Biomedical Engineering / Biomedizinische Technik最新文献

Automatic voice pathology detection and classification plays an important role in the diagnosis and prevention of voice disorders. To accurately describe the pronunciation characteristics of patients with dysarthria and improve the effect of pathological voice detection, this study proposes a pathological voice detection method based on a multi-modal network structure. First, speech signals and electroglottography (EGG) signals are mapped from the time domain to the frequency domain spectrogram via a short-time Fourier transform (STFT). The Mel filter bank acts on the spectrogram to enhance the signal's harmonics and denoise. Second, a pre-trained convolutional neural network (CNN) is used as the backbone network to extract sound state features and vocal cord vibration features from the two signals. To obtain a better classification effect, the fused features are input into the long short-term memory (LSTM) network for voice feature selection and enhancement. The proposed system achieves 95.73% for accuracy with 96.10% F1-score and 96.73% recall using the Saarbrucken Voice Database (SVD); thus, enabling a new method for pathological speech detection.

The aim of this review was to determine whether smartphone applications are reliable and valid to measure range of motion (RoM) in lower extremity joints. A literature search was performed up to October 2020 in the databases PubMed and Cochrane Library. Studies that reported reliability or validity of smartphone applications for RoM measurements were included. The study quality was assessed with the QUADAS-2 tool and baseline information, validity and reliability were extracted. Twenty-five studies were included in the review. Eighteen studies examined knee RoM, whereof two apps were analysed as having good to excellent reliability and validity for knee flexion ("DrGoniometer", "Angle") and one app showed good results for knee extension ("DrGoniometer"). Eight studies analysed ankle RoM. One of these apps showed good intra-rater reliability and excellent validity for dorsiflexion RoM ("iHandy level"), another app showed excellent reliability and moderate validity for plantarflexion RoM ("Coach's Eye"). All other apps concerning lower extremity RoM had either insufficient results, lacked study quality or were no longer available. Some apps are reliable and valid to measure RoM in the knee and ankle joint. No app can be recommended for hip RoM measurement without restrictions.

Programming in deep brain stimulation (DBS) is a labour-intensive process for treating advanced motor symptoms. Specifically for patients with medication-refractory tremor in multiple sclerosis (MS). Wearable sensors are able to detect some manifestations of pathological signs, such as intention tremor in MS. However, methods are needed to visualise the response of tremor to DBS parameter changes in a clinical setting while patients perform the motor task finger-to-nose. To this end, we attended DBS programming sessions of a MS patient and intention tremor was effectively quantified by acceleration amplitude and frequency. A new method is introduced which results in the generation of therapeutic maps for a systematic review of the programming procedure in DBS. The maps visualise the combination of tremor acceleration power, clinical rating scores, total electrical energy delivered to the brain and possible side effects. Therapeutic maps have not yet been employed and could lead to a certain degree of standardisation for more objective decisions about DBS settings. The maps provide a base for future research on visualisation tools to assist physicians who frequently encounter patients for DBS therapy.

The magnitude of forces and moments applied on teeth during orthodontic treatment is crucial to achieve the desired tooth movement. The aim of this study is to introduce a modular 3D printable orthodontic measurement apparatus (M3DOMA), which can be used for measurements of forces and moments acting on teeth during treatment with aligners. The measurement device was characterized regarding signal to noise ratio (SNR) of the sensors, repeatability of measurements, influence of thermoforming, as well as reliability. Forces and moments were evaluated for an activation range of 0.1-0.4 mm, comparing them among different activation patterns with two aligner thicknesses. The sensors exhibited a SNR from 13-33 dB. Repeatability with repeated measurements showed standard deviations ≤0.015 N and 0.769 Nmm. The influence of thermoforming represented by standard deviation of forces ranges from 0.019-0.147 N. The device showed a range of intra class correlation (ICC) for repeated measurements for all sensors from 0.932 to 0.999. Hence the reliability of the device has been proven to be excellent.

