Deep Learning for Daily Monitoring of Parkinson's Disease Outside the Clinic Using Wearable Sensors.

Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson's disease (PD). However, the unsupervised and "open world" nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these "walk-like" events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD.

Sensor Location Optimization of Wireless Wearable fNIRS System for Cognitive Workload Monitoring Using a Data-Driven Approach for Improved Wearability.

Functional Near-Infrared Spectroscopy (fNIRS) is a hemodynamic modality in human cognitive workload assessment receiving popularity due to its easier implementation, non-invasiveness, low cost and other benefits from the signal-processing point of view. Wearable wireless fNIRS systems used in research have promisingly shown that fNIRS could be used in cognitive workload assessment in out-of-the-lab scenarios, such as in operators' cognitive workload monitoring. In such a scenario, the wearability of the system is a significant factor affecting user comfort. In this respect, the wearability of the system can be improved if it is possible to minimize an fNIRS system without much compromise of the cognitive workload detection accuracy. In this study, cognitive workload-related hemodynamic changes were acquired using an fNIRS system covering the whole forehead, which is the region of interest in most cognitive workload-monitoring studies. A machine learning approach was applied to explore how the mean accuracy of the cognitive workload classification accuracy varied across various sensing locations on the forehead such as the Left, Mid, Right, Left-Mid, Right-Mid and Whole forehead. The statistical significance analysis result showed that the Mid location could result in significant cognitive workload classification accuracy compared to Whole forehead sensing, with a statistically insignificant difference in the mean accuracy. Thus, the wearable fNIRS system can be improved in terms of wearability by optimizing the sensor location, considering the sensing of the Mid location on the forehead for cognitive workload monitoring.

Prediction of gait intention from pre-movement EEG signals: a feasibility study.

BACKGROUND: Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence. METHODS: An eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then used to classify between 'walk vs. stop' and 'rest vs. start' classes using support vector machine (SVM) classifier with RBF kernel in a ten-fold cross-validation scheme. RESULTS: Using a fully predictive intention detection system, 76.41±4.47% accuracy, 72.85±7.48% sensitivity, and 79.93±5.50% specificity were achieved for 'rest vs. start' classification. While for 'walk vs. stop' classification, the obtained mean accuracy, sensitivity, and specificity were 74.12±4.12%, 70.24±6.45%, and 77.78±7.01% respectively. Overall average True Positive Rate achieved by this methodology was 72.06±8.27% with 1.45 False Positives/min. CONCLUSION: Extensive simulations and resulting classification results show that it is possible to achieve statistically similar intention detection accuracy using either only pre-movement EEG features or trans-movement EEG features. The classifier performance shows the potential of the proposed methodology to predict human movement intention exclusively from the pre-movement EEG signal to be applied in real-life prosthetic and neuro-rehabilitation systems.

The feasibility of using exoskeletal-assisted walking with epidural stimulation: a case report study.

Spinal cord epidural stimulation (SCES) exhibits a rehabilitation potential of restoring locomotion in individuals with spinal cord injury (SCI). However, this is linked to an intensive rehabilitation locomotion approach, which is impractical to apply among a large clinical SCI population. We, hereby, propose a rehabilitation approach of using SCES to enhance motor control during exoskeletal-assisted walking (EAW). After 24 sessions (12 weeks) of EAW swing assistance decreased from 100% to 35% in a person with C7 complete SCI. This was accompanied by 573 unassisted steps (50% of the total number of steps). Electromyographic pattern improved during EAW, reflecting the subject's ability to rhythmically activate paralyzed muscles. Rate perceived exertion increased during EAW with SCES compared to stepping without SCES. These preliminary findings suggest that using SCES with EAW may be a feasible rehabilitation approach for persons with SCI.

Exploration of Metrics for Leg Length Discrepancy Using a Wearable Gait Analysis System.

