Machine learning pathology detection with a body surface potential mapping

Wójcik, Dariusz; Kozłowski, Edward; Woś, Michał; Rymarczyk, Tomasz; Wośko, Elżbieta

doi:10.1145/3410530.3414404

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…The Body Surface Potential mapping [1] is the method which has more sensors than classical ECG signal and allows potential on the patient's heart reconstruction in order to identify heart diseases. The BSPM vest used in the research described in this article presented in Figure 1 IOP Publishing doi:10.1088/1742-6596/2408/1/012001 2 There are many available numerical recognition algorithms [2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Body surface potential mapping time series recognition using convolutional and recurrent neural networks

Rymarczyk

Wójcik²,

Maciura³

et al. 2022

J. Phys.: Conf. Ser.

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Section: Introductionmentioning

confidence: 99%

Body surface potential mapping time series recognition using convolutional and recurrent neural networks

Rymarczyk

Wójcik²,

Maciura³

et al. 2022

J. Phys.: Conf. Ser.

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Time Series Recognition with Convolutional and Recursive Neural Networks in BSPM

Wójcik

Rymarczyk

Maciura³

et al. 2023

2023 International Interdisciplinary PhD Workshop (IIPhDW)

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Measurement channels’ information relationships in body surface potential mapping

Przysucha,

Kowalski,

Oleszek

et al. 2024

JoMS

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Body Surface Potential Mappings (BSPM) measurements allow heart rate monitoring. Based on measurements obtained from BSPM, defects and heart rhythm disorders can be detected. Due to the multitude of data obtained from BSPM measurements, the crucial aspect in the potential test signals analysis on the human body is the automation of the disturbance detection process. This automation process involves the use of advanced algorithms and machine learning techniques to analyze the recorded data and identify patterns indicative of heart rhythm disorders. In the article, the information dependencies in the measurement channels were analyzed and presented. PCA analysis was used to determine informational relationships between the channels. The study was carried out on the data from simulations made for the BSPM measuring vest on a simulation system created for this purpose and based on the ECG signal generator. As a result of the conducted research indicated informational relationships between the channels for signals with disturbances: Atrial fibrillation, Brachycardia, Normal signal, PVC, Tachycardia, Ventricular Fibrillation, and Ventricular Tachycardia.

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Machine learning pathology detection with a body surface potential mapping

Cited by 3 publications

References 4 publications

Body surface potential mapping time series recognition using convolutional and recurrent neural networks

Body surface potential mapping time series recognition using convolutional and recurrent neural networks

Time Series Recognition with Convolutional and Recursive Neural Networks in BSPM

Measurement channels’ information relationships in body surface potential mapping

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