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Blood pressure (BP) is an important clinical vital sign that varies from beat-to-beat. Nevertheless, these variations cannot be captured by the conventional cuff-based BP monitors. This study proposes and evaluates novel cuffless frameworks to continuously estimate the 10-beat averaged systolic BP (SBP) and diastolic BP (DBP) during dynamic exercise by fusing information from multiple biosensors using five machine learning algorithms. Over 100 thousand beats of data were collected from 62 subjects (aged 59 ± 10 years), each underwent a maximal exercise stress test. The average length of recording for each subject was 35 minutes. The BP ranges were 75-280 mmHg for SBP and 36-157 mmHg for DBP respectively. Multiple physiological parameters were measured continuously and used as inputs to five machine learning algorithms for estimating the 10-beat SBP and DBP averages before, during and after the cycling exercise. The mean absolute error (MAE) of Gaussian process regression (GPR) model was 4.8 mmHg and 3.4 mmHg for SBP and DBP, respectively. The MAE of multiple linear regression (MLR), regression tree (RT), ensemble of trees (ETs), and support vector machine (SVM) models varied from 6.1 mmHg to 17.6 mmHg and from 4.0 mmHg to 9.7 mmHg for SBP and DBP, respectively. The GPR model outperformed the other four models and showed promising results in estimating the 10-beat averages of both SBP and DBP without a cuff in a general elderly population under dynamic conditions.

Estimating hospital mortality of patients is important in assisting clinicians to make decisions and hospital providers to allocate resources. This paper proposed a multi-task recurrent neural network with attention mechanisms to predict patients' hospital mortality, using reconstruction of patients' physiological time series as an auxiliary task. Experiments were conducted on a large public electronic health record database, i.e., MIMIC-III. Fifteen physiological measurements during the first 24 h of critical care were used to predict death before hospital discharge. Compared with the conventional simplified acute physiology score (SAPS-II), the proposed multi-task learning model achieved better sensitivity (0.503 ± 0.020 versus 0.365 ± 0.021), when predictions were made based on the same 24-h observation period. The multi-task learning model is recommended to be updated daily with at least a 6-h observation period, in order for it to perform similarly or better than the SAPS-II. In the future, the need for intervention can be considered as another task to further optimize the performance of the multi-task learning model.

Pulse arrival time (PAT) has been broadly investigated for its potential for cuffless blood pressure (BP) estimation and ease of measurement by wearable devices. It is also of great significance to explore whether PAT conveys complementary information to BP for vascular health assessment. In this paper, the differences between the 24-h ambulatory BP and wearable-based PAT were compared among 12 young normotensives and 15 elderly hypertensives in terms of the mean and coefficients of variation (CoVs). The correlations of the nocturnal normalized PAT (n-PAT) and BP with two arterial stiffness-related parameters (i.e., the intrinsic elastic modulus E0 and the vascular modulation factor α) estimated by a proposed model-based method were also compared. The results showed that the inter-subject variances of the nocturnal average n-PAT were significantly different between the hypertensives and the normotensives (P

A computer-aided detection (CAD) tool for locating and detecting polyps can help reduce the chance of missing polyps during colonoscopy. Nevertheless, state-of-the-art algorithms were either computationally complex or suffered from low sensitivity and therefore unsuitable to be used in real clinical setting. In this paper, a novel regression-based Convolutional Neural Network (CNN) pipeline is presented for polyp detection during colonoscopy. The proposed pipeline was constructed in two parts: 1) to learn the spatial features of colorectal polyps, a fast object detection algorithm named ResYOLO was pre-trained with a large non-medical image database and further fine-tuned with colonoscopic images extracted from videos; and 2) temporal information was incorporated via a tracker named Efficient Convolution Operators (ECO) for refining the detection results given by ResYOLO. Evaluated on 17,574 frames extracted from 18 endoscopic videos of the AsuMayoDB, the proposed method was able to detect frames with polyps with a precision of 88.6%, recall of 71.6% and processing speed of 6.5 frames per second, i.e. the method can accurately locate polyps in more frames and at a faster speed compared to existing methods. In conclusion, the proposed method has great potential to be used to assist endoscopists in tracking polyps during colonoscopy.

An automatic colonic polyp localization algorithm named RYCO is developed to tackle the challenges of precise polyp location indication together with fast processing time when general limited and accessible computation resource is given.