The purpose of this study was to explore effective metrics for differentiating levels ofleg length discrepancy (LLD) using a wearable device. DESIGN: A wearable device for measuring ground reaction forces and kinetic features was developed in this study. PARTICIPANTS: Eight volunteers without previously diagnosed LLD walked along a 120.0 m walkway with a 2.5 cm and 3 cm foot spacer to simulate LLD. MAIN OUTCOME MEASURES: The p-values of thirteen kinetic and kinematic metrics between normal and LLD walking. RESULTS: Difference in stance time duration, difference in heel reposition time, and ratio difference of loading effect showed statistical difference between normal walking and simulated LLD walking. CONCLUSION: The metrics with statistical difference may serve as effective indicators oflow levels ofLLD and be implemented into a point-of-care system for gait analysis.

Movement artefact removal from NIRS signal using multi-channel IMU data.

BACKGROUND: The non-invasive nature of near-infrared spectroscopy (NIRS) makes it a widely accepted method for blood oxygenation measurement in various parts of the human body. One of the main challenges in this method lies in the successful removal of movement artefacts in the detected signal. In this respect, multi-channel inertia measurement unit (IMU) containing accelerometer, gyroscope and magnetometer can be used for better modelling of movement artefact than using accelerometer only, which as a result, movement artefact can be more accurately removed. METHODS: A wearable two-channel continuous wave NIRS system, incorporating an IMU sensor which contain accelerometer, gyroscope and magnetometer in it, was developed to record NIRS signal along with the simultaneous recording of movement artefacts related signal using the IMU. Four healthy subjects volunteered in the recording of the NIRS signals. During the recording from the first two subject, movement artefacts were simulated in one of the NIRS channels by tapping the photodiode sensor nearby. The corresponding IMU data for the simulated movement artefacts were used to estimate the artefacts in the corrupted signal by autoregressive with exogenous input method and subtracted from the corrupted signal to remove the artefacts in the NIRS signal. Signal-to-noise ratio (SNR) improvement was used to evaluate the performance of the movement artefacts removal process. The performance of the movement artefacts estimation and removal were compared using accelerometer only, accelerometer and gyroscope, and accelerometer, gyroscope and magnetometer data from IMU sensor to estimate the artefact in NIRS reading. For the remaining two subjects the NIRS signal was recorded by natural movement artefacts impact and the results of artefacts removal was compared using accelerometer only, accelerometer and gyroscope, and accelerometer, gyroscope and magnetometer data from IMU sensor to estimate the artefact in NIRS reading. RESULTS: The quantitative and qualitative results revealed that the SNR improvement increases with the number of IMU channels used in the artefacts estimation, and there were approximately 5-11 dB increase in SNR when nine channel IMU data were used rather than using only three channel accelerometer data only. The artefact removal from natural movements also demonstrated that the combination of gyroscope and magnetometer sensors with accelerometer provided better estimation and removal of the movement artefacts, which was revealed by the minimal change of the HbO2 and Hb level before, during and after movement artefacts occurred in the NIRS signal. CONCLUSION: The movement artefacts in NIRS can be more accurately estimated and removed by using accelerometer, gyroscope and magnetograph signals from an integrated IMU sensor than using accelerometer signal only.

Smart Data-Driven Optimization of Powered Prosthetic Ankles Using Surface Electromyography.

The advent of powered prosthetic ankles provided more balance and optimal energy expenditure to lower amputee gait. However, these types of systems require an extensive setup where the parameters of the ankle, such as the amount of positive power and the stiffness of the ankle, need to be setup. Currently, calibrations are performed by experts, who base the inputs on subjective observations and experience. In this study, a novel evidence-based tuning method was presented using multi-channel electromyogram data from the residual limb, and a model for muscle activity was built. Tuning using this model requires an exhaustive search over all the possible combinations of parameters, leading to computationally inefficient system. Various data-driven optimization methods were investigated and a modified Nelder⁻Mead algorithm using a Latin Hypercube Sampling method was introduced to tune the powered prosthetic. The results of the modified Nelder⁻Mead optimization were compared to the Exhaustive search, Genetic Algorithm, and conventional Nelder⁻Mead method, and the results showed the feasibility of using the presented method, to objectively calibrate the parameters in a time-efficient way using biological evidence.

