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Intelligent Biosignal Processing in Wearable and Implantable Sensors
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Intelligent Biosignal Processing in Wearable and Implantable Sensors

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Intelligent Biosensors and Bio-Signal Processing".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 77318

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Hariton-Nicolae Costin

E-Mail Website
Guest Editor
Institute of Computer Science of the Romanian Academy, Iasi Branch, 700481 Iasi, Romania
Interests: biosignal processing; biomedical image processing; artificial intelligence (neural networks, fuzzy systems, bio-inspired algorithms); (bio)sensors/transducers; e-health and telemedicine; assistive technologies
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Saeid Sanei

E-Mail Website
Guest Editor
School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
Interests: signal processing; biomedical signal processing; machine learning; body sensor networking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

We are very pleased to invite you to contribute to this Special Issue related to this emerging domain in health care and technical sciences, i.e. intelligent monitoring and processing in portable and/or implantable (bio)sensors.

Biological signals, or biosignals, are space, time, or space-time records of biological events. Among them, those presenting respiratory cycles, heart rate and cardiac cycles, blood oxygenation, brain activity, muscle movement, and human gait are more popular. A variety of active and passive sensors, some with on-board processing capability, are available to provide the most accurate and efficient recordings of the above data. The underlying information, however, may not always be visualized by the naked eye and therefore, signal processing, machine learning, and artificial intelligence (AI) techniques have been constantly under research and development to provide a better understanding and recognition of human body state using raw data records. Although the objective is to have noninvasive and less intrusive sensors, the use of implanted sensors is inevitable for particular in vivo recordings where the human bioindicators need to be monitored for a longer time, or during surgical interventions.

Significant developments have been made in the last few decades in the field of artificial intelligence. For instance, the introduction of deep learning methodology or bio/natured-inspired algorithms has significantly improved the learning, classification, optimization and prediction accuracy, especially when dealing with big data and high-resolution images. Also, substantial developments have occurred in the area of biomedical signal processing, measurement techniques, and health monitoring, such as for vital biosignals and for biomedical systems in general.

Due to the continuous progress of portable, wearable, and implantable devices, with big processing power and high sensory accuracy, the use into the biomedical field has received intense interest, and these technologies became pervasive in our days. Thus, the development of new sensors and signal processing algorithms in the field are mandatory to increase diagnosis and prognosis power. As a result, there is a need to integrate different systems and technologies for real-time signal detection and medical diagnosis.

This task leads to the so-called third generation of pervasive health applications. This emerging branch of research aims to combine continuous health monitoring with other sources of medical information and knowledge. Thus, the main objective in third-generation applications is to integrate intelligent agents that implement technologies such as stream and real time processing, data mining, machine learning, genetic and multi-omics data. These agents are thus responsible for extracting information from a variety of sources including clinical research, patient records, laboratory generated data (e.g., genomics, proteomics, or metabonomics), and they are capable of improving and personalizing clinical care. This multi-modal information must be then fused, and the analysis system examines patients from a system level. In this way, the decision-making process (e.g., of diagnosis) is governed by the latest evidence in biomedical and health informatics. In addition, the use of smart sensors paves the path for personalized medicine, which is one of the objectives of future healthcare. With more intelligent systems developed through advanced processing and learning algorithms, the number of sensors can be also reduced, which is another objective for less intrusive monitoring.

This Special Issue addresses major advances in integration and intelligent processing of data coming from wearable, portable, or implantable devices for health care, and is intended to highlight new research opportunities in biomedical informatics and clinical environment. Incorporation of machine learning/artificial intelligence on-chip can lead to the realization of smart sensors. The purpose of this special issue is to address on-going research activities in the design of intelligent sensors focusing on the applications in the areas of biomedical implantable, portable and wearable sensor instruments.

