Volume 66, Issue 3

Arterial blood pressure is one of the most often measured vital parameters in clinical practice. State-of-the-art noninvasive ABP measurement technologies have noticeable limitations and are mainly based on uncomfortable techniques of complete or partial arterial occlusion by cuffs. Most commonplace devices provide only intermittent measurements, and continuous systems are bulky and difficult to apply correctly for nonprofessionals. Continuous cuffless ABP measurements are still an unmet clinical need and a topic of ongoing research, with only few commercially available devices. This paper discusses surrogate-based noninvasive blood pressure measurement techniques. It covers measurement methods of continuously and noninvasively inferring BP from surrogate signals without applying external pressures, except for reference or initialization purposes. The BP is estimated by processing signal features, so called surrogates, which are modulated by variations of BP. Discussed techniques include well-known approaches such as pulse transit time and pulse arrival time techniques, pulse wave analysis or combinations thereof. Despite a long research history, these methods have not found widespread use in clinical and ambulatory practice, in part due to technical limitations and the lack of a standardized regulatory framework. This work summarizes findings from an invited workshop of experts in the fields covering clinical expertise, engineering aspects, commercialization and standardization issues. The goal is to provide an application driven outlook, starting with clinical needs, and extending to technical actuality. It provides an outline of recommended research directions and includes a detailed overview of clinical use case scenarios for these technologies, opportunities, and limitations.

This paper proposes a smart, automated heart health-monitoring (SAHM) device using a single photoplethysmography (PPG) sensor that can monitor cardiac health. The SAHM uses an Orthogonal Matching Pursuit (OMP)-based classifier along with low-rank motion artifact removal as a pre-processing stage. Major contributions of the proposed SAHM device over existing state-of-the-art technologies include these factors: (i) the detection algorithm works with robust features extracted from a single PPG sensor; (ii) the motion compensation algorithm for the PPG signal can make the device wearable; and (iii) the real-time analysis of PPG input and sharing through the Internet. The proposed low-cost, compact and user-friendly PPG device can also be prototyped easily. The SAHM system was tested on three different datasets, and detailed performance analysis was carried out to show and prove the efficiency of the proposed algorithm.

The implantation of a left ventricular assist device (LVAD) is often the only therapy in terminal heart failure (HF). However, despite technical advancements, the physical fitness of the patients is still limited. One strategy to improve the benefits of ventricular assist device therapy might be the implementation of load adaptive control strategies. Two control strategies and a constant speed controller (CS) were implemented in an acute animal model where four healthy pigs received LVAD implantations. In the first strategy (preload recruitable stroke work [SW] controller, PRS), the desired pump work was computed in relation to the end-diastolic volume. In the second strategy, the controller was programmed to keep a fixed ratio of the mean hydraulic power of the assist device to the mean hydraulic power of the left ventricle (power relation controller, PR). Preload reduction, afterload increase experiments and short-term coronary artery occlusions were conducted to test the behavior of the control strategies under variable conditions. Within the experiments, the PR controller demonstrated the best preload sensitivity. The PRS controller had the best response to an increased afterload and to a reduced ventricular contractility in terms of effectively preventing ventricular overloading and increasing VAD support. No significant differences in systemic flow were observed.

Seizures are the most common brain dysfunction. Electroencephalography (EEG) is required for their detection and treatment initially. Studies show that if seizures are detected at their early stage, instant and effective treatment can be given to the patients. In this paper, an automated system for seizure onset detection is proposed. As the power spectrum of normal person’s EEG and EEG of someone with epilepsy is plotted, powers present at different frequencies are found to be different for both. The proposed algorithm utilizes this frequency discrimination property of EEG with some statistical features to detect the seizure onset using simple linear classifier. The tests conducted on EEG data of 30 patients, obtained from the two different datasets, show the presence of all 183 seizures with mean latency of 0.9 s and 1.02 false detections per hour. The main contribution of this study is the use of simple features and classifier in the field of seizures onset detection that reduces the computational complexity of the algorithm. Also, the classifier used is patient independent. This patient independency in the classification system would be helpful in the implementation of the proposed algorithm to develop an online detection system.

Change in cortisol affects brain EEG signals. So, the identification of the significant EEG features which are sensitized to cortisol concentration was the aim of the present study. From 468 participated healthy subjects, the salivary samples were taken to test the cortisol concentration and EEG signal recording was done simultaneously. Then, the subjects were categorized into three classes based on the salivary cortisol concentration (<5, 5–15 and >15 nmol/l). Some linear and nonlinear features extracted and finally, in order to investigate the relationship between cortisol level and EEG features, the following steps were taken on features in sequence: Genetic Algorithm, Neighboring Component Analysis, polyfit, artificial neural network and support vector machine classification. Two classifications were considered as following: state 1 categorized the subjects into three groups (three classes) and the second state put them into two groups (group 1: class 1 and 3, group 2: class 2). The best classification was done using ANN in the second state with the accuracy=94.1% while it was 92.7% in the first state. EEG features carefully predicted the cortisol level. This result is applicable to design the intelligence brain computer machines to control stress and brain performance.

