A New Venous Entry Detection Method Based on Electrical Bio-impedance Sensing

Sensing Technologies for Extravasation Detection: A Review

Peripheral intravenous catheters are administered for various purposes, such as blood sampling or the infusion of contrast agents and drugs. Extravasation happens when the catheter is unintentionally directed outside of the vein due to movement of the intravascular catheter, enhanced vascular permeability, or occlusion of the upstream vein. In this article, extravasation and its mechanism are discussed. Subsequently, the sensorized devices (e.g., single sensor and multimodal detection) to identify the extravasation phenomena are highlighted. In this review article, we have shed light on both physiological and engineering points of view of extravasation and its detection approaches. This review provides an overview on the most recent and relevant technologies that can help in the early detection of extravasation.

Tissue Recognition Based on Electrical Impedance Classified by Support Vector Machine in Spinal Operation Area

OBJECTIVE

One of the major difficulties in spinal surgery is the injury of important tissues caused by tissue misclassification, which is the source of surgical complications. Accurate recognization of the tissues is the key to increase safety and effect as well as to reduce the complications of spinal surgery. The study aimed at tissue recognition in the spinal operation area based on electrical impedance and the boundaries of electrical impedance between cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus.

METHODS

Two female white swines with body weight of 40 kg were used to expose cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus under general anesthesia and aseptic conditions. The electrical impedance of these tissues at 12 frequencies (in the range of 10-100 kHz) was measured by electrochemical analyzer with a specially designed probe, at 22.0-25.0°C and 50%-60% humidity. Two types of tissue recognition models - one combines principal component analysis (PCA) and support vector machine (SVM) and the other combines combines SVM and ensemble learning - were constructed, and the boundaries of electrical impedance of the five tissues at 12 frequencies of current were figured out. Linear correlation, two-way ANOVA, and paired T-test were conducted to analyze the relationship between the electrical impedance of different tissues at different frequencies.

RESULTS

The results suggest that the differences of electrical impedance mainly came from tissue type (p < 0.0001), the electrical impedance of five kinds of tissue was statistically different from each other (p < 0.0001). The tissue recognition accuracy of the algorithm based on principal component analysis and support vector machine ranged from 83%-100%, and the overall accuracy was 95.83%. The classification accuracy of the algorithm based on support vector machine and ensemble learning was 100%, and the boundaries of electrical impedance of five tissues at various frequencies were calculated.

CONCLUSION

The electrical impedance of cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus had significant differences in 10-100 kHz frequency. The application of support vector machine realized the accurate tissue recognition in the spinal operation area based on electrical impedance, which is expected to be translated and applied to tissue recognition during spinal surgery.

Smart Bio-Impedance-Based Sensor for Guiding Standard Needle Insertion

A venipuncture is the most common non-invasive medical procedure, and is frequently used with patients; however, a high probability of post-injection complications accompanies intravenous injection. The most common complication is a hematoma, which is associated with puncture of the uppermost and lowermost walls. To simplify and reduce complications of the venipuncture procedure, and as well as automation of this process, a device that can provide information of the needle tip position into patient’s tissues needs to be developed. This paper presents a peripheral vascular puncture control system based on electrical impedance measurements. A special electrode system was designed to achieve the maximum sensitivity for puncture identification using a traditional needle, which is usually used in clinical practice. An experimental study on subjects showed that the electrical impedance signal changed significantly once the standard needle entered the blood vessel. On basis of theoretical and experimental studies, a decision rule of puncture identification based on the analysis of amplitude-time parameters of experimental signals was proposed. The proposed method was tested on 15 test and 9 control samples, with the results showing that 97% accuracy was obtained.

