The longevity of hip prostheses is contingent on the stability of the implant within the cavity of the femur bone. The cemented fixation was mostly adopted owing to offering the immediate stability from cement-stem and cement-bone bonding interfaces after implant surgery. Yet cement damage and stress shielding of the bone were proven to adversely affect the lifelong stability of the implant, especially among younger subjects who tend to have an active lifestyle. The geometry and material distribution of the implant can be optimized more efficiently with a three-dimensional realistic design of a functionally graded material (FGM). We report an efficient numerical technique for achieving this objective, for maximum performance stress shielding and the rate of early accumulation of cement damage were concurrently minimized. Results indicated less stress shielding and similar cement damage rates with a 2D-FGM implant compared to 1D-FGM and Titanium alloy implants.
Researchers and clinicians are increasingly using plantar pressure and force measurement system to evaluate foot functions. This research evaluates the quality and reliability of a Tekscan HR mat to study the plantar pressures and forces acting during walking, running, jumping, and standing of healthy subjects. The following regions of the foot were investigated: heel, mid foot, metatarsophalangeal joint, hallux, and the toes. The arches of both feet of the three healthy subjects in the gait analysis were presented which addresses the balancing issues of the body during locomotion. The results indicated that the peaks at the big toe (79.4 ± 8.5 N/cm2, p = 0.0001) were the maximum compared to forefoot (40.3 ± 3.3 N/cm2, p = 0.001), to midfoot (7.5 ± 1.3 N/cm2, p = 0.001), and to heel (27.8 ± 3.9 N/cm2, p = 0.0002) for jump activity. The running activity demonstrated similar results as jump where the maximum peak pressures were absorbed at the big toe region. The heel region during running (86.3 ± 12.6 N/cm2, p = 0.001) showed three times the pressure peak compared to the jump land (27.8 ± 3.9 N/cm2, p = 0.0002) activity. The measurement system proved to be highly capable of detecting heel strike and toe-off moments.
Optical coherence tomography is a micrometer-scale imaging modality that permits label-free, cross-sectional imaging of biological tissue microstructure using tissue backscattering properties. After its invention in the 1990s, OCT is now being widely used in several branches of neuroscience as well as other fields of biomedical science. This review study reports an overview of OCT's applications in several branches or subbranches of neuroscience such as neuroimaging, neurology, neurosurgery, neuropathology, and neuroembryology. This study has briefly summarized the recent applications of OCT in neuroscience research, including a comparison, and provides a discussion of the remaining challenges and opportunities in addition to future directions. The chief aim of the review study is to draw the attention of a broad neuroscience community in order to maximize the applications of OCT in other branches of neuroscience too, and the study may also serve as a benchmark for future OCT-based neuroscience research. Despite some limitations, OCT proves to be a useful imaging tool in both basic and clinical neuroscience research.
The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.
Over the past years, coregistered EEG-fMRI has emerged as a powerful tool for neurocognitive research and correlated studies, mainly because of the possibility of integrating the high temporal resolution of the EEG with the high spatial resolution of fMRI. However, additional work remains to be done in order to improve the quality of the EEG signal recorded simultaneously with fMRI data, in particular regarding the occurrence of the gradient artefact. We devised and presented in this paper a novel approach for gradient artefact correction based upon optimised moving-average filtering (OMA). OMA makes use of the iterative application of a moving-average filter, which allows estimation and cancellation of the gradient artefact by integration. Additionally, OMA is capable of performing the attenuation of the periodic artefact activity without accurate information about MRI triggers. By using our proposed approach, it is possible to achieve a better balance than the slice-average subtraction as performed by the established AAS method, regarding EEG signal preservation together with effective suppression of the gradient artefact. Since the stochastic nature of the EEG signal complicates the assessment of EEG preservation after application of the gradient artefact correction, we also propose a simple and effective method to account for it.
Background. In this study, the cut-out risk of Dynamic Hip Screw (DHS) was investigated in nine different positions of the lag screw for two fracture types by using Finite Element Analysis (FEA). Methods. Two types of fractures (31-A1.1 and A2.1 in AO classification) were generated in the femur model obtained from Computerized Tomography images. The DHS model was placed into the fractured femur model in nine different positions. Tip-Apex Distances were measured using SolidWorks. In FEA, the force applied to the femoral head was determined according to the maximum value being observed during walking. Results. The highest volume percentage exceeding the yield strength of trabecular bone was obtained in posterior-inferior region in both fracture types. The best placement region for the lag screw was found in the middle of both fracture types. There are compatible results between Tip-Apex Distances and the cut-out risk except for posterior-superior and superior region of 31-A2.1 fracture type. Conclusion. The position of the lag screw affects the risk of cut-out significantly. Also, Tip-Apex Distance is a good predictor of the cut-out risk. All in all, we can supposedly say that the density distribution of the trabecular bone is a more efficient factor compared to the positions of lag screw in the cut-out risk.
