Signal Feature Detection Research Articles

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences.

Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.

Research on Traffic Signal Characteristic Detection Device of Railway Railside System

The research direction of this paper is to study the railway railside traffic signal feature detection device. This device is a multi-sensor cooperative device using MPU6050 accelerometer, NRF24L01 wireless data transmission sensor and HC-SR04 ultrasonic ranging sensor on the Arduino tool platform. The feature quantity that can be detected by the device includes the vibration acceleration of the railway track and the real-time distance between the train and the device, and judges according to whether the measured parameter is in a normal range, and alarms when the train running state is abnormal.

A robust indicator based on singular value decomposition for flaw feature detection from noisy ultrasonic signals

Singular value decomposition (SVD) has been proven to be an effective de-noising tool for flaw echo signal feature detection in ultrasonic non-destructive evaluation (NDE). However, the uncertainty in the arbitrary manner of the selection of an effective singular value weakens the robustness of this technique. Improper selection of effective singular values will lead to bad performance of SVD de-noising. What is more, the computational complexity of SVD is too large for it to be applied in real-time applications. In this paper, to eliminate the uncertainty in SVD de-noising, a novel flaw indicator, named the maximum singular value indicator (MSI), based on short-time SVD (STSVD), is proposed for flaw feature detection from a measured signal in ultrasonic NDE. In this technique, the measured signal is first truncated into overlapping short-time data segments to put feature information of a transient flaw echo signal in local field, and then the MSI can be obtained from the SVD of each short-time data segment. Research shows that this indicator can clearly indicate the location of ultrasonic flaw signals, and the computational complexity of this STSVD-based indicator is significantly reduced with the algorithm proposed in this paper. Both simulation and experiments show that this technique is very efficient for real-time application in flaw detection from noisy data.

Research and Analysis of ECG Signal Feature Detection Based on Multi-Resolution Analysis and Baselines Wander

Electrocardiosignal feature extraction is the base of electrocard iologic automatic diagnosis. By using wavelet transform multi-resolution analysis, the noise in electrocard iosignal is removed; and by using proximity signals of wavelet transform the base linew ander is filtered. The high frequency noise is handled and eliminated with the default threshold; and the average value of the electrocardiosignals is set to zero. In detection of rpeak, because leak detection will occur when only 23 detail signals is considered, thus the 23 and 24 detail signals are integrated to avoid miss detection effectively. The methods avoiding error detection bring excellent effects. For calculating average cardiac electric axis, among the methods of area method, time voltage method and amplitude method, the area method offers the highest accuracy.

Development of a Mobile Phone Based e-Health Monitoring Application

The use of Electrocardiogram (ECG) system is important in primary diagnosis and survival analysis of the heart diseases. Growing portable mobile technologies have provided possibilities for medical monitoring for human vital signs and allow patient move around freely. In this paper, a mobile health monitoring application program is described. This system consists of the following sub-systems: real-time signal receiver, ECG signal processing, signal display in mobile phone, and data management as well five user interface screens. We verified the signal feature detection using the MIT-BIH arrhythmia database. The detection algorithms were implemented in the mobile phone application program. This paper describes the application system that was developed and tested successfully.

Gain of the human dura in vivo and its effects on invasive brain signal feature detection

Invasive brain signal recordings generally rely on bioelectrodes implanted on the cortex underneath the dura. Subdural recordings have strong advantages in terms of bandwidth, spatial resolution and signal quality. However, subdural electrodes also have the drawback of compromising the long-term stability of such implants and heighten the risk of infection. Epidurally implanted electrodes might provide a viable alternative to subdural electrodes, offering a compromise between signal quality and invasiveness. Determining the feasibility of epidural electrode implantation for e.g., clinical research, brain–computer interfacing (BCI) and cognitive experiments, requires the characterization of the electrical properties of the dura, and its effect on signal feature detection. In this paper we report measurements of brain signal attenuation by the human dura in vivo. In addition, we use signal detection theory to study how the presence of the dura between the sources and the recording electrodes affects signal power features in motor BCI experiments. For noise levels typical of clinical brain signal recording equipment, we observed no detrimental effects on signal feature detection due to the dura. Subdural recordings were found to be more robust with respect to increased instrumentation noise level as compared to their epidural counterpart nonetheless. Our findings suggest that epidural electrode implantation is a viable alternative to subdural implants from the feature detection viewpoint.

Efficient Signal Feature Detection method using Spectral Correlation Function in the Fading channel

The cognitive radio communication is taking the attentions because the development of the technique came to be possible to analyze wireless signals. In the IEEE 802.22 WRAN Systems[1], how to detect a spectrum and signals is continuously studied. In this paper, we propose the efficient signal detection method using SCF (Spectral Correlation Function). It is easy to detect the signal feature when we are using the SCF. Because most modulated signals have the cyclo-stationarity which is unique for each signal. But the fading channel effected serious influence even though it detects the feature of the signal. We applied LMS(Least Mean Square) filter for the compensation of the signal which is effected the serious influence in the fading channel. And we analyze some signal patterns through the SCF. And we show the unique signal feature of each signal through the SCF method. It is robust for low SNR(Signal to Noise Ratio) environment and we can distinguish it in the fading channel using LMS Filter.