More than one fifth of American adults lives with chronic pain. As pain chronifies, the underlying neuronal circuitry undergoes maladaptive spatial and temporal changes. We previously developed and used an advanced CMOS sensor to record video of ventral tegmental area (VTA) activity in response to acute pain and pain chronification. Here we use both discriminative and generative AI approaches to spatiotemporally characterize the VTA's complex response to pain and quantify changes in its circuit dynamics murine chronic pain models. We trained a time-attention convolutional neural network (TA-CNN) and used its gradient-weighted class activation maps (Grad-CAMs) to spatially isolate activity which differentiates pre- and post-surgical responses to stimulation. Next, we implemented an unsupervised vector quantized variational autoencoder (VQ-VAE) to learn a dense, discrete representation of the VTA's response in terms of a codebook of spatiotemporal motifs. The TA-CNN's (test set accuracy 0.787) CAMs help isolate post-surgery activity differences to the inferior segments of the VTA for partial sciatic nerve ligation (PNL) subjects but not sham subjects. The VQ-VAE (validation mean squared error 0.00732) identifies distinct spatiotemporal motifs which spatially correspond to observed VTA sub-regions. Furthermore, these motifs show changes in both spatial organization and time-response to pain after PNL but not after sham operation. These motifs also exhibit intensified spatiotemporal responses to varying intensities of mechanical stimulation in post-PNL recordings. The use of AI to fit complex space-time dynamics to an ordered latent representation or code paves the way for nuanced analysis of previously difficult-to-approach problems.

Chronic pain is a widespread phenomenon affecting over 21% of the United States population. Despite the significant impact of pain on a patient's quality of life, the detection and identification of pain relies on subjective methods such as self-reporting. To address the challenges in identifying and treating chronic pain, a quantifiable biomarker for pain is needed. Here we present novel AI-driven method for the identification and isolation of localized pain signals in the brain during both acute and chronic pain. By using Matching Pursuit (MP) to decompose Local Field Potential (LFP) recordings from the Anterior Cingulate Cortex, the Nucleus Accumbens, and the Prelimbic Cortex, we can learn a latent representation with a conditional variational autoencoder (CVAE) and track changes in latent signal components in response to acute, sub-chronic, and chronic pain after injury. This method allows for both the identification of LFP signal components which are the primary drivers of observed aggregate changes in brain activity during pain, as well as for the tracking of said components over time. The model achieves an average per-feature RMSE of 0.130 on validation data and produces functionally separable latent representations of input MP atoms. The combination of MP for feature extraction and CVAE for latent space development allows for the extraction of both generalized and subject-specific pain motifs involved in chronic pain. These AI-driven biomarkers provide a basis for precision identification and quantitative monitoring of pain over time.

We have been faced with an unprecedented challenge in combating the COVID-19/SARS-CoV2 outbreak that is threatening the fabric of our civilization, causing catastrophic human losses and a tremendous economic burden globally. During this difficult time, there has been an urgent need for biomedical engineers, clinicians, and healthcare industry leaders to work together to develop novel diagnostics and treatments to fight the pandemic including the development of portable, rapidly deployable, and affordable diagnostic testing kits, personal protective equipment, mechanical ventilators, vaccines, and data analysis and modeling tools. In this position paper, we address the urgent need to bring these inventions into clinical practices. This paper highlights and summarizes the discussions and new technologies in COVID-19 healthcare, screening, tracing, and treatment-related presentations made at the IEEE EMBS Public Forum on COVID-19. The paper also provides recent studies, statistics and data and new perspectives on ongoing and future challenges pertaining to the COVID-19 pandemic.

Conventional biometric modalities, such as the face, fingerprint, and iris, are vulnerable against imitation and circumvention. Accordingly, secure biometric modalities with cancelable properties are needed for personal identification, especially in smart healthcare applications. Here we developed a person identification model using high-density surface electromyography (HD-sEMG) as biometric traits. In this model, the HD-sEMG biometric templates are cancelable and could be customized by the users through finger isometric contractions. A deep feature learning approach, implemented by convolutional neural networks (CNNs) is used to capture user-specific patterns from HD-sEMG signals and make identification decisions. This model has been validated on twenty-two subjects, with training and testing data acquired from two different days. The rank-1 identification accuracy and equal error rate for 44 identities (22 subjects × 2 accounts) can reach 87.23% and 4.66%, respectively. The cross-day identification accuracy of the proposed model is higher than the results of previous methods reported in the literature. The usability and efficiency of the proposed model are also investigated, indicating its potentials for practical applications.

