitoring and connectivity with a smartphone and microSD card storage.The software is implemented in Android app and web-based platforms via Internet of Things (IoT). This component provides cloud-based and local storage and uses AI for arrhythmia detection. The arrhythmia detection algorithm shows 98.7% accuracy using Artificial Neural Network and K-Nearest Neighbors methods, and 98.1% using Decision Tree method on test data set.

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Segmentation of the ECG Signal by Means of a Linear Regression Algorithm

Cited by 51 publications

References 26 publications

Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias

Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Design and Implementation of an Ultra-Low-Power ECG Patch and Smart Cloud-Based Platform

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