Electromagnetic Navigation Systems (eMNS) can be used to control a variety of multiscale devices within the human body for remote surgery. Accurate modeling of the magnetic fields generated by the electromagnets of an eMNS is crucial for the precise control of these devices. Existing methods assume a linear behavior of these systems, leading to significant modeling errors within nonlinear regions exhibited at higher magnetic fields, preventing these systems from operating at full capacity. In this paper, we use a random forest (RF) and an artificial neural network (ANN) to model the nonlinear behavior of the magnetic fields generated by an eMNS. Both machine learning methods outperformed the state-of-the-art linear multipole electromagnet model (MPEM). The RF and the ANN model reduced the root mean squared error (RMSE) of the MPEM when predicting the field magnitude by approximately 40% and 87%, respectively, over the entire current range of the eMNS. At high current regions, especially between 30 and 35 A, the field-magnitude RMSE improvement of the ANN model over the MPEM was 37 mT, equivalent to 90% error reduction. This study demonstrates the feasibility of using machine learning to model an eMNS for medical applications, and its ability to account for complex nonlinear behavior at high currents. The use of machine learning thus shows promise in developing accurate field predicting models, and ultimately improving surgical procedures that use magnetic navigation. © 2020 IEEE., 2020 IEEE International Conference on Robotics and Automation (ICRA), ISBN:978-1-7281-7395-5, ISBN:978-1-7281-7394-8, ISBN:978-1-7281-7396-2

We have designed a deep-learning model, an “Artificial Intelligent Endoscopist (a.k.a. AI-doscopist)”, to localise colonic neoplasia during colonoscopy. This study aims to evaluate the agreement between endoscopists and AI-doscopist for colorectal neoplasm localisation. AI-doscopist was pre-trained by 1.2 million non-medical images and fine-tuned by 291,090 colonoscopy and non-medical images. The colonoscopy images were obtained from six databases, where the colonoscopy images were classified into 13 categories and the polyps’ locations were marked image-by-image by the smallest bounding boxes. Seven categories of non-medical images, which were believed to share some common features with colorectal polyps, were downloaded from an online search engine. Written informed consent were obtained from 144 patients who underwent colonoscopy and their full colonoscopy videos were prospectively recorded for evaluation. A total of 128 suspicious lesions were resected or biopsied for histological confirmation. When evaluated image-by-image on the 144 full colonoscopies, the specificity of AI-doscopist was 93.3%. AI-doscopist were able to localise 124 out of 128 polyps (polyp-based sensitivity = 96.9%). Furthermore, after reviewing the suspected regions highlighted by AI-doscopist in a 102-patient cohort, an endoscopist has high confidence in recognizing four missed polyps in three patients who were not diagnosed with any lesion during their original colonoscopies. In summary, AI-doscopist can localise 96.9% of the polyps resected by the endoscopists. If AI-doscopist were to be used in real-time, it can potentially assist endoscopists in detecting one more patient with polyp in every 20–33 colonoscopies.

Objective: Wireless capsule endoscope (WCE) is a revolutionary approach to diagnose small bowel pathologies. Currently available WCEs are mostly passive devices with image capturing function only, while on-going efforts have been placed on robotizing WCEs or to enhance them with therapeutic functions. In this paper, the authors present a novel inflatable WCE for haemostasis in the gastrointestinal (GI) tracts by balloon tamponade effect. Methods: The proposed wireless capsule consists of a balloon that can be inflated using the endothermic reaction of acid and base. When the balloon reached a precalculated pressure level, it is able to stop at a bleeding site in the bowel, and achieve haemostasis by tamponade effect. The prototype is 14 mm in diameter, with three sections of 13, 35, and 12 mm in length, respectively. The three sections are linked together with flexible joints and enclosed in a silicone balloon. The prototypes were tested in ex vivo porcine intestine models. Results: In the ten ex vivo trials conducted, the inflatable wireless capsule achieved average balloon pressure of 46.0 mmHg and withstood average maximum longitudinal pulling force at 1.46 N. An in vivo study was carried out as a proof-of-concept for treating bleeding in a porcine model. The proposed inflatable WCE succeeded in the animal test by controlling haemostasis within 5 min. No rebleeding was observed in the next 20 min. Conclusion: The results suggested that the inflatable capsule with a real-time bleeding detection algorithm can be implemented. Moreover, the proposed inflatable WCE prototype can achieve haemorrhage control in the lower GI. Significance: To our best knowledge, this is the first study that demonstrated the potential to treat GI haemorrhage by an inflatable WCE. The proposed capsule enables the development of a closed-loop system based on a body sensor network to provide early treatment of GI bleeding for p-medicine.

Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. Although polypectomy at early stage reduces CRC incidence, 90% of the polyps are small and diminutive, where removal of them poses risks to patients that may outweigh the benefits. Correctly detecting and predicting polyp type during colonoscopy allows endoscopists to resect and discard the tissue without submitting it for histology, saving time, and costs. Nevertheless, human visual observation of early stage polyps varies. Therefore, this paper aims at developing a fully automatic algorithm to detect and classify hyperplastic and adenomatous colorectal polyps. Adenomatous polyps should be removed, whereas distal diminutive hyperplastic polyps are considered clinically insignificant and may be left in situ . A novel transfer learning application is proposed utilizing features learned from big nonmedical datasets with 1.4-2.5 million images using deep convolutional neural network. The endoscopic images we collected for experiment were taken under random lighting conditions, zooming and optical magnification, including 1104 endoscopic nonpolyp images taken under both white-light and narrowband imaging (NBI) endoscopy and 826 NBI endoscopic polyp images, of which 263 images were hyperplasia and 563 were adenoma as confirmed by histology. The proposed method identified polyp images from nonpolyp images in the beginning followed by predicting the polyp histology. When compared with visual inspection by endoscopists, the results of this study show that the proposed method has similar precision (87.3% versus 86.4%) but a higher recall rate (87.6% versus 77.0%) and a higher accuracy (85.9% versus 74.3%). In conclusion, automatic algorithms can assist endoscopists in identifying polyps that are adenomatous but have been incorrectly judged as hyperplasia and, therefore, enable timely resection of these polyps at an early stage before they develop into invasive cancer.

Endoscopic submucosal dissection (ESD) is an effective and less invasive treatment for early gastric and colorectal cancers. Nevertheless, performing ESD through a flexible endoscope is technically challenging due to the limited controllability of the endoscopic instruments. Therefore, a surgical robotic system with high dexterity and better ergonomics is much needed. A master–slave dual-arm robot with customizing workspace for ESD is proposed for this study. The design is such that the procedure is completed without the need for changing the instrument. Our robot is mainly driven by tendon–sheath (TS) mechanism. Modeling and use of a controller for elongation compensation of tendon due to friction to increase position control accuracy are presented. Experiment shows that the controller greatly enhances the controllability of the robot. We have demonstrated that the design of the robot arm allows it to lift up to 48 g, which is sufficient for ESD. Six subjects, including three endoscopists and upper gastrointestinal surgeons, were invited to evaluate the system in an ex vivo experiment. Experienced surgeons were able to complete a dissection within 30 min. We have demonstrated a prototype robot that can facilitate the ESD procedure with bimanual tissue dissection. The system is competent to remove a lesion area precisely. Further studies are needed to evaluate the system in vivo .

This paper aims to develop an objective index for anxiety based on features derived from electroencephalogram (EEG) and photoplethysmogram (PPG) collected from wearable headset and glasses. The 20 subjects were asked to ride at his most comfortable speed in Task 1 and ride while imagining competing with another person in Task 2. A Competitive State Anxiety Inventory-2 questionnaire was conducted before each task to evaluate the anxiety level of each participant. Various features were extracted from EEG and PPG. The results of this paper showed that the mean value and average power of alpha band wavelet coefficients and that of beta band are highly correlated with the anxiety level ( $r=-0.49$ and −0.58, $p for alpha band, and $r=-0.51$ and −0.58, $p for beta band, respectively). Features extracted from partial autocorrelation of EEG showed moderate correlation with the anxiety level. Mean pulse rate also acts as a potential anxiety marker for individualized anxiety measurement. Using both EEG and PPG features, the classification accuracy of three-level anxiety by principle component analysis and $k$ -nearest neighbors can achieve 62.5% across subjects. To conclude, wearable sensors have the potential to be used for assessing anxiety level objectively and unobtrusively to facilitate on-site sports performance enhancement and mental-stress-related studies.

Noninvasive and real-time cuffless blood pressure (BP) measurement realizes the idea of unobtrusive and continuous BP monitoring which is essential for diagnosis and prevention of cardiovascular diseases associated with hypertension. In this paper, a wearable sensor patch system that integrates flexible piezoresistive sensor (FPS) and epidermal electrocardiogram (ECG) sensors for cuffless BP measurement is presented. By developing parametric models on the FPS sensing mechanism and optimizing operational conditions, a highly stable epidermal pulse monitoring method is established and beat-to-beat BP measurement from the ECG and epidermal pulse signals is demonstrated. In particular, this study highlights the compromise between sensor sensitivity and signal stability. As compared with the current optical-based cuffless BP measurement devices, the sensing patch requires much lower power consumption (3 nW) and is capable of detecting subtle physiological signal variations, e.g., pre and postexercises, thus providing a promising solution for low-power, real-time, and home-based BP monitoring.

