Regeneration of sciatic nerve crush injury by a hydroxyapatite nanoparticle-containing collagen type I hydrogel

The current study aimed to enhance the efficacy of peripheral nerve regeneration using a hydroxyapatite nanoparticle-containing collagen type I hydrogel. A solution of type I collagen, extracted from the rat tails, was incorporated with hydroxyapatite nanoparticles (with the average diameter of ~212 nm) and crosslinked with 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDC) to prepare the hydrogel. The Schwann cell cultivation on the prepared hydrogel demonstrated a significantly higher cell proliferation than the tissue culture plate, as positive control, after 48 h (n = 3, P < 0.005) and 72 h (n = 3, P < 0.01). For in vivo evaluation, the prepared hydrogel was administrated on the sciatic nerve crush injury in Wistar rats. Four groups were studied: negative control (with injury but without interventions), positive control (without injury), collagen hydrogel and hydroxyapatite nanoparticle-containing collagen hydrogel. After 12 weeks, the administration of hydroxyapatite nanoparticle-containing collagen significantly (n = 4, P < 0.005) enhanced the functional behavior of the rats compared with the collagen hydrogel and negative control groups as evidenced by the sciatic functional index, hot plate latency and compound muscle action potential amplitude measurements. The overall results demonstrated the applicability of the produced hydrogel for the regeneration of peripheral nerve injuries.

Personalization of NonEEG-based seizure detection systems

Seizures affect each patient differently, so personalization is a vital part of developing a reliable nonEEG based seizure detection system. This personalization must be done while the patient is undergoing video EEG monitoring in an epilepsy monitoring unit (EMU) because seizure detection by EEG is considered to be the ground truth. We propose the use of confidence interval analysis for determining how many seizures must be captured from a patient before we can reliably personalize such a seizure detection system for him/her. Our analysis indicates that 6 to 8 seizures are required. In addition, we create seizure likelihood tables for future use by said system by comparing the number of times a prespecified biosignal activity level is induced by seizure to the total number of occurrences of that level of activity. We focus on complex partial seizures in this paper because they are more difficult to detect than are generalized seizures.

An adaptive deep learning approach for PPG-based identification

Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

Distance metric learning using random forest for cytometry data

Visualization and clustering of single-cell mass cytometry (CyTOF) data are analytic techniques to identify different cell types. Most of such techniques, such as Euclidean norm, lose their effectiveness when the data dimension increases due to the curse of dimensionality. In this paper, we propose a new cell distance (called CytoRFD) that works based on Random Forest (RF) concept. The experimental results show that the proposed distance can achieve a much higher quality and effectiveness in large data analysis than traditional metrics specially for CyTOF data.

Epileptic seizure detection using wristworn biosensors

Single signal seizure detection algorithms suffer from high false positive rates. We have found a set of signals which can be easily monitored by a wristworn device and which produce a distinctive pattern during seizure for patients in an epilepsy monitoring unit (EMU). This pattern is much less likely to be reproduced by nonseizure events in the patient's daily life than are changes in heart rate alone. We collected 108 hours of data from three EMU patients who suffered a combined total of seven seizures, then developed a time series analysis/pattern recognition based algorithm which distinguishes the seizures from nonseizure events with 100% accuracy.

Clustering-based limb identification for pressure ulcer risk assessment

Bedridden patients have a high risk of developing pressure ulcers. Risk assessment for pressure ulceration is critical for preventive care. For a reliable assessment, we need to identify and track the limbs continuously and accurately. In this paper, we propose a method to identify body limbs using a pressure mat. Three prevalent sleep postures (supine, left and right postures) are considered. Then, predefined number of limbs (body parts) are identified by applying Fuzzy C-Means (FCM) clustering on key attributes. We collected data from 10 adult subjects and achieved average accuracy of 93.2% for 10 limbs in supine and 7 limbs in left/right postures.

A wireless monitoring system for Hydrocephalus shunts

Patients with Hydrocephalus are usually treated by diverting the excess Cerebrospinal Fluid (CSF) to other parts of the body using shunts. More than 40 percentage of shunts implanted fail within the first two years. Obstruction in the shunts is one of the major causes of failure (45 percent) and the detection of obstruction reduces the complexity of the revision surgery. This paper describes a proposed wireless monitoring system for clog detection and flow measurement in shunts. A prototype was built using multiple pressure sensors along the shunt catheters for sensing the location of clog and flow rate. Regular monitoring of flow rates can be used to adjust the valve in the shunt to prevent over drainage or under drainage of CSF. The accuracy of the flow measurement is more than 90 percent.

A wrist-worn biosensor system for assessment of neurological status

EEG based monitoring for the purpose of assessing a patient's neurological status is conspicuous and uncomfortable at best. We are analyzing a set of physiological signals that may be monitored comfortably by a wrist worn device. We have found that these signals and machine based classification allows us to accurately discriminate among four stress states of individuals. Further, we have found a clear change in these signals during the 70 minutes preceding a single convulsive epileptic seizure. Our classification accuracy on all data has been greater than 90% to date.

Classifying bed inclination using pressure images

Pressure ulcer is one of the most prevalent problems for bed-bound patients in hospitals and nursing homes. Pressure ulcers are painful for patients and costly for healthcare systems. Accurate in-bed posture analysis can significantly help in preventing pressure ulcers. Specifically, bed inclination (back angle) is a factor contributing to pressure ulcer development. In this paper, an efficient methodology is proposed to classify bed inclination. Our approach uses pressure values collected from a commercial pressure mat system. Then, by applying a number of image processing and machine learning techniques, the approximate degree of bed is estimated and classified. The proposed algorithm was tested on 15 subjects with various sizes and weights. The experimental results indicate that our method predicts bed inclination in three classes with 80.3% average accuracy.

Sleep state classification using pressure sensor mats

Sleep state detection is valuable in assessing patient's sleep quality and in-bed general behavior. In this paper, a novel classification approach of sleep states (sleep, pre-wake, wake) is proposed that uses only surface pressure sensors. In our method, a mobility metric is defined based on successive pressure body maps. Then, suitable statistical features are computed based on the mobility metric. Finally, a customized random forest classifier is employed to identify various classes including a new class for pre-wake state. Our algorithm achieves 96.1% and 88% accuracies for two (sleep, wake) and three (sleep, pre-wake, wake) class identification, respectively.

Bed posture classification for pressure ulcer prevention

Pressure ulcer is an age-old problem imposing a huge cost to our health care system. Detecting and keeping record of the patient's posture on bed, help care givers reposition patient more efficiently and reduce the risk of developing pressure ulcer. In this paper, a commercial pressure mapping system is used to create a time-stamped, whole-body pressure map of the patient. An image-based processing algorithm is developed to keep an unobtrusive and informative record of patient's bed posture over time. The experimental results show that proposed algorithm can predict patient's bed posture with up to 97.7% average accuracy. This algorithm could ultimately be used with current support surface technologies to reduce the risk of ulcer development.