Biomedical Engineering: Applications, Basis and Communications

Innovatively new behaviors of heart rate variability caused by special heart-transition process were observed in the long-term, well-experienced Zen practitioners while practicing the heart-to-heart imprint sealing (HHIS) Zen meditation. HHIS Zen practice involves specific neurocardiac-cardiorespiratory interaction while on the way of realizing the heart-dominant, detached brain. Results of analyzing the electrocardiogram and respiratory signals of 10 experienced practitioners reveal several distinctive characteristics: (1) remarkably linear correlation between standard deviation of the normal R-to-R intervals, SDNN, and total power in very-low-frequency (VLF, 0.0033–0.04Hz) band of power spectrum of the heart-rate sequence, (2) time-varying VLF power dominating over the low-frequency and high-frequency power in heart rate variability (HRV) variations, (3) intermittent transition into slowly, deeply abdominal respiration inducing a boost of heart rates, (4) heart-rate baseline slowly fluctuating at 0.005–0.0067Hz, about 1.5–2 cycles in 5-min period, and (5) remarkable respiratory sinus arrhythmia (RSA) synchrony between heart rate and respiration rhythm. This paper proposes a rational scientific hypothesis for the neurocardiac-cardiorespiratory mechanism. The unique scheme of HHIS Zen meditation involves the spiritual-qi concentration and refinement for pinpointing into the particular energy centers, mailuns. Ignition by a subtle, deepest abdominal respiration, electrical impulses rapidly transmit from solar plexus to branchial plexuses to activate unique heart-transition process. Simultaneously, another branch streams upward the spinal cord to cervical plexus and brainstem that effectively harmonizes neurocardiac interactions. To investigate the underlying behaviors, time-domain and frequency-domain HRV based on continuous wavelet transform were employed.

The objective of this study is to propose an accurate emotion recognition methodology. To this end, a novel fusion framework based on wavelet transform (WT), and matching pursuit (MP) algorithm was offered. Electrocardiogram (ECG) and galvanic skin response (GSR) of 11 healthy students were collected while subjects listened to emotional music clips. In both fusion techniques, Coiflet wavelet (Coif5 at level 14) was chosen as a wavelet family and MP dictionary, respectively. After employing the proposed fusion framework, some statistical measures were extracted. To describe emotions, three schemes were adopted: two-dimensional model (five classes), valence-(three classes), and arousal-(three classes) based emotion categories. Subsequently, the probabilistic neural network (PNN) was applied to classify affective states. The experiments indicate that the MP-based fusion approach outperform the wavelet-based fusion technique or methods using only ECG or GSR indices. Considering the proposed fusion techniques, the maximum classification rate of 99.64% and 92.31% was reached for the fusion methodology based on the MP algorithm (five classes of emotion) and wavelet-based fusion technique (three classes of valence), respectively.

Locomotor state transitions are challenging for transfemoral (TF) amputees due to the lack of active knee control even in the current powered prosthetic devices. Myoelectric activation has been used successfully to classify steady-state locomotion states, but classification of transitions between locomotion states remains a challenge, especially for TF amputees. The purpose of this study was to determine if lower-extremity muscle activation differences between pre-transition and transition gait cycles occur in the involved or uninvolved limb of TF amputees during locomotion state transitions. Surface electromyography (EMG) was collected from residual muscles on the involved limb and from the uninvolved limb from five TF amputees as they transitioned between different locomotion states (level ground, ramp ascent/descent, stair ascent/descent). Statistical parametric mapping (SPM) was used to assess differences in activation. When analyzed as a group, the only significant differences were observed in the vastus lateralis of the uninvolved limb. High inter-subject variation reduced the significance of other pattern differences. Further inspection revealed that the individual subjects expressed three different recruitment patterns. These recruitment patterns may indicate compensatory strategies adopted by the subjects over the years since amputation. Furthermore, the separate recruitment patterns suggest the need for individualized locomotion transition classification algorithms rather than a general classification scheme.

Lumbar disc diseases are the commonest complaint of Lower Back Pain (LBP). In this paper, a new method for automatic diagnosis of lumbar disc herniation is proposed which is based on clinical Magnetic Resonance Images (MRI) data. We use T2-W sagittal and myelograph images. Our method uses Otsu thresholding method to extract the spinal cord from MR images of Lumbar disc. In the next step, a third-order polynomial is aligned on the extracted spinal cords, and in the end of preprocessing step all the T2-W sagittal images are prepared for extracting disc boundary and labeling. After labeling and extracting a ROI for each disc, intensity and shape features are used for classification. The presented Method is applied on 30 clinical cases, each containing 7 discs (210 lumbar discs) for the herniation diagnosis. The results revealed 92.38% and 93.80% accuracy for Artificial Neural Network and Support Vector Machine (SVM) classifiers, respectively. The results indicate the superiority of the proposed method to those mentioned in similar studies.