Algorithms for localising colorectal polyps have been studied extensively; however, they were often trained and tested using the same database. In this study, we present a new application of a unified and real-time object detector based on You-Only-Look-Once (YOLO) convolutional neural network (CNN) for localizing polyps with bounding boxes in endoscopic images. The model was first pre-trained with non-medical images and then fine-tuned with colonoscopic images from three different databases, including an image set we collected from 106 patients using narrow-band (NB) imaging endoscopy. YOLO was tested on 196 white light (WL) images of an independent public database. YOLO achieved a precision of 79.3% and sensitivity of 68.3% with time efficiency of 0.06 sec/frame in the localization task when trained by augmented images from multiple WL databases. In conclusion, YOLO has great potential to be used to assist endoscopists in localising colorectal polyps during endoscopy. CNN features of WL and NB endoscopic images are different and should be considered separately. A large-scale database that covers different scenarios, imaging modalities and scales is lacking but crucial in order to bring this research into reality.

Current markers for heart failure (HF) diagnosis and prognosis are mainly for the evaluation of cardiac functions. Since previous studies have reported that HF patients demonstrated abnormal vascular responses to external stimuli, it is speculated that vascular tone, a measure of activation level of vascular wall, may be able to reflect these abnormalities to assist HF detection. Nevertheless, vascular tone is difficult to be objectively quantified using existing tools. In this study, a vascular tone index was estimated from noninvasive blood pressure and pulse transit time measurements using system identification technique. This method was evaluated in 35 subjects (10 healthy, 13 with HF risk factors and 12 HF patients) in a regular maximal exercise test. It was found that the vascular tone index significantly increased by 24.4 ± 26.6% (p

Body sensors are now commonly used as ingestible, wearable and implantable devices for clinical diagnosis and continuous physiological monitoring. Nevertheless, they usually have limited resources. Recent advancements in technologies provide a possible solution to mitigate the resource limitation of these devices by connecting them with mobile devices and cloud services. To evaluate the feasibility of the cloud-enabled body sensor networks, this paper presents simulation results on testing the feasibility of 24-hour operating time and concurrent user support for the cloud-enabled applications.

Ambulatory blood pressure monitoring (ABPM) has become an essential tool in the diagnosis and management of hypertension. Current standard ABPM devices use an oscillometric cuff-based method which can cause physical discomfort to the patients with repeated inflations and deflations, especially during nighttime leading to sleep disturbance. The ability to measure ambulatory BP accurately and comfortably without a cuff would be attractive. This study validated the accuracy of a cuff-less approach for ABPM using pulse arrival time (PAT) measurements on both healthy and hypertensive subjects for potential use in hypertensive management, which is the first of its kind. The wearable cuff-less device was evaluated against a standard cuff-based device on 24 subjects of which 15 have known hypertension. BP measurements were taken from each subject over a 24-h period by the cuff-less and cuff-based devices every 15 to 30 minutes during daily activities. Mean BP of each subject during daytime, nighttime and over 24-h were calculated. Agreement between mean nighttime systolic BP (SBP) and diastolic (DBP) measured by the two devices evaluated using Bland-Altman plot were ?1.4 ± 6.6 and 0.4 ± 6.7 mmHg, respectively. Receiver operator characteristics (ROC) statistics was used to assess the diagnostic accuracy of the cuff-less approach in the detection of BP above the hypertension threshold during nighttime (>120/70 mmHg). The area under ROC curves were 0.975/0.79 for nighttime. The results suggest that PAT-based approach is accurate and promising for ABPM without the issue of sleep disturbances associated with cuff-based devices.

Mobile health (mHealth) is an emerging concept which refers to the combinational use of mobile computing, communication technologies, and medical sensors for healthcare [1]. In addition to the passive provision of health-related information via mobile platforms, its scope also extends to more active applications such as disease management, medical prognosis, diagnosis, and even treatment. A typical mHealth system is characterized by its features of mobility, wireless connectivity, location independence, and timely responsewhich together provide seamless and personalized healthcare.

Presents an introductory editorial on the special issue for this issue of the publication which examines the fields of biomedical and health informatics.

Body sensor networks (BSN) have emerged as an active field of research to connect and operate sensors within, on or at close proximity to the human body. BSN have unique roles in health applications, particularly to support real-time decision making and therapeutic treatments. Nevertheless, challenges remain in designing BSN nodes with antennas that operate efficiently around, ingested or implanted inside the human body, as well as new methods to process the heterogeneous and growing amount of data on-node and in a distributed system for optimized performance and power consumption. As the battery operating time and sensor size are two important factors in determining the usability of BSN nodes, ultralow power transceivers, energy-aware network protocol, data compression, on-node processing, and energy-harvesting techniques are highly demanded to ultimately achieve a self-powered BSN.