Potential topics include, but are not limited, to the following:

  • Emerging smart wearable and implantable sensors and devices, along with related artificial intelligence methods for data processing
  • Machine Learning, deep learning, nature/bio-inspired algorithms and artificial intelligence on-chip for smart portable/implantable sensors
  • Real-time and low-power signal processing for smart sensors, based on AI techniques
  • Flexible, printable, and biocompatible sensors and systems, smart organic, inorganic and hybrid electronic sensors which are portable, wearable or implantable, along with smart processing of raw data coming from them
  • Nanotechnology based smart sensors for and biomedical applications and their related advanced data processing
  • Smart sensor applications in healthcare and clinic environment
  • Trends in smart sensor technologies and intelligent data processing
  • Sensor-based threats to industrial and biomedical applications
  • Reviews/Surveys on intelligent biosignal processing in portable or implantable sensors
  • Body Sensor networks; short- and long-range communication trends, channel modelling, channel equalization, energy harvesting, and quality of service
  • Pervasive computing and distributed processing for sensor networks
  • Cooperative and decentralized sensor networks

Prof. Dr. Hariton-Nicolae Costin
Prof. Dr. Saeid Sanei
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sensors and biosensors – new architectures and signal processing methods
  • Artificial intelligence methods for biosignal processing
  • Machine learning techniques for biosignal processing in portable/implantable sensors
  • Bioinspired algorithms for biosignal processing
  • Non-invasive and portable glucometer accepted by clinical setting
  • Clinical applications of smart sensors using intelligent processing of data
  • Body sensor networking

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Published Papers (17 papers)

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Editorial

Jump to: Research

5 pages, 194 KiB  
Editorial
Intelligent Biosignal Processing in Wearable and Implantable Sensors
by Hariton-Nicolae Costin and Saeid Sanei
Biosensors 2022, 12(6), 396; https://doi.org/10.3390/bios12060396 - 9 Jun 2022
Cited by 4 | Viewed by 2447
Abstract
Wearable technology including sensors, sensor networks, and the associated devices have opened up space in a variety of applications [...] Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)