Sudden death caused by abnormal QTc and atrial fibrillation (AF) has been reported in stroke. Heart rate variability (HRV) is reduced with missing beats of RRI during arrhythmic episode and abnormal QTc variation during acute stroke. In this study, we develop a hybrid signal processing by Pan Tompkins QRS detection and Kalman filter estimator for meaningful missing beats and searching AF with prolonged QTc. We use this hybrid model to investigate RRIs of Lead II ECG in thirty acute stroke patients with long QTc and AF (LQTc-AF) and normal QTc without AF (NQTc-nonAF) and then assess them by HRV. In LQTc-AF Kalman, higher mean heart rate with lower mean RRIs compared to NQTc-nonAF Kalman was characterized. LQTc-AF Kalman showed significant increase in SDNN, HF, SD2, SD2/SD1, and sample entropy. SDNN and HF associated with high RMSSD, pNN50, and SD1 reflect predominant parasympathetic drive for sympathovagal balance in LQTc-AF Kalman. Greater SD2, SD2/SD1, and sample entropy indicate more scatter of Poincaré plot. Compared with conventional Labchart, fractal scaling exponent of α1 (DFA) is higher in LQTc-AF Kalman. Remarkable complexity with parasympathetic drive in LQTc-AF Kalman suggests an influence of missing beats during stroke.
External cardiac loop recorder (ELR) is a kind of ECG monitoring system that records cardiac activities of a subject continuously for a long time. When the heart palpitations are not the frequent and nonspecific character, it is difficult to diagnose the disease. In such a case, ELR is used for long-term monitoring of heart signal of the patient. But the cost of ELR is very high. Therefore, it is not prominently available in developing countries like India. Since the design of ELR includes the ECG electrodes, instrumentation amplifier, analog to digital converter, and signal processing unit, a comparative review of each part of the ELR is presented in this paper in order to design a cost effective, low power, and compact kind of ELR. This review will also give different choices available for selecting and designing each part of the ELR system. Finally, the review will suggest the better choice for designing a cost effective external cardiac loop recorder that helps to make it available even for rural people in India.
Electrical Impedance Myography (EIM) is a noninvasive neurophysiologic technique to diagnose muscle health. Besides muscle properties, the EIM measurements vary significantly with the change of some other anatomic and nonanatomic factors such as skin fat thickness, shape and thickness of muscle, and electrode size and spacing due to its noninvasive nature of measurement. In this study, genetic algorithm was applied along with finite element model of EIM as an optimization tool in order to figure out an optimized EIM electrode setup, which is less affected by these factors, specifically muscle thickness variation, but does not compromise EIM's ability to detect muscle diseases. The results obtained suggest that a particular arrangement of electrodes and minimization of electrode surface area to its practical limit can overcome the effect of undesired factors on EIM parameters to a larger extent.
Purpose. Injecting triamcinolone acetonide (TA) into a keloid is physically challenging due to the density of keloids. The purpose was to investigate the effects of various syringe and needle combinations on the injection force to determine the most ergonomic combination. Materials and Methods. A load cell was used to generate and measure the injection force. Phase 1: the injection force of 5 common syringes was measured by injecting water into air. The syringe that required the lowest injection force was evaluated with various needle gauges (25, 27, and 30 G) and lengths (16, 25, and 38 mm) by injecting TA (40 mg/mL) into air. The needle-syringe combination with the lowest injection force (CLIF) was deemed the most ergonomic combination. Phase 2: comparisons between the CLIF and a standard combination (SC) were performed by injecting TA into air and tap water into a keloid specimen. Intraclass Correlation Coefficient (ICC) and independent t-test were used. Results. Increasing the syringe caliber, injection speed, and needle gauge and length significantly increased the injection force (p value < 0.001). The SC required a maximum force of 40.0 N to inject water into keloid, compared to 25.0 N for the CLIF. Injecting TA into keloid using the SC would require an injection force that was 103.5% of the maximum force female thumbs could exert compared to 64.8% for the CLIF. ICC values were greater than 0.4. Conclusions. The 1 mL polycarbonate syringe with a 25 G, 16 mm needle (CLIF) was the most ergonomic combination. The SC required a substantial injection force, which may represent a physical challenge for female thumbs.
