A novel vehicle stationary detection utilizing map matching and IMU sensors.

Precise navigation is a vital need for many modern vehicular applications. The global positioning system (GPS) cannot provide continuous navigation information in urban areas. The widely used inertial navigation system (INS) can provide full vehicle state at high rates. However, the accuracy diverges quickly in low cost microelectromechanical systems (MEMS) based INS due to bias, drift, noise, and other errors. These errors can be corrected in a stationary state. But detecting stationary state is a challenging task. A novel stationary state detection technique from the variation of acceleration, heading, and pitch and roll of an attitude heading reference system (AHRS) built from the inertial measurement unit (IMU) sensors is proposed. Besides, the map matching (MM) algorithm detects the intersections where the vehicle is likely to stop. Combining these two results, the stationary state is detected with a smaller timing window of 3 s. A longer timing window of 5 s is used when the stationary state is detected only from the AHRS. The experimental results show that the stationary state is correctly identified and the position error is reduced to 90% and outperforms previously reported work. The proposed algorithm would help to reduce INS errors and enhance the performance of the navigation system.

Reduction of the dimensionality of the EEG channels during scoliosis correction surgeries using a wavelet decomposition technique.

This paper presents a comparison between the electroencephalogram (EEG) channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete system to find the least affected channel by the noise. The presented system consists of five stages: filtering, wavelet decomposing (Level 4), processing the signal bands using four different criteria (mean, energy, entropy and standard deviation), finding the useful channel according to the criteria's value and, finally, generating a combinational signal from Channels 1 and 2. Experimentally, two channels of EEG data were recorded from six patients who underwent scoliosis correction surgeries in the Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) (the Medical center of National University of Malaysia). The combinational signal was tested by power spectral density, cross-correlation function and wavelet coherence. The experimental results show that the system-outputted EEG signals are neatly switched without any substantial changes in the consistency of EEG components. This paper provides an efficient procedure for analyzing EEG signals in order to avoid averaging the channels that lead to redistribution of the noise on both channels, reducing the dimensionality of the EEG features and preparing the best EEG stream for the classification and monitoring stage.

Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department.

Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient's emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician's burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in term of generalization. It was therefore chosen as the technique to develop the primary triage prediction model. This primary triage model will be combined with the secondary triage prediction model to produce the final triage category as a tool to assist the medical officer in the emergency department.

Evolution of electroencephalogram signal analysis techniques during anesthesia.

Biosignal analysis is one of the most important topics that researchers have tried to develop during the last century to understand numerous human diseases. Electroencephalograms (EEGs) are one of the techniques which provides an electrical representation of biosignals that reflect changes in the activity of the human brain. Monitoring the levels of anesthesia is a very important subject, which has been proposed to avoid both patient awareness caused by inadequate dosage of anesthetic drugs and excessive use of anesthesia during surgery. This article reviews the bases of these techniques and their development within the last decades and provides a synopsis of the relevant methodologies and algorithms that are used to analyze EEG signals. In addition, it aims to present some of the physiological background of the EEG signal, developments in EEG signal processing, and the effective methods used to remove various types of noise. This review will hopefully increase efforts to develop methods that use EEG signals for determining and classifying the depth of anesthesia with a high data rate to produce a flexible and reliable detection device.

Development of a computer-based automated pure tone hearing screening device: a preliminary clinical trial.

Hearing screening is important for the early detection of hearing loss. The requirements of specialized equipment, skilled personnel, and quiet environments for valid screening results limit its application in schools and health clinics. This study aimed to develop an automated hearing screening kit (auto-kit) with the capability of realtime noise level monitoring to ensure that the screening is performed in an environment that conforms to the standard. The auto-kit consists of a laptop, a 24-bit resolution sound card, headphones, a microphone, and a graphical user interface, which is calibrated according to the American National Standards Institute S3.6-2004 standard. The auto-kit can present four test tones (500, 1000, 2000, and 4000 Hz) at 25 or 40 dB HL screening cut-off level. The clinical results at 40 dB HL screening cut-off level showed that the auto-kit has a sensitivity of 92.5% and a specificity of 75.0%. Because the 500 Hz test tone is not included in the standard hearing screening procedure, it can be excluded from the auto-kit test procedure. The exclusion of 500 Hz test tone improved the specificity of the auto-kit from 75.0% to 92.3%, which suggests that the auto-kit could be a valid hearing screening device. In conclusion, the auto-kit may be a valuable hearing screening tool, especially in countries where resources are limited.

Analysis of the effect of ageing on rising edge characteristics of the photoplethysmogram using a modified Windkessel model.

Ageing is one of the main contributing factors towards increasing arterial stiffness, leading to changes in peripheral pulses propagation. Therefore the characteristics of the photoplethysmogram (PPG) pulse, especially the rising edge and peak position, are greatly affected. In this study, the PPG pulse rising edge and corresponding peak position have been investigated non-invasively in human subjects as a function of age. Fifteen healthy subjects were selected and grouped in five age intervals, from 20 to 59 years, based on their comparable systolic-diastolic blood pressure and PPG amplitude. As expected, the peripheral pulse shows a steep rise and early peak in younger subjects. With age, the slope becomes blunted and in older subjects, the rise is very gradual and the pulse peak appears much later. Qualitative results were further verified by a modified 10-element Windkessel model to quantify the lumped parameter changes with ageing. This verification highlighted some specific changes in vascular parameters with aging. The rising edge could be considered as one parameter in determining the age-dependent vascular state.