Principles and Practice of Sleep Medicine

The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

In this article, a new fusion scheme based on the Dempster–Shafer Evidence Theory (DSET) is introduced for Epileptic Seizure Detection (ESD) in brain disorders. Firstly, various features in temporal, spectral, and temporal-spectral domains are extracted from Electroencephalogram (EEG) signals. Afterward, a Correlation analysis via the Pearson Correlation Coefficient (PCC) is conducted on the extracted features to select and remove highly correlated features. It leads to the second feature set with about half numbers of the first feature set. Next, three separate filter-type feature selection techniques, including Relief-F (RF), Compensation Distance Evaluation Technique (CDET), and Fisher Score (FS), are conducted to this second feature set for ranking features. Following that, a feature fusion is engaged by the DSET through the individual feature ranking results to generate high qualified feature sets. Indeed, the DSET-based feature fusion is devoted to enhancing the feature selection confidence using the least superb ranked features. In the classification stage, an Ensemble Decision Tree (EDT) classifier, along with two common validation procedures, including hold out and 10-fold cross-validation, is appropriated to classify the selected features from the EEG signals as normal, pre-ictal (epileptic background), and ictal (epileptic seizure) classes. Finally, several test scenarios are investigated using experimental data of Bonn University to evaluate the proposed ESD performance. Moreover, a comparison with other research works on the same dataset and classes is accomplished. The obtained results indicate the effectiveness of the proposed feature fusion approach and superior accuracy compared to the traditional methods.

Multi-Feature Fusion Approach for Epileptic Seizure Detection From EEG Signals

Passive Infrared (PIR) sensors are commonly used in indoor applications to detect human presence. PIR sensors detect human presence by detecting the change in infrared radiation across the polarity of the sensor. Due to this, PIR sensors are unable to accurately detect stationary human subjects, which results in false negatives. In the pursuit of creating a low-cost solution for detecting stationary occupants in a closed space, the novel approach to mount a PIR sensor on a moving platform was developed (MI-PIR). This approach was developed for the system to artificially induce the motion that is necessary for stationary human detection. Utilizing the raw analog output of the PIR sensor and an artificial neural network (ANN), the closed space was accurately classified for room occupancy, the number of occupants, the approximate location of the human targets, and the differentiation of targets. This novel approach provides the advantages of a utilizing a single PIR sensor for human presence detection, while eliminating the major known drawback to this type of sensor. Scanning the room using a PIR sensor also allows for an expanded field of view (FoV) and a simpler deployment, in comparison to other approaches using a PIR sensor. Finally, MI-PIR expands the functionalities of PIR sensors by using an ANN to detect various other occupancy parameters. The experimental results show that the system can detect room classification with 99% accuracy, 91% accuracy in occupancy count estimation, 93% accuracy in relative location prediction, and 93% accuracy in human target differentiation. These results show promise for an application of tracking and monitoring an at-risk patient in an indoor setting.

A Motion Induced Passive Infrared (PIR) Sensor for Stationary Human Occupancy Detection

https://doi.org/10.1016/j.scs.2022.103719

Metabolism-based ventilation monitoring and control method for COVID-19 risk mitigation in gymnasiums and alike places

Review of signal processing applications of Pyroelectric Infrared (PIR) sensors with a focus on respiration rate and heart rate detection

Sudden Unexplained Death in Epilepsy (SUDEP) is a fatal threat to patients who suffer from convulsive seizures The causes of the SUDEP are still ambiguous, and the patients who suffer from epileptic seizures may face death during sleep, likely after an unwitnessed convulsive seizure An important step towards SUDEP prevention is reliable seizure detection during sleep that is inexpensive and unobtrusive In this work, we developed a non-contact, nonintrusive, privacy-preserving system that can detect convulsive movements experienced by human subjects Detection is accomplished by a combination of uncooled low-cost, low-power, low-resolution (8 × 8) IR array sensor, and a deep learning algorithm implemented with a Convolutional Neural Network (CNN) The thermopile sensor array is placed 1m from subjects who are reclining in bed The CNN training set consists of thermal video streams from 40 healthy subjects mimicking convulsive movements or lying in bed without making convulsive movements After training, the CNN was tested on thermal video streams not included in the training set and had a 992% accuracy in classifying convulsive movements and non-convulsive episodes, with no false negatives to distinguish between the occurrence and non-occurrence of convulsive movements The performance results show that the thermopile sensor array has the potential to detect convulsive seizures while maintaining patient privacy and not requiring direct patient contact

/pdf/convulsive-movement-detection-using-low-resolution-3arba99jhn.pdf

Convulsive Movement Detection Using Low-Resolution Thermopile Sensor Array

This paper addresses the problem of detecting epileptic seizures experienced by a human subject during sleep. Commonly used solutions to this problem mostly rely on detecting motion due to seizures using contact-based sensors or video-based sensors. We seek a low-cost, low-power alternative that can sense motion without making direct contact with the subject and provides high detection accuracy. We investigate the use of Passive InfraRed (PIR) sensors to sense human body motion caused by epileptic seizures during sleep which makes the body shake and causes the PIR sensor to generate an oscillatory output signal. This signal can be distinguished from that of ordinary motions during sleep using analysis with machine learning algorithms. The supervised hidden Markov model algorithm (HMM) and a 1-D and 2-D convolutional neural network (ConvNet) are used to classify the data set of the PIR sensor output into the occurrence of epileptic seizures, ordinary motions, or absence of motion. The method was tested on the PIR signals captured at 1 m from 33 recruited healthy subjects who, after watching seizure videos, either moved their body on a bed to simulate a seizure, ordinary motion, or lay still. The HMM algorithm attained 97.03% accuracy, while 1D-ConvNet and 2D-ConvNet attained an accuracy of 96.97% and 98.98%, respectively. All simulated seizures were successfully detected, with errors occurring only in distinguishing between ordinary motion and no motion, thereby demonstrating the potential for using PIR sensors in the epileptic seizure detection.

Real-Time Epileptic Seizure Detection During Sleep Using Passive Infrared Sensors

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Review of signal processing applications of Pyroelectric Infrared (PIR) sensors with a focus on respiration rate and heart rate detection

Convulsive Movement Detection Using Low-Resolution Thermopile Sensor Array

Real-Time Epileptic Seizure Detection During Sleep Using Passive Infrared Sensors