Applications of smart textiles in electromedicine

Korzeniewska, Ewa; Krawczyk, A.

doi:10.1109/isef45929.2019.9096883

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…For the final verification of the generalization capacity of the newly developed LSTM network, additional validation was carried out using real data. In our laboratory, a prototype of a special vest has been developed, which allows you to perform tomographic measurements and reconstructions, as well as ECG measurements [51,52]. Figure 14 shows the arrangement of electrodes seen from the inside of the vest.…”

Section: Model Validation In Real Conditionsmentioning

confidence: 99%

The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification

Kłosowski

Rymarczyk

Wójcik³

et al. 2020

Electronics

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This paper refers to the method of using the deep neural long-short-term memory (LSTM) network for the problem of electrocardiogram (ECG) signal classification. ECG signals contain a lot of subtle information analyzed by doctors to determine the type of heart dysfunction. Due to the large number of signal features that are difficult to identify, raw ECG data is usually not suitable for use in machine learning. The article presents how to transform individual ECG time series into spectral images for which two characteristics are determined, which are instantaneous frequency and spectral entropy. Feature extraction consists of converting the ECG signal into a series of spectral images using short-term Fourier transformation. Then the images were converted using Fourier transform again to two signals, which includes instantaneous frequency and spectral entropy. The data set transformed in this way was used to train the LSTM network. During the experiments, the LSTM networks were trained for both raw and spectrally transformed data. Then, the LSTM networks trained in this way were compared with each other. The obtained results prove that the transformation of input signals into images can be an effective method of improving the quality of classifiers based on deep learning.

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Section: Model Validation In Real Conditionsmentioning

confidence: 99%

The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification

Kłosowski

Rymarczyk

Wójcik³

et al. 2020

Electronics

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“…Thanks to tomography, it is possible to obtain images of the interior of the object, which is why it is not a technique of measuring absolute physical quantities, but a method of visualizing relative differences in the structure of the analyzed object. In industry, various types of tomography can be used, which, due to the physical phenomenon used, can be divided into: electrical tomography, computed tomography, ultrasound, radiotomographic imaging, and even optical tomography [1][2][3][4][5][6][7][8]. There are basically two types of electrical tomography -capacitive and impedance.…”

Section: Introductionmentioning

confidence: 99%

The use of dual machine learning in industrial electrical tomography

Rymarczyk

Kłosowski

Cieplak

et al. 2022

J. Phys.: Conf. Ser.

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Machine learning techniques are playing a key role in tomography. Process tomography, also known as industrial tomography, uses a variety of physical phenomena. Contrary to the commonly used computed tomography in medicine, electrical, ultrasound, radio and even optical tomography are used in industry. In electrical tomography we distinguish between impedance and capacitance tomography. This manuscript presents an algorithmic method to allow accurate measurements of reactors and industrial vessels using electrical impedance tomography. Reactors may contain liquids which undergo phase changes resulting in crystallization or gassing. The tomograph can detect gas crystals or bubbles. The innovative contribution of the authors is the development of an original algorithm that allows the conversion of input measurements to 2D images. First, the algorithm trains multiple single-output neural networks, each of which generates a single image pixel. Secondly, two models were used (support vector machines and artificial neural networks), which were assigned to individual pixels of the image. The image was reconstructed using two methods, not one, so the new method was called dual machine learning (DML). In order to assess the effectiveness of the new approach, both homogeneous methods (SVM and ANN) were compared with the new DML method. The results confirmed the higher effectiveness of the new approach.

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Smart bio-textiles for medicine and healthcare applications

Arik

2024

Smart Textiles From Natural Resources

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Applications of smart textiles in electromedicine

Cited by 5 publications

References 16 publications

The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification

The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification

The use of dual machine learning in industrial electrical tomography

Smart bio-textiles for medicine and healthcare applications

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