Cancer will be the leading cause of death in a few decades. In line with minimal invasive lung cancer surgery, surgeons loose most of their tactile tissue information and need an additional tool of intraoperative tissue navigation during surgery. Confocal laser microscopy is a well-established method of tissue investigation. In this ex-vivo pilot study, we evaluated an endoscopic confocal laser microscope (eCLM) that does not need any fluorescent dye as a diagnostic tool in non-malignant and malignant pulmonary tissue and distal stapler resection margins, respectively. In seven cases, an eCLM was used for examining pulmonary tissue ex-vivo . Images of non-malignant and non-small cell lung cancer tissue and distal stapler resection margins were characterized in terms of specific signal-patterns. No fluorescent dye was used. Correlations to findings in conventional histology were systematically recorded and described. Healthy lung tissue showed hyperreflectoric alveolar walls with dark alveolar spaces. Hyperreflective nets indicated the tumor stroma; whereas the hyperreflective areas indicated the tumor cell clusters. Compared to adenocarcinoma tissue, tissue from squamous cell carcinoma showed more distinctive hyperreflective stroma nets. eCLM characteristics seen in non-malignant and malignant tissue were also visible in distal stapler resection margins and so therefore it was feasible to distinguish between healthy lung tissue and lung cancer. This pilot study shows that the assessment of pulmonary tissue with this eCLM for minimally invasive surgical approach without any fluorescent dye is feasible. It enables to differentiate between benign and malignant tissue in pulmonary specimen by easy to evaluate and reproducible parameters.

The damage in the spinal cord due to vertebral fractures may result in loss of sensation and muscle function either permanently or temporarily. The neurological condition of the patient can be improved only with the early detection and the treatment of the injury in the spinal cord. This paper proposes a spinal cord segmentation and injury detection system based on the proposed Crow search-Rider Optimization-based DCNN (CS-ROA DCNN) method, which can detect the injury in the spinal cord in an effective manner. Initially, the segmentation of the CT image of the spinal cord is performed using the adaptive thresholding method, followed by which the localization of the disc is performed using the Sparse FCM clustering algorithm (Sparse-FCM). The localized discs are subjected to a feature extraction process, where the features necessary for the classification process are extracted. The classification process is done using DCNN trained using the proposed CS-ROA, which is the integration of the Crow Search Algorithm (CSA) and Rider Optimization Algorithm (ROA). The experimentation is performed using the evaluation metrics, such as accuracy, sensitivity, and specificity. The proposed method achieved the high accuracy, sensitivity, and specificity of 0.874, 0.8961, and 0.8828, respectively that shows the effectiveness of the proposed CS-ROA DCNN method in spinal cord injury detection.

The aim of this study was to investigate experimentally the fatigue and stability of three bar materials with distal extension at the molar region and to numerically analyse the biomechanical properties of the bar materials connected to overdentures in a patient individual model. A milled bar was designed for the mandible on four implants in the canine and second premolar region. Three bar materials were investigated: titanium (Ti), cobalt chromium (CoCr), and polyetherketonketon (PEKK). Firstly, static and fatigue tests were performed based on EN ISO 14801 in a commercial permanent loading set-up. Unilateral axial force was applied on the distal extension of the bars. Secondly, numerical models were created. Different bar materials and loading scenarios were analysed. The static fracture limit of the three materials was 1,750 N, 780 N, 310 N for Ti, CoCr, and PEKK, respectively. The Wöhler curves showed comparable fatigue limits of 200 N, 160 N, and 150 N for titanium, CoCr, and PEKK, respectively. The stress at the distal extension was 2,600 MPa (Ti), 1,000 MPa (CoCr), and 270 MPa (PEKK). All loading simulations with the PEKK bar showed higher stresses in the implants and in the bone bed as well as higher displacements of the over denture in comparison to metal bars. PEKK showed different mechanical behaviour compared to Ti and CoCr. The distribution of stresses within the PEKK bar was wider than the area of loading which probably leads to fatigue of the whole bar and not only the part under load.

Materials with low-strength and low-impedance properties, such as elastomers and polymeric foams are major contributors to prosthetic liner design. Polyethylene-Light (Pelite™) is a foam liner that is the most frequently used in prosthetics but it does not cater to all amputees’ limb and skin conditions. The study aims to investigate the newly modified Foam Liner, a combination of two different types of foams (EVA + PU + EVA) as the newly modified Foam Liner in terms of compressive and tensile properties in comparison to Pelite™, polyurethane (PU) foam, and ethylene-vinyl acetate (EVA) foam. Universal testing machine (AGS-X, Shimadzu, Kyoto, Japan) has been used to measure the tensile and compressive stress. Pelite™ had the highest compressive stress at 566.63 kPa and tensile stress at 1145 kPa. Foam Liner fell between EVA and Pelite™ with 551.83 kPa at compression and 715.40 kPa at tension. PU foam had the lowest compressive stress at 2.80 kPa and tensile stress at 33.93 kPa. Foam Liner has intermediate compressive elasticity but has high tensile elasticity compared to EVA and Pelite™. Pelite™ remains the highest in compressive and tensile stiffness. Although it is good for amputees with bony prominence, constant pressure might result in skin breakdown or ulcer. Foam Liner would be the best for amputees with soft tissues on the residual limbs to accommodate movement.

Acute and planned transportations of patients are major tasks for emergency medical services (EMS) and often result in substantial physical strains with a major impact on the workers’ health, because current transportation aids cannot provide sufficient support, especially on stairs. A new stair-climbing and self-balancing approach (SEBARES) has been developed and its usability is evaluated in the context of this paper. Twelve participants operated a prototype in a transportation scenario and user forces, user joint angles and the perceived usability were evaluated. Results show that user forces were within long-term acceptable ergonomic limits for over 90% of the transportation time and a mainly healthy upright posture of the back could be maintained. This resulted in a healthy working posture for 85% of the time, according to the OWAS method, and a good perceived usability. A comparison to the most ergonomic aid according to literature, a caterpillar stair chair, reveals that similar upright postures are assumed, while the operation of SEBARES required only 47% of the forces to operate the caterpillar stair chair. A comparison to a previous field study indicates a reduction of strenuous working postures by a factor of three, which further confirms the ergonomic advantages of this concept.