Advances in Intelligent Biosignals Processing and Analysis

Topic Information

Dear Colleagues,

Computer-aided diagnosis (CAD) systems are powerful tools for supporting the interpretation of biomedical signals and making clinical decisions. In fact, biomedical signals, including mono- and multi-dimensional signals, yield a great deal of information to be analyzed, correlated with data from other domains, comprehensively evaluated and, possibly, in a short time.

In recent years, precision medicine (PM) has emerged as an approach for disease treatment and prevention that takes into account each person’s variability in genes, environment, and lifestyle. To implement such an approach, radiomics and radiogenomics emerged as modern research fields which, combining large amounts of data extracted from biological signals and genomic analyses, allowed predicting pathological states of patients using intelligent models based on machine learning and deep learning.

Topics of interest could include but are not limited to:

• Biomedical image and signal processing;
• Intelligent decision support systems based on biomedical signals;
• Radiomics and radiogenomics;
• Bioengineering systems;
• Modeling of machine learning and deep learning models for biomedical signals analysis;
• Precision medicine frameworks based on intelligent systems.

Dr. Antonio Brunetti
Dr. Domenico Buongiorno
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400
Bioengineering bioengineering	3.8	4.0	2014	15.6 Days	CHF 2700
Journal of Imaging jimaging	2.7	5.9	2015	20.9 Days	CHF 1800
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600
Biology biology	3.6	5.7	2012	16.1 Days	CHF 2700

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Published Papers (17 papers)

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20 pages, 4231 KiB  

Open AccessArticle

Reducing Power Line Interference from sEMG Signals Based on Synchrosqueezed Wavelet Transform

by Jingcheng Chen, Yining Sun, Shaoming Sun and Zhiming Yao

Sensors 2023, 23(11), 5182; https://doi.org/10.3390/s23115182 - 29 May 2023

Cited by 4 | Viewed by 1901

Abstract

Power line interference (PLI) is a major source of noise in sEMG signals. As the bandwidth of PLI overlaps with the sEMG signals, it can easily affect the interpretation of the signal. The processing methods used in the literature are mostly notch filtering [...] Read more.

Power line interference (PLI) is a major source of noise in sEMG signals. As the bandwidth of PLI overlaps with the sEMG signals, it can easily affect the interpretation of the signal. The processing methods used in the literature are mostly notch filtering and spectral interpolation. However, it is difficult for the former to reconcile the contradiction between completely filtering and avoiding signal distortion, while the latter performs poorly in the case of a time-varying PLI. To solve these, a novel synchrosqueezed-wavelet-transform (SWT)-based PLI filter is proposed. The local SWT was developed to reduce the computation cost while maintaining the frequency resolution. A ridge location method based on an adaptive threshold is presented. In addition, two ridge extraction methods (REMs) are proposed to fit different application requirements. Parameters were optimized before further study. Notch filtering, spectral interpolation, and the proposed filter were evaluated on the simulated signals and real signals. The output signal-to-noise ratio (SNR) ranges of the proposed filter with two different REMs are 18.53–24.57 and 18.57–26.92. Both the quantitative index and the time–frequency spectrum diagram show that the performance of the proposed filter is significantly better than that of the other filters. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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23 pages, 4743 KiB  

Open AccessArticle

Volatile-Based Diagnosis for Pathogenic Wood-Rot Fungus Fulvifomes siamensis by Electronic Nose (E-Nose) and Solid-Phase Microextraction/Gas Chromatography/Mass Spectrometry

by Jhing Yein Tan, Ziteng Zhang, Hazirah Junin Izzah, Yok King Fong, Daryl Lee, Marek Mutwil and Yan Hong

Sensors 2023, 23(9), 4538; https://doi.org/10.3390/s23094538 - 6 May 2023

Cited by 2 | Viewed by 2391

Abstract

Wood rot fungus Fulvifomes siamensis infects multiple urban tree species commonly planted in Singapore. A commercial e-nose (Cyranose 320) was used to differentiate some plant and fungi volatiles. The e-nose distinctly clustered the volatiles at 0.25 ppm, and this sensitivity was further increased [...] Read more.

