Automated Biosignal Quality Analysis of Electrocardiograms

Abdelazez, Mohamed; Rajan, Sreeraman; Chan, Adrian D. C.

doi:10.1109/mim.2021.9400951

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…Where possible, we consider quality criteria from other biomedical signal fields. Automated processes are developed for multiple different parts of the diagnostic field, such as in the electrocardiogram [9]. This comprehensive approach aims at developing a procedure and criteria, ensuring comparable, reliable, and objective results regarding the long-term stability and capability of neural implants to consistently produce high-quality signals over many years.…”

Section: Methodsmentioning

confidence: 99%

Long-Term Quality of Neural Signals: Criteria for Intracortical Implants

Charlotte,

Ulrich P.

2024

Preprint

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Intracortical implants offer a promising avenue for restoring autonomy to paralyzed individuals, with their efficacy contingent upon the consistent delivery of stable, high-quality signals. Despite thorough testing, e.g., for biocompatibility and electrical function, the evaluation of long-term performance is often limited, likely due to associated high costs and time constraints. To address this gap and lower the threshold of longterm functional testing, we suggest the establishment of standardized testing protocols and uniform quality criteria applicable across various types of intracortical implants. These criteria should be designed to provide a reliable, objective, and transparent methodology for assessing signal longevity and stability. Such standardization has the potential to expedite the evolution of advanced implant technologies from limited experimental use to widespread clinical applications.

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Section: Methodsmentioning

confidence: 99%

Long-Term Quality of Neural Signals: Criteria for Intracortical Implants

Charlotte,

Ulrich P.

2024

Preprint

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“…Step 1] The 0 order scattering coefficient of the wavelet scattering transform is calculated through a convolution operation with the input data signal 𝑦 and is defined by Equation (1). 𝜙 𝑛 represents the scale function.…”

Section: Wavelet Scattering Transformmentioning

confidence: 99%

“…From various physiological signals in the human body, important functions and the health status of the body can be indicated through phonocardiogram (PCG), electrocardiography (ECG), electroencephalography (EEG), electromyography (EMG), etc. [1]. PCG and ECG can diagnose heart-related diseases and diseases, EEG can diagnose brain-related diseases and diseases such as Epilepsy and Brain Tumors, and EMG can diagnose problems such as muscle diseases and nerve damage.…”

Section: Introductionmentioning

confidence: 99%

Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models

Lee,

Kwak

2023

Applied Sciences

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Analyzing the condition and function of the heart is very important because cardiovascular diseases (CVDs) are responsible for high mortality rates worldwide and can lead to strokes and heart attacks; thus, early diagnosis and treatment are important. Phonocardiogram (PCG) signals can be used to analyze heart rate characteristics to detect heart health and detect heart-related diseases. In this paper, we propose a method for designing using wavelet analysis techniques and an ensemble of deep learning models from phonocardiogram (PCG) for heart sound classification. For this purpose, we use wavelet scattering transform (WST) and continuous wavelet transform (CWT) as the wavelet analysis approaches for 1D-convolutional neural network (CNN) and 2D-CNN modeling, respectively. These features are insensitive to translations of the input on an invariance scale and are continuous with respect to deformations. Furthermore, the ensemble model is combined with 1D-CNN and 2D-CNN. The proposed method consists of four stages: a preprocessing stage for dividing signals at regular intervals, a feature extraction stage through wavelet scattering transform (WST) and continuous wavelet transform (CWT), a design stage of individual 1D-CNN and 2D-CNN, and a classification stage of heart sound by the ensemble model. The datasets used for the experiment were the PhysioNet/CinC 2016 challenge dataset and the PASCAL classifying heart sounds challenge dataset. The performance evaluation is performed by precision, recall, F1-score, sensitivity, and specificity. The experimental results revealed that the proposed method showed good performance on two datasets in comparison to the previous methods. The ensemble method of the proposed deep learning model surpasses the performance of recent studies and is suitable for predicting and diagnosing heart-related diseases by classifying heart sounds through phonocardiogram (PCG) signals.

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