Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference最新文献

Pain is a highly unpleasant sensory experience, for which currently no objective diagnostic test exists to measure it. Identification and localisation of pain, where the subject is unable to communicate, is a key step in enhancing therapeutic outcomes. Numerous studies have been conducted to categorise pain, but no reliable conclusion has been achieved. This is the first study that aims to show a strict relation between Electrodermal Activity (EDA) signal features and the presence of pain and to clarify the relation of classified signals to the location of the pain. For that purpose, EDA signals were recorded from 28 healthy subjects by inducing electrical pain at two anatomical locations (hand and forearm) of each subject. The EDA data were preprocessed with a Discrete Wavelet Transform to remove any irrelevant information. Chi-square feature selection was used to select features extracted from three domains: time, frequency, and cepstrum. The final feature vector was fed to a pool of classification schemes where an Artificial Neural Network classifier performed best. The proposed method, evaluated through leave-one-subject-out cross-validation, provided 90% accuracy in pain detection (no pain vs. pain), whereas the pain localisation experiment (hand pain vs. forearm pain) achieved 66.67% accuracy.Clinical relevance- This is the first study to provide an analysis of EDA signals in finding the source of the pain. This research explores the viability of using EDA for pain localisation, which may be helpful in the treatment of noncommunicable patients.

Directional neural connectivity is essential to understanding how neurons encode and transmit information in the neural network. The previous studies on single neuronal encoding models illustrate how the neurons modulate the stimulus, underlying movement, and interactions with other neurons. And these encoding models have been used in the Bayesian decoders of the brain-machine interface (BMI) to explain how the neural population represents the movement intentions. However, the existing methods only consider rough correlations between neurons without directional connections, while the synapses between real neurons have explicit directions. Therefore, in these models, we cannot specify the proper functional neural connectivity and how the neurons cooperate to represent the movement intentions in truth. Therefore, we propose representing the directional neural connectivity in the Bayesian decoder in BMI. Our method derives a chain-likelihood based on Bayes' rule to form the single-directional influence between neurons. According to the derived structure, the prior causality relationship can be used to build more precise neural encoding models. Therefore, our method can represent the functional neural circuit more precisely and benefit the decoding in the BMI. We validate the proposed method in synthetic data simulating the rat's two-lever discrimination task. The results demonstrate that our method outperforms the existing methods by representing directional-neural connectivity. Besides, our method is more efficient in training because it employs fewer parameters. Consequently, our method can be used to evaluate the causality between neurons at the behavior level.Clinical Relevance-This paper proposes a decoder that can represent single-directional neural connectivity, which is potential to validate the causality relationship between neurons at behavior level.

Exosuits are a relatively new trend in wearable robotics to answer the flaws of their exoskeleton counterparts, but they remain impractical as the lack of rigidity in their frames makes the integration of crucial components into a single unit a challenge. While some simple solutions exist, almost all current research focuses on the output performance of exosuits rather than the needs of potential beneficiaries of this technology. To address this, a novel mechanism of complete portability for exosuits was developed and tested to improve exosuit practicality and adoption. Designed for elbow flexion, the device produced 12.21-13.66Nm of assistive torque and could be mostly concealed by the wearer's clothing without impacting performance. The proof-of-concept design proved successful and demonstrated many advantages over current portability methods, particularly in size and convenience, weighing only 1.7kg. This device provides the sense of normalcy crucial for a technology to seamlessly integrate into the daily lives of its end users. It is extendable and upgradeable with access to advanced materials and manufacturing methods.Clinical Relevance- Exoskeletons are currently the only marketed wearable robotic device for full limb support. This research is the foundation for a new series of exosuits that could drastically enhance the adoptability, accessibility, and versatility of exosuits in physical rehabilitation and general physical enhancement, becoming a superior alternative or addition.