MicroRNAs play an important role in gene regulation for many biological systems, including nicotine and alcohol addiction. However, the underlying mechanism behind miRNAs and mRNA interaction is not well characterized. Microarrays are commonly used to quantify the expression levels of mRNAs and/or miRNAs simultaneously. In this study, we performed a Bayesian network analysis to identify mRNA and miRNA interactions following perinatal exposure to nicotine and/or alcohol. We utilized three sets of microarray data to predict the regulation relationship between mRNA and miRNAs. Following perinatal alcohol exposure, we identified two miRNAs: miR-542-5p and miR-874-3p, that exhibited a strong mutual influence on several mRNA in gene regulatory pathways, mainly Axon guidance and Dopaminergic synapses. Finally, we confirmed our predicted addiction pathways based on the Bayesian network analysis with the widely used Kyoto Encyclopedia of Genes and Genomes (KEGG)-based database and identified comparable relevant miRNA-mRNA pairs. We believe the Bayesian network can provide insight into the complexity biological process related to addiction and can potentially be applied to other diseases.

In this article, we propose a novel neuromuscular password-based user authentication method. The method consists of two parts: surface electromyogram (sEMG) based finger muscle isometric contraction password (FMICP) and neuromuscular biometrics. FMICP can be entered through isometric contraction of different finger muscles in a prescribed order without actual finger movement, which makes it difficult for observers to obtain the password. In our study, the isometric contraction patterns of different finger muscles were recognized through high-density sEMG signals acquired from the right dorsal hand. Moreover, both time-frequency-space domain features at macroscopic level (interference-pattern EMG) and motor neuron firing rate features at microscopic level (via decomposition) were extracted to represent neuromuscular biometrics, serving as a second defense. The FMICP and macro-micro neuromuscular biometrics together form a neuromuscular password. The proposed neuromuscular password achieved an equal error rate (EER) of 0.0128 when impostors entered a wrong FMICP. Even when impostors entered the correct FMICP, the neuromuscular biometrics, as the second defense, inhibited impostors with an EER of 0.1496. To the best of our knowledge, this is the first study to use individually unique neuromuscular information during unobservable muscle isometric contractions for user authentication, with training and testing data acquired on different days.

Because of the rapid and serious nature of acute cardiovascular disease (CVD) especially ST segment elevation myocardial infarction (STEMI), a leading cause of death worldwide, prompt diagnosis and treatment is of crucial importance to reduce both mortality and morbidity. During a pandemic such as coronavirus disease-2019 (COVID-19), it is critical to balance cardiovascular emergencies with infectious risk. In this work, we recommend using wearable device based mobile health (mHealth) as an early screening and real-time monitoring tool to address this balance and facilitate remote monitoring to tackle this unprecedented challenge. This recommendation may help to improve the efficiency and effectiveness of acute CVD patient management while reducing infection risk.

With the soaring development of body sensor network (BSN)-based health informatics, information security in such medical devices has attracted increasing attention in recent years. Employing the biosignals acquired directly by the BSN as biometrics for personal identification is an effective approach. Noncancelability and cross-application invariance are two natural flaws of most traditional biometric modalities. Once the biometric template is exposed, it is compromised forever. Even worse, because the same biometrics may be employed as tokens for different accounts in multiple applications, the exposed template can be used to compromise other accounts. In this work, we propose a cancelable and cross-application discrepant biometric approach based on high-density surface electromyogram (HD-sEMG) for personal identification. We enrolled two accounts for each user. HD-sEMG signals from the right dorsal hand under isometric contractions of different finger muscles were employed as biometric tokens. Since isometric contraction, in contrast to dynamic contraction, requires no actual movement, the users' choice to login to different accounts is greatly protected against impostors. We realized a promising identification accuracy of 85.8% for 44 identities (22 subjects × 2 accounts) with training and testing data acquired 9 days apart. The high identification accuracy of different accounts for the same user demonstrates the promising cancelability and cross-application discrepancy of the proposed HD-sEMG-based biometrics. To the best of our knowledge, this is the first study to employ HD-sEMG in personal identification applications, with signal variation across days considered.

