Band 67, Heft 2

Additive manufacturing (AM) has continuously grown in recent decades. Enhanced quality, further development of technology, and fall in prices make AM applicable and capable for various industrial applications, also for the manufacture of medical devices. 3D printing offers the possibility for an unprecedented adaptation to the anatomy of each patient, generating medical devices on a case-by-case basis. In many jurisdictions, custom-made devices qualify for an exemption to pre-market approval standards. This regulation is called into question by new technologies, like AM. Therefore, this article compares the current regulatory requirements for custom-made devices in Europe, the United States, and Australia and discusses the impact on 3D printed devices. It concludes that not all jurisdictions have yet adjusted their regulatory framework for custom-made devices to technological advances. Remaining uncertainties must be eliminated in order to help manufacturers comply with the regulatory requirements, emphasizing key aspects of AM.

Due to the presence of electric fields and piezoelectricity in various living tissues, piezoelectric materials have been incorporated into biomedical applications especially for tissue regeneration. The piezoelectric scaffolds can perfectly mimic the environment of natural tissues. The ability of scaffolds which have been made from piezoelectric materials in promoting cell proliferation and regeneration of damaged tissues has encouraged researchers in biomedical areas to work on various piezoelectric materials for fabricating tissue engineering scaffolds. In this review article, the way that cells of different tissues like cardio, bone, cartilage, bladder, nerve, skin, tendon, and ligament respond to electric fields and the mechanism of tissue regeneration with the help of piezoelectric effect will be discussed. Furthermore, all of the piezoelectric materials are not suitable for biomedical applications even if they have high piezoelectricity since other properties such as biocompatibility are vital. Seen in this light, the proper piezoelectric materials which are approved for biomedical applications are mentioned. Totally, the present review introduces the recent materials and technologies that have been used for tissue engineering besides the role of electric fields in living tissues.

Surface Electromyographic (sEMG) signal is a prime source of information to activate prosthetic hand such that it is able to restore a few basic hand actions of amputee, making it suitable for rehabilitation. In this work, a non-invasive single channel sEMG amplifier is developed that captures sEMG signal for three typical hand actions from the lower elbow muscles of able bodied subjects and amputees. The recorded sEMG signal detrends and has frequencies other than active frequencies. The Empirical Mode Decomposition Detrending Fluctuation Analysis (EMD–DFA) is attempted to de-noise the sEMG signal. A feature vector is formed by extracting eight features in time domain, seven features each in spectral and wavelet domain. Prominent features are selected by Fuzzy Entropy Measure (FEM) to ease the computational complexity and reduce the recognition time of classification. Classification of different hand actions is attempted based on multi-class approach namely Partial Least Squares Discriminant Analysis (PLS–DA) to control the prosthetic hand. It is inferred that an accuracy of 89.72% & 84% is observed for the pointing action whereas the accuracy for closed fist is 81.2% & 79.54% while for spherical grasp it is 80.6% & 76% respectively for normal subjects and amputees. The performance of the classifier is compared with Linear Discriminant Analysis (LDA) and an improvement of 5% in mean accuracy is observed for both normal subjects and amputees. The mean accuracy for all the three different hand actions is significantly high (83.84% & 80.18%) when compared with LDA. The proposed work frame provides a fair mean accuracy in classifying the hand actions of amputees. This methodology thus appears to be useful in actuating the prosthetic hand.

In recent years surface electromyography signals-based machine learning models are rapidly establishing. The efficacy of prosthetic arm growth for transhumeral amputees is aided by efficient classifiers. The paper aims to propose a stacking classifier-based classification system for sEMG shoulder movements. It presents the possibility of various shoulder motions classification of transhumeral amputees. To improve the system performance, adaptive threshold method and wavelet transformation have been applied for features extraction. Six different classifiers Support Vector Machines (SVM), Tree, Random Forest (RF), K-Nearest Neighbour (KNN), AdaBoost and Naïve Bayes (NB) are designed to extract the sEMG data classification accuracy. With cross-validation, the accuracy of RF, Tree and Ada Boost is 97%, 92% and 92% respectively. Stacking classifiers provides an accuracy as 99.4% after combining the best predicted multiple classifiers.

The process of diagnosing tremor patients often leads to misdiagnoses. Therefore, existing technical methods for analysing tremor are needed to more effectively distinguish between different diseases. For this purpose, a system has been developed that classifies measured tremor signals in real time. To achieve this, the hand tremor of 561 subjects has been measured in different hand positions. Acceleration and surface electromyography are recorded during the examination. For this study, data from subjects with Parkinson’s Disease, Essential Tremor, and physiological tremor are considered. In a first signal analysis feature extraction is performed, and the resulting features are examined for their discriminative value. In a second step, three classification models based on different pattern recognition techniques are developed to classify the subjects with respect to their tremor type. With a trained decision tree, the three tremor types can be classified with a relative diagnostic accuracy of 83.14%. A neural network achieves 84.24% and the combination of both classifiers yields a relative diagnostic accuracy of 85.76%. The approach is promising and involving more features of the recorded time series will improve the discriminative value.

As a common mental disorder, depression is placing an increasing burden on families and society. However, the current methods of depression detection have some limitations, and it is essential to find an objective and efficient method. With the development of automation and artificial intelligence, computer-aided diagnosis has attracted more and more attention. Therefore, exploring the use of deep learning (DL) to detect depression has valuable potential. In this paper, convolutional neural network (CNN) is applied to build a diagnostic model for depression based on electroencephalogram (EEG). EEG recordings are analyzed by three different CNN structures, namely EEGNet, DeepConvNet and ShallowConvNet, to dichotomize depression patients and healthy controls. EEG data were collected in the resting state from three electrodes (Fp1, Fz, Fp2) among 80 subjects (40 depressive patients and 40 normal subjects). After the preprocessing step, the DL structures are employed to classify the data, and their recognition performance is evaluated by comparing the classification results. The classification performance shows that depression was effectively detected using EEGNet with 93.74% accuracy, 94.85% sensitivity and 92.61% specificity. In the process of optimizing the parameters of EEGNet structure, the highest accuracy can reach 94.27%. Compared with traditional diagnostic methods, EEGNet is highly worthy for the future depression detection and valuable in terms of accuracy and speed.

In this study, breast cancer classification as benign or malignant was made using images obtained by histopathological procedures, one of the medical imaging techniques. First of all, different noise types and several intensities were added to the images in the used data set. Then, the noise in images was removed by applying the Wavelet Transform (WT) process to noisy images. The performance rates in the denoising process were found out by evaluating Peak Signal to Noise Rate (PSNR) values of the images. The Gaussian noise type gave better results than other noise types considering PSNR values. The best PSNR values were carried out with the Gaussian noise type. After that, the denoised images were classified by Convolution Neural Network (CNN), one of the deep learning techniques. In this classification process, the proposed CNN model and the VggNet-16 model were used. According to the classification result, better results were obtained with the proposed CNN model than VggNet-16. The best performance (86.9%) was obtained from the data set created Gaussian noise with 0.3 noise intensity.