Removing the motion artifacts from measured photoplethysmography (PPG) signals is one of the important issues to be tackled for the accurate measurement of arterial oxygen saturation during movement. In this paper, the motion artifacts were reduced by exploiting the quasi-periodicity of the PPG signal and the independence between the PPG and the motion artifact signals. The combination of independent component analysis and block interleaving with low-pass filtering can reduce the motion artifacts under the condition of general dual-wavelength measurement. Experiments with synthetic and real data were performed to demonstrate the efficacy of the proposed algorithm.

Rehabilitation within three months plays a significant role in the recovery of damaged motor functions following the onset of a stroke. To increase the effectiveness of rehabilitation, it is important to perform rehabilitative exercises with movement intention. This study analyzed the association between electroencephalogram (EEG) and electromyogram (EMG) signals in healthy individuals in an attempt to verify the differences between the two signals in corticomuscular connectivity as well as the time delay in the flow of information in accordance with the presence of movement intention. To examine the relationship between the brain and muscles, coherence and mutual information analyses were performed on the EEG signals in the motor cortex and EMG signals in the flexor digitorum superficialis muscle during grasping training. Coherence and mutual information between EEG and EMG signals were significantly higher and the time delay of information flow was shorter when subjects performed active exercise with movement intention than when they performed passive exercise without movement intention. These findings could be applied to the rehabilitation of stroke patients to develop a rehabilitative training system with heightened effectiveness through verification of the presence of movement intention in the patients.

The delay performance of compression algorithms is particularly important when time-critical data transmission is required. In this paper, we propose a wavelet-based electrocardiogram (ECG) compression algorithm with a low delay property for instantaneous, continuous ECG transmission suitable for telecardiology applications over a wireless network. The proposed algorithm reduces the frame size as much as possible to achieve a low delay, while maintaining reconstructed signal quality. To attain both low delay and high quality, it employs waveform partitioning, adaptive frame size adjustment, wavelet compression, flexible bit allocation, and header compression. The performances of the proposed algorithm in terms of reconstructed signal quality, processing delay, and error resilience were evaluated using the Massachusetts Institute of Technology University and Beth Israel Hospital (MIT-BIH) and Creighton University Ventricular Tachyarrhythmia (CU) databases and a code division multiple access-based simulation model with mobile channel noise

In this paper, the three-dimensional geometry of the human cochlea is modeled by the helico-spiral seashell model. The 3-D helico-spiral model, the generalized representation of the Archimedian spiral model, provides a framework for measuring cochlear features based on consistent estimation of model parameters. Nonlinear least square minimization based algorithms are developed for the identification of rotation, center and intrinsic parameters of the helico-spiral representation. Two algorithms are designed for the rotation axis aligned to the modiolar axis: one is more susceptible in the presence of noise, while the other allows applicability to two-dimensional data sets. The estimated center and intrinsic parameters allow the calculation of length, height and angular positions needed for frequency mapping of multichannel cochlear implant electrodes. Model performance is evaluated with numerically synthesized curves with different levels of added random noise, histologic data and real human cochlear spiral computed tomography data.

Prediction of abdominal aortic aneurysm (AAA) growth is of essential importance for the early treatment and surgical intervention of AAA. Capturing key features of vascular growth, such as blood flow and intraluminal thrombus (ILT) accumulation play a crucial role in uncovering the intricated mechanism of vascular adaptation, which can ultimately enhance AAA growth prediction capabilities. However, local correlations between hemodynamic metrics, biological and morphological characteristics, and AAA growth rates present high inter-patient variability that results in that the temporal-spatial biochemical and mechanical processes are still not fully understood. Hence, this study aims to integrate the physics-based knowledge with deep learning with a patch-based convolutional neural network (CNN) approach by incorporating important multiphysical features relating to its pathogenesis for validating its impact on AAA growth prediction. For this task, we observe that the unstructured multiphysical features cannot be directly employed in the kernel-based CNN. To tackle this issue, we propose a parameterization of features to leverage the spatio-temporal relations between multiphysical features. The proposed architecture was tested on different combinations of four features including radius, intraluminal thrombus thickness, time-average wall shear stress, and growth rate from 54 patients with 5-fold cross-validation with two metrics, a root mean squared error (RMSE) and relative error (RE). We conduct extensive experiments on AAA patients, the results show the effect of leveraging multiphysical features and demonstrate the superiority of the presented architecture to previous state-of-the-art methods in AAA growth prediction.