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

Wearable sensors have emerged as an active field of research in human-computer interaction. This study explores the use of wearable sensors to detect human motion for precise control of a two-arm surgical robot designed for gripping and dissecting tissues. The wearable sensory sheath was designed with flexible e-textile bipolar electrodes to collect forearm electromyogram (EMG) and inertial measurement units (IMU) to capture arm motions of the user. Four pairs of bipolar electrodes were used to collect EMG from the forearm muscles and two IMU for detecting rotation and translation of each arm of the subject. Features were extracted from the EMG and linear discriminant analysis was used as the decoding method to classify the signals of the muscles. A calibration procedure was setup in the beginning for calibrating the IMU sensors to familiarize the user with the working space environment and the mapped-motions of the robot arms. A training session was then conducted for each user to control wrist flexion, wrist extension, hand opening and hand closure of the robot arms. Six users were asked to perform random arm and hand movements to ensure satisfactory mapping of the movements of the surgical robot. To evaluate the system, two tasks which were important in controlling surgical robots were designed: (1) using the dissector to mark dots along a straight line and (2) lifting a weight from one location to another. The results of this study found that the performance of different users in operating the motion controller and the wearable sensory sheath were similar in accuracy. Most users completed the same task in a shorter time with a standard motion controller than the wearable sensory sheath. The results show that most users adapt to a standard motion controller faster than the wearable sensors although the latter can be calibrated individually and is a user-specific approach for the control of robot.

This paper presents a task specific robot for the procedure of endoscopic sub mucosal dissection (ESD). This robot has two arms with nine degrees of freedom (DOF), and the capability of tissue elevation and dissection. An optimal design of these robot arms requires the use of shape memory alloy (SMA) wire and steel wire actuators to develop an improved actuation mechanism, which enables force to be transmitted to the distal tip of the robot arms for an efficient tissue dissection. The details of the robot such as robot arm design and kinematics, driven unit mechanism and novel controller design are presented. Two endoscopists, who are also upper gastrointestinal surgeons, independently evaluated our robot in a bench porcine model ex vivo. Both of them were able to complete the procedure within 75-85% of the marked sub mucosal tissue and within 30 minutes.

THIS special issue includes seven interesting articles that tackle some of the existing challenges and design issues related to the use of RF and Communication Technologies for wireless implants. An in-body medical device is a miniature device that is inserted in the human body to collect physiological signals and images or to act like a prosthetic device restoring certain body functions. In order for these devices to communicate with the external world, radio frequency (RF) wireless technologies are often used to maintain their operation for a longer period of time. This is achieved by integrating wireless communication technologies within these devices. In some situations, a body worn device is used to receive the medical information from the devices in the human body and then transmit to remote stations for remote medical monitoring.

Background A tendon-sheath system (TSS) has the advantages of being relatively compact in size, flexible and low cost, and therefore is favoured in building flexible endoscopic robots to pass through long and tortuous human lumen. TSS, however, is prone to nonlinear behaviors such as backlash, hysteresis and direction dependent properties. A compensation technique is required to improve its positioning performance. Methods Tension and elongation models of TSS are analyzed. A feedforward motion compensation controller is designed to compensate the asymmetric backlash behavior of the TSS in real time. Results Motion tracking experiments were conducted on a TSS driven two DOFs continuum manipulator. The results showed that using the proposed compensation methods, tracking error can be reduced by 74%. Conclusions The proposed compensation method is useful for controlling flexible continuum robots, which are anticipated to have emerging roles in assisting surgeons to perform the increasingly technically challenging endoscopic procedures. Copyright © 2016 John Wiley & Sons, Ltd.

Tendon-sheath actuation mechanism is widely used in surgical robot, especially in endoscopic surgery, due to its capable of providing remote force and action transmission through long and flexible channel. However, hysteresis, backlash, nonlinear friction are the drawbacks of this mechanism. Our surgical robot use continuum manipulator which is useful in endoscopic surgery, due to its flexible and simple structure. Unlike other literatures that focus on tendon-sheath compensation only, the continuum manipulator is also taken into application level analysis. A model based feedforward motion compensation for tendon-sheath driven continuum manipulator is presented. The model is validated by using optical tracking system to trace the distal end position. Experiment result shows that the proposed model reduces the position error less than 5%.