This study investigated the effects different sampling rates may produce on the quality of muscle fatigue detection algorithms. sEMG signals were obtained from isometric contractions of the arm. Subsampled signals resulting in technically relevant sampling rates were computationally deduced from the original recordings. The spectral based fatigue recognition methods mean and median frequency as well as spectral moment ratio were included in this investigation, as well as the sample and the fuzzy approximate entropy. The resulting fatigue indices were evaluated with respect to noise and separability of different load levels. We concluded that the spectral moment ratio provides the best results in fatigue detection over a wide range of sampling rates.
This work investigates the performance of eventsynchronous noise cancelling algorithms to separate ECG artifacts from thoracic EMG recordings. In focus are the precise detection of heart beats, the exact time alignment of the QRS segments and the construction of a template QRS signal. Among the utilized methods is a modified structural intensity (SI) approach based on the observation of extrema in low pass filtered versions of the second derivative as a detector. An adoption of this approach is also used to obtain the exact time alignment of QRS segments. Artificial test signals are constructed based on addition of ECG and sEMG with different magnitudes. We show that approaches based on the observation of extrema in different scales yield superior results.
This work investigates elimination methods for cardiogenic artifacts in respiratory surface electromyographic (sEMG) signals and compares their performance with respect to subsequent fatigue detection with different fatigue algorithms. The analysis is based on artificially constructed test signals featuring a clearly defined expected fatigue level. Test signals are additively constructed with different proportions from sEMG and electrocardiographic (ECG) signals. Cardiogenic artifacts are eliminated by high-pass filtering (HP), template subtraction (TS), a newly introduced two-step approach (TSWD) consisting of template subtraction and a wavelet-based damping step and a pure wavelet-based damping (DSO). Each method is additionally combined with the exclusion of QRS segments (gating). Fatigue is subsequently quantified with mean frequency (MNF), spectral moments ratio of order five (SMR5) and fuzzy approximate entropy (fApEn). Different combinations of artifact elimination methods and fatigue detection algorithms are tested with respect to their ability to deliver invariant results despite increasing ECG contamination. Both DSO and TSWD artifact elimination methods displayed promising results regarding the intermediate, “cleaned” EMG signal. However, only the TSWD method enabled superior results in the subsequent fatigue detection across different levels of artifact contamination and evaluation criteria. SMR5 could be determined as the best fatigue detection algorithm. This study proposes a signal processing chain to determine neuromuscular fatigue despite the presence of cardiogenic artifacts. The results furthermore underline the importance of selecting a combination of algorithms that play well together to remove cardiogenic artifacts and to detect fatigue. This investigation provides guidance for clinical studies to select optimal signal processing to detect fatigue from respiratory sEMG signals.
The following work describes a new backscattered ultrasonic RF data of the human hem. The assumption is that it is possible to relate ultrasonic RF data to structural properties of myocardium due to chronic heart failure.transoesophageal multiplane probe. The region of interest The uluasonic RF data was collected using a was at a mean depth of 7.5-10 cm. The analog RF-data was digitized with 8 bits at a sampling rate of I6MHz using a modified ultrasound system. The W-data was filtered and and blood. Different features were extracted from the signal segmented in areas representing myocardium, pericardium originating in the myocardium. These features were applied to a Multilayer Perceptron which distinguishes between five different states of disease.were used in the investigation. They were taken in vivo from A total number of 200 data sets of ultrasonic RF-data 33 different patients. Additionally, with each patient a biopsy was performed to get samples of myocardial tissue. INTRODUC?TONUltrasonic imaging is a powerful noninvasive tool in modern medical diagnostics of the human heart. It is widely compared to X-ray and MRI. even if used frequently. used and causes minimal risk to the health of patients There have been many approaches to estimate the concentration of collagen in myocardium using ultrasonic echoes [l]. It is assumed that an increase of collagen in the myocardium rises the number of scauerer. This amplifies the backscattered ultrasonic echoes and the speckle noise. The statistics of speckles in ultrasonic B-scans were investigated Texture features based on gray level statistics and in [2] and the envelope of the ultrasonic echoes in 131. cwccurence matrices were also applied to ultrasonic B-images in order to classify the myocardium [41. More promising were those works that directly investigated the RFdata. It was shown in [5] that the backscatter coefficient increases with an increase of collagen. In vitro tests were performed using myocardium of dogs. Mathematical models were applied to describe and simulate the scatterer and their impact on the ultrasound signal [6],[71.classify myocardial tissue based on ultrasonic echoes of the Our goal is the development of a noninvasive method to heart. Currently. this can only be done by a biopsy which is a catheter is passed from the veins in the leg up through the highly invasive and stressful procedure for the patients.
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