Manual heart disease diagnosis with the electrocardiogram (ECG) is intractable due to the intertwined signal features and lengthy diagnosis procedure, especially for the 24-hour dynamic ECG signals. Consequently, even experienced cardiologists may face difficulty in producing all accurate ECG reports. In recent years, Artificial Intelligence (AI), particularly neural network-based automatic ECG diagnosis methods have exhibited promising performance, suggesting a potential alternative to the labor-intensive examination conducted by cardiologists. However, many existing approaches failed to adequately consider the temporal and channel dimensions when assembling features and ignored interpretability. And clinical theory underscores the necessity of prolonged signal observations for diagnosing certain ECG conditions such as tachycardia. Moreover, specific heart diseases manifest primarily through distinct ECG leads represented as channels. In response to these challenges, this paper introduces a novel neural network architecture for ECG classification (diagnosis). The proposed model incorporates Lead Fusing blocks, transformer-XL (meaning extra long) encoder-based Encoder modules, and hierarchical temporal attentions. Importantly, this classifier operates directly on raw ECG time-series signals rather than cardiac cycles. Signal integration begins with the Lead Fusing blocks, followed by the Encoder modules and hierarchical temporal attentions, enabling the extraction of long-dependent features. Furthermore, existing convolution-based methods have been argued to compromise interpretability, whereas the proposed neural network provides improved clarity in this regard. Experimental evaluations on a comprehensive public dataset confirm the superiority of the proposed classifier over state-of-the-art methods. Moreover, a visualization method was employed to generate a location map that demonstrates the areas of the signal emphasized by the model, thereby enhancing interpretability. • Our model extracts long-dependent features of ECG signals based on the Transformer-XL encoder. • The proposed network offers the improved interpretability. • Our classifier achieves superior performance over other state-of-the-art methods.

In minimally invasive surgery (MIS), the field of view (FOV) control is crucial. Autonomous endoscope robots have been developed to facilitate MIS procedures by enabling autonomous surgical target tracking, thus reducing the workload on surgeons. However, existing visual servoing-based target tracking methods for autonomous endoscopes often overlook the insecurity stemming from restricted workspace conditions. Instances, such as collisions between the endoscope robot’s tip and the patient’s chest or abdominal wall pose risks to patient tissue, while extensive motion of the endoscope shaft may damage incision port tissue. Addressing these security concerns, this article proposes a novel approach called virtual fixture-based restricted workspace constraint (RWSC) to reconstruct the endoscope robot’s movement range. A quadratic programming (QP) optimization framework is employed to govern the robot’s motion, ensuring autonomous target tracking while adhering to RWSCs. To solve the QP problem, we propose an adaptive zeroing neural network (ZNN) featuring a newly designed activation function (AF). This AF enhances the ZNN with predefined-time convergence and noise rejection capabilities, making it especially suitable for time-sensitive and noise-prone surgical applications. Theoretical analysis and experimental results demonstrate that our adaptive ZNN achieves shorter convergence times than existing neural dynamic-based QP solvers. Physical validations show the efficacy of the proposed RWSCs in limiting the workspace of the endoscope robot, while the FOV control strategy enables autonomous target tracking of flexible endoscopes under diverse constraints and objectives.

In response to torso-oriented magnetic localization tasks that require the system to have interactivity and flexibility with guaranteed accuracy, a novel bio-inspired magnetic sensor array configuration is proposed in this paper. Precisely, the ideas of the natural characteristics of octopus flexible tentacles and the "wrap" morphology are integrated into the design of the magnetic localization system based on the sensor array method. It is worth mentioning that such a design enhances the interactivity and flexibility of the localization system compared to the general planar sensor array strategy. Apart from the concept introduction, the geometry analysis of the proposed configuration is presented based on the constant curvature model. Besides, the magnetic localization algorithm for the system is presented by constructing a magnetic tracking optimization function. Eventually, the proposed concept and developed algorithm are examined in the sensor-array-simulation environment to manifest their effectiveness and applicability. The experimental results indicate that the octopus-inspired-configuration sensor array achieves a mean accuracy at a centimeter-level in our cases, and has better accuracy with a mean value of ē as 0.0178 m and ${\overline {SQR} _{ave{\text{ }}}}$ as 0.0883 for the center interest space compared to general planar configuration one. Moreover, the effect of the configuration error is analyzed. These results verify the feasibility and superiority of the proposed concept and hold significant practical significance in addressing the challenge associated with magnetic localization tasks toward the clinical application scenarios.

This paper is concerned with the issue of the kinematic model-based bending control for the magnetically actuated robotic endoscope and its application for automatic retroflexion. By the utilization of the Cosserat rod theory and the transformation in the magnetic tip of the endoscope, the comprehensive kinematic model of the magnetically-actuated robotic endoscope is established. Afterward, a magnetic control scheme for the bending motion is proposed by co-developing an error feedback PID control strategy and the model-based feedback approach. Moreover, as one unique kind of bending motion, retroflexion is taken into account, and the strategy aimed at the bid of compact space retroflexion is presented by virtue of the introduction of serial waypoints pursuing the position of the magnetic tip being close to the midline as possible. Eventually, the developed modeling and bending control scheme and the compact space retroflexion strategy are examined in a magnetically actuated robotic endoscope system to manifest the effectiveness and applicability of the theoretical approach. The experimental results indicate that the designed controller can drive the endoscope to bend to the desired pose and show a reduction of about 47.01% in the sweeping area and 79.25% in the last distance to midline achieved by conducting compact space retroflexion in comparison to “U” type one.

This paper presents a long-range magnetic actuated and guided endoscope for uniport video-assisted thoracic surgery (VATS). In VATS, the incision is quite narrow and part of the chest wall may be very thick. So, the magnetic endoscope system is required to produce sufficient attractive force at a considerable distance with a compact dimension. In this paper, a magnetic endoscope system is developed to meet the aforementioned clinical demands. In the system, both the internal and external units consist of two cylindrical magnets at both ends and a semi-cylindrical magnet in the middle. Coupled with the magnetic field from the external unit, the internal endoscope can achieve anchoring, tilting, panning, and translating to provide the desired view for the surgeon. The rotation of the endoscope is dynamically modeled by combining magnetic theory and coordinate transformation. The prototype is made with a boundary box of 10×14×56 mm, which can be inserted through the narrow incision in VATS. In the experiment, the developed models of anchoring, tilting, and panning were verified. The magnet configuration in the system can achieve a static anchoring distance of 95 mm and exhibits enhancement in attractive force compared with other designs.

In this paper, we proposed an autonomous robotic flexible endoscope system for the laparoscopic bariatric surgery (LBS). This system comprises a UR5 robot and a flexible endoscope equipped with a novel continuum joint, named reinforced double helix continuum mechanism. Compared with the simple helix structure, the compressional and torsional stiffness of the proposed joint are improved significantly. To automate the robotic flexible endoscope, image-based visual servoing technique is employed. A deep learning algorithm named TernausNet-16 is improved and incorporated into the control framework to detect surgical instruments inside the camera view. The experimental studies verified the effectiveness and feasibility of the robotic flexible endoscope system for the visual serviong control scheme assisted by deep learning methods.