Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population. Besides, falls are also a major safety threat to bikers, skiers, construction workers, and others. Fortunately, with the advancements of technologies, the number of proposed fall detection systems and devices has increased dramatically and some of them are already in the market. Fall detection devices/systems can be categorized based on their architectures as wearable devices, ambient systems, image processing-based systems, and hybrid systems, which employ a combination of two or more of these methodologies. In this review paper, a comparison is made among these major fall detection systems, devices, and algorithms in terms of their proposed approaches and measure of performance. Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized. Challenges with privacy issues, limited real fall data, and ergonomic design deficiency are also discussed.

The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen’s and Kale’s (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods.

In this study, we developed a machine learning model for automated seizure detection using system identification techniques on EEG recordings. System identification builds mathematical models from a time series signal and uses a small number of parameters to represent the entirety of time domain signal epochs. Such parameters were used as features for the classifiers in our study. We analyzed 69 seizure and 55 non-seizure recordings and an additional 10 continuous recordings from Thomas Jefferson University Hospital, alongside a larger dataset from the CHB-MIT database. By dividing EEGs into epochs (1 s, 2 s, 5 s, and 10 s) and employing fifth-order state-space dynamic systems for feature extraction, we tested various classifiers, with the decision tree and 1 s epochs achieving the highest performance: 96.0% accuracy, 92.7% sensitivity, and 97.6% specificity based on the Jefferson dataset. Moreover, as the epoch length increased, the accuracy dropped to 94.9%, with a decrease in sensitivity to 91.5% and specificity to 96.7%. Accuracy for the CHB-MIT dataset was 94.1%, with 87.6% sensitivity and 97.5% specificity. The subject-specific cases showed improved results, with an average of 98.3% accuracy, 97.4% sensitivity, and 98.4% specificity. The average false detection rate per hour was 0.5 ± 0.28 in the 10 continuous EEG recordings. This study suggests that using a system identification technique, specifically, state-space modeling, combined with machine learning classifiers, such as decision trees, is an effective and efficient approach to automated seizure detection.

Millions of people around the globe are impacted by falls annually, making it a significant public health concern. Falls are particularly challenging to detect in real time, as they often occur suddenly and with little warning, highlighting the need for innovative detection methods. This study aimed to assist in the advancement of an accurate and efficient fall detection system using electroencephalogram (EEG) data to recognize the reaction to a postural disturbance. We employed a state-space-based system identification approach to extract features from EEG signals indicative of reactions to postural perturbations and compared its performance with those of traditional autoregressive (AR) and Shannon entropy (SE) methods. Using EEG epochs starting from 80 ms after the onset of the event yielded improved performance compared with epochs that started from the onset. The classifier trained on the EEG data achieved promising results, with a sensitivity of up to 90.9%, a specificity of up to 97.3%, and an accuracy of up to 95.2%. Additionally, a real-time algorithm was developed to integrate the EEG and accelerometer data, which enabled accurate fall detection in under 400 ms and achieved an over 99% accuracy in detecting unexpected falls. This research highlights the potential of using EEG data in conjunction with other sensors for developing more accurate and efficient fall detection systems, which can improve the safety and quality of life for elderly adults and other vulnerable individuals.

The functional development of the fetal brain is critical for long-term health, yet current diagnostic tools lack the capability to examine it effectively. This paper presents a non-invasive method for extracting fetal brain signals using abdominal recordings obtained before elective cesarean sections, with neonatal brain activity recorded post-delivery serving as a reference. The recorded abdominal signals were preprocessed using digital filters and separated into independent components using the blind source separation technique: Independent component analysis. These components were analyzed to identify potential fetal brain signals based on their similarity to postnatal brain activity and dissimilarity to maternal cardiac signals, which are the primary source of interference in abdominal recordings. Features related to time and frequency characteristics were extracted, and a feature selection was conducted to identify the most informative ones. Preliminary results using simulated data demonstrated effective signal separation, with some segments closely resembling postnatal brain activity in spectral features. Low-frequency bands showed the strongest potential for distinguishing fetal brain activity from maternal interference. This approach demonstrates a feasible pathway for non-invasive fetal brain monitoring with implications for early detection of neurological development issues.