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18 pages, 3463 KiB

Open AccessArticle

A Novel Equivalent Time Sampling-Based Method for Pulse Transit Time Estimation with Applications into the Cardiovascular Disease Diagnosis

by Giorgia Fiori, Fabio Fuiano, Silvia Conforto, Salvatore Andrea Sciuto and Andrea Scorza

Sensors 2023, 23(11), 5005; https://doi.org/10.3390/s23115005 - 23 May 2023

Viewed by 1292

Abstract

The increasing incidence of cardiovascular diseases (CVDs) is reflected in additional costs for healthcare systems all over the world. To date, pulse transit time (PTT) is considered a key index of cardiovascular health status and for diagnosis of CVDs. In this context, the [...] Read more.

The increasing incidence of cardiovascular diseases (CVDs) is reflected in additional costs for healthcare systems all over the world. To date, pulse transit time (PTT) is considered a key index of cardiovascular health status and for diagnosis of CVDs. In this context, the present study focuses on a novel image analysis-based method for PTT estimation through the application of equivalent time sampling. The method, which post-processes color Doppler videos, was tested on two different setups: a Doppler flow phantom set in pulsatile mode and an in-house arterial simulator. In the former, the Doppler shift was due to the echogenic properties of the blood mimicking fluid only, since the phantom vessels are non-compliant. In the latter, the Doppler signal relied on the wall movement of compliant vessels in which a fluid with low echogenic properties was pumped. Therefore, the two setups allowed the measurement of the flow average velocity (FAV) and the pulse wave velocity (PWV), respectively. Data were collected through an ultrasound diagnostic system equipped with a phased array probe. Experimental outcomes confirm that the proposed method can represent an alternative tool for the local measurement of both FAV in non-compliant vessels and PWV in compliant vessels filled with low echogenic fluids. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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14 pages, 4396 KiB

Open AccessArticle

Investigating the Structural and Functional Changes in the Optic Nerve in Patients with Early Glaucoma Using the Optical Coherence Tomography (OCT) and RETeval System

by Marsida Bekollari, Maria Dettoraki, Valentina Stavrou, Aikaterini Skouroliakou and Panagiotis Liaparinos

Sensors 2023, 23(9), 4504; https://doi.org/10.3390/s23094504 - 5 May 2023

Cited by 2 | Viewed by 1987

Abstract

The present manuscript introduces an investigation of the structural and functional changes in the optic nerve in patients undergoing glaucoma treatment by comparing optical coherence tomography (OCT) measurements and RETeval system parameters. For such a purpose, 140 eyes were examined at the Ophthalmology [...] Read more.

The present manuscript introduces an investigation of the structural and functional changes in the optic nerve in patients undergoing glaucoma treatment by comparing optical coherence tomography (OCT) measurements and RETeval system parameters. For such a purpose, 140 eyes were examined at the Ophthalmology Clinic of the “Elpis” General Hospital of Athens between October 2022 and April 2023. A total of 59 out of 140 eyes were from patients with early glaucoma under treatment (case group), 63 were healthy eyes (control group) and 18 were excluded. The experimental measurements were statistically analyzed using the SPSS software package. The main outcomes are summarized below: (i) there was no statistical difference between the right and left eye for both groups, (ii) statistical differences were found between age interval subgroups (30–54 and 55–80 years old) for the control group, mainly for the time response part of the RETeval parameters. Such difference was not indicated by the OCT system, and (iii) a statistical difference occurred between the control and case group for both OCT (through the retinal nerve fiber layer–RNFL thickness) and the RETeval parameters (through the photopic negative response–PhNR). RNFL was found to be correlated to b-wave (ms) and W-ratio parameters. In conclusion, the PhNR obtained by the RETeval system could be a valuable supplementary tool for the objective examination of patients with early glaucoma. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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19 pages, 12844 KiB

Open AccessArticle

A Remote Monitoring System for Rodent Infestation Based on LoRaWAN

by Shin-Chi Lai, Szu-Ting Wang, Kuan-Lin Liu and Chang-Yu Wu

Sensors 2023, 23(9), 4185; https://doi.org/10.3390/s23094185 - 22 Apr 2023

Viewed by 2505

Abstract

Rodent infestations are a common problem that can result in several issues, including diseases, damage to property, and crop loss. Conventional methods of controlling rodent infestations often involve using mousetraps and applying rodenticides manually, leading to high manpower expenses and environmental pollution. To [...] Read more.

