PCA as an effective tool for the detection of R-peaks in an ECG signal processing

Gupta, Varun; Saxena, Nitin Kumar; Kanungo, Abhas; Kumar, Parvin; Diwania, Sourav

doi:10.1007/s13198-022-01650-0

Cited by 33 publications

(5 citation statements)

References 88 publications

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“…It exhibits a commendable ability to precisely pinpoint the R-peaks within the array data, enabling us to subsequently compute the temporal separation between these R-peaks in alignment with the data sampling frequency. However, it is worth noting that more sophisticated algorithms, as referenced in [22], [23] are available for the accurate localization of QRS peaks. When such algorithms are applied to determine the positions of these peaks with precision, the timing of these peak occurrences can prove valuable in the diagnosis of heart diseases.…”

Section: Figure 5 Comparison Of the Results Of Wavelet (Micro) With T...mentioning

confidence: 99%

“…Research such as study [22] highlights the integration of chaos analysis, short-time Fourier transform (STFT), and principal component analysis (PCA) to automate QRS complex detection, offering impressive sensitivity and accuracy rates. In a similar vein, Gupta et al [23] introduces a novel application of PCA for R-peak detection, avoiding the need for pre-processing in noisy ECG signals. Meanwhile, Gupta et al [24] presents a computer-aided diagnosis system utilizing chaos analysis to extract non-linear patterns in ECG signals, enhancing arrhythmia detection.…”

Section: Introductionmentioning

confidence: 99%

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Electrocardiogram signal processing algorithm on microcontroller using wavelet transform method

Phuphanin,

Tasakorn,

Srivichai

2024

IJECE

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The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.

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Section: Figure 5 Comparison Of the Results Of Wavelet (Micro) With T...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electrocardiogram signal processing algorithm on microcontroller using wavelet transform method

Phuphanin,

Tasakorn,

Srivichai

2024

IJECE

View full text Add to dashboard Cite

show abstract

“…The method proposed in the [108] 3. The authors of [110] used principal component analysis and achieved an accuracy of 99.85%. The researchers of [111] used moving average, low pass, high pass filters to expunge the noise from the input ECG signal and used sequential minimal optimization-support vector machine network and obtained significant performance.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders

Chaitanya

Sharma

Rahul

et al. 2023

Biomed. Phys. Eng. Express

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Coronavirus disease (COVID-19) is a class of SARS-CoV-2 virus which is initially identified in the later half of the year 2019 and then evolved as a pandemic. If it is not identified in the early stage then the infection and mortality rates increase with time. A timely and reliable approach for COVID-19 identification has become important in order to prevent the disease from spreading rapidly. In recent times, many methods have been suggested for the detection of COVID-19 disease have various flaws, to increase diagnosis performance, fresh investigations are required. In this article, automatically diagnosing COVID-19 using ECG images and deep learning approaches like as Visual Geometry Group (VGG) and AlexNet architectures have been proposed. The proposed method is able to classify between COVID-19, myocardial infarction, normal sinus rhythm, and other abnormal heart beats using Lead-II ECG image only. The efficacy of the technique proposed is validated by using a publicly available ECG image database. We have achieved an accuracy of 77.42% using Alexnet model and 75% accuracy with the help of VGG19 model.

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“…The limitations of our CNN-LSTM-SE model are as follows: The ECG segments input by the model should contain at least one complete ECG beat (P wave, PR segment [ 45 – 47 ], QRS complex, ST-T segment, U wave) to ensure more accurate classification results of the model. From the interpretability visualization results of the model, it can be known that if the input ECG segment does not contain a complete ECG beat, it may lead to the loss of some important features associated with four grades of heart failure, which affects the decision results of the model.…”

Section: Discussionmentioning

confidence: 99%

Heart failure classification using deep learning to extract spatiotemporal features from ECG

Zhang,

Yuan-Lu,

Tang

et al. 2024

BMC Med Inform Decis Mak

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Background Heart failure is a syndrome with complex clinical manifestations. Due to increasing population aging, heart failure has become a major medical problem worldwide. In this study, we used the MIMIC-III public database to extract the temporal and spatial characteristics of electrocardiogram (ECG) signals from patients with heart failure. Methods We developed a NYHA functional classification model for heart failure based on a deep learning method. We introduced an integrating attention mechanism based on the CNN-LSTM-SE model, segmenting the ECG signal into 2 to 20 s long segments. Ablation experiments showed that the 12 s ECG signal segments could be used with the proposed deep learning model for superior classification of heart failure. Results The accuracy, positive predictive value, sensitivity, and specificity of the NYHA functional classification method were 99.09, 98.9855, 99.033, and 99.649%, respectively. Conclusions The comprehensive performance of this model exceeds similar methods and can be used to assist in clinical medical diagnoses.

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PCA as an effective tool for the detection of R-peaks in an ECG signal processing

Cited by 33 publications

References 88 publications

Electrocardiogram signal processing algorithm on microcontroller using wavelet transform method

Electrocardiogram signal processing algorithm on microcontroller using wavelet transform method

Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders

Heart failure classification using deep learning to extract spatiotemporal features from ECG

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