Controlled neo-endothelialization is critical to the patency of vascular grafts. Expanded polyethylene terephthalate (PET) vascular grafts were grafted with polyethylene glycol (PEG), irradiated with ultraviolet light, and subsequently coated with silk fibroin (SF) and EDC in a dip-coating process. Endothelial cells were cultivated on the coated samples for 1, 3, 5, and 7 days, and characterized by fluorescence microscopy and scanning electron microscopy (SEM). The quantitative analyse of CCK-8 method was used to assess ECs proliferation. The results reveal the correlation between grafting components and cell adhesion. We demonstrated that PET with SF grafting facilitated cell adhesion and spreading. Following 7 days of cell culture in vitro, PET-PEG6000-SF (PEG molecular weight 6,000) displayed spreading of cells over a significantly larger area. Rapid endothelialization on a modified PET surface resulted in large tissue pack that can be observed by SEM.

The diagnostic and clinical overlap of early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI) and Alzheimer disease (AD) is a vital oncological issue in dementia disorder. This study is designed to examine Whole brain (WB), grey matter (GM) and Hippocampus (HC) morphological variation and identify the prominent biomarkers in MR brain images of demented subjects to understand the severity progression. Curve evolution based on shape constraint is carried out to segment the complex brain structure such as HC and GM. Pre-trained models are used to observe the severity variation in these regions. This work is evaluated on ADNI database. The outcome of the proposed work shows that curve evolution method could segment HC and GM regions with better correlation. Pre-trained models are able to show significant severity difference among WB, GM and HC regions for the considered classes. Further, prominent variation is observed between AD vs. EMCI, AD vs. MCI and AD vs. LMCI in the whole brain, GM and HC. It is concluded that AlexNet model for HC region result in better classification for AD vs. EMCI, AD vs. MCI and AD vs. LMCI with an accuracy of 93, 78.3 and 91% respectively.

Seizures, the main symptom of epilepsy, are provoked due to a neurological disorder that underlies the disease. The accurate detection of seizures is a crucial step in any procedure of treatment. In the present study, electrocorticogram (ECoG) signals were recorded from awake and freely moving animals implanted with cortical electrodes before and after pentylenetetrazol, the chemo-convulsant injection. ECoG signals were segmented into 4-s epochs and labeled. Twenty-four linear and non-linear features were extracted from the time and frequency domains of the ECoG signals. The extracted features either individually or in combinations were fed to an automatic support vector machine (SVM) classification system. SVM classifier was trained with 5 min of ictal and non-ictal labeled ECoG signals to build the hyperplane that separates two sets of training signals. Sensitivity, specificity, and accuracy were determined for the testing dataset using the different feature combinations. It has been found that some linear features either individually or in combinations outperform non-linear features in terms of the accuracy for seizure detection. The maximum accuracy achieved by the system was 95.3% and has been obtained only after linear and non-linear features were combined. ECoG signals were classified without pre-processing or removal of artifacts to reduce the required computational time to be suitable for online implementation purposes. This may prove the detection system's robustness and supports its use in online seizure detection protocols.

The gait assessment is instrumental for evaluating the efficiency of rehabilitation of persons with a motor impairment of the lower extremities. The protocol for quantifying the gait performance needs to be simple and easy to implement; therefore, a wearable system and user-friendly computer program are preferable. We used the Gait Master (instrumented insoles) with the industrial quality ground reaction forces (GRF) sensors and 6D inertial measurement units (IMU). WiFi transmitted 10 signals from the GRF sensors and 12 signals from the accelerometers and gyroscopes to the host computer. The clinician was following in real-time the acquired data to be assured that the WiFi operated correctly. We developed a method that uses principal component analysis (PCA) to provide a clinician with easy to interpret cyclograms showing the difference between the recorded and healthy-like gait performance. The cyclograms formed by the first two principal components in the PCA space show the step-to-step reproducibility. We suggest that a cyclogram and its orientation to the coordinate system PC1 vs. PC2 allow a simple assessment of the gait. We show results for six healthy persons and five patients with hemiplegia.