An electrical bioimpedance scanning system for subsurface tissue detection in Robot Assisted Minimally Invasive Surgery

In Robot Assisted Minimally Invasive Surgery, discriminating critical subsurface structures is essential to make the surgical procedure safer and more efficient. In this paper, a novel robot assisted electrical bio-impedance scanning (RAEIS) system is developed and validated using a series of experiments. The proposed system constructs a tri-polar sensing configuration for tissue homogeneity inspection. Specifically, two robotic forceps are used as electrodes for applying electric current and measuring reciprocal voltages relative to a ground electrode which is placed distal from the measuring site. Compared to the other existing electrical bioimpedance sensing technology, the proposed system is able to use miniaturized electrodes to measure a site flexibly with enhanced subsurfacial detection capability. In this paper, we present the concept, the modeling of the sensing method, the hardware design, and the system calibration. Subsequently, a series of experiments are conducted for system evaluation including finite element simulation, saline solution bath experiments and experiments based on ex vivo animal tissues. The experimental results demonstrate that the proposed system can measure the resistivity of the material with high accuracy, and detect a subsurface non-homogeneous object with 100% success rate. The proposed parameters estimation algorithm is able to approximate the resistivity and the depth of the subsurface object effectively with one fast scanning.

Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data

Strawberry is one of the most popular fruits in the market. To meet the demanding consumer and market quality standards, there is a strong need for an on-site, accurate and reliable grading system during the whole harvesting process. In this work, a total of 923 strawberry fruit were measured directly on-plant at different ripening stages by means of bioimpedance data, collected at frequencies between 20 Hz and 300 kHz. The fruit batch was then splitted in 2 classes (i.e. ripe and unripe) based on surface color data. Starting from these data, six of the most commonly used supervised machine learning classification techniques, i.e. Logistic Regression (LR), Binary Decision Trees (DT), Naive Bayes Classifiers (NBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron Networks (MLP), were employed, optimized, tested and compared in view of their performance in predicting the strawberry fruit ripening stage. Such models were trained to develop a complete feature selection and optimization pipeline, not yet available for bioimpedance data analysis of fruit. The classification results highlighted that, among all the tested methods, MLP networks had the best performances on the test set, with 0.72, 0.82 and 0.73 for the F[Formula: see text], F[Formula: see text] and F[Formula: see text]-score, respectively, and improved the training results, showing good generalization capability, adapting well to new, previously unseen data. Consequently, the MLP models, trained with bioimpedance data, are a promising alternative for real-time estimation of strawberry ripeness directly on-field, which could be a potential application technique for evaluating the harvesting time management for farmers and producers.

SmartProbe: a bioimpedance sensing system for head and neck cancer tissue detection

OBJECTIVES

This study presents SmartProbe, an electrical bioimpedance (EBI) sensing system based on a concentric needle electrode (CNE). The system allows the use of commercial CNEs for accurate EBI measurement, and was specially developed for in-vivo real-time cancer detection.

APPROACH

Considering the uncertainties in EBI measurements due to the CNE manufacturing tolerances, we propose a calibration method based on statistical learning. This is done by extracting the correlation between the measured impedance value |Z|, and the material conductivity σ, for a group of reference materials. By utilizing this correlation, the relationship of σ and |Z| can be described as a function and reconstructed using a single measurement on a reference material of known conductivity.

MAIN RESULTS

This method simplifies the calibration process, and is verified experimentally. Its effectiveness is demonstrate by results that show less than 6% relative error. An additional experiment is conducted for evaluating the system's capability to detect cancerous tissue. Four types of ex-vivo human tissue from the head and neck region, including mucosa, muscle, cartilage and salivary gland, are characterized using SmartProbe. The measurements include both cancer and surrounding healthy tissue excised from 10 different patients operated on for head and neck cancer. The measured data is then processed using dimension reduction and analyzed for tissue classification. The final results show significant differences between pathologic and healthy tissues in muscle, mucosa and cartilage specimens.

SIGNIFICANCE

These results are highly promising and indicate a great potential for SmartProbe to be used in various cancer detection tasks.

Design of a Drop-in EBI Sensor Probe for Abnormal Tissue Detection in Minimally Invasive Surgery

It is a common challenge for the surgeon to detect pathological tissues and determine the resection margin during a minimally invasive surgery. In this study, we present a drop-in sensor probe based on the electrical bioimpedance spectroscopic technology, which can be grasped by a laparoscopic forceps and controlled by the surgeon to inspect suspicious tissue area conveniently. The probe is designed with an optimized electrode and a suitable shape specifically for Minimally Invasive Surgery (MIS). Subsequently, a series of ex vivo experiments are carried out with porcine liver tissue for feasibility validation. During the experiments, impedance measured at frequencies from 1 kHz to 2 MHz are collected on both normal tissues and water soaked tissue. In addition, classifiers based on discriminant analysis are developed. The result of the experiment indicate that the sensor probe can be used to measure the impedance of the tissue easily and the developed tissue classifier achieved accuracy of 80% and 100% respectively.