Wood rot fungus Fulvifomes siamensis infects multiple urban tree species commonly planted in Singapore. A commercial e-nose (Cyranose 320) was used to differentiate some plant and fungi volatiles. The e-nose distinctly clustered the volatiles at 0.25 ppm, and this sensitivity was further increased to 0.05 ppm with the use of nitrogen gas to purge the system and set up the baseline. Nitrogen gas baseline resulted in a higher magnitude of sensor responses and a higher number of responsive sensors. The specificity of the e-nose for F. siamensis was demonstrated by distinctive clustering of its pure culture, fruiting bodies collected from different tree species, and in diseased tissues infected by F. siamensis with a 15-min incubation time. This good specificity was supported by the unique volatile profiles revealed by SPME GC-MS analysis, which also identified the signature volatile for F. siamensis—1,2,4,5-tetrachloro-3,6-dimethoxybenzene. In field conditions, the e-nose successfully identified F. siamensis fruiting bodies on different tree species. The findings of concentration-based clustering and host-tree-specific volatile profiles for fruiting bodies provide further insights into the complexity of volatile-based diagnosis that should be taken into consideration for future studies. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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19 pages, 4245 KiB  

Open AccessArticle

A New Algorithm for Estimating a Noiseless, Evenly Sampled, Heart Rate Modulating Signal

by Enrico M. Staderini, Harish Kambampati, Amith K. Ramakrishnaiah, Stefano Mugnaini, Andrea Magrini and Sandro Gentili

Bioengineering 2023, 10(5), 552; https://doi.org/10.3390/bioengineering10050552 - 4 May 2023

Viewed by 1448

Abstract

Heart rate variability (HRV) is commonly intended as the variation in the heart rate (HR), and it is evaluated in the time and frequency domains with various well-known methods. In the present paper, the heart rate is considered as a time domain signal, [...] Read more.

Heart rate variability (HRV) is commonly intended as the variation in the heart rate (HR), and it is evaluated in the time and frequency domains with various well-known methods. In the present paper, the heart rate is considered as a time domain signal, at first as an abstract model in which the HR is the instantaneous frequency of an otherwise periodic signal, such as with an electrocardiogram (ECG). In this model, the ECG is assumed to be a frequency modulated signal, or carrier signal, where HRV or $HRV\left(t\right)$ is the time-domain signal which is frequency modulating the carrier ECG signal around its average frequency. Hence, an algorithm able to frequency demodulate the ECG signal to extract the signal $HRV\left(t\right)$ is described, with possibly enough time resolution to analyse fast time-domain variations in the instantaneous HR. After exhaustive testing of the method on simulated frequency modulated sinusoidal signals, the new procedure is eventually applied on actual ECG tracings for preliminary nonclinical testing. The purpose of the work is to use this algorithm as a tool and a more reliable method for the assessment of heart rate before any further clinical or physiological analysis. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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13 pages, 2840 KiB  

Open AccessArticle

Pericoronary Adipose Tissue Radiomics from Coronary Computed Tomography Angiography Identifies Vulnerable Plaques

by Justin N. Kim, Lia Gomez-Perez, Vladislav N. Zimin, Mohamed H. E. Makhlouf, Sadeer Al-Kindi, David L. Wilson and Juhwan Lee

Bioengineering 2023, 10(3), 360; https://doi.org/10.3390/bioengineering10030360 - 14 Mar 2023

Cited by 4 | Viewed by 2268

Abstract

Pericoronary adipose tissue (PCAT) features on Computed Tomography (CT) have been shown to reflect local inflammation and increased cardiovascular risk. Our goal was to determine whether PCAT radiomics extracted from coronary CT angiography (CCTA) images are associated with intravascular optical coherence tomography (IVOCT)-identified [...] Read more.