Research

Jump to: Editorial

16 pages, 3117 KiB  
Article
Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern
by Jiachen Wang, Yun-Hsuan Chen, Jie Yang and Mohamad Sawan
Biosensors 2022, 12(6), 384; https://doi.org/10.3390/bios12060384 - 2 Jun 2022
Cited by 7 | Viewed by 3966
Abstract
To apply EEG-based brain-machine interfaces during rehabilitation, separating various tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed. Previous studies were focusing on classifying different MI tasks based on complex algorithms. In this paper, we implement intelligent, straightforward, [...] Read more.
To apply EEG-based brain-machine interfaces during rehabilitation, separating various tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed. Previous studies were focusing on classifying different MI tasks based on complex algorithms. In this paper, we implement intelligent, straightforward, comprehensible, time-efficient, and channel-reduced methods to classify ME versus MI and left- versus right-hand MI. EEG of 30 healthy participants undertaking motional tasks is recorded to investigate two classification tasks. For the first task, we first propose a “follow-up” pattern based on the beta rebound. This method achieves an average classification accuracy of 59.77% ± 11.95% and can be up to 89.47% for finger-crossing. Aside from time-domain information, we map EEG signals to feature space using extraction methods including statistics, wavelet coefficients, average power, sample entropy, and common spatial patterns. To evaluate their practicability, we adopt a support vector machine as an intelligent classifier model and sparse logistic regression as a feature selection technique and achieve 79.51% accuracy. Similar approaches are taken for the second classification reaching 75.22% accuracy. The classifiers we propose show high accuracy and intelligence. The achieved results make our approach highly suitable to be applied to the rehabilitation of paralyzed limbs. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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19 pages, 4313 KiB  
Article
Computer-Aided Detection of Fiducial Points in Seismocardiography through Dynamic Time Warping
by Chien-Hung Chen, Wen-Yen Lin and Ming-Yih Lee
Biosensors 2022, 12(6), 374; https://doi.org/10.3390/bios12060374 - 30 May 2022
Cited by 7 | Viewed by 2811
Abstract
Accelerometer-based devices have been employed in seismocardiography fiducial point detection with the aid of quasi-synchronous alignment between echocardiography images and seismocardiogram signals. However, signal misalignments have been observed, due to the heartbeat cycle length variation. This paper not only analyzes the misalignments and [...] Read more.
Accelerometer-based devices have been employed in seismocardiography fiducial point detection with the aid of quasi-synchronous alignment between echocardiography images and seismocardiogram signals. However, signal misalignments have been observed, due to the heartbeat cycle length variation. This paper not only analyzes the misalignments and detection errors but also proposes to mitigate the issues by introducing reference signals and adynamic time warping (DTW) algorithm. Two diagnostic parameters, the ratio of pre-ejection period to left ventricular ejection time (PEP/LVET) and the Tei index, were examined with two statistical verification approaches: (1) the coefficient of determination (R2) of the parameters versus the left ventricular ejection fraction (LVEF) assessments, and (2) the receiver operating characteristic (ROC) classification to distinguish the heart failure patients with reduced ejection fraction (HFrEF). Favorable R2 values were obtained, R2 = 0.768 for PEP/LVET versus LVEF and R2 = 0.86 for Tei index versus LVEF. The areas under the ROC curve indicate the parameters that are good predictors to identify HFrEF patients, with an accuracy of more than 92%. The proof-of-concept experiments exhibited the effectiveness of the DTW-based quasi-synchronous alignment in seismocardiography fiducial point detection. The proposed approach may enable the standardization of the fiducial point detection and the signal template generation. Meanwhile, the program-generated annotation data may serve as the labeled training set for the supervised machine learning. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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16 pages, 2541 KiB  
Article
An Artifact-Resistant Feature SKNAER for Quantifying the Burst of Skin Sympathetic Nerve Activity Signal
by Yantao Xing, Yike Zhang, Zhijun Xiao, Chenxi Yang, Jiayi Li, Chang Cui, Jing Wang, Hongwu Chen, Jianqing Li and Chengyu Liu
Biosensors 2022, 12(5), 355; https://doi.org/10.3390/bios12050355 - 20 May 2022
Cited by 11 | Viewed by 3179
Abstract
Evaluation of sympathetic nerve activity (SNA) using skin sympathetic nerve activity (SKNA) signal has attracted interest in recent studies. However, signal noises may obstruct the accurate location for the burst of SKNA, leading to the quantification error of the signal. In this study, [...] Read more.
