Advancing spinal cord injury care through non-invasive autonomic dysreflexia detection with AI.

This paper presents an AI-powered solution for detecting and monitoring Autonomic Dysreflexia (AD) in individuals with spinal cord injuries. Current AD detection methods are limited, lacking non-invasive monitoring systems. We propose a model that combines skin nerve activity (SKNA) signals with a deep neural network (DNN) architecture to overcome this limitation. The DNN is trained on a meticulously curated dataset obtained through controlled colorectal distension, inducing AD events in rats with spinal cord surgery above the T6 level. The proposed system achieves an impressive average classification accuracy of 93.9% ± 2.5%, ensuring accurate AD identification with high precision (95.2% ± 2.1%). It demonstrates a balanced performance with an average F1 score of 94.4% ± 1.8%, indicating a harmonious balance between precision and recall. Additionally, the system exhibits a low average false-negative rate of 4.8% ± 1.6%, minimizing the misclassification of non-AD cases. The robustness and generalizability of the system are validated on unseen data, maintaining high accuracy, F1 score, and a low false-negative rate. This AI-powered solution represents a significant advancement in non-invasive, real-time AD monitoring, with the potential to improve patient outcomes and enhance AD management in individuals with spinal cord injuries. This research contributes a promising solution to the critical healthcare challenge of AD detection and monitoring.

Acclimation Protocol to Minimize Stress in Immobilized Rats During Non-Invasive Multimodal Sensing of the Autonomic Nervous System.

INTRODUCTION: Rodent models are often used in spinal cord injury investigations to measure physiological parameters but require rats to be restrained during data collection to prevent motion and stress-induced artifacts. MATERIALS AND METHODS: A 4-week acclimation protocol was developed to reduce sympathetic activity during experimentation to collect clean data. Physiological parameters were analyzed throughout the acclimation protocol using surface-based electrodes and an implanted sensor. The sensor was used to extract systolic blood pressure, skin nerve activity, and heart rate variability parameters. RESULTS: Our protocol exposed a minimal increase in sympathetic activity during experimentation despite long periods of restraint. The data suggest that the acclimation protocol presented successfully minimized changes in physiological parameters because of prolonged restraint. CONCLUSIONS: This is necessary to ensure that physiological recordings are not affected by undue stress because of the process of wearing the sensor. This is important when determining the effects of stress when studying dysautonomia after spinal cord injury, Parkinson's disease, and other neurological disorders.

Use of Artificial Intelligence Techniques to Assist Individuals with Physical Disabilities.

Assistive technologies (AT) enable people with disabilities to perform activities of daily living more independently, have greater access to community and healthcare services, and be more productive performing educational and/or employment tasks. Integrating artificial intelligence (AI) with various agents, including electronics, robotics, and software, has revolutionized AT, resulting in groundbreaking technologies such as mind-controlled exoskeletons, bionic limbs, intelligent wheelchairs, and smart home assistants. This article provides a review of various AI techniques that have helped those with physical disabilities, including brain-computer interfaces, computer vision, natural language processing, and human-computer interaction. The current challenges and future directions for AI-powered advanced technologies are also addressed. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 26 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Time and frequency domain analysis of physiological features during autonomic dysreflexia after spinal cord injury.

Introduction: Autonomic dysreflexia (AD) affects about 70% of individuals with spinal cord injury (SCI) and can have severe consequences, including death if not promptly detected and managed. The current gold standard for AD detection involves continuous blood pressure monitoring, which can be inconvenient. Therefore, a non-invasive detection device would be valuable for rapid and continuous AD detection. Methods: Implanted rodent models were used to analyze autonomic dysreflexia after spinal cord injury. Skin nerve activity (SKNA) features were extracted from ECG signals recorded non-invasively, using ECG electrodes. At the same time, blood pressure and ECG data sampled was collected using an implanted telemetry device. Heart rate variability (HRV) features were extracted from these ECG signals. SKNA and HRV parameters were analyzed in both the time and frequency domain. Results: We found that SKNA features showed an increase approximately 18 seconds before the typical rise in systolic blood pressure, indicating the onset of AD in a rat model with upper thoracic SCI. Additionally, low-frequency components of SKNA in the frequency domain were dominant during AD, suggesting their potential inclusion in an AD detection system for improved accuracy. Discussion: Utilizing SKNA measurements could enable early alerts to individuals with SCI, allowing timely intervention and mitigation of the adverse effects of AD, thereby enhancing their overall well-being and safety.

Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.

The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endurance augmentation using exoskeleton/exosuit. For system development, the conventional classification-based brain-computer interface (BCI) controls external devices by providing discrete control signals to the actuator. A continuous kinematic reconstruction from EEG signal is better suited for practical BCI applications. The state-of-the-art multivariable linear regression (mLR) method provides a continuous estimate of hand kinematics, achieving a maximum correlation of up to 0.67 between the measured and the estimated hand trajectory. In this work, three novel source aware deep learning models are proposed for motion trajectory prediction (MTP). In particular, multilayer perceptron (MLP), convolutional neural network-long short-term memory (CNN-LSTM), and wavelet packet decomposition (WPD) for CNN-LSTM are presented. In addition, novelty in the work includes the utilization of brain source localization (BSL) [using standardized low-resolution brain electromagnetic tomography (sLORETA)] for the reliable decoding of motor intention. The information is utilized for channel selection and accurate EEG time segment selection. The performance of the proposed models is compared with the traditionally utilized mLR technique on the reach, grasp, and lift (GAL) dataset. The effectiveness of the proposed framework is established using the Pearson correlation coefficient (PCC) and trajectory analysis. A significant improvement in the correlation coefficient is observed when compared with the state-of-the-art mLR model. Our work bridges the gap between the control and the actuator block, enabling real-time BCI implementation.

Advanced Energy Kernel-Based Feature Extraction Scheme for Improved EMG-PR-Based Prosthesis Control Against Force Variation.

The EMG signal is a widely focused, clinically viable, and reliable source for controlling bionics and prosthesis devices with the aid of machine-learning algorithms. The decisive step in the EMG pattern recognition (EMG-PR)-based control scheme is to extract the features with minimum neural information loss. This article proposes a novel feature extraction method based on advanced energy kernel-based features (AEKFs). The proposed method is evaluated on a scientific dataset which contains six types of upper limb motion with three different force variations. Furthermore, the EMG signal is acquired for eight upper limb gestures for the testing algorithm on the DSP processor. The efficiency of the proposed feature set has been investigated using classification accuracy (CA), Davies-Bouldin (DB) index-based separability measurement, and time complexity as performance metrics. Moreover, the proposed AEKF features, along with the LDA classifier, have been implemented on the DSP processor (ARM cortex M4) for real-time viability. Offline metrics comparison with the existing approaches prove that AEKF features exhibit lower time complexity along with a higher CA of 97.33%. The algorithm is tested on the DSP processor and CA is reported ≈ 92 %. MATLAB 2015a has been deployed in Intel Core i7, 3.40-GHz RAM for all offline analyses.

A Novel Time-Domain based Feature for EMG-PR Prosthetic and Rehabilitation Application.

EMG signal is widely accepted in human-machine interaction applications, such as prosthesis control and rehabilitation devices. The existing feature extraction methods struggle to separate a variety of EMG based activities. In the proposed work, a novel feature defined as PAP (peak average power) has been proposed. This feature has been validated for NinaPro database which includes isometric, isotonic, grasp and finger force based upper limb motions. Further, the comparison of classification accuracy has been performed with well-known time domain based features. Significant classification performance enhancement has been observed in terms of accuracy with LDA and QDA techniques. In this experiment, three datasets have been created and analysis was performed. Consequently, the results show an average enhancement of 17.60%, 7.52% and 15.37% using the proposed approach for LDA in dataset-1, dataset-2, and dataset-3 respectively. Similarly for the same datasets, when QDA is used the proposed approach overrules the existing techniques with the average enhanced performance of 13.52%, 12.72%, and 15.40%. All the analysis has been done using MATLAB 2015a in the i7 core.