A study on ECG signal characterization and practical implementation of some ECG characterization techniques

Appathurai, Ahilan; Carol, J. Jerusalin; Raja, Chandrasekaran; Kumar, S. N.; Daniel, Ashy V.; Malar, A. Jasmine Gnana; Fred, A. Lenin; Krishnamoorthy, Sujatha

doi:10.1016/j.measurement.2019.02.040

Cited by 51 publications

(13 citation statements)

References 43 publications

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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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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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“…Three kinds of noise are present in an ECG signal, namely, baseline drift, power frequency interference, and EMG interference. Median filter, digital low-pass filter, and digital high-pass filter are often used to remove high-frequency and low-frequency noise from signals such as baseline drift and power frequency interference noise [ 27 ], and good results have been achieved. Conventional filtering methods for suppressing EMG interference cause R -peak clipping and the distortion of QRS complex wave in an ECG signal [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

A New Multichannel Parallel Network Framework for the Special Structure of Multilead ECG

Zhang³

et al. 2020

Journal of Healthcare Engineering

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Electrocardiogram (ECG) contains the rhythmic features of continuous heartbeat and morphological features of ECG waveforms and varies among different diseases. Based on ECG signal features, we propose a combination of multiple neural networks, the multichannel parallel neural network (MLCNN-BiLSTM), to explore feature information contained in ECG. The MLCNN channel is used in extracting the morphological features of ECG waveforms. Compared with traditional convolutional neural network (CNN), the MLCNN can accurately extract strong relevant information on multilead ECG while ignoring irrelevant information. It is suitable for the special structures of multilead ECG. The Bidirectional Long Short-Term Memory (BiLSTM) channel is used in extracting the rhythmic features of ECG continuous heartbeat. Finally, by initializing the core threshold parameters and using the backpropagation algorithm to update automatically, the weighted fusion of the temporal-spatial features extracted from multiple channels in parallel is used in exploring the sensitivity of different cardiovascular diseases to morphological and rhythmic features. Experimental results show that the accuracy rate of multiple cardiovascular diseases is 87.81%, sensitivity is 86.00%, and specificity is 87.76%. We proposed the MLCNN-BiLSTM neural network that can be used as the first-round screening tool for clinical diagnosis of ECG.

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“…Compared to conventional CS, SNR of the GWO-CS is increased except the sparsity level 60. Nevertheless, the optimization of sensing matrix [17] using ROA and the step size optimization of SAMP increase the SNR of CS to 6% and 7.5% than the GWO-CS and CS. The comparative analysis of the different CS frameworks in terms of reconstruction probability for varying sparsity level is shown in Fig.…”

Section: The Performance Analysis Based On Sparsity Levelmentioning

confidence: 98%

Optimized Compressive Sensing Based ECG Signal Compression and Reconstruction

Mishra¹,

Jain²

2022

Intelligent Automation &Amp; Soft Computing

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In wireless body sensor network (WBSN), the set of electrocardiograms (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient. However, due to the size of the ECG data, the performance of the signal compression and reconstruction is degraded. For efficient wireless transmission of ECG data, compressive sensing (CS) frame work plays significant role recently in WBSN. So, this work focuses to present CS for ECG signal compression and reconstruction. Although CS minimizes mean square error (MSE), compression rate and reconstruction probability of the CS is further to be improved. In this paper, we provide an efficient compressive sensing framework which strives to improve the reconstruction process, by adjusting the sensing matrix during the compression phase using the rain optimization algorithm (ROA). With the optimal sensing matrix, the compressed signal is reconstructed using Step Size optimized Sparsity Adaptive Matching Pursuit algorithm (SAMP). The results of this work demonstrate that the optimised CS framework achieves a higher compression rate and probability of reconstruction than the standard CS framework.

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A study on ECG signal characterization and practical implementation of some ECG characterization techniques

Cited by 51 publications

References 43 publications

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

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

A New Multichannel Parallel Network Framework for the Special Structure of Multilead ECG

Optimized Compressive Sensing Based ECG Signal Compression and Reconstruction

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