Evaluation of sympathetic nerve activity (SNA) using skin sympathetic nerve activity (SKNA) signal has attracted interest in recent studies. However, signal noises may obstruct the accurate location for the burst of SKNA, leading to the quantification error of the signal. In this study, we use the Teager–Kaiser energy (TKE) operator to preprocess the SKNA signal, and then candidates of burst areas were segmented by an envelope-based method. Since the burst of SKNA can also be discriminated by the high-frequency component in QRS complexes of electrocardiogram (ECG), a strategy was designed to reject their influence. Finally, a feature of the SKNA energy ratio (SKNAER) was proposed for quantifying the SKNA. The method was verified by both sympathetic nerve stimulation and hemodialysis experiments compared with traditional heart rate variability (HRV) and a recently developed integral skin sympathetic nerve activity (iSKNA) method. The results showed that SKNAER correlated well with HRV features (r = 0.60 with the standard deviation of NN intervals, 0.67 with low frequency/high frequency, 0.47 with very low frequency) and the average of iSKNA (r = 0.67). SKNAER improved the detection accuracy for the burst of SKNA, with 98.2% for detection rate and 91.9% for precision, inducing increases of 3.7% and 29.1% compared with iSKNA (detection rate: 94.5% (p < 0.01), precision: 62.8% (p < 0.001)). The results from the hemodialysis experiment showed that SKNAER had more significant differences than aSKNA in the long-term SNA evaluation (p < 0.001 vs. p = 0.07 in the fourth period, p < 0.01 vs. p = 0.11 in the sixth period). The newly developed feature may play an important role in continuously monitoring SNA and keeping potential for further clinical tests. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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24 pages, 4312 KiB  
Article
An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques
by Omneya Attallah
Biosensors 2022, 12(5), 299; https://doi.org/10.3390/bios12050299 - 5 May 2022
Cited by 45 | Viewed by 4245
Abstract
Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance diagnostic accuracy [...] Read more.
Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance diagnostic accuracy and avoid the limitations of these tools. Earlier studies indicated multiple structures of cardiovascular alterations in COVID-19 cases which motivated the realization of using ECG data as a tool for diagnosing the novel coronavirus. This study introduced a novel automated diagnostic tool based on ECG data to diagnose COVID-19. The introduced tool utilizes ten deep learning (DL) models of various architectures. It obtains significant features from the last fully connected layer of each DL model and then combines them. Afterward, the tool presents a hybrid feature selection based on the chi-square test and sequential search to select significant features. Finally, it employs several machine learning classifiers to perform two classification levels. A binary level to differentiate between normal and COVID-19 cases, and a multiclass to discriminate COVID-19 cases from normal and other cardiac complications. The proposed tool reached an accuracy of 98.2% and 91.6% for binary and multiclass levels, respectively. This performance indicates that the ECG could be used as an alternative means of diagnosis of COVID-19. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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14 pages, 692 KiB  
Article
The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography
by Jesús Cano, Lorenzo Fácila, Juan M. Gracia-Baena, Roberto Zangróniz, Raúl Alcaraz and José J. Rieta
Biosensors 2022, 12(5), 289; https://doi.org/10.3390/bios12050289 - 1 May 2022
Cited by 13 | Viewed by 3720
Abstract
The detection of hypertension (HT) is of great importance for the early diagnosis of cardiovascular diseases (CVDs), as subjects with high blood pressure (BP) are asymptomatic until advanced stages of the disease. The present study proposes a classification model to discriminate between normotensive [...] Read more.
The detection of hypertension (HT) is of great importance for the early diagnosis of cardiovascular diseases (CVDs), as subjects with high blood pressure (BP) are asymptomatic until advanced stages of the disease. The present study proposes a classification model to discriminate between normotensive (NTS) and hypertensive (HTS) subjects employing electrocardiographic (ECG) and photoplethysmographic (PPG) recordings as an alternative to traditional cuff-based methods. A total of 913 ECG, PPG and BP recordings from 69 subjects were analyzed. Then, signal preprocessing, fiducial points extraction and feature selection were performed, providing 17 discriminatory features, such as pulse arrival and transit times, that fed machine-learning-based classifiers. The main innovation proposed in this research uncovers the relevance of previous calibration to obtain accurate HT risk assessment. This aspect has been assessed using both close and distant time test measurements with respect to calibration. The k-nearest neighbors-classifier provided the best outcomes with an accuracy for new subjects before calibration of 51.48%. The inclusion of just one calibration measurement into the model improved classification accuracy by 30%, reaching gradually more than 96% with more than six calibration measurements. Accuracy decreased with distance to calibration, but remained outstanding even days after calibration. Thus, the use of PPG and ECG recordings combined with previous subject calibration can significantly improve discrimination between NTS and HTS individuals. This strategy could be implemented in wearable devices for HT risk assessment as well as to prevent CVDs. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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32 pages, 12174 KiB  