In this paper we study the heart sound segmentation problem using Deep Neural Networks. The impact of available electrocardiogram (ECG) signals in addition to phonocardiogram (PCG) signals is evaluated. To incorporate ECG, two different models considered, which are built upon a 1D U-net - an early fusion one that fuses ECG in an early processing stage, and a late fusion one that averages the probabilities obtained by two networks applied independently on PCG and ECG data. Results show that, in contrast with traditional uses of ECG for PCG gating, early fusion of PCG and ECG information can provide more robust heart sound segmentation. As a proof of concept, we use the publicly available PhysioNet dataset. Validation results provide, on average, a sensitivity of 97.2%, 94.5%, and 95.6% and a Positive Predictive Value of 97.5%, 96.2%, and 96.1% for Early-fusion, Late-fusion, and unimodal (PCG only) models, respectively, showing the advantages of combining both signals at early stages to segment heart sounds.Clinical relevance- Cardiac auscultation is the first line of screening for cardiovascular diseases. Its low cost and simplicity are especially suitable for screening large populations in underprivileged countries. The proposed analysis and algorithm show the potential of effectively including electrocardiogram information to improve heart sound segmentation performance, thus enhancing the capacity of extracting useful information from heart sound recordings.

The QRS complex is the most prominent feature of the electrocardiogram (ECG) that is used as a marker to identify the cardiac cycles. Identification of QRS complex locations enables arrhythmia detection and heart rate variability estimation. Therefore, accurate and consistent localization of the QRS complex is an important component of automated ECG analysis which is necessary for the early detection of cardiovascular diseases. This study evaluates the performance of six popular publicly available QRS complex detection methods on a large dataset of over half a million ECGs in a diverse population of patients. We found that a deep-learning method that won first place in the 2019 Chinese physiological challenge (CPSC-1) outperforms the remaining five QRS complex detection methods with an F1 score of 98.8% and an absolute sdRR error of 5.5 ms. We also examined the stratified performance of the studied methods on various cardiac conditions. All six methods had a lower performance in the detection of QRS complexes in ECG signals of patients with pacemakers, complete atrioventricular block, or indeterminate cardiac axis. We also concluded that, in the presence of different cardiac conditions, CPSC-1 is more robust than Pan-Tompkins which is the most popular model for QRS complex detection. We expect that this study can potentially serve as a guide for researchers on the appropriate QRS detection method for their target applications.Clinical Relevance-This study highlights the overall performance of publicly available QRS detection algorithms in a large dataset of diverse patients. We showed that there are specific cardiac conditions that are associated with the poor performance of QRS detection algorithms and may adversely influence the performance of algorithms that rely on accurate and reliable QRS detection.

X-ray dark field signals, measurable in many x-ray phase contrast imaging (XPCI) setups, stem from unresolvable microstructures in the scanned sample. This makes them ideally suited for the detection of certain pathologies, which correlate with changes in the microstructure of a sample. Simulations of x-ray dark field signals can aid in the design and optimization of XPCI setups, and the development of new reconstruction techniques. Current simulation tools, however, require explicit modelling of the sample microstructures according to their size and spatial distribution. This process is cumbersome, does not translate well between different samples, and considerably slows down simulations. In this work, a condensed history approach to modelling x-ray dark field effects is presented, under the assumption of an isotropic distribution of microstructures, and applied to edge illumination phase contrast simulations. It substantially simplifies the sample model, can be easily ported between samples, and is two orders of magnitude faster than conventional dark field simulations, while showing equivalent results.Clinical relevance- Dark field signal provides information on the microstructure distribution within the investigated sample, which can be applied in areas such as histology and lung x-ray imaging. Efficient simulation tools for this dark field signal aid in optimizing scanning setups, acquisition schemes and reconstruction techniques.