Pericoronary adipose tissue (PCAT) features on Computed Tomography (CT) have been shown to reflect local inflammation and increased cardiovascular risk. Our goal was to determine whether PCAT radiomics extracted from coronary CT angiography (CCTA) images are associated with intravascular optical coherence tomography (IVOCT)-identified vulnerable-plaque characteristics (e.g., microchannels (MC) and thin-cap fibroatheroma (TCFA)). The CCTA and IVOCT images of 30 lesions from 25 patients were registered. The vessels with vulnerable plaques were identified from the registered IVOCT images. The PCAT-radiomics features were extracted from the CCTA images for the lesion region of interest (PCAT-LOI) and the entire vessel (PCAT-Vessel). We extracted 1356 radiomic features, including intensity (first-order), shape, and texture features. The features were reduced using standard approaches (e.g., high feature correlation). Using stratified three-fold cross-validation with 1000 repeats, we determined the ability of PCAT-radiomics features from CCTA to predict IVOCT vulnerable-plaque characteristics. In the identification of TCFA lesions, the PCAT-LOI and PCAT-Vessel radiomics models performed comparably (Area Under the Curve (AUC) ± standard deviation 0.78 ± 0.13, 0.77 ± 0.14). For the identification of MC lesions, the PCAT-Vessel radiomics model (0.89 ± 0.09) was moderately better associated than the PCAT-LOI model (0.83 ± 0.12). In addition, both the PCAT-LOI and the PCAT-Vessel radiomics model identified coronary vessels thought to be highly vulnerable to a similar standard (i.e., both TCFA and MC; 0.88 ± 0.10, 0.91 ± 0.09). The most favorable radiomic features tended to be those describing the texture and size of the PCAT. The application of PCAT radiomics can identify coronary vessels with TCFA or MC, consistent with IVOCT. Furthermore, the use of CCTA radiomics may improve risk stratification by noninvasively detecting vulnerable-plaque characteristics that are only visible with IVOCT. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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13 pages, 1740 KiB  

Open AccessArticle

Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process

by Amjed S. Al Fahoum, Ansam Omar Abu Al-Haija and Hussam A. Alshraideh

Bioengineering 2023, 10(2), 249; https://doi.org/10.3390/bioengineering10020249 - 13 Feb 2023

Cited by 33 | Viewed by 3331

Abstract

A low-cost, fast, dependable, repeatable, non-invasive, portable, and simple-to-use vascular screening tool for coronary artery diseases (CADs) is preferred. Photoplethysmography (PPG), a low-cost optical pulse wave technology, is one method with this potential. PPG signals come from changes in the amount of blood [...] Read more.

A low-cost, fast, dependable, repeatable, non-invasive, portable, and simple-to-use vascular screening tool for coronary artery diseases (CADs) is preferred. Photoplethysmography (PPG), a low-cost optical pulse wave technology, is one method with this potential. PPG signals come from changes in the amount of blood in the microvascular bed of tissue. Therefore, these signals can be used to figure out anomalies within the cardiovascular system. This work shows how to use PPG signals and feature selection-based classifiers to identify cardiorespiratory disorders based on the extraction of time-domain features. Data were collected from 360 healthy and cardiovascular disease patients. For analysis and identification, five types of cardiovascular disorders were considered. The categories of cardiovascular diseases were identified using a two-stage classification process. The first stage was utilized to differentiate between healthy and unhealthy subjects. Subjects who were found to be abnormal were then entered into the second stage classifier, which was used to determine the type of the disease. Seven different classifiers were employed to classify the dataset. Based on the subset of features found by the classifier, the Naïve Bayes classifier obtained the best test accuracy, with 94.44% for the first stage and 89.37% for the second stage. The results of this study show how vital the PPG signal is. Many time-domain parts of the PPG signal can be easily extracted and analyzed to find out if there are problems with the heart. The results were accurate and precise enough that they did not need to be looked at or analyzed further. The PPG classifier built on a simple microcontroller will work better than more expensive ones and will not make the patient nervous. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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11 pages, 4304 KiB  

Open AccessArticle

Reduction in the Motion Artifacts in Noncontact ECG Measurements Using a Novel Designed Electrode Structure

by Jianwen Ding, Yue Tang, Ronghui Chang, Yu Li, Limin Zhang and Feng Yan

Sensors 2023, 23(2), 956; https://doi.org/10.3390/s23020956 - 14 Jan 2023

Cited by 12 | Viewed by 3082

Abstract

A noncontact ECG is applicable to wearable bioelectricity acquisition because it can provide more comfort to the patient for long-term monitoring. However, the motion artifact is a significant source of noise in an ECG recording. Adaptive noise reduction is highly effective in suppressing [...] Read more.

