Biodegradable form stable phase change material for cold storage packaging of meat

In this study, tetradecane was selected as a phase change material (PCM) to provide a thermal buffering system for meat packaging. Firstly, tetradecane was capsulated within a calcium alginate shell; then the conditions for achieving the highest efficiency were obtained. The capsules were characterized using Scanning electron microscopy, Thermo-gravimetric analysis and Differential scanning calorimetry. The results revealed the stability of the thermal properties of the encapsulated PCMs and the efficient capsulation of tetradecane in the capsules. Next, the alginate films were prepared using various concentrations of sodium alginate (SA) and CaCl₂ incorporated with capsulated tetradecane. Observations indicated that the film prepared with 5% SA and 12% CaCl₂ had the least leakage and the best structural integration during phase change cycles and storage at 25 °C. The use of PCM-incorporated packages could successfully delay the meat temperature elevation during phase change cycles and incubation at 25 °C. After 7.5 h incubation at 25 °C and 15 phase change cycles, PCM-incorporated samples had lower weight loss and higher hardness, gumminess and chewiness as compared to the control. Further, the physicochemical parameters of the meat in PCM-incorporated package were less changed, as compared to the fresh sample. So, the prepared package could be effectively used in meat packaging.

Mung bean protein films incorporated with cumin essential oil: development and characterization

Biodegradable films based on mung bean protein (1, 3 and 5%) incorporated with cumin essential oil (EO) (0, 0.25 and 0.5 ml/g protein) were developed. Adding cumin oil and increasing the protein content enhanced the thickness, tensile strength and yellowness. Films incorporated with EO exhibited less water vapor permeability and water solubility, as compared to the control films. A higher antioxidant activity was also obtained by increasing the EO and protein ratios. Films with higher levels of protein displayed lower thermal stability with a lower degradation temperature, as suggested by thermo-gravimetric analyses. In addition, the incorporation of EO reduced thermal stability, as confirmed by the higher weight loss and lower degradation temperature. Furthermore, mung bean protein films containing 0.5 ml cumin oil/g protein had suitable physical characteristics, antioxidant activities, water barrier properties and thermal stability; thus, they can be used as appropriate biodegradable packaging materials for food preservation.

Formation and characterization of calcium alginate hydrogel beads filled with cumin seeds essential oil

Preparation and characterization of novel encapsulation system based on calcium alginate hydrogels filled with cumin essential oil has been investigated. Firstly, the effect of sodium alginate concentration, CaCl₂ level, hardening time, encapsulation and emulsion fabrication methods was studied on loading capacity of the hydrogels using a Resolution-V fractional factorial design (2 _V^5-1 FFD), followed by response surface methodology (RSM). At the optimum point, the in-vitro release of phenolic compounds in simulated gastric and intestinal mediums were 96.02 ± 0.96% and 10.65 ± 1.23% after 180 min, respectively. The Scanning electron microscopy (SEM) images demonstrated a relatively smooth surface with small pore size. Based on SEM images and Fourier-transform infrared spectrums, the cumin essential oil was encapsulated successfully in calcium alginate beads. Thus, calcium alginate hydrogel could be introduced as a promising carrier for encapsulating biochemical active compounds with favorable features.

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.