Rodent infestations are a common problem that can result in several issues, including diseases, damage to property, and crop loss. Conventional methods of controlling rodent infestations often involve using mousetraps and applying rodenticides manually, leading to high manpower expenses and environmental pollution. To address this issue, we introduce a system for remotely monitoring rodent infestations using Internet of Things (IoT) nodes equipped with Long Range (LoRa) modules. The sensing nodes wirelessly transmit data related to rodent activity to a cloud server, enabling the server to provide real-time information. Additionally, this approach involves using images to auxiliary detect rodent activity in various buildings. By capturing images of rodents and analyzing their behavior, we can gain insight into their movement patterns and activity levels. By visualizing the recorded information from multiple nodes, rodent control personnel can analyze and address infestations more efficiently. Through the digital and quantitative sensing technology proposed at this stage, it can serve as a new objective indicator before and after the implementation of medication or other prevention and control methods. The hardware cost for the proposed system is approximately USD 43 for one sensor module and USD 17 for one data collection gateway (DCG). We also evaluated the power consumption of the sensor module and found that the 3.7 V 18,650 Li-ion batteries in series can provide a battery life of two weeks. The proposed system can be combined with rodent control strategies and applied in real-world scenarios such as restaurants and factories to evaluate its performance. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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11 pages, 645 KiB

Open AccessArticle

Potential Use of Wearable Inertial Sensors to Assess and Train Deep Cervical Flexors: A Feasibility Study with Real Time Synchronization of Kinematic and Pressure Data during the Craniocervical Flexion Test

by Elena Bocos-Corredor, Tomás Pérez-Fernández, Raquel Perez-Dominguez, Sonia Liébana, Susan Armijo-Olivo, Rafael Raya and Aitor Martin-Pintado-Zugasti

Sensors 2023, 23(8), 3911; https://doi.org/10.3390/s23083911 - 12 Apr 2023

Viewed by 1272

Abstract

The aim of the study was to develop a novel real-time, computer-based synchronization system to continuously record pressure and craniocervical flexion ROM (range of motion) during the CCFT (craniocervical flexion test) in order to assess its feasibility for measuring and discriminating the values [...] Read more.

The aim of the study was to develop a novel real-time, computer-based synchronization system to continuously record pressure and craniocervical flexion ROM (range of motion) during the CCFT (craniocervical flexion test) in order to assess its feasibility for measuring and discriminating the values of ROM between different pressure levels. This was a descriptive, observational, cross-sectional, feasibility study. Participants performed a full-range craniocervical flexion and the CCFT. During the CCFT, a pressure sensor and a wireless inertial sensor simultaneously registered data of pressure and ROM. A web application was developed using HTML and NodeJS technologies. Forty-five participants successfully finished the study protocol (20 males, 25 females; 32 (11.48) years). ANOVAs showed large effect significant interactions between pressure levels and the percentage of full craniocervical flexion ROM when considering the 6 pressure reference levels of the CCFT (p < 0.001; η² = 0.697), 11 pressure levels separated by 1 mmHg (p < 0.001; η² = 0.683), and 21 pressure levels separated by 0.5 mmHg (p < 0.001; η² = 0.671). The novel time synchronizing system seems a feasible option to provide real-time monitoring of both pressure and ROM, which could serve as reference targets to further investigate the potential use of inertial sensor technology to assess or train deep cervical flexors. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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17 pages, 2656 KiB

Open AccessArticle

A Frequency Estimation Scheme Based on Gaussian Average Filtering Decomposition and Hilbert Transform: With Estimation of Respiratory Rate as an Example

by Yue-Der Lin, Yong-Kok Tan, Tienhsiung Ku and Baofeng Tian

Sensors 2023, 23(8), 3785; https://doi.org/10.3390/s23083785 - 7 Apr 2023

Viewed by 1655

Abstract

Frequency estimation plays a critical role in vital sign monitoring. Methods based on Fourier transform and eigen-analysis are commonly adopted techniques for frequency estimation. Because of the nonstationary and time-varying characteristics of physiological processes, time-frequency analysis (TFA) is a feasible way to perform [...] Read more.

