Characteristics of physical movements are indicative of infants' neuro-motor development and brain dysfunction. For instance, infant seizure, a clinical signal of brain dysfunction, could be ...identified and predicted by monitoring its physical movements. With the advance of wearable sensor technology, including the miniaturization of sensors, and the increasing broad application of micro- and nanotechnology, and smart fabrics in wearable sensor systems, it is now possible to collect, store, and process multimodal signal data of infant movements in a more efficient, more comfortable, and non-intrusive way. This review aims to depict the state-of-the-art of wearable sensor systems for infant movement monitoring. We also discuss its clinical significance and the aspect of system design.
Complexity science has provided new perspectives and opportunities for understanding a variety of complex natural or social phenomena, including brain dysfunctions like epilepsy. By delving into the ...complexity in electrophysiological signals and neuroimaging, new insights have emerged. These discoveries have revealed that complexity is a fundamental aspect of physiological processes. The inherent nonlinearity and non-stationarity of physiological processes limits the methods based on simpler underlying assumptions to point out the pathway to a more comprehensive understanding of their behavior and relation with certain diseases. The perspective of complexity may benefit both the research and clinical practice through providing novel data analytics tools devoted for the understanding of and the intervention about epilepsies. This review aims to provide a sketchy overview of the methods derived from different disciplines lucubrating to the complexity of bio-signals in the field of epilepsy monitoring. Although the complexity of bio-signals is still not fully understood, bundles of new insights have been already obtained. Despite the promising results about epileptic seizure detection and prediction through offline analysis, we are still lacking robust, tried-and-true real-time applications. Multidisciplinary collaborations and more high-quality data accessible to the whole community are needed for reproducible research and the development of such applications.
Atrial fibrillation (AF) is one of the most common sustained arrhythmias, affecting about 1% of the population around the world. Rapid popularization of portable and wearable devices in recent years ...makes widespread personalized and mobile healthcare get closer to reality than ever before. This paper presents a method aiming for automatic detection of AF from short single lead electrocardiogram (ECG) recordings. Since AF is a kind of arrhythmia being likely to alter the dynamics of heart rhythms and/or the morphological characteristics in ECG tracings, heart rate variability (HRV)-based metrics and frequency analysis are adopted as feature extractors. We validate our method on a public available data set comprised of short ECG recordings of normal rhythm (N), AF (A), and other arrhythmias (O) by support vector machine and bagging trees. For two-class classification problems (N versus A), accuracy varies from 92.0% to 96.6% under different additional noise levels. For three-class classification problem (N versus A versus O), accuracy as high as 82.0% is obtained. Experimental results suggest than even for a relatively short ECG recording, nonlinear descriptors of HRV are still efficient and robust for AF detection.
The increased prevalence of chronic disease in aging population entails health risks and imposes significant economic and social burden. It is essential to provide comfortable, cost-effective, and ...easy-to-use unobtrusive and wearable systems for personal well-being and healthcare. Novel flexible material-based non-invasive and wearable sensors offer an efficient and cost-effective solution, which enables the continuous and real-time monitoring of important physiological signs of the human beings, the assessment of personal health conditions and that provides feedback from remote and home monitoring. In this paper, novel flexible material-based wearable sensors, devised into body sensor networks to capture and monitor vital bio-signals, including electroencephalography (EEG), electrocardiography (ECG) and respiratory, are proposed. Silver nanowires (Ag NWs) and polydimethylsiloxane composite material, carbon foam, and graphene-based fiber are used to sense the EEG, ECG, and respiratory, respectively. With different flexible materials, the smart hat and smart jacket are designed to affix the sensors, which enable long-term health monitoring of vital signals seamlessly. Meanwhile, the corresponding acquisition circuits are developed and mounted with the proposed electrodes on the garments. More importantly, a comprehensive protocol is designed to validate the performance of the proposed system, while some standard sensors and commercial devices are used for comparison. The evaluation results demonstrate the proposed system represents a comparable performance with the existing system. In summary, the proposed sensing system offers an unobtrusive, detachable, expandable, user-friendly, and comfortable solution for physiological signal monitoring. It can be expected to use for the remote healthcare monitoring and provide personalized information of health, fitness, and diseases.
To characterize the irregularity of the spectrum of a signal, spectral entropy and its variants are widely adopted measures. However, spectral entropy is invariant under the permutation of the power ...spectrum estimations on a predefined grid. This erases the inherent order structure in the spectrum. To disentangle the order structure and extract meaningful information from raw digital signal, a novel analysis method is necessary. In this paper, we tried to unfold this order structure by defining descriptors mapping real- and vector-valued power spectrum estimation of a signal into a scalar value. The proposed descriptors showed its potential in diverse problems. Significant differences were observed from brain signals and surface electromyography of different pathological/physiological states. Drastic change accompanied by the alteration of the underlying process of signals enables it as a candidate feature for seizure detection and endpoint detection in speech signal. Since the order structure in the spectrum of physiological signal carries previously ignored information, which cannot be properly extracted by existing techniques, this paper takes one step forward along this direction by proposing computationally efficient descriptors with guaranteed information gain. To the best of our knowledge, this is the first work revealing the effectiveness of the order structure in the spectrum in physiological signal processing.