Article
Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson’s Disease Management Optimization
by Robert Radu Ileșan, Claudia-Georgiana Cordoș, Laura-Ioana Mihăilă, Radu Fleșar, Ana-Sorina Popescu, Lăcrămioara Perju-Dumbravă and Paul Faragó
Biosensors 2022, 12(4), 189; https://doi.org/10.3390/bios12040189 - 23 Mar 2022
Cited by 24 | Viewed by 5329
Abstract
Parkinson’s disease (PD) is the second most common progressive neurodegenerative disorder, affecting 6.2 million patients and causing disability and decreased quality of life. The research is oriented nowadays toward artificial intelligence (AI)-based wearables for early diagnosis and long-term PD monitoring. Our primary objective [...] Read more.
Parkinson’s disease (PD) is the second most common progressive neurodegenerative disorder, affecting 6.2 million patients and causing disability and decreased quality of life. The research is oriented nowadays toward artificial intelligence (AI)-based wearables for early diagnosis and long-term PD monitoring. Our primary objective is the monitoring and assessment of gait in PD patients. We propose a wearable physiograph for qualitative and quantitative gait assessment, which performs bilateral tracking of the foot biomechanics and unilateral tracking of arm balance. Gait patterns are assessed by means of correlation. The surface plot of a correlation coefficient matrix, generated from the recorded signals, is classified using convolutional neural networks into physiological or PD-specific gait. The novelty is given by the proposed AI-based decisional support procedure for gait assessment. A proof of concept of the proposed physiograph is validated in a clinical environment on five patients and five healthy controls, proving to be a feasible solution for ubiquitous gait monitoring and assessment in PD. PD management demonstrates the complexity of the human body. A platform empowering multidisciplinary, AI-evidence-based decision support assessments for optimal dosing between drug and non-drug therapy could lay the foundation for affordable precision medicine. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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22 pages, 3994 KiB  
Article
Explainable Artificial Intelligence and Wearable Sensor-Based Gait Analysis to Identify Patients with Osteopenia and Sarcopenia in Daily Life
by Jeong-Kyun Kim, Myung-Nam Bae, Kangbok Lee, Jae-Chul Kim and Sang Gi Hong
Biosensors 2022, 12(3), 167; https://doi.org/10.3390/bios12030167 - 7 Mar 2022
Cited by 40 | Viewed by 6206
Abstract
Osteopenia and sarcopenia can cause various senile diseases and are key factors related to the quality of life in old age. There is need for portable tools and methods that can analyze osteopenia and sarcopenia risks during daily life, rather than requiring a [...] Read more.
Osteopenia and sarcopenia can cause various senile diseases and are key factors related to the quality of life in old age. There is need for portable tools and methods that can analyze osteopenia and sarcopenia risks during daily life, rather than requiring a specialized hospital setting. Gait is a suitable indicator of musculoskeletal diseases; therefore, we analyzed the gait signal obtained from an inertial-sensor-based wearable gait device as a tool to manage bone loss and muscle loss in daily life. To analyze the inertial-sensor-based gait, the inertial signal was classified into seven gait phases, and descriptive statistical parameters were obtained for each gait phase. Subsequently, explainable artificial intelligence was utilized to analyze the contribution and importance of descriptive statistical parameters on osteopenia and sarcopenia. It was found that XGBoost yielded a high accuracy of 88.69% for osteopenia, whereas the random forest approach showed a high accuracy of 93.75% for sarcopenia. Transfer learning with a ResNet backbone exhibited appropriate performance but showed lower accuracy than the descriptive statistical parameter-based identification result. The proposed gait analysis method confirmed high classification accuracy and the statistical significance of gait factors that can be used for osteopenia and sarcopenia management. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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20 pages, 2668 KiB  
Article
A Study on Dictionary Selection in Compressive Sensing for ECG Signals Compression and Classification
by Monica Fira, Hariton-Nicolae Costin and Liviu Goraș
Biosensors 2022, 12(3), 146; https://doi.org/10.3390/bios12030146 - 27 Feb 2022
Cited by 12 | Viewed by 3071
Abstract
The paper proposes a comparative analysis of the projection matrices and dictionaries used for compressive sensing (CS) of electrocardiographic signals (ECG), highlighting the compromises between the complexity of preprocessing and the accuracy of reconstruction. Starting from the basic notions of CS theory, this [...] Read more.