EMG-based energy expenditure optimization for active prosthetic leg tuning.

In recent years, active prosthetic legs have been developed and deployed commercially that help amputees to initiate gait with less effort and more symmetry in the pattern. However, the process of initial set up and tuning is highly time and cost consuming. It requires prosthetic experts to observe the gait and the feedback from amputees to manually tune the parameters subjectively. In this study, an electromyography (EMG)-based energy expenditure optimization method was presented to automatically tune the prosthetic limb. For this purpose, a wide variety of lower body muscles were observed and the energy expenditure was modeled based on their electrical activity. The tuning optimization was implemented based on a grid-searching protocol designed in this study. This method resulted in a power value comparable to manual tuning, which provided enough force to facilitate gait for amputees. This study shows the feasibility of automatic tuning and removal of the need for referral to an expert.

Electroencephalogram-Based Brain-Computer Interface and Lower-Limb Prosthesis Control: A Case Study.

OBJECTIVE: The purpose of this study was to establish the feasibility of manipulating a prosthetic knee directly by using a brain-computer interface (BCI) system in a transfemoral amputee. Although the other forms of control could be more reliable and quick (e.g., electromyography control), the electroencephalography (EEG)-based BCI may provide amputees an alternative way to control a prosthesis directly from brain. METHODS: A transfemoral amputee subject was trained to activate a knee-unlocking switch through motor imagery of the movement of his lower extremity. Surface scalp electrodes transmitted brain wave data to a software program that was keyed to activate the switch when the event-related desynchronization in EEG reached a certain threshold. After achieving more than 90% reliability for switch activation by EEG rhythm-feedback training, the subject then progressed to activating the knee-unlocking switch on a prosthesis that turned on a motor and unlocked a prosthetic knee. The project took place in the prosthetic department of a Veterans Administration medical center. The subject walked back and forth in the parallel bars and unlocked the knee for swing phase and for sitting down. The success of knee unlocking through this system was measured. Additionally, the subject filled out a questionnaire on his experiences. RESULTS: The success of unlocking the prosthetic knee mechanism ranged from 50 to 100% in eight test segments. CONCLUSION: The performance of the subject supports the feasibility for BCI control of a lower extremity prosthesis using surface scalp EEG electrodes. Investigating direct brain control in different types of patients is important to promote real-world BCI applications.

Obstructive sleep apnea detection using spectrum and bispectrum analysis of single-lead ECG signal.

A novel method for the automatic diagnosis of obstructive sleep apnea (OSA) from an electrocardiogram (ECG) is presented. This method aims to detect OSA utilizing exclusively ECG recordings during sleep and present a minute-by-minute signal processing technique. In the proposed algorithm, a wide range of features based on heart rate variability (HRV) and ECG-derived respiratory (EDR) signals are considered. The novelty of this study arises from employing bispectral analysis to the HRV and EDR signals in order to illustrate quadratic phase-coupling that can be observed among signal components with different frequencies. From this perspective, in the proposed algorithm, a new feature set based on a higher order spectrum of HRV and EDR signals is introduced and it is utilized to extract information regarding their non-linearity and non-Gaussianity. This feature vector is then fed into the input of a least-square support vector machine classifier. To implement the proposed method, the apnea-ECG database, which contains 70 nocturnal ECG records gathered from volunteer men and women, is used in this work. Results obtained from cross-validating 35 data records show that the normal recordings could be separated from the apneic recordings with an accuracy of 95.57% and a sensitivity and specificity of 98.64% and 92.51%, respectively. In addition, 35 other records were used for a pure independent validation of the proposed method and the obtained accuracy, sensitivity and specificity was 94.12%, 93.46% and 94.79% respectively in OSA episode detection. The performance of our proposed technique is better than in other existing approaches. It can be used as a reliable tool for automatic OSA identification and as a result, it will improve medical services.