A noncontact ECG is applicable to wearable bioelectricity acquisition because it can provide more comfort to the patient for long-term monitoring. However, the motion artifact is a significant source of noise in an ECG recording. Adaptive noise reduction is highly effective in suppressing motion artifact, usually through the use of external sensors, thus increasing the design complexity and cost. In this paper, a novel ECG electrode structure is designed to collect ECG data and reference data simultaneously. Combined with the adaptive filter, it effectively suppresses the motion artifact in the ECG acquisition. This method adds one more signal acquisition channel based on the single-channel ECG acquisition system to acquire the reference signal without introducing other sensors. Firstly, the design of the novel ECG electrode structure is introduced based on the principle of noise reduction. Secondly, a multichannel signal acquisition circuit system and ECG electrodes are implemented. Finally, experiments under normal walking conditions are carried out, and the performance is verified by the experiment results, which shows that the proposed design effectively suppresses motion artifacts and maintains the stability of the signal quality during the noncontact ECG acquisition. The signal-to-noise ratio of the ECG signal after noise reduction is 14 dB higher than that of the original ECG signal with the motion artifact. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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24 pages, 39938 KiB  

Open AccessArticle

Robust Identification System for Spanish Sign Language Based on Three-Dimensional Frame Information

by Jesús Galván-Ruiz, Carlos M. Travieso-González, Alejandro Pinan-Roescher and Jesús B. Alonso-Hernández

Sensors 2023, 23(1), 481; https://doi.org/10.3390/s23010481 - 2 Jan 2023

Cited by 6 | Viewed by 2198

Abstract

Nowadays, according to the World Health Organization (WHO), of the world’s population suffers from a hearing disorder that makes oral communication with other people challenging. At the same time, in an era of technological evolution and digitization, designing tools that could help these [...] Read more.

Nowadays, according to the World Health Organization (WHO), of the world’s population suffers from a hearing disorder that makes oral communication with other people challenging. At the same time, in an era of technological evolution and digitization, designing tools that could help these people to communicate daily is the base of much scientific research such as that discussed herein. This article describes one of the techniques designed to transcribe Spanish Sign Language (SSL). A Leap Motion volumetric sensor has been used in this research due to its capacity to recognize hand movements in 3 dimensions. In order to carry out this research project, an impaired hearing subject has collaborated in the recording of 176 dynamic words. Finally, for the development of the research, Dynamic Time Warping (DTW) has been used to compare the samples and predict the input with an accuracy of 95.17%. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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17 pages, 4934 KiB  

Open AccessArticle

Evaluation of Integrated CNN, Transfer Learning, and BN with Thermography for Breast Cancer Detection

by N. Aidossov, Vasilios Zarikas, Aigerim Mashekova, Yong Zhao, Eddie Yin Kwee Ng, Anna Midlenko and Olzhas Mukhmetov

Appl. Sci. 2023, 13(1), 600; https://doi.org/10.3390/app13010600 - 1 Jan 2023

Cited by 16 | Viewed by 2665

Abstract

Breast cancer comprises a serious public health concern. The three primary techniques for detecting breast cancer are ultrasound, mammography, and magnetic resonance imaging (MRI). However, the existing methods of diagnosis are not practical for regular mass screening at short time intervals. Thermography could [...] Read more.