The paper proposes a comparative analysis of the projection matrices and dictionaries used for compressive sensing (CS) of electrocardiographic signals (ECG), highlighting the compromises between the complexity of preprocessing and the accuracy of reconstruction. Starting from the basic notions of CS theory, this paper proposes the construction of dictionaries (constructed directly by cardiac patterns with R-waves, centered or not-centered) specific to the application and the results of their testing. Several types of projection matrices are also analyzed and discussed. The reconstructed signals are analyzed quantitatively and qualitatively by standard distortion measures and by the classification of the reconstructed signals. We used a k-nearest neighbors (KNN) classifier to evaluate the reconstructed models. The KNN module was trained with the models from the mega-dictionary used in the classification block and tested with the models reconstructed with class-specific dictionaries. In addition to the KNN classifier, a neural network was used to test the reconstructed signals. The neural network was a multilayer perceptron (MLP). Moreover, the results are compared with those obtained with other compression methods, and ours proved to be superior. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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15 pages, 3635 KiB  
Article
Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry
by Rajkumar Vaghashiya, Sanghoon Shin, Varun Chauhan, Kaushal Kapadiya, Smit Sanghavi, Sungkyu Seo and Mohendra Roy
Biosensors 2022, 12(3), 144; https://doi.org/10.3390/bios12030144 - 27 Feb 2022
Cited by 14 | Viewed by 4507
Abstract
The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been developed, such as automatic analysis of complete blood count (CBC), cell viability, 2D cell [...] Read more.
The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been developed, such as automatic analysis of complete blood count (CBC), cell viability, 2D cell morphology, 3D cell tomography, etc. The developed auto characterization algorithm so far for this custom-developed LSIT cytometer was based on the handcrafted features of the cell diffraction patterns from the LSIT cytometer, that were determined from our empirical findings on thousands of samples of individual cell types, which limit the system in terms of induction of a new cell type for auto classification or characterization. Further, its performance suffers from poor image (cell diffraction pattern) signatures due to their small signal or background noise. In this work, we address these issues by leveraging the artificial intelligence-powered auto signal enhancing scheme such as denoising autoencoder and adaptive cell characterization technique based on the transfer of learning in deep neural networks. The performance of our proposed method shows an increase in accuracy >98% along with the signal enhancement of >5 dB for most of the cell types, such as red blood cell (RBC) and white blood cell (WBC). Furthermore, the model is adaptive to learn new type of samples within a few learning iterations and able to successfully classify the newly introduced sample along with the existing other sample types. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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14 pages, 2459 KiB  
Article
Decoding Vagus-Nerve Activity with Carbon Nanotube Sensors in Freely Moving Rodents
by Joseph T. Marmerstein, Grant A. McCallum and Dominique M. Durand
Biosensors 2022, 12(2), 114; https://doi.org/10.3390/bios12020114 - 11 Feb 2022
Cited by 7 | Viewed by 3829
Abstract
The vagus nerve is the largest autonomic nerve and a major target of stimulation therapies for a wide variety of chronic diseases. However, chronic recording from the vagus nerve has been limited, leading to significant gaps in our understanding of vagus nerve function [...] Read more.
The vagus nerve is the largest autonomic nerve and a major target of stimulation therapies for a wide variety of chronic diseases. However, chronic recording from the vagus nerve has been limited, leading to significant gaps in our understanding of vagus nerve function and therapeutic mechanisms. In this study, we use a carbon nanotube yarn (CNTY) biosensor to chronically record from the vagus nerves of freely moving rats for over 40 continuous hours. Vagal activity was analyzed using a variety of techniques, such as spike sorting, spike-firing rates, and interspike intervals. Many spike-cluster-firing rates were found to correlate with food intake, and the neural-firing rates were used to classify eating and other behaviors. To our knowledge, this is the first chronic recording and decoding of activity in the vagus nerve of freely moving animals enabled by the axon-like properties of the CNTY biosensor in both size and flexibility and provides an important step forward in our ability to understand spontaneous vagus-nerve function. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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15 pages, 1535 KiB  
Article
Detection of Liver Dysfunction Using a Wearable Electronic Nose System Based on Semiconductor Metal Oxide Sensors