Design and Integration of Electrical Bio-impedance Sensing in Surgical Robotic Tools for Tissue Identification and Display

The integration of intra-operative sensors into surgical robots is a hot research topic since this can significantly facilitate complex surgical procedures by enhancing surgical awareness with real-time tissue information. However, currently available intra-operative sensing technologies are mainly based on image processing and force feedback, which normally require heavy computation or complicated hardware modifications of existing surgical tools. This paper presents the design and integration of electrical bio-impedance sensing into a commercial surgical robot tool, leading to the creation of a novel smart instrument that allows the identification of tissues by simply touching them. In addition, an advanced user interface is designed to provide guidance during the use of the system and to allow augmented-reality visualization of the tissue identification results. The proposed system imposes minor hardware modifications to an existing surgical tool, but adds the capability to provide a wealth of data about the tissue being manipulated. This has great potential to allow the surgeon (or an autonomous robotic system) to better understand the surgical environment. To evaluate the system, a series of ex-vivo experiments were conducted. The experimental results demonstrate that the proposed sensing system can successfully identify different tissue types with 100% classification accuracy. In addition, the user interface was shown to effectively and intuitively guide the user to measure the electrical impedance of the target tissue, presenting the identification results as augmented-reality markers for simple and immediate recognition.

New electrical impedance methods for the in situ measurement of the complex permittivity of anisotropic skeletal muscle using multipolar needles

This paper provides a rigorous analysis on the measurement of the permittivity of two-dimensional anisotropic biological tissues such as skeletal muscle using the four-electrode impedance technique. The state-of-the-art technique requires individual electrodes placed at the same depth in contact with the anisotropic material, e.g. using monopolar needles. In this case, the minimum of measurements in different directions needed to estimate the complex permittivity and its anisotropy direction is 3, which translates into 12 monopolar needle insertions (i.e. 3 directions × 4 electrodes in each direction). Here, we extend our previous work and equip the reader with 8 new methods for multipolar needles, where 2 or more electrodes are spaced along the needle's shaft in contact with the tissue at different depths. Using multipolar needles, the new methods presented reduce the number of needle insertions by a factor of 2 with respect to the available methods. We illustrate the methods with numerical simulations and new experiments on ex vivo ovine skeletal muscle (n = 3). Multi-frequency longitudinal and transverse permittivity data from 30 kHz to 1 MHz is made publicly available in the supplementary material. The methods presented here for multipolar needles bring closer the application of needle electrical impedance to patients with neuromuscular diseases.

Design and evaluation of a new bioelectrical impedance sensor for micro-surgery: application to retinal vein cannulation

PURPOSE

Nowadays, millions of people suffer from retinal vein occlusion, a blind-making eye disease. No curative treatment currently exists for this vascular disorder. However, a promising treatment consists in injecting a thrombolytic drug directly inside the affected retinal vessel. Successfully puncturing miniature vessels with diameters between 50 and 400 [Formula: see text] remains a real challenge, amongst others due to human hand tremor, poor visualisation and depth perception. As a consequence, there is a significant risk of double-puncturing the targeted vessel. Sub-surfacic injection of thrombolytic agent could potentially lead to severe retinal damage.

METHODS

A new bio-impedance sensor has been developed to visually display the instant of vessel puncture. The physical working principle of the sensor has been analysed, and a representative electrical model has been derived. Based on this model, the main design parameters were derived to maximise the sensor sensitivity. A detailed characterisation and experimental validation of this concept were conducted.

RESULTS

Stable, repeatable and robust impedance measurements were obtained. In an experimental campaign, 35 puncture attempts on ex vivo pig eyes vessels were conducted. A confusion matrix shows a detection accuracy of 80% if there is a puncture, a double puncture or no puncture. The 20% of inaccuracy most probably comes from the limitations of the employed eye model and the experimental conditions.

CONCLUSIONS

The developed bio-impedance sensor has shown great promise to help in avoiding double punctures when cannulating retinal veins. Compared to other puncture detection methods, the proposed sensor is simple and therefore potentially more affordable. Future research will include validation in an in vivo situation involving vitreoretinal surgeons.