Breast cancer comprises a serious public health concern. The three primary techniques for detecting breast cancer are ultrasound, mammography, and magnetic resonance imaging (MRI). However, the existing methods of diagnosis are not practical for regular mass screening at short time intervals. Thermography could be a solution to this issue because it is a non-invasive and low-cost method that can be used routinely as a self-screening method. The research significance of this work lies in the implementation and integration of multiple different AI techniques for achieving diagnosis based on breast thermograms from several data sources. The data sources contain 306 images. The concept of transfer learning with several pre-trained models is implemented. Bayesian Networks (BNs) are also used to have interpretability of the diagnosis. A novel feature extraction from images (related to temperature) has been implemented and feeds the BNs. Finally, all methods and the classification results of pre-trained models are compared. It is found that the best result amongst the transfer learning concept is achieved with MobileNet, which delivered 93.8% accuracy. Furthermore, the BN achieves an accuracy of 90.20%, and finally, the expert model that combines CNNs and BNs gives an accuracy of 90.85%, even with a limited amount of data available. The integration of CNN and BN aims to overcome the hardship of interpretability. These approaches demonstrate high performance with added interpretability compared to previous works. In conclusion, the deep neural network provides promising results in breast cancer detection. It could be an ideal candidate for Breast Self-Exam (BSE), the goal recommended by WHO for mass screening. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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15 pages, 1578 KiB  

Open AccessArticle

sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning

by Kaikui Zheng, Shuai Liu, Jinxing Yang, Metwalli Al-Selwi and Jun Li

Sensors 2022, 22(24), 9949; https://doi.org/10.3390/s22249949 - 16 Dec 2022

Cited by 4 | Viewed by 1915

Abstract

Conventional classification of hand motions and continuous joint angle estimation based on sEMG have been widely studied in recent years. The classification task focuses on discrete motion recognition and shows poor real-time performance, while continuous joint angle estimation evaluates the real-time joint angles [...] Read more.

Conventional classification of hand motions and continuous joint angle estimation based on sEMG have been widely studied in recent years. The classification task focuses on discrete motion recognition and shows poor real-time performance, while continuous joint angle estimation evaluates the real-time joint angles by the continuity of the limb. Few researchers have investigated continuous hand action prediction based on hand motion continuity. In our study, we propose the key state transition as a condition for continuous hand action prediction and simulate the prediction process using a sliding window with long-term memory. Firstly, the key state modeled by GMM-HMMs is set as the condition. Then, the sliding window is used to dynamically look for the key state transition. The prediction results are given while finding the key state transition. To extend continuous multigesture action prediction, we use model pruning to improve reusability. Eight subjects participated in the experiment, and the results show that the average accuracy of continuous two-hand actions is 97% with a 70 ms time delay, which is better than LSTM (94.15%, 308 ms) and GRU (93.83%, 300 ms). In supplementary experiments with continuous four-hand actions, over 85% prediction accuracy is achieved with an average time delay of 90 ms. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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29 pages, 12888 KiB  

Open AccessArticle

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

by Fatemeh Esmaeili, Erica Cassie, Hong Phan T. Nguyen, Natalie O. V. Plank, Charles P. Unsworth and Alan Wang

Bioengineering 2022, 9(10), 529; https://doi.org/10.3390/bioengineering9100529 - 6 Oct 2022

Cited by 9 | Viewed by 2297

Abstract

Nanomaterial-based aptasensors are useful devices capable of detecting small biological species. Determining suitable signal processing methods can improve the identification and quantification of target analytes detected by the biosensor and consequently improve the biosensor’s performance. In this work, we propose a data augmentation [...] Read more.