Automatic seizure detection has been often treated as a classification problem that aims at determining the label of electroencephalogram (EEG) signals by computer science, as the EEG monitoring is a ...helpful adjunct to the diagnosis of epilepsy. In most existing work, the traditional signal energy of the EEG has been applied for classification, since the energy pattern of epileptic seizures differs from that of non-seizures. Although they are effective, the accuracy either heavily depends on additional information besides energy or is limited by the shortcoming of energy-based features. To address this issue, the proposed approach achieves the classification based on the instantaneous energy of the EEG signals instead. The proposed approach first measures the instantaneous energy related to changes in the EEG signals. Then, energy behavior over time is characterized by instantaneous energy-based features from different aspects. Finally, the classification is carried out on the features to produce output labels. By processing instantaneous energy, the information of energy evolution is involved. As such, the accuracy is improved without bringing in extra information besides energy, or complicated transformation. In multi-class problems, the proposed approach has obtained promising results for identifying the ictal EEG, which indicates the tremendous potential of the proposed approach for epileptic seizure detection.
This paper presents a novel descriptor aiming at anomaly detection in sequential data, like epileptic seizure detection with EEG time series. The descriptor is derived from the eigenvalue ...decomposition (EVD) of a Hankel-form data matrix generated from the raw time series. Simulation trials imply that the descriptor is capable of characterizing the structural aspect of a time series. In addition, we deploy the proposed descriptor as a feature extractor and apply it on Bonn Seizure Database which is widely used in seizure detection. The high accuracies on classification problems are comparable with the state-of-the-art so validate the effectiveness of our method.
Functional near-infrared spectroscopy (fNIRS) is a non-invasive multi-channel imaging tool for assessing brain activities, which has shown its high potential in brain-computer interface (BCI) ...technique. Most previous studies have focused on constructing high dimensional features from whole channels, adding to the complexity of their classifiers. Another multi-channel source for BCI is electroencephalograph (EEG), which possesses different spatial and temporal features from fNIRS. In EEG field, Common Spatial Pattern (CSP) algorithm is widely used aimed at dimensionality reduction. In our article, we modified it based on the characteristics of fNIRS and evaluated its effectiveness in discriminating Mental Arithmetic (MA) against resting status in an open-access dataset. The Modified Common Spatial Pattern algorithm significantly outperforms CSP algorithm in fNIRS-based BCI and shows its potential in further BCI related explorations.
Objective: To characterize the irregularity of the spectrum of a signal, spectral entropy is a widely adopted measure. However, such a metric is invariant under any permutation of the estimations of ...the powers of individual frequency components on a predefined grid. This erases the order structure inherent in the spectrum which is also an important aspect of irregularity of the signal. To disentangle the order structure and extract meaningful information from raw digital signal, novel analysis method is necessary. Approach: A novel method to depict the order structure by simply ranking power estimations on frequency grid of a evenly spaced signal is proposed. Two descriptors mapping real- and vector-valued power spectrum estimation of a signal into scalar value are defined in a heuristic manner. By definition, the proposed descriptor is capable of distinguishing signals with identical spectrum entropies. Main Results: The proposed descriptor showed its potential in diverse problems. Significant (p<0.001) differences were observed from brain signals and surface electromyography of different pathological/physiological states. Drastic change accompanied by the alteration of the underlying process of signals enables it as candidate feature for seizure detection and endpoint detection in speech signal. Significance: This letter explores the previously ignored order structure in the spectrum of physiological signal. We take one step forward along this direction by proposing two computationally efficient descriptors with guaranteed information gain. As far as the authors are concerned, this is the first work revealing the effectiveness of the order structure in the spectrum in physiological signal processing.
A novel wearable sensor system for seizure monitoring of neonates comprised of smart clothing, video recording and cloud platform is presented. Textile electrodes and Inertial Measurement Unit (IMU) ...are embedded in the smart clothing to obtain ECG signal and motion signal whereby epileptic seizure detection algorithm is performed. Moreover, a video monitoring module provides real-time information about patients. The cloud platform receives the pre-processed data and enables remote monitoring, centralized signal processing and data management. Comparison with commercial instruments shows that the smart clothing is capable of acquiring high-quality signals. Pilot tests under disinfection operations at Children's Hospital of Fudan University confirm clinical feasibility of the proposed system. The scalability and modularity of the unobtrusive wearable front end and the design of system architecture based on cloud enable the whole system with great potential in clinical practice and home monitoring scenarios.
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