We showcase two proof-of-concept approaches for enhancing the Vision Transformer (ViT) model by integrating ophthalmology resident gaze data into its training. The resulting Fixation-Order-Informed ViT and Ophthalmologist-Gaze-Augmented ViT show greater accuracy and computational efficiency than ViT for detection of the eye disease, glaucoma.Clinical relevance- By enhancing glaucoma detection via our gaze-informed ViTs, we introduce a new paradigm for medical experts to directly interface with medical AI, leading the way for more accurate and interpretable AI 'teammates' in the ophthalmic clinic.

Characterization of sleep stages is essential in the diagnosis of sleep-related disorders but relies on manual scoring of overnight polysomnography (PSG) recordings, which is onerous and labor-intensive. Accordingly, we aimed to develop an accurate deep-learning model for sleep staging in children suffering from pediatric obstructive sleep apnea (OSA) using pulse oximetry signals. For this purpose, pulse rate (PR) and blood oxygen saturation (SpO₂) from 429 childhood OSA patients were analyzed. A CNN-RNN architecture fed with PR and SpO₂ signals was developed to automatically classify wake (W), non-Rapid Eye Movement (NREM), and REM sleep stages. This architecture was composed of: (i) a convolutional neural network (CNN), which learns stage-related features from raw PR and SpO₂ data; and (ii) a recurrent neural network (RNN), which models the temporal distribution of the sleep stages. The proposed CNN-RNN model showed a high performance for the automated detection of W/NREM/REM sleep stages (86.0% accuracy and 0.743 Cohen's kappa). Furthermore, the total sleep time estimated for each children using the CNN-RNN model showed high agreement with the manually derived from PSG (intra-class correlation coefficient = 0.747). These results were superior to previous works using CNN-based deep-learning models for automatic sleep staging in pediatric OSA patients from pulse oximetry signals. Therefore, the combination of CNN and RNN allows to obtain additional information from raw PR and SpO₂ data related to sleep stages, thus being useful to automatically score sleep stages in pulse oximetry tests for children evaluated for suspected OSA.Clinical Relevance-This research establishes the usefulness of a CNN-RNN architecture to automatically score sleep stages in pulse oximetry tests for pediatric OSA diagnosis.

Fetal phonocardiogram (fPCG), or the electronic recording of fetal heart sounds, is a safe and easily available signal that can be used to monitor fetal wellbeing. In the proposed work an attempt is made to identify twin pregnancies using fPCG data recorded from the fetus with 1/3^rd power in octave band filtered output as features to train K-Nearest Neighbor (KNN) and support vector machine (SVM) classifiers. The SVM classifier with the quadratic kernel is able to identify singletons and twins with a positive predictive value of 100% and 79.1% respectively. The KNN classifier with k=10 neighbors is able to identify singletons and twins with a positive predictive value of 100% and 81.8% respectively.Clinical Relevance: Identifying twin pregnancies from singleton is an essential clinical protocol followed during late pregnancy as there may be complications like twin-twin transfusion syndrome, selective fetal growth restriction, and preterm labor in twin pregnancy [1], [2]. Ultrasound imaging is the most commonly used technique for twin pregnancy detection, though it is often not affordable or available in rural or low-income populations. Utilization of fPCG in such circumstances has immense clinical potential.

Tissue oxygenation assessment using hyperspectral imaging is an emerging technique for the diagnosis and pre- and post-treatment monitoring of ischemic patients. However, the high spectral resolution of hyperspectral imaging leads to large data sizes and a long imaging time. In this study, we propose a method that utilizes multi-objective evolutionary algorithms to determine the optimal hyperspectral band combination when developing a deep learning model for predicting tissue oxygenation from hyperspectral images. Our results confirm that the deep learning model effectively predicts tissue oxygenation images for various oxygenation states. Moreover, we demonstrate that a high-performance prediction model can be developed using only a small number of spectral bands, indicating the potential for more efficient non-contact tissue oxygenation mapping with the proposed method.Clinical Relevance- The proposed method allows for the non-contact and efficient acquisition of two-dimensional tissue oxygenation information in various oxygenation states.