Nanomaterial-based aptasensors are useful devices capable of detecting small biological species. Determining suitable signal processing methods can improve the identification and quantification of target analytes detected by the biosensor and consequently improve the biosensor’s performance. In this work, we propose a data augmentation method to overcome the insufficient amount of available original data and long short-term memory (LSTM) to automatically predict the analyte concentration from part of a signal registered by three electrochemical aptasensors, with differences in bioreceptors, analytes, and the signals’ lengths for specific concentrations. To find the optimal network, we altered the following variables: the LSTM layer structure (unidirectional LSTM (LSTM) and bidirectional LSTM (BLSTM)), optimizers (Adam, RMSPROP, SGDM), number of hidden units, and amount of augmented data. Then, the evaluation of the networks revealed that the highest original data accuracy increased from 50% to 92% by exploiting the data augmentation method. In addition, the SGDM optimizer showed a lower performance prediction than that of the ADAM and RMSPROP algorithms, and the number of hidden units was ineffective in improving the networks’ performances. Moreover, the BLSTM nets showed more accurate predictions than those of the ULSTM nets on lengthier signals. These results demonstrate that this method can automatically detect the analyte concentration from the sensor signals. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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12 pages, 3826 KiB  

Open AccessArticle

Effectiveness of Remote PPG Construction Methods: A Preliminary Analysis

by Fridolin Haugg, Mohamed Elgendi and Carlo Menon

Bioengineering 2022, 9(10), 485; https://doi.org/10.3390/bioengineering9100485 - 20 Sep 2022

Cited by 14 | Viewed by 5493

Abstract

The contactless recording of a photoplethysmography (PPG) signal with a Red-Green-Blue (RGB) camera is known as remote photoplethysmography (rPPG). Studies have reported on the positive impact of using this technique, particularly in heart rate estimation, which has led to increased research on this [...] Read more.

The contactless recording of a photoplethysmography (PPG) signal with a Red-Green-Blue (RGB) camera is known as remote photoplethysmography (rPPG). Studies have reported on the positive impact of using this technique, particularly in heart rate estimation, which has led to increased research on this topic among scientists. Therefore, converting from RGB signals to constructing an rPPG signal is an important step. Eight rPPG methods (plant-orthogonal-to-skin (POS), local group invariance (LGI), the chrominance-based method (CHROM), orthogonal matrix image transformation (OMIT), GREEN, independent component analysis (ICA), principal component analysis (PCA), and blood volume pulse (PBV) methods) were assessed using dynamic time warping, power spectrum analysis, and Pearson’s correlation coefficient, with different activities (at rest, during exercising in the gym, during talking, and while head rotating) and four regions of interest (ROI): the forehead, the left cheek, the right cheek, and a combination of all three ROIs. The best performing rPPG methods in all categories were the POS, LGI, and OMI methods; each performed well in all activities. Recommendations for future work are provided. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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12 pages, 2203 KiB  

Open AccessArticle

Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram

by Qunfeng Tang, Zhencheng Chen, Rabab Ward, Carlo Menon and Mohamed Elgendi

Bioengineering 2022, 9(8), 402; https://doi.org/10.3390/bioengineering9080402 - 18 Aug 2022

Cited by 7 | Viewed by 3239

Abstract

The continuous prediction of arterial blood pressure (ABP) waveforms via non-invasive methods is of great significance for the prevention and treatment of cardiovascular disease. Photoplethysmography (PPG) can be used to reconstruct ABP signals due to having the same excitation source and high signal [...] Read more.

The continuous prediction of arterial blood pressure (ABP) waveforms via non-invasive methods is of great significance for the prevention and treatment of cardiovascular disease. Photoplethysmography (PPG) can be used to reconstruct ABP signals due to having the same excitation source and high signal similarity. The existing methods of reconstructing ABP signals from PPG only focus on the similarities between systolic, diastolic, and mean arterial pressures without evaluating their global similarity. This paper proposes a deep learning model with a W-Net architecture to reconstruct ABP signals from PPG. The W-Net consists of two concatenated U-Net architectures, the first acting as an encoder and the second as a decoder to reconstruct ABP from PPG. Five hundred records of different lengths were used for training and testing. The experimental results yielded high values for the similarity measures between the reconstructed ABP signals and their reference ABP signals: the Pearson correlation, root mean square error, and normalized dynamic time warping distance were 0.995, 2.236 mmHg, and 0.612 mmHg on average, respectively. The mean absolute errors of the SBP and DBP were 2.602 mmHg and 1.450 mmHg on average, respectively. Therefore, the model can reconstruct ABP signals that are highly similar to the reference ABP signals. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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17 pages, 5253 KiB  