We report on a Kinect-based, augmented reality, real-time physiotherapy platform tailored to Parkinson’s disease (PD) patients. The platform employs a Kinect sensor to extract real-time 3D skeletal data (joint information) from a patient facing the sensor (at 30 frames per second). In addition, a small collection of exercises practiced in traditional physiotherapy for PD patients has been implemented in the Unity 3D game engine. Each exercise employs linear or circular movement patterns and poses very light-weight processing demands on real-time computations. During an exercise, trainer instruction demonstrates correct execution and Kinect-provided 3D joint data are fed to the game engine and compared to exercise-specific control routines to assess proper posture and body control in real time. When an exercise is complete, performance metrics appropriate for that exercise are computed and displayed on screen to enable the attending physiotherapist to fine-tune the exercise to the abilities/needs of an individual patient as well as to provide performance feedback to the patient. The platform can operate in a physiotherapist’s office and, following appropriate validation, in a home environment. Finally, exercises can be parameterized meaningfully, depending on the intended purpose (motor assessment versus plain exercise at home).
Most of the 3D printing applications of preoperative models have been focused on dental and craniomaxillofacial area. The purpose of this paper is to demonstrate the possibilities in other application areas and give examples of the current possibilities. The approach was to communicate with the surgeons with different fields about their needs related preoperative models and try to produce preoperative models that satisfy those needs. Ten different kinds of examples of possibilities were selected to be shown in this paper and aspects related imaging, 3D model reconstruction, 3D modeling, and 3D printing were presented. Examples were heart, ankle, backbone, knee, and pelvis with different processes and materials. Software types required were Osirix, 3Data Expert, and Rhinoceros. Different 3D printing processes were binder jetting and material extrusion. This paper presents a wide range of possibilities related to 3D printing of preoperative models. Surgeons should be aware of the new possibilities and in most cases help from mechanical engineering side is needed.
Clinical studies on application of functional electrical stimulation (FES) to motor rehabilitation have been increasing. However, muscle fatigue appears early in the course of repetitive movement production training by FES. Although M-wave variables were suggested to be reliable indices of muscle fatigue in long lasting constant electrical stimulation under the isometric condition, the ability of M-wave needs more studies under intermittent stimulation condition, because the intervals between electrical stimulations help recovery of muscle activation level. In this paper, M-waves elicited by double pulses were examined in muscle fatigue evaluation during repetitive movements considering rehabilitation training with surface electrical stimulation. M-waves were measured under the two conditions of repetitive stimulation: knee extension force production under the isometric condition and the dynamic movement condition by knee joint angle control. Amplitude of M-wave elicited by the 2nd pulse of a double pulse decreased during muscle fatigue in both measurement conditions, while the change in M-waves elicited by single pulses in a stimulation burst was not relevant to muscle fatigue in repeated activation with stimulation interval of 1 s. Fatigue index obtained from M-waves elicited by 2nd pulses was suggested to provide good estimation of muscle fatigue during repetitive movements with FES.
An algorithm based on the ability of TEO to track the changes in the envelope of ECG signal is proposed for identifying PVCs in ECG. Teager energy is calculated from DCT coefficients of ECG signal. This method can be considered as computationally efficient algorithm when compared with the well-known DCT cepstrum technique. EPE is derived from the teager energy of DCT coefficients in DCT-TEO method and from the cepstrum of DCT coefficients in the existing method. EPE determines the decay rate of the action potential of ECG beat and provides sufficient information to identify the PVC beats in ECG data. EPEs obtained by DCT-TEO and existing DCT cepstrum models are compared. The proposed algorithm has resulted in performance measures like sensitivity of 98-100%, positive predictivity of 100%, and detection error rate of 0.03%, when tested on MIT-BIH database signals consisting of PVC and normal beats. Result analysis reveals that the DCT-TEO algorithm worked well in clear identification of PVCs from normal beats compared to the existing algorithm, even in the presence of artifacts like baseline wander, PLI, and noise with SNR of up to -5 dB.
Cervical cancer is the second most common female cancer worldwide and a disease of concern due to its high rate of incidence of about 500,000 women annually and is responsible for about 280,000 deaths in a year. The mortality and morbidity of cervical cancer are reduced through mass screening via Pap smear, but this technique suffers from very high false negativity of around 30% to 40% and hence the sensitivity of this technique is not more than 60%. Electrical bioimpedance study employing cytosensors over a frequency range offers instantaneous and quantitative means to monitor cellular events and is an upcoming technique in real time to classify cells as normal and abnormal ones. This technology is exploited for label-free detection of diseases by identifying and measuring nonbiological parameters of the cell which may carry the disease signature.