Recently, a contactless method for measuring a biological signal using a video camera has garnered attention. Especially, video plethysmography, a technique for obtaining a pulse wave from a video, is useful for managing the health of people on a daily basis. However, any body movement of a person subjected to the measurement leads to the generation of irregular noise in video plethysmography and reduces the accuracy of the recorded biological information, e.g., heart rate, during the measurement. Blind source separation is a popular technique for eliminating noise from the results of video plethysmography comprising different multiple-color channels. However, it is difficult to apply this technique to a single-color video such as a near-infrared video. Herein, a new method that combines singular spectrum analysis with the circular autocorrelation function is introduced to eliminate irregular noise in single-color video plethysmography. Applying the proposed method on videos collected from 39 individuals improved the estimation accuracy of instantaneous heart rate by approximately 44% over a conventional method using a linear filter. Furthermore, the proposed method also enabled more precise estimations of the heart rate than that achieved using multi-color video plethysmography.

We believe that this special issue will serve to increase the public awareness and foster discussions on the multiple worldwide BRAIN initiatives, both within and outside the IEEE, providing an impetus for development of long-term cost-effective healthcare solutions. We also believe that the topics presented in this special issue will serve as scientific evidence for health and policy advocates of the value of neurotechnologies for improving the neurological and mental health and wellbeing of the general population. Below we briefly highlight the papers and technologies in this special issue.

In this review, we present an implantable microimaging device to observe brain activities of small experimental animals such as mice and rats. Three categories of such devices are described: an optical fiber system, a head-mountable fluorescent microscope, and an ultrasmall image sensor that can be directly implanted into the brain. Among them, we focus on the third one, because this is a powerful tool to explore brain activities in deep brain region in a freely moving mouse. The device structure and performance are shown with some examples of deep brain images of mice.

The papers in this special section were presented at three well-known conferences organized in 2014: EAI Mobihealth, IEEE HealthCom, and IEEE Biomedical and Health Informatics. EAI Mobihealth is an annually organized conference, which started in 2010, to address the demands of the rapidly evolving disciplines of wireless communications, mobile computing, and sensing technologies in healthcare. The IEEE-Healthcom is held every year since 1999 in different countries in Asia, Europe, and in America. It aims at bringing together interested parties working in the field of healthcare to exchange ideas, discuss innovative and emerging solutions, and develop collaborations. The IEEE Biomedical Health Informatics Conference started in 2013 and is organized every year providing the forum to showcase enabling technologies of computing, devices, imaging, sensors, and systems that optimize the acquisition, transmission, processing, storage, retrieval, visualization, and analysis of medical data. The aim of this special section is to present an overview of recent advances in sensing technologies, monitoring of patients, security and privacy of data transfer, provision of collaborative environments, data gathering and analysis from various sources, and predictive models, which all finally target the best strategy for patient monitoring and treatment.

Provides an overview of the technical articles and features presented in this issue.

The aim of this special section is to provide an overview of the emerging biomedical informatics technologies and their application in research and clinical environments. Recent developments in biomedical informatics have created methods, techniques and tools, which are based on the analysis of heterogeneous data, data mining, decision support systems, multiscale modeling, etc. The distance from the development of such systems and the real clinical environments is still long enough, and only some of them have been used in a clinical scale.

Coronary heart disease (CHD) is the most prevalent cause of death worldwide. Atherosclerosis which is the condition of plaque buildup on the inside of the coronary artery wall is the main cause of CHD. Rupture of unstable atherosclerotic coronary plaque is known to be the cause of acute coronary syndrome. Vulnerability of atherosclerotic plaque has been related to a large lipid core covered by a fibrous cap. Non-invasive assessment of plaque characterization is necessary due to prognostic importance of early stage identification. The purpose of this study is to use the additional attenuation data provided by dual energy computed tomography (DECT) for plaque characterization. We propose to train supervised learners on pixel values recorded from DECT monochromatic X-ray and material basis pairs images, for more precise classification of fibrous and lipid plaques. The interaction of the pixel values from different image types is taken into consideration, as single pixel value might not be informative enough to separate fibrous from lipid. Organic phantom plaques scanned in a fabricated beating heart phantom were used as ground truth to train the learners. Our results show that support vector machines, artificial neural networks and random forests provide accurate results both on phantom and patient data.