by Andreas Voss, Rico Schroeder, Steffen Schulz, Jens Haueisen, Stefanie Vogler, Paul Horn, Andreas Stallmach and Philipp Reuken
Biosensors 2022, 12(2), 70; https://doi.org/10.3390/bios12020070 - 26 Jan 2022
Cited by 16 | Viewed by 4976
Abstract
The purpose of this exploratory study was to determine whether liver dysfunction can be generally classified using a wearable electronic nose based on semiconductor metal oxide (MOx) gas sensors, and whether the extent of this dysfunction can be quantified. MOx gas sensors are [...] Read more.
The purpose of this exploratory study was to determine whether liver dysfunction can be generally classified using a wearable electronic nose based on semiconductor metal oxide (MOx) gas sensors, and whether the extent of this dysfunction can be quantified. MOx gas sensors are attractive because of their simplicity, high sensitivity, low cost, and stability. A total of 30 participants were enrolled, 10 of them being healthy controls, 10 with compensated cirrhosis, and 10 with decompensated cirrhosis. We used three sensor modules with a total of nine different MOx layers to detect reducible, easily oxidizable, and highly oxidizable gases. The complex data analysis in the time and non-linear dynamics domains is based on the extraction of 10 features from the sensor time series of the extracted breathing gas measurement cycles. The sensitivity, specificity, and accuracy for distinguishing compensated and decompensated cirrhosis patients from healthy controls was 1.00. Patients with compensated and decompensated cirrhosis could be separated with a sensitivity of 0.90 (correctly classified decompensated cirrhosis), a specificity of 1.00 (correctly classified compensated cirrhosis), and an accuracy of 0.95. Our wearable, non-invasive system provides a promising tool to detect liver dysfunctions on a functional basis. Therefore, it could provide valuable support in preoperative examinations or for initial diagnosis by the general practitioner, as it provides non-invasive, rapid, and cost-effective analysis results. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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10 pages, 1782 KiB  
Article
Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG
by Shuo Wang, Jingjing Zheng, Bin Zheng and Xianta Jiang
Biosensors 2022, 12(2), 57; https://doi.org/10.3390/bios12020057 - 21 Jan 2022
Cited by 11 | Viewed by 3561
Abstract
Pattern recognition using surface Electromyography (sEMG) applied on prosthesis control has attracted much attention in these years. In most of the existing methods, the sEMG signal during the firmly grasped period is used for grasp classification because good performance can be achieved due [...] Read more.
Pattern recognition using surface Electromyography (sEMG) applied on prosthesis control has attracted much attention in these years. In most of the existing methods, the sEMG signal during the firmly grasped period is used for grasp classification because good performance can be achieved due to its relatively stable signal. However, using the only the firmly grasped period may cause a delay to control the prosthetic hand gestures. Regarding this issue, we explored how grasp classification accuracy changes during the reaching and grasping process, and identified the period that can leverage the grasp classification accuracy and the earlier grasp detection. We found that the grasp classification accuracy increased along the hand gradually grasping the object till firmly grasped, and there is a sweet period before firmly grasped period, which could be suitable for early grasp classification with reduced delay. On top of this, we also explored corresponding training strategies for better grasp classification in real-time applications. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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20 pages, 3985 KiB  
Article
Atrial Fibrillation Prediction from Critically Ill Sepsis Patients
by Syed Khairul Bashar, Eric Y. Ding, Allan J. Walkey, David D. McManus and Ki H. Chon
Biosensors 2021, 11(8), 269; https://doi.org/10.3390/bios11080269 - 9 Aug 2021
Cited by 18 | Viewed by 5126
Abstract
Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, [...] Read more.
Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time–frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients’ AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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18 pages, 1022 KiB  
Article
Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network
by Andreas Bahr, Matthias Schneider, Maria Avitha Francis, Hendrik M. Lehmann, Igor Barg, Anna-Sophia Buschhoff, Peer Wulff, Thomas Strunskus and Franz Faupel
Biosensors 2021, 11(7), 203; https://doi.org/10.3390/bios11070203 - 23 Jun 2021
Cited by 27 | Viewed by 5845
Abstract
The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly attractive option. For such implantable medical devices, efficient and low energy consumption, small size, and efficient processing architectures are essential. To [...] Read more.