Frequency estimation plays a critical role in vital sign monitoring. Methods based on Fourier transform and eigen-analysis are commonly adopted techniques for frequency estimation. Because of the nonstationary and time-varying characteristics of physiological processes, time-frequency analysis (TFA) is a feasible way to perform biomedical signal analysis. Among miscellaneous approaches, Hilbert–Huang transform (HHT) has been demonstrated to be a potential tool in biomedical applications. However, the problems of mode mixing, unnecessary redundant decomposition and boundary effect are the common deficits that occur during the procedure of empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD). The Gaussian average filtering decomposition (GAFD) technique has been shown to be appropriate in several biomedical scenarios and can be an alternative to EMD and EEMD. This research proposes the combination of GAFD and Hilbert transform that is termed the Hilbert–Gauss transform (HGT) to overcome the conventional drawbacks of HHT in TFA and frequency estimation. This new method is verified to be effective for the estimation of respiratory rate (RR) in finger photoplethysmography (PPG), wrist PPG and seismocardiogram (SCG). Compared with the ground truth values, the estimated RRs are evaluated to be of excellent reliability by intraclass correlation coefficient (ICC) and to be of high agreement by Bland–Altman analysis. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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14 pages, 4889 KiB

Open AccessArticle

Convolutional Neural Network for Individual Identification Using Phase Space Reconstruction of Electrocardiogram

by Hsiao-Lung Chan, Hung-Wei Chang, Wen-Yen Hsu, Po-Jung Huang and Shih-Chin Fang

Sensors 2023, 23(6), 3164; https://doi.org/10.3390/s23063164 - 16 Mar 2023

Cited by 2 | Viewed by 1727

Abstract

Electrocardiogram (ECG) biometric provides an authentication to identify an individual on the basis of specific cardiac potential measured from a living body. Convolutional neural networks (CNN) outperform traditional ECG biometrics because convolutions can produce discernible features from ECG through machine learning. Phase space [...] Read more.

Electrocardiogram (ECG) biometric provides an authentication to identify an individual on the basis of specific cardiac potential measured from a living body. Convolutional neural networks (CNN) outperform traditional ECG biometrics because convolutions can produce discernible features from ECG through machine learning. Phase space reconstruction (PSR), using a time delay technique, is one of the transformations from ECG to a feature map, without the need of exact R-peak alignment. However, the effects of time delay and grid partition on identification performance have not been investigated. In this study, we developed a PSR-based CNN for ECG biometric authentication and examined the aforementioned effects. Based on a population of 115 subjects selected from the PTB Diagnostic ECG Database, a higher identification accuracy was achieved when the time delay was set from 20 to 28 ms, since it produced a well phase-space expansion of P, QRS, and T waves. A higher accuracy was also achieved when a high-density grid partition was used, since it produced a fine-detail phase-space trajectory. The use of a scaled-down network for PSR over a low-density grid with 32 × 32 partitions achieved a comparable accuracy with using a large-scale network for PSR over 256 × 256 partitions, but it had the benefit of reductions in network size and training time by 10 and 5 folds, respectively. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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13 pages, 5887 KiB

Open AccessArticle

Motion Smoothness-Based Assessment of Surgical Expertise: The Importance of Selecting Proper Metrics

by Farzad Aghazadeh, Bin Zheng, Mahdi Tavakoli and Hossein Rouhani

Sensors 2023, 23(6), 3146; https://doi.org/10.3390/s23063146 - 15 Mar 2023

Cited by 2 | Viewed by 1233

Abstract

The smooth movement of hand/surgical instruments is considered an indicator of skilled, coordinated surgical performance. Jerky surgical instrument movements or hand tremors can cause unwanted damages to the surgical site. Different methods have been used in previous studies for assessing motion smoothness, causing [...] Read more.

The smooth movement of hand/surgical instruments is considered an indicator of skilled, coordinated surgical performance. Jerky surgical instrument movements or hand tremors can cause unwanted damages to the surgical site. Different methods have been used in previous studies for assessing motion smoothness, causing conflicting results regarding the comparison among surgical skill levels. We recruited four attending surgeons, five surgical residents, and nine novices. The participants conducted three simulated laparoscopic tasks, including peg transfer, bimanual peg transfer, and rubber band translocation. Tooltip motion smoothness was computed using the mean tooltip motion jerk, logarithmic dimensionless tooltip motion jerk, and 95% tooltip motion frequency (originally proposed in this study) to evaluate their capability of surgical skill level differentiation. The results revealed that logarithmic dimensionless motion jerk and 95% motion frequency were capable of distinguishing skill levels, indicated by smoother tooltip movements observed in high compared to low skill levels. Contrarily, mean motion jerk was not able to distinguish the skill levels. Additionally, 95% motion frequency was less affected by the measurement noise since it did not require the calculation of motion jerk, and 95% motion frequency and logarithmic dimensionless motion jerk yielded a better motion smoothness assessment outcome in distinguishing skill levels than mean motion jerk. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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18 pages, 3601 KiB

Open AccessArticle

Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction

by Bach-Tung Pham, Phuong Thi Le, Tzu-Chiang Tai, Yi-Chiung Hsu, Yung-Hui Li and Jia-Ching Wang

Sensors 2023, 23(6), 2993; https://doi.org/10.3390/s23062993 - 9 Mar 2023

Cited by 4 | Viewed by 4337

Abstract

An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on [...] Read more.