These tutorials/keynote speeches discuss the following: the effects of nicotine exposure on the complexity and the genetic patterns of dopamine neurons in VTA; from 6-Ps medicine to cardiovascular health informatics; computer-aided interpretation of vascular images towards valid diagnosis and risk stratification of atherosclerosis; turning data into predictions of gene and protein function; from reading to writing (and rewriting) the code of life: the future of biology - scientific, ethical, legal, civil and social issues.

Clinics in developing nations often use paper-based records that are hard to manage or are inefficient. Electronic medical records (EMR) systems could help increase the efficiency and efficacy of these clinics. Even though some EMR systems have been developed for developing countries, they lack customizability. This paper gives some background information about EMR systems: how they are used in developed countries, and how FileMaker, an off-the-shelf database software, could be used to rapidly deploy EMR systems in clinics and hospitals of developing countries. An existing EMR database developed by Banner Alzheimer's Institute serves as a proof of concept that FileMaker is a viable EMR solution.

This issue covers advances in neural and cognitive engineering to highlight and understand the organizational principles and underlying mechanisms of neural systems and cognition, and to study the behavior dynamics and complexities of neural systems in nature. There are ten papers in this special issue.

Quantitative assessment of motor abilities in stroke survivors can provide valuable feedback to guide clinical interventions. Numerous clinical scales were developed in the past to assess levels of impairment and functional limitation in individuals after stroke. The Functional Ability Scale is one of these clinical scales. It is a 75-point scale used to evaluate the functional ability of subjects by grading movement quality during performance of 15 motor tasks. Performance of these motor tasks requires subjects to reach for objects (e.g., a pencil on a table) and manipulate them (e.g., lift the pencil). In this paper, we show that accelerometer data recorded during performance of a subset of the motor tasks pertaining to the Functional Ability Scale can be relied upon to derive accurate estimates of the scores provided by a clinician using this scale. Accelerometer-based estimates of clinical scores were obtained by segmenting the recordings into movement components (reaching, manipulation, release/return), extracting data features, selecting features that maximized the separation among classes associated with different clinical scores, feeding these features to Random Forests to estimate scores for individual motor tasks, and using a linear equation to estimate the total Functional Ability Scale score based on the sum of the clinical scores for individual motor tasks derived from the accelerometer data. Results showed that it is possible to achieve estimates of the total Functional Ability Scale score marked by a bias of only 0.04 points of the scale and a standard deviation of only 2.43 points when using as few as three sensors to collect data during performance of only six motor tasks.

During the last decade there has been a renewed interest in the development of advanced, active hand prosthetic devices for amputees. In contrast to passive prostheses, active devices can be controlled by the user's intention. Active prosthetic devices have been substantially improved by integrating robot technology to achieve more functionalities and lifelike movements. Despite important progress in the technological development of prosthetics, their clinical application is still limited by the quantity and quality of biological signals that can be used for understanding the user's intention, and by the relatively poor performance of the algorithms that translate the user's intention into a desired movement. In this review we describe a solution to some of these limitations, i.e., the flexible, multichannel, implantable intraneural and intramuscular electrodes to interface the body's peripheral nerves or muscles. We aim to review the historic development, the underlying technology, and the design concepts of these electrodes. Moreover, the signal processing methods applied to these recordings and their use for the control of prosthetic devices will be discussed. Although the focus is on hand prostheses, the interface approach described is general.

This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson's disease. A support vector machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different SVM kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms and motor complications. Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.

It is known that cancer develops when cells in a part of the body begin to grow out of control. Because cancer cells continue to grow and divide with no order, they never differentiate into the specific tissue, and thus, they are functionally different from normal cells. However, there are some genes that help to prevent cells' malignant behavior, and therefore, are referred to as tumor suppressor genes. Here, we have investigated the structural and functional relationships of p53, oncogene and interleukin 2 (IL2) proteins using the resonant recognition model (RRM), a physico-mathematical approach based on digital signal processing methods. In addition, using the RRM concepts, we have designed the peptide analoges that would exhibit tumor-suppression-like activity and be used in anticancer vaccine development.