The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly attractive option. For such implantable medical devices, efficient and low energy consumption, small size, and efficient processing architectures are essential. To meet these requirements, epileptic seizure detection by analysis and classification of brain signals with a convolutional neural network (CNN) is an attractive approach. This work presents a CNN for epileptic seizure detection capable of running on an ultra-low-power microprocessor. The CNN is implemented and optimized in MATLAB. In addition, the CNN is also implemented on a GAP8 microprocessor with RISC-V architecture. The training, optimization, and evaluation of the proposed CNN are based on the CHB-MIT dataset. The CNN reaches a median sensitivity of 90% and a very high specificity over 99% corresponding to a median false positive rate of 6.8 s per hour. After implementation of the CNN on the microcontroller, a sensitivity of 85% is reached. The classification of 1 s of EEG data takes t=35 ms and consumes an average power of P≈140 μW. The proposed detector outperforms related approaches in terms of power consumption by a factor of 6. The universal applicability of the proposed CNN based detector is verified with recording of epileptic rats. This results enable the design of future medical devices for epilepsy treatment. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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20 pages, 5350 KiB  
Article
On the Classification of ECG and EEG Signals with Various Degrees of Dimensionality Reduction
by Monica Fira, Hariton-Nicolae Costin and Liviu Goraș
Biosensors 2021, 11(5), 161; https://doi.org/10.3390/bios11050161 - 19 May 2021
Cited by 16 | Viewed by 3890
Abstract
Classification performances for some classes of electrocardiographic (ECG) and electroencephalographic (EEG) signals processed to dimensionality reduction with different degrees are investigated. Results got with various classification methods are given and discussed. So far we investigated three techniques for reducing dimensionality: Laplacian eigenmaps (LE), [...] Read more.
Classification performances for some classes of electrocardiographic (ECG) and electroencephalographic (EEG) signals processed to dimensionality reduction with different degrees are investigated. Results got with various classification methods are given and discussed. So far we investigated three techniques for reducing dimensionality: Laplacian eigenmaps (LE), locality preserving projections (LPP) and compressed sensing (CS). The first two methods are related to manifold learning while the third addresses signal acquisition and reconstruction from random projections under the supposition of signal sparsity. Our aim is to evaluate the benefits and drawbacks of various methods and to find to what extent they can be considered remarkable. The assessment of the effect of dimensionality decrease was made by considering the classification rates for the processed biosignals in the new spaces. Besides, the classification accuracies of the initial input data were evaluated with respect to the corresponding accuracies in the new spaces using different classifiers. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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22 pages, 4295 KiB  
Article
Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN
by Junsheng Yu, Xiangqing Wang, Xiaodong Chen and Jinglin Guo
Biosensors 2021, 11(3), 69; https://doi.org/10.3390/bios11030069 - 4 Mar 2021
Cited by 34 | Viewed by 5554
Abstract
Premature ventricular contractions (PVCs), common in the general and patient population, are irregular heartbeats that indicate potential heart diseases. Clinically, long-term electrocardiograms (ECG) collected from the wearable device is a non-invasive and inexpensive tool widely used to diagnose PVCs by physicians. However, analyzing [...] Read more.
Premature ventricular contractions (PVCs), common in the general and patient population, are irregular heartbeats that indicate potential heart diseases. Clinically, long-term electrocardiograms (ECG) collected from the wearable device is a non-invasive and inexpensive tool widely used to diagnose PVCs by physicians. However, analyzing these long-term ECG is time-consuming and labor-intensive for cardiologists. Therefore, this paper proposed a simplistic but powerful approach to detect PVC from long-term ECG. The suggested method utilized deep metric learning to extract features, with compact intra-product variance and separated inter-product differences, from the heartbeat. Subsequently, the k-nearest neighbors (KNN) classifier calculated the distance between samples based on these features to detect PVC. Unlike previous systems used to detect PVC, the proposed process can intelligently and automatically extract features by supervised deep metric learning, which can avoid the bias caused by manual feature engineering. As a generally available set of standard test material, the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database is used to evaluate the proposed method, and the experiment takes 99.7% accuracy, 97.45% sensitivity, and 99.87% specificity. The simulation events show that it is reliable to use deep metric learning and KNN for PVC recognition. More importantly, the overall way does not rely on complicated and cumbersome preprocessing. Full article
(This article belongs to the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors)
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