An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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15 pages, 1704 KiB

Open AccessArticle

Joint Masked Face Recognition and Temperature Measurement System Using Convolutional Neural Networks

by Tsung-Han Tsai, Ji-Xiu Lu, Xuan-Yu Chou and Chieng-Yang Wang

Sensors 2023, 23(6), 2901; https://doi.org/10.3390/s23062901 - 7 Mar 2023

Cited by 6 | Viewed by 2445

Abstract

With the outbreak of COVID-19, epidemic prevention has become a way to prevent the spread of epidemics. Many public places, such as hospitals, schools, and office places, require disinfection and temperature measurement. To implement epidemic prevention systems and reduce the risk of infection, [...] Read more.

With the outbreak of COVID-19, epidemic prevention has become a way to prevent the spread of epidemics. Many public places, such as hospitals, schools, and office places, require disinfection and temperature measurement. To implement epidemic prevention systems and reduce the risk of infection, it is a recent trend to measure body temperature through non-contact sensing systems with thermal imaging cameras. Compared to fingerprints and irises, face recognition is accurate and does not require close contact, which significantly reduces the risk of infection. However, masks block most facial features, resulting in the low accuracy of face recognition systems. This work combines masked face recognition with a thermal imaging camera for use as an automated attendance system. It can record body temperature and recognize the person at the same time. Through the designed UI system, we can search the attendance information of each person. We not only provide the design method based on convolutional neural networks (CNNs), but also provide the complete embedded system as a real demonstration and achieve a 94.1% accuracy rate of masked face recognition in the real world. With the face recognition system combined with a thermal imaging camera, the purpose of screening body temperature when checking in at work can be achieved. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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18 pages, 6608 KiB

Open AccessArticle

VLSI Design Based on Block Truncation Coding for Real-Time Color Image Compression for IoT

by Shih-Lun Chen, He-Sheng Chou, Shih-Yao Ke, Chiung-An Chen, Tsung-Yi Chen, Mei-Ling Chan, Patricia Angela R. Abu, Liang-Hung Wang and Kuo-Chen Li

Sensors 2023, 23(3), 1573; https://doi.org/10.3390/s23031573 - 1 Feb 2023

Cited by 1 | Viewed by 2086

Abstract

It has always been a major issue for a hospital to acquire real-time information about a patient in emergency situations. Because of this, this research presents a novel high-compression-ratio and real-time-process image compression very-large-scale integration (VLSI) design for image sensors in the Internet [...] Read more.

It has always been a major issue for a hospital to acquire real-time information about a patient in emergency situations. Because of this, this research presents a novel high-compression-ratio and real-time-process image compression very-large-scale integration (VLSI) design for image sensors in the Internet of Things (IoT). The design consists of a YEF transform, color sampling, block truncation coding (BTC), threshold optimization, sub-sampling, prediction, quantization, and Golomb–Rice coding. By using machine learning, different BTC parameters are trained to achieve the optimal solution given the parameters. Two optimal reconstruction values and bitmaps for each 4 × 4 block are achieved. An image is divided into 4 × 4 blocks by BTC for numerical conversion and removing inter-pixel redundancy. The sub-sampling, prediction, and quantization steps are performed to reduce redundant information. Finally, the value with a high probability will be coded using Golomb–Rice coding. The proposed algorithm has a higher compression ratio than traditional BTC-based image compression algorithms. Moreover, this research also proposes a real-time image compression chip design based on low-complexity and pipelined architecture by using TSMC 0.18 μm CMOS technology. The operating frequency of the chip can achieve 100 MHz. The core area and the number of logic gates are 598,880 μm² and 56.3 K, respectively. In addition, this design achieves 50 frames per second, which is suitable for real-time CMOS image sensor compression. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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18 pages, 451 KiB

Open AccessArticle

Finding Explanations in AI Fusion of Electro-Optical/Passive Radio-Frequency Data

by Asad Vakil, Erik Blasch, Robert Ewing and Jia Li

Sensors 2023, 23(3), 1489; https://doi.org/10.3390/s23031489 - 29 Jan 2023

Cited by 2 | Viewed by 1634

Abstract

In the Information Age, the widespread usage of blackbox algorithms makes it difficult to understand how data is used. The practice of sensor fusion to achieve results is widespread, as there are many tools to further improve the robustness and performance of a [...] Read more.