Recent advances in medical technology have significantly improved the human health in developed countries. However, these advances remain out of touch for much of the worldpsilas population. We still face unprecedented healthcare challenges in the 21st century. The prevalence of major diseases today, from the global AIDS pandemic to antibiotic-resistant tuberculosis, cuts across the healthcare, political, economic, social, and biomedical disciplines: These diseases will continue affecting the world unless major measures are taken to develop comprehensive prevention and treatment programs. Thus, biomedical engineers are expected to play a critical role in developing novel and affordable medical technology and drugs to solve global healthcare problems, especially in the developing countries. In this talk, we discuss the healthcare systems, financing, delivery and management in the world, recent advances in information technologies in biomedicine and their use in diagnosing, treating, and preventing diseases, using novel technologies to develop new drugs, technology regulation, and ethical issues surrounding the use of novel technologies.

Provides an overview of the technical articles and features presented in this issue.

Proteins are the biomolecular workhorses driving the most biological processes in any living organism. These processes are based on selective interactions between particular proteins. So far the rules governing the coding of the protein's biological function, i.e. its ability to selectively interact with other biomolecules, have not been elucidated The resonant recognition model (RRM) is a novel physicomathematical approach established to analyze the interaction between a protein and its target. The RRM assumes that the specificities of protein interactions are based on the resonant electromagnetic energy transfer at the specific frequency for each interaction. One of the main applications of this model is to predict the location of a protein's biological active site(s) using digital signal processing. This paper incorporates the continuous wavelet transform (CWT) into the RRM to predict the active sites for a chosen protein example. We have investigated the oncogene functional group using digital signal analysis methods, in particular Fourier transform and CWT determined oncogenes' characteristic frequency and functional active sites; and performed the design of the peptide analogous. The results obtained provide new insights into the structure-function relationships of the analyzed oncogene protein family.

Now that the human genome has been sequenced, the measurement, processing, and analysis of specific genomic information in real time are gaining considerable interest because of their importance to better the understanding of the inherent genomic function, the early diagnosis of disease, and the discovery of new drugs. Traditional methods to process and analyze deoxyribonucleic acid (DNA) or ribonucleic acid data, based on the statistical or Fourier theories, are not robust enough and are time-consuming, and thus not well suited for future routine and rapid medical applications, particularly for emergency cases. In this paper, we present an overview of some recent applications of signal processing techniques for DNA structure prediction, detection, feature extraction, and classification of differentially expressed genes. Our emphasis is placed on the application of wavelet transform in DNA sequence analysis and on cellular neural networks in microarray image analysis, which can have a potentially large effect on the real-time realization of DNA analysis. Finally, some interesting areas for possible future research are summarized, which include a biomodel-based signal processing technique for genomic feature extraction and hybrid multidimensional approaches to process the dynamic genomic information in real time.

Provides an overview of the technical articles and features presented in this issue.

Recent developments in networking and computing have enabled collaborative biomedical engineering research by geographically separated participants. One of the most promising goals is to use these technologies to extend human intellectual capabilities in medical decision making. These emerging technologies are poised to drastically reduce healthcare cost by providing service at remote locations. This also increases diagnosis capacity since information is made available to experts at any location. In this paper, we propose a novel application of a recently developed interactive and distributed system in medical consultation and education. Our approach builds on the notion that interactive and distributive capabilities of the system are crucial for medical consultation and education. The presented application uses a multiuser, collaborative environment with multimodal human/machine communication in the dimensions of sight, sound, and touch. The experimental setup, consisting of two user stations, and the multimodal interfaces, including sight (eye-tracking), sound (automatic speech), and touch (microbeam pen), were tested and evaluated. The system uses a collaborative workspace as a common visualization space. Users communicate with the application through a fusion agent by eye-tracking, speech, and microbeam pen. The audio/video teleconferencing is also included to help the radiologists to communicate with each other simultaneously while they are working on the mammograms. The system used in this study has three software agents: a fusion agent, a conversational agent, and an analytic agent. The fusion agent interprets multimodal commands by integrating the multimodal inputs. The conversational agent answers the user's questions and detects human-related or semantic errors and notifies the user about the results of the image analysis. The analytic agent enhances the digitized images using the wavelet denoising algorithm if requested by the user. To show how well the system performs in practice, we used the system for medical consultation on mammograms. Results also show that the relevant information about the region of interest (ROI) of the mammograms chosen by the users is extracted automatically and used to enhance the mammograms.