Open AccessArticle

CPAP Adherence Assessment via Gaussian Mixture Modeling of Telemonitored Apnea Therapy

by Jose F. Rodrigues, Jr., Sebastien Bailly, Jean-Louis Pepin, Lorraine Goeuriot, Gabriel Spadon and Sihem Amer-Yahia

Appl. Sci. 2022, 12(15), 7618; https://doi.org/10.3390/app12157618 - 28 Jul 2022

Viewed by 1638

Abstract

Sleep disorders pose serious cardiovascular threats if not treated effectively. However, adherence to Continuous Positive Airway Pressure (CPAP), the most recommended therapy, is known to be challenging to monitor. Telemonitored CPAP equipment has improved the follow-up of CPAP adherence (hours of use per [...] Read more.

Sleep disorders pose serious cardiovascular threats if not treated effectively. However, adherence to Continuous Positive Airway Pressure (CPAP), the most recommended therapy, is known to be challenging to monitor. Telemonitored CPAP equipment has improved the follow-up of CPAP adherence (hours of use per night) by producing far larger amounts of data collected daily. The analysis of such data have relied on averaging the entire therapeutic history and interpreting it without a proper reference concerning the level of adherence. By contrast, we contribute with an unsupervised machine-learning methodology that (i) translates the adherence data to a scale of discrete numbers that hold correspondence to the most usual 30-day-long patterns as observed in a real-word database; (ii) avoids the loss of information aggregation problem by creating summaries of the time series that capture the dynamic nature of the everyday-use CPAP. Our experiments have detected eight particular adherence behaviors validated with information-oriented statistical criteria; we successfully applied them to the time series of a French hospital to produce summaries that reflect the adherence of any 30 days of interest. Our method can aid physicians in more precisely evaluating the therapy adherence, as well as fostering systems to alert of problems in the treatment automatically. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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20 pages, 4733 KiB  

Open AccessArticle

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net

by Nicola Altini, Antonio Brunetti, Valeria Pia Napoletano, Francesca Girardi, Emanuela Allegretti, Sardar Mehboob Hussain, Gioacchino Brunetti, Vito Triggiani, Vitoantonio Bevilacqua and Domenico Buongiorno

Bioengineering 2022, 9(8), 343; https://doi.org/10.3390/bioengineering9080343 - 26 Jul 2022

Cited by 5 | Viewed by 3262

Abstract

In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it [...] Read more.

In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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26 pages, 15845 KiB  

Open AccessArticle

A Novel Prototype Biosensor Array Electrode System for Detecting the Bacterial Pathogen Salmonella typhimurium

by Palaniappan Ramasamy, Gajalakshmi Dakshinamoorthy, Shanmugam Jayashree, Dhamodharan Prabhu, Sundararaj Rajamanikandan, Palaniyandi Velusamy, Govindan Dayanithi and Robert E. B. Hanna

Biosensors 2022, 12(6), 389; https://doi.org/10.3390/bios12060389 - 4 Jun 2022

Cited by 2 | Viewed by 3385

Abstract

Salmonellosis caused by Salmonella sp. has long been reported all over the world. Despite the availability of various diagnostic methods, easy and effective detection systems are still required. This report describes a dialysis membrane electrode interface disc with immobilized specific antibodies to capture [...] Read more.

