Noisy ECG Signal Analysis for Automatic Peak Detection

D’Aloia, Matteo; Longo, Annalisa; Rízzí, María

doi:10.3390/info10020035

Cited by 58 publications

(27 citation statements)

References 25 publications

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“…Wavelet transform is a suitable tool for studying nonstationary signals. In fact, both the property of time-frequency localization and the multirate filtering option make wavelet transform an effective tool in signal processing analysis [48]. It decomposes the signal into several components with various scales or resolutions.…”

Section: Wavelet Transformmentioning

confidence: 99%

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

2019

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The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.

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Section: Wavelet Transformmentioning

confidence: 99%

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

2019

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“…D'Aloia et al [21] developed an effective approach for peak point detection and localization in noisy electrocardiogram (ECG) signals. Six stages characterize the implemented method, which adopts the Hilbert transform and a thresholding technique for the detection of zones inside the ECG signal that could contain a peak.…”

Section: Heart and Cardiovascular Diseasesmentioning

confidence: 99%

eHealth and Artificial Intelligence

Impedovo

Pirlo

2019

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Artificial intelligence is changing the healthcare industry from many perspectives: diagnosis, treatment, and follow-up. A wide range of techniques has been proposed in the literature. In this special issue, 13 selected and peer-reviewed original research articles contribute to the application of artificial intelligence (AI) approaches in various real-world problems. Papers refer to the following main areas of interest: feature selection, high dimensionality, and statistical approaches; heart and cardiovascular diseases; expert systems and e-health platforms.

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“…De Chazal et al [2] used morphological features and weighted linear discrete analysis (LDA) combined with a packaging feature selection function to screen for heart disease. It is well known that the morphological approach is sensitive to ECG signal noise and has many limitations in the classification performance robustness of the model [12]. Thanks to the development of deep learning technology, many feature extraction processing tasks can be completed by convolutional computation.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology

Zhen-yu

Chen

et al. 2020

Electronics

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Electrocardiogram (ECG) signal evaluation is routinely used in clinics as a significant diagnostic method for detecting arrhythmia. However, it is very labor intensive to externally evaluate ECG signals, due to their small amplitude. Using automated detection and classification methods in the clinic can assist doctors in making accurate and expeditious diagnoses of diseases. In this study, we developed a classification method for arrhythmia based on the combination of a convolutional neural network and long short-term memory, which was then used to diagnose eight ECG signals, including a normal sinus rhythm. The ECG data of the experiment were derived from the MIT-BIH arrhythmia database. The experimental method mainly consisted of two parts. The input data of the model were two-dimensional grayscale images converted from one-dimensional signals, and detection and classification of the input data was carried out using the combined model. The advantage of this method is that it does not require performing feature extraction or noise filtering on the ECG signal. The experimental results showed that the implemented method demonstrated high classification performance in terms of accuracy, specificity, and sensitivity equal to 99.01%, 99.57%, and 97.67%, respectively. Our proposed model can assist doctors in accurately detecting arrhythmia during routine ECG screening.

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Noisy ECG Signal Analysis for Automatic Peak Detection

Cited by 58 publications

References 25 publications

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

eHealth and Artificial Intelligence

An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology

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