In the Information Age, the widespread usage of blackbox algorithms makes it difficult to understand how data is used. The practice of sensor fusion to achieve results is widespread, as there are many tools to further improve the robustness and performance of a model. In this study, we demonstrate the utilization of a Long Short-Term Memory (LSTM-CCA) model for the fusion of Passive RF (P-RF) and Electro-Optical (EO) data in order to gain insights into how P-RF data are utilized. The P-RF data are constructed from the in-phase and quadrature component (I/Q) data processed via histograms, and are combined with enhanced EO data via dense optical flow (DOF). The preprocessed data are then used as training data with an LSTM-CCA model in order to achieve object detection and tracking. In order to determine the impact of the different data inputs, a greedy algorithm (explainX.ai) is implemented to determine the weight and impact of the canonical variates provided to the fusion model on a scenario-by-scenario basis. This research introduces an explainable LSTM-CCA framework for P-RF and EO sensor fusion, providing novel insights into the sensor fusion process that can assist in the detection and differentiation of targets and help decision-makers to determine the weights for each input. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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19 pages, 3491 KiB

Open AccessArticle

Inhalation Injury Grading Using Transfer Learning Based on Bronchoscopy Images and Mechanical Ventilation Period

by Yifan Li, Alan W. Pang, Jad Zeitouni, Ferris Zeitouni, Kirby Mateja, John A. Griswold and Jo Woon Chong

Sensors 2022, 22(23), 9430; https://doi.org/10.3390/s22239430 - 2 Dec 2022

Cited by 1 | Viewed by 4414

Abstract

The abbreviated injury score (AIS) is commonly used as a grading system for inhalation injuries. While inhalation injury grades have inconsistently been shown to correlate positively with the time mechanical ventilation is needed, grading is subjective and relies heavily on the clinicians’ experience [...] Read more.

The abbreviated injury score (AIS) is commonly used as a grading system for inhalation injuries. While inhalation injury grades have inconsistently been shown to correlate positively with the time mechanical ventilation is needed, grading is subjective and relies heavily on the clinicians’ experience and expertise. Additionally, no correlation has been shown between these patients’ inhalation injury grades and outcomes. In this paper, we propose a novel inhalation injury grading method which uses deep learning algorithms in bronchoscopy images to determine the injury grade from the carbonaceous deposits, blistering, and fibrin casts in the bronchoscopy images. The proposed method adopts transfer learning and data augmentation concepts to enhance the accuracy performance to avoid overfitting. We tested our proposed model on the bronchoscopy images acquired from eighteen patients who had suffered inhalation injuries, with the degree of severity 1, 2, 3, 4, 5, or 6. As performance metrics, we consider accuracy, sensitivity, specificity, F-1 score, and precision. Experimental results show that our proposed method, with both transfer learning and data augmentation components, provides an overall 86.11% accuracy. Moreover, the experimental results also show that the performance of the proposed method outperforms the method without transfer learning or data augmentation. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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14 pages, 1424 KiB

Open AccessArticle

Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification

by Mohamed Hammad, Souham Meshoul, Piotr Dziwiński, Paweł Pławiak and Ibrahim A. Elgendy

Sensors 2022, 22(23), 9347; https://doi.org/10.3390/s22239347 - 1 Dec 2022

Cited by 9 | Viewed by 1764

Abstract

An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide [...] Read more.

An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel based on convolutional neural networks (CNNs) that can transfer knowledge from many lightweight deep learning models and decant it into one model to aid in the diagnosis of arrhythmia by using electrocardiogram (ECG) signals. Thus, we gained a multimodel able to classify arrhythmia from ECG signals. Our system’s effectiveness is examined by using a publicly accessible database and a comparison to the current methodologies for arrhythmia classification. The results we achieved by using our multimodel are better than those obtained by using a single model and better than most of the previous detection methods. It is worth mentioning that this model produced accurate classification results on small collection of data. Experts in this field can use this model as a guide to help them make decisions and save time. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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