Salmonellosis caused by Salmonella sp. has long been reported all over the world. Despite the availability of various diagnostic methods, easy and effective detection systems are still required. This report describes a dialysis membrane electrode interface disc with immobilized specific antibodies to capture antigenic Salmonella cells. The interaction of a specific Salmonella antigen with a mouse anti-Salmonella monoclonal antibody complexed to rabbit anti-mouse secondary antibody conjugated with HRP and the substrate o-aminophenol resulted in a response signal output current measured using two electrode systems (cadmium reference electrode and glassy carbon working electrode) and an agilent HP34401A 6.5 digital multimeter without a potentiostat or applied potential input. A maximum response signal output current was recorded for various concentrations of Salmonella viz., 3, 30, 300, 3000, 30,000 and 300,000 cells. The biosensor has a detection limit of three cells, which is very sensitive when compared with other detection sensors. Little non-specific response was observed using Streptococcus, Vibrio, and Pseudomonas sp. The maximum response signal output current for a dialysis membrane electrode interface disc was greater than that for gelatin, collagen, and agarose. The device and technique have a range of biological applications. This novel detection system has great potential for future development and application in surveillance for microbial pathogens. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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12 pages, 3205 KiB  

Open AccessArticle

Real-Time Gait Phase Detection Using Wearable Sensors for Transtibial Prosthesis Based on a kNN Algorithm

by Atcharawan Rattanasak, Peerapong Uthansakul, Monthippa Uthansakul, Talit Jumphoo, Khomdet Phapatanaburi, Bura Sindhupakorn and Supakit Rooppakhun

Sensors 2022, 22(11), 4242; https://doi.org/10.3390/s22114242 - 2 Jun 2022

Cited by 15 | Viewed by 3710

Abstract

Those with disabilities who have lost their legs must use a prosthesis to walk. However, traditional prostheses have the disadvantage of being unable to move and support the human gait because there are no mechanisms or algorithms to control them. This makes it [...] Read more.

Those with disabilities who have lost their legs must use a prosthesis to walk. However, traditional prostheses have the disadvantage of being unable to move and support the human gait because there are no mechanisms or algorithms to control them. This makes it difficult for the wearer to walk. To overcome this problem, we developed an insole device with a wearable sensor for real-time gait phase detection based on the kNN (k-nearest neighbor) algorithm for prosthetic control. The kNN algorithm is used with the raw data obtained from the pressure sensors in the insole to predict seven walking phases, i.e., stand, heel strike, foot flat, midstance, heel off, toe-off, and swing. As a result, the predictive decision in each gait cycle to control the ankle movement of the transtibial prosthesis improves with each walk. The results in this study can provide 81.43% accuracy for gait phase detection, and can control the transtibial prosthetic effectively at the maximum walking speed of 6 km/h. Moreover, this insole device is small, lightweight and unaffected by the physical factors of the wearer. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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22 pages, 15278 KiB  

Open AccessArticle

Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method

by Jun Fu, Teng Lv and Bao Li

Sensors 2022, 22(10), 3652; https://doi.org/10.3390/s22103652 - 11 May 2022

Cited by 18 | Viewed by 3073

Abstract

Navigating safely in complex marine environments is a challenge for submarines because proper path planning underwater is difficult. This paper decomposes the submarine path planning problem into global path planning and local dynamic obstacle avoidance. Firstly, an artificial potential field ant colony algorithm [...] Read more.

Navigating safely in complex marine environments is a challenge for submarines because proper path planning underwater is difficult. This paper decomposes the submarine path planning problem into global path planning and local dynamic obstacle avoidance. Firstly, an artificial potential field ant colony algorithm (APF-ACO) based on an improved artificial potential field algorithm and improved ant colony algorithm is proposed to solve the problem of submarine underwater global path planning. Compared with the Optimized ACO algorithm proposed based on a similar background, the APF-ACO algorithm has a faster convergence speed and better path planning results. Using an inflection point optimization algorithm greatly reduces the number and length of inflection points in the path. Using the Clothoid curve fitting algorithm to optimize the path results, a smoother and more stable path result is obtained. In addition, this paper uses a three-dimensional dynamic obstacle avoidance algorithm based on the velocity obstacle method. The experimental results show that the algorithm can help submarines to identify threatening dynamic obstacles and avoid collisions effectively. Finally, we experimented with the algorithm in the submarine underwater semi-physical simulation system, and the experimental results verified the effectiveness of the algorithm. Full article

(This article belongs to the Topic Advances in Intelligent Biosignals Processing and Analysis)

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