The features of electroencephalographic (EEG) signals include important information about the function of the brain. One of the most common EEG signal features is alpha wave, which is indicative of relaxation or mental inactivity. Until now, the analysis and the feature extraction procedures of these signals have not been well developed. This study presents a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) for extracting and predicting the alpha power band of EEG signals during Muslim prayer (Salat). Proposed models can acquire information related to the alpha power variations during Salat from other physiological parameters such as heart rate variability (HRV) components, heart rate (HR), and respiration rate (RSP). The models were developed by systematically optimizing the initial ANFIS model parameters. Receiver operating characteristic (ROC) curves were performed to evaluate the performance of the optimized ANFIS models. Overall prediction accuracy of the proposed models were achieved of 94.39%, 92.89%, 93.62%, and 94.31% for the alpha power of electrodes positions at O1, O2, P3, and P4, respectively. These models demonstrated many advantages, including efficiency, accuracy, and simplicity. Thus, ANFIS could be considered as a suitable tool for dealing with complex and nonlinear prediction problems.

Copy number changes or alterations are a form of genetic variation in the human genome. Genomic DNA copy number alterations (CNAs) are associated with the development and progression of cancers. Array-based comparative genomic hybridization (a-CGH) is a technique used to identify copy number changes in genomic DNA. It yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of automated statistics algorithms for learning about the genomic alterations from array CGH data. Specifically, there is a need for algorithms that can identify gains and losses in the number of copies based on statistical considerations, rather than merely detect trends in the data. For this purpose the proposed study introduces three different approaches; Circular binary segmentation, Bayesian approach, relying on the hidden Markov model and effective Gaussian mixture (GM) clustering for the analysis of array CGH profiles. Publicly available data on pancreatic adenocarcinoma and Coriell cell line bacterial artificial chromosome (BAC) array were used for the analysis to illustrate the reliability and success of the techniques.

A need assessment exercise at various resource-limited hospitals in Ghana revealed that a conventional method of monitoring uterine contractions is employed. This method is time consuming and ineffective with a likelihood of misrepresenting data on uterine contractions. There is therefore a need for a system that can potentially overcome the identified challenges. In this paper, the authors present the proof of concept for development of an automated uterine contraction monitoring system designed for use in resource-limited settings. Following the engineering design process, data were gathered to draft product specifications. Various concepts were evaluated and a mathematical model of chosen concept was built and simulated. A functional prototype was constructed to test the system’s ability to measure the frequency and average duration of muscle contractions over a specified interval. The results indicate the capability of the chosen concept to meet design specifications. The design can also be enhanced to provide the intensity of contractions.

Rapid growth of Diabetes mellitus in people causes damage to posterior part of eye vessel structures. Diabetic retinopathy (DR) is an important hurdle in diabetic people and it causes lesion formation in retina due to retinal vessel structures damage. Bright lesions (BLs) or exudates are initial clinical signs of DR. Early BLs detection can help avoiding vision loss. The severity can be recognized based on number of BLs formed in the color fundus image. Manually diagnosing a large amount of images is time consuming. So a computerized DR grading and BLs detection system is proposed. In this paper for BLs detection, curvelet fusion enhancement is done initially because bright objects maps to largest coefficients in an image by utilizing the curvelet transform, so that BLs can be recognized in the retina easily. Then optic disk (OD) appearance is similar to BLs and vessel structures are barriers for lesion exact detection and moreover OD falsely classified as BLs and that increases false positives in classification. So these structures are segmented and eliminated by thresholding techniques. Various features were obtained from detected BLs. Publicly available databases are used for DR severity testing. 260 fundus images were used for the performance evaluation of proposed work. The support vector machine classifier (SVM) used to separate fundus images in various levels of DR based on feature set extracted. The proposed system that obtained the statistical measures were sensitivity 100%, specificity 95.4% and accuracy 97.74%. Compared to existing state-of-art techniques, the proposed work obtained better results in terms of sensitivity, specificity and accuracy.