This paper leads to developing a Labview based ECG patient monitoring system for cardiovascular patient using Simple Mail Transfer Protocol technology. The designed device has been divided into three parts. First part is ECG amplifier circuit, built using instrumentation amplifier (AD620) followed by signal conditioning circuit with the operation amplifier (lm741). Secondly, the DAQ card is used to convert the analog signal into digital form for the further process. Furthermore, the data has been processed in Labview where the digital filter techniques have been implemented to remove the noise from the acquired signal. After processing, the algorithm was developed to calculate the heart rate and to analyze the arrhythmia condition. Finally, SMTP technology has been added in our work to make device more communicative and much more cost-effective solution in telemedicine technology which has been key-problem to realize the telediagnosis and monitoring of ECG signals. The technology also can be easily implemented over already existing Internet.
A framework for automated detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features is proposed and examined. The various stages involved in the proposed methodology include enhancement of microscopic images, segmentation of background cells, features extraction, and finally the classification. An appropriate and efficient method is employed in each of the design steps of the proposed framework after making a comparative analysis of commonly used method in each category. For highlighting the details of the tissue and structures, the contrast limited adaptive histogram equalization approach is used. For the segmentation of background cells, k-means segmentation algorithm is used because it performs better in comparison to other commonly used segmentation methods. In feature extraction phase, it is proposed to extract various biologically interpretable and clinically significant shapes as well as morphology based features from the segmented images. These include gray level texture features, color based features, color gray level texture features, Law’s Texture Energy based features, Tamura’s features, and wavelet features. Finally, the K-nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application. The performance of the proposed framework is evaluated using well-known parameters for four fundamental tissues (connective, epithelial, muscular, and nervous) of randomly selected 1000 microscopic biopsy images.
An automated robust feature extraction technique is proposed in this paper based on inherent structural distribution of heart sound to analyze the phonocardiogram signal in presence of environmental noise and interference of lung sound signal. The structural complexity of the heart sound signal is estimated in terms of sample entropy using a nonlinear signal processing framework. The effectiveness of the feature is evaluated using a support vector machine under two different circumstances which include Gaussian noise and pulmonary perturbation. The analysis framework has been executed on a composite data set of 60 healthy and 60 pathological individuals for different SNR levels (−5 to 10 dB) and the performance accuracy is close to that of the clean signal. In addition, a comparative study has been done with conventional approaches which includes waveform analysis, spectral domain inspection, and spectrogram evaluation. The experimental results show that sample entropy based classification method gives an accuracy of 96.67% for clean data and 91.66% for noisy data of SNR 10 dB. The result suggests that the proposed method performs significantly well over the visual and audio test.
Ability of humans to maintain balance in an upright stance and during movement activities is one of the most natural skills affecting everyday life. This ability progressively deteriorates with increasing age, and balance impairment, often aggravated by age-related diseases, can result in falls that adversely impact the quality of life. Falls represent serious problems of health concern associated with aging. Many investigators, involved in different science disciplines such as medicine, engineering, psychology, and sport, have been attracted by a research of the human upright stance. In a clinical practice, stabilometry based on the force plate is the most widely available procedure used to evaluate the balance. In this paper, we have proposed a low-cost extension of the conventional stabilometry by the multimedia technology that allows identifying potentially disturbing effects of visual sensory information. Due to the proposed extension, a stabilometric assessment in terms of line integral of center of pressure (COP) during moving scene stimuli shows higher discrimination power between young healthy and elderly subjects with supposed stronger visual reliance.
The quantification of changes in the trabecular bone structure induced by musculoskeletal diseases like osteoarthritis, osteoporosis, rheumatoid arthritis, and others by means of a texture analysis is a valuable tool which is expected to improve the diagnosis and monitoring of a disease. The reaction of texture parameters on different alterations in the architecture of the fine trabecular network and inherent imaging factors such as spatial resolution or image noise has to be understood in detail to ensure an accurate and reliable determination of the current bone state. Therefore, a digital model for the quantitative analysis of cancellous bone structures was developed. Five parameters were used for texture analysis: entropy, global and local inhomogeneity, local anisotropy, and variogram slope. Various generic structural changes of cancellous bone were simulated for different spatial resolutions. Additionally, the dependence of the texture parameters on tissue mineralization and noise was investigated. The present work explains changes in texture parameter outcomes based on structural changes originating from structure modifications and reveals that a texture analysis could provide useful information for a trabecular bone analysis even at resolutions below the dimensions of single trabeculae.
