Feasibility research of non-invasive methods for interstitial fluid level measurement

Pockevicius, V.; Cepenas, Mindaugas; Miklusis, Donatas; Markevičius, Vytautas; Zabulienė, Lina; Navikas, Dangirutis; Valinevicius, Algimantas; Andriukaitis, Darius

doi:10.3233/bme-171699

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In particular, one ECG signal is sampled at the 360 Hz frequency using Nyquist's theorem for collecting the maximum frequency of 180 Hz. In addition, the frequency of approximation and detail coefficients in each decomposition level are described as in [38], [39]. In the approximation component with the very low frequency a m , the decomposition of level-m and a hard threshold λ a are utilized for eliminating the noise from the approximation m a using the following formula…”

Section: A Ecg Datasetsmentioning

confidence: 99%

Deep Learning Framework with ECG Feature-Based Kernels for Heart Disease Classification

Hải

2021

ELEKTRON ELEKTROTECH

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Heart disease classification with high accuracy can support the physician’s correct decision on patients. This paper proposes a kernel size calculation based on P, Q, R, and S waves of one heartbeat to enhance classification accuracy in a deep learning framework. In addition, Electrocardiogram (ECG) signals were filtered using wavelet transform with dmey wavelet, in which the shape of the dmey is closed to that of one heartbeat. With this selected dmey, each heartbeat was standardized with 300 samples for calculation of kernel sizes so that it contains most features in each heartbeat. Therefore, in this research, with 103,459 heart rhythms from the MIT-BIH Arrhythmia Database, the proposed approach for calculation of kernel sizes is effective with seven convolutional layers and other fully connected layers in a Deep Neural Network (DNN). In particular, with five types of heart disease, the result of the high classification accuracy is about 99.4 %. It means that the proposed kernel size calculation in the convolutional layers can achieve good classification performance and it may be developed for classifying different types of disease.

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Section: A Ecg Datasetsmentioning

confidence: 99%

Deep Learning Framework with ECG Feature-Based Kernels for Heart Disease Classification

Hải

2021

ELEKTRON ELEKTROTECH

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“…In order to overcome this limitation, a wavelet adaptive filter is proposed in [16]. Recently, various techniques have been proposed based on wavelet transforms [17]- [20], interpolation algorithms [21], sparse signal decomposition [22], principal component analysis [23], independent component analysis [24], and empirical mode decomposition (EMD) [25] method. EMD is computationally very demanding in comparison to the other techniques [7].…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Baseline-Wander Noise in ECG Signals Using Discrete Wavelet Transform and Decision Tree Classifier

Haider¹,

Alhussein²

2019

ELEKTRON ELEKTROTECH

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An electrocardiogram (ECG) signal is usually contaminated with various noises, such as baseline-wander, power-line interference, and electromyogram (EMG) noise.Denoising must be performed to extract meaningful information from ECG signals for clinical detection of heart diseases. This work is focused on baseline-wander noise as it shares the same frequency spectrum as the ST segment of ECG signals. Hence, it is important to estimate the baseline-wander prior to its removal from ECG signals. This paper presents a method for classifying each segment of the ECG signal's baseline-wander as minimal, moderate or large. We use the C4.5 decision tree algorithm to model the classifier using the WEKA data-mining tool. We test the proposed method on ECG signals obtained from the MIT-BIH arrhythmia database (48 ECG recordings, each slightly longer than 30 min). We use 36 ECG recordings for training the classifier with the remaining 12 ECG recordings as the test data for classification. We partition each recording into 5 second, non-overlapping segments, which result in 361 segments for each record. The classification results show that the model classifier achieves an average sensitivity of 97.36 %, specificity of 99.50 %, and overall accuracy of 98.89 % in classifying the baseline-wander noise in ECG signals. The proposed method effectively addresses the question of identifying the minimal baselinewander segments. Moreover, the proposed framework may help in devising an algorithm for the selective filtering of moderate and large baseline-wander segments to achieve the best tradeoff between accuracy and computational cost.

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On-Demand Current Pulse Activation of RF Monopole Antenna Biosensor Arrays with Nitrocellulose Membranes

Shanmugadas

Lundsquit

Topsakal

et al. 2022

2022 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM)

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Feasibility research of non-invasive methods for interstitial fluid level measurement

Cited by 4 publications

References 14 publications

Deep Learning Framework with ECG Feature-Based Kernels for Heart Disease Classification

Deep Learning Framework with ECG Feature-Based Kernels for Heart Disease Classification

Detection and Classification of Baseline-Wander Noise in ECG Signals Using Discrete Wavelet Transform and Decision Tree Classifier

On-Demand Current Pulse Activation of RF Monopole Antenna Biosensor Arrays with Nitrocellulose Membranes

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